Regarding therapy, Coriat et al. reported that everolimus and sunitinib were studied in two phase III randomized trials of G1-G2 pancreatic NETs and concluded that “there is no validation of a targeted therapy except bevacizumab.” However, given that NET G3 are well differentiated, as the vast majority of tumor populations included in the two aforementioned trials, everolimus and sunitinib should be considered among the possible therapeutic options.

It cannot be excluded that some NET G3 were included in those studies, provided that Ki-67 was unknown in the main part of the tumors (on this basis the terms “G1” and “G2” should not be used).

Furthermore, data on a limited number of patients with pancreatic NET G3 treated with everolimus were presented at the most recent European Neuroendocrine Tumor Society meeting [3].


Finally, we disagree with Coriat and colleagues’ statement that bevacizumab was validated in pancreatic NECs, based on the current evidence.

We believe that biological therapies, such as molecular targeted agents and somatostatin receptor-targeted treatments, should be prospectively investigated in patients with advanced NET G3 in comparison with platinum-based chemotherapy, which is the current debatable standard treatment.


We thank Fazio and Milione [1] for their comment on our position paper recently published in The Oncologist [2].

As highlighted in our paper, evidence-based medicine in response to chemotherapy is lacking, and the choice of the best therapeutic option remains a challenge.

Fazio and Milione described in their letter that the main primary localization of a well-differentiated grade 3 neuroendocrine tumor (NET G-3) is the pancreas.

They identified in their retrospective cohort that the incidence of pancreatic NET G-3 is 42% of all NET G-3 (10 out of 24).

Similarly, in our position paper, we stressed that the main localizations of NET G-3 are the pancreas, the stomach, and the colon.


In addition, we factually reported that there is no validated phase III study on NET G-3 and there are no prospective data ongoing on the targeted therapy efficacy.

Furthermore, we agree with their comment that in “low” NET G-3, the proven efficacy of targeted therapy should be evaluated and that in some cases such therapy, either everolimus or sunitinib, should be considered.


Fazio and Milione believe that somatostatin receptor-targeted treatments might have a therapeutic efficacy in NET G-3.

In 2016, the NETTER-1 phase III study validated the efficacy of the 177Lu-DOTA0-Tyr3-Octreotate (Lutathera, Advanced Accelerator Applications, Saint-Genis-Pouilly, France) in patients with inoperable, progressive, somatostatin receptor-positive midgut grade 1–2 neuroendocrine tumor.


The NETTER-1 study presented as an inclusion criterion patients with a Ki-67 level below 20.

No data on the “aggressive” grade 2 NET subgroup treated in the NETTER-1 study that helped to propose 177Lu-DOTA0-Tyr3-Octreotate in NET G-3 have been published.


Moreover, it has been reported that patients with pancreatic tumors had higher rates of positive somatostatin receptor scintigraphy (46%), lower rates of Ki-67 (70% with Ki-67 <55%), and longer overall survival.


Such data pave the way for an evaluation in NET G-3 with a Ki-67 below 55% of the efficacy of the 177Lu-DOTA0-Tyr3-Octreotate (Lutathera).

Finally, the 2016 European Neuroendocrine Tumor Society (ENETS) guidelines state that chemotherapy as first-line treatment with a temozolomide plus capecitabine or streptozotocin plus 5-fluorouracil regimen [3] appears as the recommended option in NET G-3.


As stressed by Milione et al. in a recent review, there is a clinicopathologic heterogeneity of NET G-3 that deserves specific clinical trials [4].

Cell differentiation and Ki-67 level appear as the key points in the management of NET G-3 with a Ki-67 cut-off.


Homelessness in the United States is a risk factor for a myriad of poor health outcomes, including infectious dis- eases, chronic diseases, mental health disorders, and sub- stance abuse disorders (1).

However, much is unknown about the full extent of the health risks associated with homeless- ness.

Gaining a better understanding of the epidemiologic context of the experience of homelessness is valuable to public health for 2 reasons: First, epidemiologic data can inform public health practitioners and clinical providers in designing interventions that protect people who are cur- rently experiencing homelessness, and second, these data can provide evidence to support housing and homelessness prevention.


To calculate rates of disease for people experiencing homelessness, data sources that quantify the number of people exposed to homelessness are necessary.

Although several agencies collect data on homelessness in the United States, 2 federal agencies provide yearly, publicly available, local-level data: the US Department of Housing and Urban Development (HUD) and the US Department of Education (ED).

HUD defines any individual (of any age) or family as homeless if they lack a fixed, regular, and adequate nighttime residence.

This includes situations where their primary nighttime residence is a public or private place not meant for human habitation, if they are living in a temporary shelter (including emergency homeless shelters and transitional housing), or if they are exiting an institution where they have resided for 90 days or less and resided in an emergency shelter or place not meant for human habitation immediately before entering that institution (2).

ED defines homelessness differently than HUD, counting school-aged children who lack a fixed, regular, and adequate nighttime residence, inclusive of those who are 1) sharing the housing of other people; 2) living in motels, hotels, trailer parks, or campgrounds; 3) living in emergency or transitional shelters; 3) abandoned in hospitals; or 4) residing in places not designed for or ordinarily used as regular sleeping accommodations for human beings (such as cars, parks, public spaces, abandoned buildings, substandard housing, bus or train stations, or similar settings) (3).

While these federal definitions reflect eligibility for the homeless assistance programs of HUD and ED, the data collection requirements may result in capturing fewer adults and Through consultation with federal agencies, we identified and reviewed 3 publicly available data sources that enu- merate persons experiencing homelessness with the purpose of outlining the challenges and opportunities for use in epidemiologic analyses.

To provide examples of the impli- cations of the data sources, we describe the use of these data sets in the context of calculating the incidence of tuberculosis and other infectious diseases.


ED publicly releases school-district-level data with an unduplicated number of homeless students enrolled in public schools, regardless of age, at any time point during the school year.

These data are collected in alignment with the education portion of the McKinney-Vento Act to ensure that students who are experiencing homelessness do not face barriers to school enrollment and are provided with academic support.

Data are available from the 2013-2014 to 2017-2018 school years.

These local-level data include type of homelessness (sheltered, unsheltered, doubled up, motel/hotel) and some characteristics of the students, such as the number of homeless students who have disabilities, are English learners, are migrants, or are unaccompanied.

Counts are suppressed if the number of students in a category is less than 2 (9).


These data provide the number of enrolled school-aged children who have been recorded to have been exposed to homelessness (inclusive of those doubled up, staying in hotels/motels, or residing in other temporary situations as described above) over the course of a school year (Table 1).

ED data can serve to define a retrospective exposure group of students within the population.

Using ED data, the definition of homelessness in case data should be "a case in a student enrolled in public school who experienced homelessness (as defined) at some point during the school year." Utilizing these data as a denominator for an incident case of a disease, the state or local case report form would ask about a student's exposure to homelessness over the course of the school year.


ED data are not comparable to HUD data due to differing definitions of homelessness.

ED data also may not be com- parable across years: Prior to the 2016-2017 school year, students awaiting foster-care placement were counted as homeless.

In more recent years, states have begun excluding those students from their counts.

Like the HMIS data, ED data cannot be used to estimate traditional person-time for incidence calculations and cannot be used in combination with a case report form that collects data on immediate homelessness.


The Homeless Management Information System (HMIS) is a set of locally administered information systems used by CoCs to confidentially collect data on individuals and fami- lies who access any homeless services throughout the year, including but not limited to emergency shelters, transitional housing, permanent supportive housing, or rapid rehousing (6).

People who are unsheltered are only included in HMIS data through street outreach programs, if they utilize pro- grams that participate in the HMIS, such as day shelters, or if a local CoC decides to add their own customized fields on unsheltered people to their database.

People entering Vio- lence Against Women Act-funded programs for survivors of domestic violence are not included in the HMIS.

Lastly, Native Americans who may be experiencing homelessness or seeking services on tribal lands are not included.


Like the PIT, the purpose of the HMIS is to allow com- munities to follow performance and allocate resources while providing data to Congress and the public to better inform homeless policy and decision-making (7).

Each CoC sub- contracts with an HMIS software provider of their choosing but must follow HUD data standards.

All CoCs have been


More epidemiologic research is needed to protect the health of people experiencing homelessness.

The data from HUD and ED provide an opportunity to expand current re- search and develop actionable analyses.

However, as de- scribed here, the limitations of the data must be kept in mind.

New data on both sheltered and unsheltered homelessness will also become available on the local level from the 2020 US Census, which can be useful for validation and may provide further geographic granularity (10).

With cautious alignment of definitions and consideration of the limita- tions, data from the 3 sources outlined here can be used as denominator estimates for understanding the epidemiology and risks associated with homelessness.


In the accompanying article, Mosites et al. ( Am J Epidemiol.

2021;190(11):2432–2436) evaluate data sources that enumerate people experiencing homelessness in the United States with respect to their strengths and limitations for conducting epidemiologic research in homeless populations.

We largely agree with their key arguments, yet offer additional points that provide important context about these data for researchers and other stakeholders.

Overall, we believe that it is possible to address many of the noted shortcomings of these data, and once addressed, the data could be more effectively leveraged to improve the health, housing stability, and quality of life of people experiencing homelessness.


Homelessness consistently ranks as a leading problem in the minds of the American public [1].

Yet, the true scope of the problem has historically been a subject of fierce debate among advocates, researchers, policy-makers, and other stakeholders.

According to the most recent estimates from the US Department of Housing and Urban Development (HUD), in 2019 an estimated 568,000 people were experiencing homelessness in the United States, and throughout 2018, more than 1.4 million spent time in a shelter [2], [3].


Estimates of the size of the homeless population have been reported by HUD every year since 2007 in the Annual Homeless Assessment Report (AHAR).

HUD is the only agency that collects data with which to estimate the number of people experiencing homelessness in the United States on a systematic and routine basis.

Additionally, the AHAR creates a descriptive profile of people experiencing homelessness [4].


However, HUD estimates probably undercount the true number of people experiencing homeless in the United States—especially those who are unsheltered, homeless youth, immigrants, and homeless families [5].

Estimate inaccuracies interfere with appropriate resource allocation and interventions.

For example, during the beginning of the coronavirus disease 2019 pandemic, it was unclear what resources were needed to protect people experiencing homelessness in different localities because of inaccurate estimates.

More accurate estimates of the size of the homeless population—especially at the county and city levels, where most programs are implemented—would have facilitated the delivery of resources to homeless people affected by coronavirus disease 2019 [6].


In their article in this issue of the Journal, Mosites et al. [ 7] argue that population-level data for epidemiologic exploration are necessary to understand the full extent of social and health-related risks associated with homelessness, and in turn to inform interventions that will ultimately improve outcomes.

The authors describe the potential epidemiologic applications of 3 main data sources: HUD Point-in-Time (PIT) estimates, which are annual counts of the sheltered and unsheltered homeless population on a single night in January; HUD annual estimates of persons using emergency shelter, derived from local Homeless Management Information System data; and US Department of Education estimates of the number of students experiencing homelessness during a school year [7].

They argue that each has serious shortcomings that limit how and when the data can be used in epidemiologic research and should thus be approached and interpreted with caution.

The authors’ argument also implicitly calls for making improvements to these data sources.


Although we agree with Mosites et al. ’s main argument about the limitations of these data, we believe epidemiologists and other stakeholders interested in understanding and improving the health outcomes of persons experiencing homelessness should keep 3 additional points in mind.

First, while flawed, the current data sources that enumerate the homeless population are vast improvements over what was available until the late 2000s.

Second, there is a wide array of proposals for improving the accuracy of enumeration efforts that could be easily implemented and would strengthen the utility of these data for epidemiologic research and other purposes.

Finally, improvements in the accuracy of these data would have benefits that would extend far beyond their potential use for epidemiologic research and even the original reasons for collecting the data.


Current homeless population-level data collection and estimates are improvements


Enumeration of the homeless population has been a longstanding methodological challenge, as conventional means of counting the general population (e.g., through the Census Bureau’s household-based surveys) are inadequate for people experiencing homelessness.

Before the first AHAR report in 2007, estimates of the homeless population were conducted sporadically and were of questionable accuracy—varying wildly from 200,000 to 3.5 million [8]–[10].

However, since 2007, annual estimates from HUD have been available, and these have been valuable, useful, and easily retrievable from HUD.

Additionally, the community-level data comprising the AHAR have also been available since 2007 and add a crucial resource for tracking trends in the size of the homeless population over time at multiple levels of geography.

The collection of these data has opened avenues for conducting important new research to understand community-level predictors of homelessness and the impact of policy interventions on homelessness [11]–[13].

For example, in 2020, the US Government Accountability Office conducted regression analyses using HUD PIT data and found that rent level is an important driver of homelessness [5].

Likewise, researchers have used PIT data to show that a concerted policy effort to expand the availability of permanent supportive housing for military veterans has been a key factor in declines in veteran homelessness over time [11].


Counts underestimate the homeless population but can be easily improved


Mosites et al. correctly note that there are serious limitations in estimates of people experiencing homelessness [7].

They call particular attention to problems with the undercounting of the unsheltered population, and those concerns are echoed by the Government Accountability Office report, as well as the New York Times [5], [14].

However, they provide little guidance on how to improve these estimates, despite numerous recent and past proposals for improvement [5], [15].


Undercounting of individuals in any given homeless population probably occurs more than expected and more for unsheltered persons than for sheltered individuals, given the wide year-to-year fluctuations in numbers reported for the unsheltered population [5], [15].

The Homeless Management Information System estimates of the sheltered population are thought to be more reliable given fewer year-to-year fluctuations, but they could also be improved [5].


Enumeration of the unsheltered population depends on many factors: weather, the number of enumerators, the use of sampling or a census, a change in methods, the ability to find people, the area of focus (e.g., urban areas vs. large rural areas), and the level of training [5].

Some of these factors introduce bias to PIT counts.

That is, some homeless people have a lower probability of being included in the count than others.

During counts, enumerators sometimes estimate the number of people inside a tent or in an abandoned building without going inside, because of risk or out of respect [5].

In addition, characteristics such as age, sex, and race/ethnicity are assumed if not confirmed through interview [5].


We feel that a few simple strategies could vastly improve the estimates and help address these issues.


First, HUD could provide clearer and more accessible guidance and training on PIT counting methods [5].

This would result in more accurate and more consistent estimates from year to year [5].

In particular, HUD could provide communities with the resources and training necessary to calculate margins of error for their estimates [5].

One way to improve accuracy for the unsheltered population PIT count, through improved estimates of the error, would be to use plant-capture methods (in which people impersonating homeless individuals are “planted” in predetermined locations to help assess what percentage of homeless persons are being missed during the count), which have previously been used in certain communities on an ad hoc basis [15].


Additional options HUD could consider for improving estimates of people experiencing homelessness include conducting the count over several days and nights or over the course of a week or increasing the number of communities that use postcount service-based surveys.

These surveys involve interviewing people receiving services from soup kitchens, drop-in centers, or other places frequented by people experiencing homelessness to assess whether they were homeless on the night of the PIT count.

It may also be worthwhile to expand homeless enumeration efforts to include people who are “doubled up” (e.g., sleeping on a friend’s sofa) and do not meet HUD’s current definition of homelessness and thus are not currently included in HUD’s estimates.

Estimating the size of this population poses additional methodological challenges, but it would be a worthwhile endeavor for understanding how conditions that may be understood as homelessness affect health.


Finally, it need not be solely the responsibility of HUD to routinely collect data on the scope of homelessness in the United States.

Key population-based health surveys, such as the National Health and Nutrition Examination Survey and the National Health Interview Survey, could promote understanding of the risk homelessness poses to health by incorporating measures of current housing status, recent exposure to homelessness, and risk of becoming homeless.


Epidemiologic purposes are just one of many uses for population-level homelessness data


Mosites et al. note that more accurate population-level data would lead to better epidemiologic research focused on homelessness [7].

While that is true, there is already abundant evidence of the negative health impacts of homelessness, and improving counts of people experiencing homelessness solely for the purpose of developing more precise estimates of these negative health impacts is likely to be insufficient for achieving improved health and housing outcomes in this population.

Indeed, there are reasons to improve these counts beyond their ability to produce better research.

Currently, these data inform Congress on the magnitude of the problem and the characteristics of the homeless population, and they directly affect the amount of funding allocated to federal homelessness programs and the ability of programs that serve homeless populations to plan and respond to homelessness [5].

For this reason, it is imperative that these data be accurate.

Improving data collection methods and accuracy is essential for supporting and serving the homeless population and other hard-to-reach groups, such as immigrants.

Beyond assessment of health risks, improved data collection will inform interventions and natural experiments which have the potential to improve health and quality of life.


CONCLUSION


The annual data from HUD are improvements over past ad hoc and sporadic information-gathering, which varied considerably.

Nevertheless, the HUD data, especially the PIT counts, are considered underestimates.

Fortunately, methods for improving the estimates are well known.

Commitment of resources from federal and state entities will be needed, as well as thoughtful use of well-regarded population-based surveys.

There is broad agreement that improved estimates of the homeless population would be desirable and could lead to progress in addressing the health and housing needs of those experiencing homelessness.

Yet, better estimates require both the political will and adequate resources.

Given the marginalized nature of this population and its significant intersection with other historically marginalized racial groups and people with disabilities, we hope that such political will and resources will be forthcoming.


We appreciate the insights provided by Roncarati et al. [ 1] in their commentary on our article [2].

The authors raised important considerations about recent progress related to data collection on homelessness, possible opportunities to improve data collection processes, and nonepidemiologic uses for data on homelessness.

In particular, we appreciate that the authors outlined specific opportunities to improve data collection.


We emphasize that the primary goal of our article was to ensure that epidemiologists and public health practitioners are aware of the available data for use in their analyses.

Despite the limitations and opportunities for improvement, these data sources are available for use now.

Use of population data on homelessness is especially timely and critical during the response to coronavirus disease 2019 in order to understand the burden of disease and estimate resource needs for people experiencing homelessness.


We agree with Roncarati et al. that data collection can be improved [1].

Efforts to improve these data, especially for epidemiologic purposes, should be a collaboration across health and social service sectors.

As the authors note, collecting information on housing status in health surveys is one way to increase understanding of risks of and exposure to homelessness.

However, because concerns about the health of people experiencing homelessness inherently cross sectors, collaboration between public health, health care, social services, and homeless service agencies is necessary to make wider improvements in the representativeness of homelessness data.


Although Roncarati et al. suggest that “there is already abundant evidence of the negative health impacts of homelessness” [1, p. 2439], quantification of health inequities, whether they are socioeconomic, geographic, or racial, remains crucial.

There are large gaps in the peer-reviewed literature related to homelessness, which can lead to misunderstanding of risk and misallocation of public health resources.

Epidemiologic analyses can help to protect the health of people currently experiencing homelessness while ultimately supporting the importance of stable housing and the end of homelessness.

We look forward to working collaboratively across sectors to achieve these goals.


The University of Tokyo Institutional Review Board approved this study.

The board waived the requirement for informed pa- tient consent because of the anonymous nature of the data.


For this study, we used the Japanese diagnosis procedure com- bination (DPC) database, which was described in detail previ- ously [21].

In short, the DPC database includes administrative claims and discharge abstract data for all inpatients discharged from more than 1000 participating hospitals; it covers approx- imately 92% (244/266) of all tertiary-care emergency hospitals in Japan and 90% (90/100) of institutions certified for training burn specialists by the Japanese Society for Burn Injuries [22].

The database includes the following information for each pa- tient: age; sex; primary diagnosis; comorbidities on admission and post-admission complications coded with the International Classification of Diseases, 10th Revision (ICD-10), codes and written in Japanese; medical procedures, including types of sur- gery, coded with original Japanese codes; daily records of drug administration and devices used; length of stay; and discharge status.

The dates of hospital admission, surgery, bedside proce- dures, drugs administered, and hospital discharge are recorded using a uniform data submission format [21-25].


Several lists of scores are also available in the database, in- cluding burn index and Japan coma scale (JCS) scores.

The burn index takes both the surface area and thickness of the burned area into consideration: burn index = full thickness of total burn surface area + 1/2 partial thickness of total burn sur- face area [22, 26].

The JCS correlates well with the Glasgow coma scale; consciousness scored at 100 points on the JCS is equivalent to a score of 6-9 on the Glasgow coma scale.

We cat- egorized the JCS scores into 4 groups: 0, alert; 1-3, delirium; 10-30, somnolence; and 100-300, coma [21-25].

To quantify the extent of comorbidities, the ICD-10 code for each comor- bidity was converted to a score, and the sum was used to calcu- late the Charlson comorbidity index (CCI) as previously described [22, 27, 28].

Briefly, the CCI is a method of predicting mortality by classifying or weighting comorbidities.

It has been widely used by health researchers to measure case mix and the burden of disease [27].

We categorized patients into 1 of 3 groups on the basis of the CCI: low, 0; medium, 1; and high, ≥2 [22].


We categorized the hospital types as academic or nonaca- demic.

We defined teaching hospitals as to whether or not the institution was designated for postgraduate clinical training.

We designated hospital volume as the number of eligible patients treated for the current study and categorized into quartiles (very low, low, high, and very high).

We defined the use of prophylactic antibiotics as treatment with first-generation ceph- alosporin and ampicillin/sulbactam (ie, targeted for burn infec- tion) within 2 days after admission without being used for perioperative management.


We identified all patients with severe burns (burn index ≥10) [22] who were recorded in the database from 1 July 2010 to 31 March 2013.

The exclusion criteria for this study were the following types of patients: out-of-hospital cardiac arrest, dis- charged within 2 days after admission (to avoid immortal time bias), administered nonprophylactic antibiotics within 2 days after admission, and underwent surgery (skin grafting) within 2 days after admission.

We thus compared patients who were administered prophylactic antibiotics within 2 days after admission ( prophylaxis group) and those who were not administered any antibiotics within that time (control group).

Because the DPC is an administrative database with informa- tion input as to when patients are discharged, patient follow- up began on the day of admission and ended on the hospital discharge date (to home, transfer to another hospital, or death).

We could not follow up patients after discharge from the hospital since the information was not available in this dis- charge database.

Although physicians in charge were obliged to record the burn index, we were unable to evaluate patients for whom these data were missing.


The endpoint used in this study was all-cause 28-day in- hospital mortality.

The secondary endpoints were in-hospital mortality and the use of carbapenem, tazobactam/piperacillin, fourth-generation cephalosporins, and drugs for methicillin- resistant Staphylococcus aureus (MRSA; eg, vancomycin, teico- planin, arbekacin sulfate, linezolid daptomycin) started after day 2 or later.


Since inhalation injury that requires mechanical ventilation sig- nificantly affects the outcome for burn patients [22, 26, 29-32], we divided the patients into 2 categories: those who had re- ceived mechanical ventilation within 2 days after admission and those who had not.

We compared categorical variables using the χ 2 or Fisher exact test.

To assess the propensity score, we fitted a logistic regression model for prophylactic an- tibiotic use as a function of patients' demographic and clinical characteristics as well as hospital factors, which included the following: age; sex; burn index; CCI, level of consciousness on admission; hospital type (academic or nonacademic); teaching hospital status and hospital volume; use of catecholamine Prophylactic Antibiotics for Severe Burns • CID 2016:62 (1 January) • 61 (dobutamine, norepinephrine, and/or dopamine); antithrom- bin use; treatment with recombinant human soluble thrombo- modulin; treatment with haptoglobin; and requirement for escharotomy and debridement [1, 21-26, 29-34].

We performed propensity score-adjusted logistic regression analyses to evalu- ate the effect of prophylactic antibiotics (with and without me- chanical ventilation, respectively), fitted with a generalized estimating equation to adjust for institutional clustering [35].


In addition, we also performed a 1-to-1 propensity score- matched analysis (of prophylactic antibiotic and control groups) using nearest-neighbor matching.

A match occurred when a patient in the prophylaxis group had an estimated score within 0.2 standard deviations of a patient in the control group.

We examined the balance in baseline variables using standardized differences, where >10% was regarded as imbalanced [36].

We performed logistic regression analysis fitted with generalized estimating equations to examine the association between pro- phylactic antibiotic use and 28-day in-hospital mortality and accounted for the paired nature of the propensity score- matched patients.

We used Cox regression analysis to assess dif- ferences in in-hospital survival rates between patients with and without prophylactic treatment in the propensity score- matched groups.

We performed all statistical analyses using IBM SPSS, version 22 (IBM Corp., Armonk, New York).


We identified 2893 severe burn patients treated at 583 hospitals during the 33-month study period (Figure 1).

We categorized the patients according to whether they received mechanical ventilation within 2 days after admission (n = 692) or not (n = 2201).

We further divided the patients into those with and without prophylaxis.

From the groups with and without mechanical ventilation, we generated 232 and 526 propensity score-matched pairs, respectively.


Overall, 35% (1013/2893) re- ceived prophylactic antibiotics: 60% (412/692) with mechanical ventilation and 27.3% (601/2201) without mechanical ventila- tion.

The mean doses and durations of prophylactic first- generation cephalosporin or ampicillin/sulbactam were 2.1 g/ day for 6.9 days and 4.6 g/day for 6.7 days, respectively.


Table 1 shows the baseline characteristics of the unmatched and propensity score-matched groups among mechanically ventilated patients.

In the unmatched groups, patients were more likely to have received prophylaxis if they were younger, required more norepinephrine or other drugs, or needed escharotomy and debridement.


Table 2 shows the baseline char- acteristics of the unmatched and propensity score-matched groups without mechanical ventilation.

Among the unmatched groups, patients were more likely to have received prophylaxis if they were treated at teaching hospitals, required higher doses of cat- echolamines or other drugs, or required debridement.


After pro- pensity score matching, the baseline patient characteristics were well balanced between the groups, as evident in Tables 1 and 2.


Overall 28-day in-hospital mortality among all patients was 15.2% (441/2893).

There was a significant 28-day in-hospital mortality difference between patients who were mechanically ventilated and those who were not (42.5%, 294/692 vs 6.7%, 147/2201).

Pro- pensity score- adjusted logistic regression analyses adjusted for institutional clustering revealed a significant association between the use of prophylactic antibiotics and lower 28-day in-hospital mortality in 692 mechanically ventilated patients (odds ratio [OR], 0.71; 95% confidence interval [CI], .52 to .96), but there was no significant relationship among the 2201 patients who were not mechanically ventilated (OR, 0.94; 95% CI, .61 to 1.5).


Among the mechanically ventilated patients, significant dif- ferences in 28-day in-hospital mortality occurred between the 2 62 • CID 2016:62 (1 January) • Tagami et al groups for both the unmatched (control vs prophylaxis, 48.6% vs 38.3%; difference, 10.2%; 95% CI, 2.7 to 17.7) and propensi- ty-matched groups (47.0% vs 36.6%; difference, 10.3%; 95% CI, 1.4 to 19.3; Table 3).

Logistic regression analyses showed a sig- nificant association between the use of prophylactic antibiotics and lower 28-day in-hospital mortality in the propensity- matched groups (OR, 0.65; 95% CI, .45 to .95).

There was a sig- nificant in-hospital mortality difference between the 2 groups for both the unmatched (56.8% vs 48.5%; difference, 8.2%; 95% CI, .6 to 15.8) and propensity-matched groups (55.6% vs 45.6%; difference, 9.9%; 95% CI, .8 to 19.0).

Cox regression anal- ysis showed a significant in-hospital mortality difference between the control and prophylaxis groups in the propensity-matched groups (hazard ratio, 0.70; 95% CI, .54 to .91; Figure 2).

In the propensity-matched groups, after day 2, there were no significant differences in the proportion administered anti-MRSA drugs (43.1% vs. 38.8%; difference, 4.3%; 95% CI, −4.6 to 13.2), carba- penem (34.1% vs. 37.9%; difference, −3.9%; 95% CI, −12.6 to 4.9), tazobactam/piperacillin (31.0% vs. 29.3%; difference, 2.0%; 95% CI, −7.8 to 12.0), or fourth-generation cephalosporins (12.5% vs. 14.2%; difference, −1.7%; 95% CI, −7.9 to 9.6).


The results of this study, which used a nationwide database, suggest that there may be a significant association between the use of prophylactic antibiotics and lower mortality in Several recommendations and guidelines for the initial man- agement of severe burns [17, 18, 20], including Japanese guide- lines (Japanese Society for Burn Injuries) [19], do not address systemic antibiotic prophylaxis or explicitly state that prophy- lactic antibiotics are not recommended.

The use of prophylactic antibiotics may not be safe because they could also promote the emergence of resistant strains of microorganisms, further im- peding the treatment of infections.

However, in terms of actual clinical practice in a general setting (ie, not for perioperative management) across Japan, the present study found that pro- phylactic antibiotics were administered to approximately 35% of all patients with severe burns and to 60% of those receiving mechanical ventilation.


These results are consistent with the findings of the system- atic review study by Avni et al [15].

They analyzed 136 paired cases from 5 studies that did not take the use of mechanical ven- tilation into consideration and determined that systemic antibi- otic prophylaxis significantly reduces all-cause mortality.


The methodological strength of our study lies in the evaluation of a large sample of patients across Japan with severe burns.

We conducted robust adjustment for measured confounders using propensity-matched analyses and focused on patients who did or did not receive prophylaxis within 2 days after admission.


In our study, the prophylactic antibiotics mainly targeted microor- ganisms, which in general heavily colonize the wound surface within the first 48 hours.

In severe burn patients, coagulase-neg- ative staphylococci and methicillin-sensitive S. aureus have been found to be the most prevalent isolates in admission cultures [4].

A gradual decrease in the number of isolates of coagulase- negative staphylococci and a marked increase in the numbers of S. aureus and Pseudomonas aeruginosa have been observed from admission to day 21 [4].

There was subsequently a constant increase in MRSA [4].

Accordingly, as the secondary outcome, we compared the incidence of patients who required anti-MRSA drug administration with the prophylaxis group and controls.

We found that the use of prophylactic antibiotics did not lead to an increase in the proportion of anti-MRSA drug administration.


Several small sample studies [10, 11] and systematic reviews [15, 16] have found reduced incidence of pneumonia in burn patients to be associated with systemic antibiotic regimens.

Several studies have indicated the presence of immune defects in the early phase following thermal injury among patients with severe burns.

The defects include impaired cytotoxic T-lymphocyte response, myeloid maturation arrest causing neutropenia, impaired neutrophil function, and decreased mac- rophage production [6-8] .The results of the current propensity score-matched analysis suggest that mechanically ventilated pa- tients with severe burns who received prophylactic antibiotics were more likely to survive than similar patients who did not receive the antibiotics.

These findings were robust, as demon- strated by the results obtained by the logistic regression and Cox regression analyses.

Although in the present study we were unable to determine the indications for mechanical venti- lation for each patient, mechanically ventilated patients were apparently in a more severe condition (higher burn index and mortality rate) than those who did not undergo the procedure.

Although we cannot infer a robust cause-and-effect relationship from this study, we can speculate that severe burn patients who required mechanical ventilation could have (severe enough to) had immune defects and may have suffered from sepsis (includ- ing pneumonia) in the early phase after burn injury.


This study has some limitations.

First, although it used a na- tionwide database, it was retrospective and observational, with- out randomization.

Even though we adopted propensity score matching to adjust for differences in baseline characteristics and disease severity, there may still have been bias in the form of confounders that were not measured.

However, we were able to identify the major factors that potentially affect mortality in patients with severe burns, such as age, sex, burn index, comorbidities, and use of mechanical ventilation [5, 20, 22, 26, 28, 33], and the baseline characteristics of selected pa- tients with severe burns were well balanced in the propensity score-matched groups.

Although large randomized trials are necessary to confirm these results, it may be not easy to perform such trials in a large number of patients for this life-threatening condition, especially for mechanically ventilated patients with Prophylactic Antibiotics for Severe Burns • CID 2016:62 (1 January) • 65 severe burns (mortality rate of 42.5%).

Thus, the present nation- wide study may provide the best attainable level of evidence on this issue.


Second, the database does not include data on micro- organisms, the actual incidence of infection, or the cause of death, that is, death caused by sepsis due to burn site infection or pneumonia.


Third, we did not consider topical antibiotics when we analyzed the data.

However, recent studies have sug- gested that topical antibiotic prophylaxis confers no beneficial effects on outcome [15, 16].


Finally, we evaluated only severe burn patients in a general setting (ie, not in a perioperative set- ting), as defined in previous studies [15, 16].

Thus, our results cannot be generalized to the use of prophylactic antibiotics for severe burns in perioperative settings.


The current nationwide database study using propensity score analyses found that the use of systemic prophylactic antibiotics in general settings may result in improved 28-day in-hospital mortality in mechanically ventilated patients with severe burns but not in patients who do not receive mechanical ventilation.

Future multination studies are required to validate our results.


All authors assisted with study conception and design and with data acquisition.

T. T., H. M., and H. Y. were responsible for the statistical analyses and the first draft of the manuscript.

All authors amended and commented on the manuscript and approved the final version.


This work was supported by grants for Research on Policy Planning and Evaluation from the Ministry of Health, Labour and Welfare, Japan (grant numbers H27-Policy-Designated-009 to K. F. and H. Y. and H27-Policy-Strategy-011 to H. Y.) and Grants-in-Aid for Scientific Research, Japan (grant number KAKENHI-15H05685 to T. T.).

The funders had no role in the execution of this study or interpretation of the results.


Potential conflicts of interest.

All authors: No potential conflicts of in- terest.

All authors have submitted the ICMJE Form for Disclosure of Poten- tial Conflicts of Interest.

Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.


TO THE EDITOR—We read with great interest the article by Tagami et al [1] suggesting that antibiotic prophylaxis may improve outcome in patients with burn injuries requiring invasive mechanical ventilation.

They reached their conclusion using a propensity score analysis from a Japanese nationwide database.

The authors deserve credit for addressing such an important question, because outcomes in severely ill patients with burn injuries remain poor, with most dying of sepsis.

Although we agree that antibiotic prophylaxis has been such questioned in critically ill patients with burn injuries [2], we also believe that too much reliance on the results reported by Tagami et al [1] may lead to inappropriate conclusions regarding the effectiveness of antibiotic prophylaxis in this specific population.


One major point is the setting in which patients with burn injuries were treated.

Such patients have longer intensive care unit (ICU) stays than most medical and surgical patients and most often receive several courses of antibiotic treatment owing to repeated sepsis.

The need to control antibiotic exposure to limit selection of drug-resistant pathogens therefore seems crucial and must be considered in the benefit-risk balance for antibiotic prophylaxis in these patients.

In the current study [1], most mechanically ventilated patients did receive antibiotic prophylaxis (60%), which seems a very high proportion given current recommendations [3].

However, we do not know the basis on which antibiotic prophylaxis was administrated.


Previous meta-analysis has shown that the risk of death was reduced with selective digestive decontamination in ICU patients and that systemic antimicrobial therapy was essential to this reduced risk [4].

Although short-term emergence of antimicrobial resistance seems limited, the risk of long-term resistance remains largely unexplored.

It is likely that some centers had a protocol of antibiotic prophylaxis for mechanically ventilated patients (ie, not patients with burn injuries specifically).

Centers with such protocols are likely to have implemented healthcare-associated infection prevention bundles, which may have a large impact on prognosis.

This important point is missed by the propensity score adjustment.


In other words, it is unclear whether some mechanically ventilated patients with burn injuries may have benefited from selective digestive decontamination and systemic antibiotic prophylaxis implemented for all mechanically ventilated patients.

This question is reinforced by the fact that most patients with burn injuries seem to have been treated in ICUs that were not dedicated burn units and had a low volume of such patients (>80% of mechanically ventilated patients with burn injuries were admitted to centers with <12 such admissions a year).

A control group to determine the effect of antibiotic prophylaxis in mechanically ventilated patients without burn injuries would have provided important insight in this regard.


Finally, we are concerned by the low doses of antibiotics used (2.1 g/d for first-generation cephalosporin or 4.6 g/d for ampicillin-sulbactam), given the high risk of underdosing in patients with burn injuries.

Underdosing may dramatically decrease efficacy and increase the risk of selecting drug-resistant bacteria.

In sum, we believe that Tagami et al [1] may have missed the specificity of treatment for patients with severe burn injuries in specialized centers.

The question of antibiotic prophylaxis and infection control in such patients remains open.


TO THE EDITOR—We thank Legrand and Lafaurie [1] for their interest and comments on our study [2].

Their major concerns were within-hospital clustering and the doses of antibiotics used in our study [2].

We agree with their view [1] that between-hospital differences in medical care for patients with severe burns, particularly in specialized centers, may influence outcomes.

To reduce these differences, we included several hospital factors (hospital type, teaching hospital status, and hospital volume), as well as patient-level demographic and clinical characteristics, among the dependent variables in the logistic regression model for estimating the propensity score analysis [2].

After the propensity score matching, the baseline patient characteristics related to the outcome of burns [3] and the hospital factors were well balanced between the groups [2, Tables 1 and 2].


Moreover, as a different confirmatory analysis for the propensity score matching, we performed propensity score-adjusted logistic regression analyses to evaluate the effects of prophylactic antibiotics fitted with a generalized estimating equation to adjust for within-hospital clustering [4].

Although neither the propensity score-matching analyses nor the logistic generalized estimating equation regression analyses could completely avoid the effect of within-hospital clustering, the consistent results from these 2 analyses may serve to reflect the robustness of our results.


As mentioned by Legrand and Lafaurie [1], the doses of antibiotics used in our study were lower than those used in Western countries.

However, these are the standard doses used in Japan and were approved by the Japanese Ministry of Health, Labour and Welfare.


Finally, we agree with the final comment by Legrand and Lafaurie [1] that the question of antibiotic prophylaxis and infection control in patients with burns remains open.

Further multinational studies are warranted to clarify the true effectiveness of prophylactic antibiotics in patients with severe burns.

Our study may help ignite this into a more “burning” question and spark the initiation of multinational trials.


Snake venoms are a complex mixture of proteins and polypeptides which cause a broad range of toxic effects when released by the snake into the prey during a bite.

Although snake toxins have proba- bly evolved to assist in prey capture and defense, they inadvertently cause a range of systemic effects when humans are bitten.

Many snake venoms contain procoagulant toxins that activate the clotting pathway, which results in venom induced consumption coagulopa- thy (VICC) in human envenoming, and this is the most common im- portant clinical manifestation of snakebites worldwide.

1,2 Australian elapids contain serine proteases, which closely resemble the mam- malian prothrombinase complex.

3 Exposure to these prothrombin activators leads to widespread activation of the coagulation pathway resulting in consumption of the key clotting factors, including fibrinogen, FV, and FVIII.

2,4 More recently, microangiopathic hemolytic anemia (MAHA), acute kidney injury, and thrombocytopenia have also been recognized to be as- sociated with VICC.

Although this is thought to be a form of throm- botic microangiopathy, the pathophysiology remains unclear. 2-4 Circulating microvesicles (MV) or small cell- derived extracellular vesicles have been implicated in hemostatic disorders-an increase in MV associated with pathological thrombosis, and a decrease contrib- uting to potential bleeding.

5-7 Several cell- derived MV are frequently found in the circulation-these include red cell, platelet, leukocyte, and endothelial cell- derived.

8,9 Flow cytometry is a popular and easily acces- sible technique for measuring MV and determining their cellular origin.

10 The role of circulating MV in snakebite patients is not known and we present the results of a unique investigation of MV in snakebite re- lated VICC and MAHA.


Changes in MV levels after envenoming could indicate direct toxin mediated damage to the endovascular system which has been hitherto difficult to evaluate or measure in a system- atic fashion.

To explore MV in snake envenoming further, we analyzed the levels of circulating platelet, endothelial, and red blood cell- derived MV in patients recruited to the Australian Snakebite Project (ASP) with confirmed envenoming from brown snake, tiger snake, or taipan.


Samples and clinical information were sourced from patients re- cruited to the ASP with elapid envenoming (including brown snake, tiger snake, and taipan envenoming), as previously described.

11 Citrated plasma was collected from each snakebite patient re- cruited to the study.

Peripheral blood was collected in 0.109 mol/L tri- sodium citrate and platelet- free plasma was prepared by double centrifugation of whole blood for 15 minutes at 2500×g. All sam- ples were processed within 2 hours of collection, and aliquots were stored at −80°C for further batched analysis.

Samples were then thawed at 37°C, 15 minutes before testing.

Control samples were obtained from healthy volunteer blood donors after appropriate consent-these were processed and stored in an identical fashion to the patient samples.


Prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen, and D- dimer concentrations were measured on a Behring Coagulation System (BCS; Dade Behring, Marburg, Germany) at a centralized laboratory.

Full blood count was mea- sured as per local routine laboratory protocol and blood films were examined by an independent pathologist for presence of red cell fragmentation for confirmation of MAHA.

VICC was defined as an international normalized ratio (INR) >3 with undetectable fibrino- gen, and MAHA as the occurrence of thrombocytopenia and red cell fragmentation.

11 Flow cytometry was done on a BD FACS Canto flow cytometer (BD Biosciences, San Jose, CA, USA) with combinations of antibodies specific for platelet marker (CD41a- PE; Clone HIP8, BD Biosciences), red cell marker (CD235a- APC; Clone GA- R2, BD Biosciences), en- dothelial cell marker (CD62e- APC; Clone 68- 5H11) and appropriate isotype controls in a final volume of 100 μL of PBS.

Megamix beads (Biocytex, Marseille, France) were used to gate events less than 1 μm in diameter and analysis was undertaken using FACSDiva software.

Control samples were obtained from healthy volunteer blood do- nors from the Australian Red Cross blood service.

They ranged in age from 21 to 60 years and had a mix of both sexes.

These subjects were screened for any significant medical conditions and samples obtained were processed within 4 hours from collection in a citrated tube in a manner similar to the snakebite patients' samples.


Microsoft Excel 2007 and SPSS, (IBM SPSS Statistics for Macintosh, Version 19.0., IBM Corp., Armonk, NY, USA) were used to collate and evaluate the data.

Prism 7.0a (Graphpad Software, La Jolla, CA, USA) was used for graphical representations and non- parametric analysis was undertaken to evaluate differences given the large standard deviations in the MV levels.

The normal co- hort, VICC without MAHA (VICC no MAHA) and VICC with MAHA


• Snake bites are associated with venom induced coagulopathy, microangiopathic hemolytic anaemia (MAHA) and thrombocytopaenia.


• The mechanisms of MAHA and role of MV in its pathophysiology in snake bite are not known.


• This results of this study show that red cell MV are increased in snake bites associated with MAHA whilst endothelial MV are reduced in all snakebites.


• Evaluation of circulating endovascular microvesicles provide an important mechanistic tool and have a potential role as predictive markers for studying the effects of snake venom.


(VICC + MAHA) groups were compared.

One way ANOVA (Kruskal- Wallis) test was used for non- parametric comparisons between the groups and a level of 0.05 was set for statistical significance.

The study was approved by the local ethics committee (Hunter New England ethics committee approval number 06/12/13/5.05) and by all major State and Territory human research ethics committees for the hospitals involved in the ASP.


A cohort of snakebite patient samples who had envenoming and VICC, and had baseline samples collected on admission to hospital, were evaluated.

There were 17 patient- samples with VICC alone (no evidence of MAHA; designated as VICC no MAHA) and 13 patient- samples with VICC with MAHA (designated as VICC + MAHA); and 28 normal healthy controls.

The age range for all snakebite patients was 24- 73 years and there were more males than females (see Table 1).

However, the differences between the age and gender between the groups compared was not statistically significant (Kruskal- Wallis test for age and z- test for gender).

All envenomation was by brown snake bites apart from five patients (two tiger snake and three where the type could not be determined due to insufficient pre- antivenom sam- ples).


results for baseline blood counts and circulating MV levels in the three groups are shown in the table and illustrated in Figure 1.

As expected, snakebite patients with MAHA had lower hemoglobin and platelets compared to controls and those without MAHA (P < 0.001).

Platelet MV were significantly elevated in snakebite patients with- out MAHA (VICC no MAHA) compared to controls (P = 0.009), but there were no differences between the snakebite patients with and without MAHA (P = 0.21).

Interestingly, the endothelial MV were significantly reduced in all snakebite samples compared to the con- trols (P < 0.001).

The levels of red cell MV in snakebite subjects with MAHA (VICC + MAHA group), however, were significantly higher than those without MAHA (P = 0.002) and when compared to con- trols (see Figure 1).


A markedly elevated red cell MV level may be used as a sur- rogate marker for MAHA whereas thrombocytopenia alone may Increased levels of red cell MV in snakebite patient samples with MAHA is consistent with red cell breakdown and hemolysis.

If this is a consistent finding then measuring MV at presentation could be useful as a predictive marker for MAHA in snakebite pa- tients.

This suggests an alternative mechanism for the observed microangiopathy other than endothelial damage alone due to the snake toxin.

The rapid generation of red cell MV may subsequently lead on to a consumptive coagulopathy and microvascular throm- bosis, which is often accompanied by thrombocytopenia that is typical of MAHA.


It is also possible that some of the "vesicles" may represent red cell fragments rather than "microvesicles" and it may be considered technically challenging to separate the two populations by flow cytometry alone.

However, majority of these fragments as seen on a blood film are usually in the size range of 1 μm or above, and in the range of the size of large platelets.

12 In previous studies evaluating the detection of fragmented red cells, using an automated hematology analyzer, have also used the principle of light scatter.

The average dimension of the fragments was about 30 fL with a unidimensional size of a few microns when compared to a red cell which ranges between 80 to 100 fL with a diameter of about 8 μm.

13,14 The gating strategy used in our exper- iments excluded events larger than 1 μm in the MV gate therefore ensuring only red cell MV were enumerated.


The reason for apparent low levels of circulating endothelial MV could be due to changes in the size or surface antigen expression of the MV which may be altered in response to envenomation, resulting in MV that are undetectable via our method of flow cytometry.

This was an unexpected finding that requires further investigation.


Limitations of this study include that we did not perform any functional studies in MV isolates or use alternative methods to eval- uate MV apart from flow cytometry.

Inter- laboratory as well as inter- instrument variability in flow cytometry is a concern despite the fact that it is an easily and widely available technique.

Also, MV smaller than 200 nm are usually not detectable by flow cytometry.

The use of isotype controls, correlation with functional testing and participa- tion in global standardization initiatives may reduce this variability.

15 Given the small sample size, we did not stratify the results according to gender or comorbidity status but this may be important to explore in a larger cohort.


AKE is a recipient of NSW Health Pathology/HCRA/CMN trans- lational research fellowship.

This study was partly supported by Calvary Mater Research funds and NSW Health Pathology-Hunter Trust Funds.

GKI is supported by a NHMRC Senior Research Fellowship ID1061041 and the Australian Snakebite Project is par- tially funded by an NHMRC Centre for Research Excellence Grant ID1110343.

We acknowledge the various hospitals who participated in ASP study and provided the samples as well as the study patholo- gist who provided review of blood films.

There are no conflicts of interest for any of the authors.


Snakebite envenoming is estimated to cause 95,000-125,000 deaths and up to 400,000 permanent injuries or disabilities worldwide on an annual basis.

This serious health threat has been recognized by the World Health Organization, and snakebite envenoming is classified as a category A neglected tropical disease [1].

In May of 2018, the World Health Assembly adopted a resolution on snakebite envenoming [2], thereby providing a mandate to develop a comprehensive plan to effectively control the burden of envenoming and reduce its debilitating impact.

The multisectoral strategy that is required to achieve this will address socioeconomic, environmental, and healthcare challenges.

Targeted actions to raise awareness have culminated in, among others, a recent international scientific conference where many essential stakeholders united in the fight against snakebite envenoming [3].


While the highest health burden from venomous snakebites falls on south Asia and sub-Saharan Africa [4], exact information on the number of envenomings, clinical effects of snakebites, and effective treatments are lacking on a global scale.

In an effort to address this, the Australian Snakebite Project registry has compiled information on a substantial proportion of all Australian snakebites since 2005 [5, 6].

This study clarified that the most commonly observed clinical envenoming phenotype in Australia is venom-induced consumptive coagulopathy (VICC) [5], an unbridled activation and consumption of the coagulation system.

This is due to the venom composition of the snakes that are most commonly involved in these envenomings [5]; Pseudonaja species contain procoagulant factor Xa-Va-like and Notechis species factor Xa-like enzymes [7-9].

Once injected into the blood stream of the prey, the Notechis factor Xa-like protease interacts with prey factor V(a) [10], and the circulating venom factor Xa-Va-like enzymes are capable of converting prothrombin of the prey into thrombin [10, 11].

Action of thrombin induces VICC, which is characterized by a (complete) consumption of fibrinogen and factors V and VIII, elevated D-dimer levels, and an international normalized ratio >3 [12, 13].


From the envenomed individuals that developed VICC as reported in the Australian Snakebite Project, approximately 10% presented with microangiopathic hemolytic anemia (MAHA) [5], resulting from intravascular red cell fragmentation with schistocytosis and thrombocytopenia.

Most MAHA cases were observed following taipan (Oxyuranus species, 26% MAHA cases) and brown snake (Pseudonaja species, 14% MAHA cases) envenoming, while bites from Notechis and Hoplocephalus species each led to MAHA in 7% of envenomed individuals.

Interestingly, this correlates with the venom composition of these snakes, with both Oxyuranus and Pseudonaja venom comprising factor Xa-Va-like prothrombin activators, while a factor Xa-like protease is found in the venom of Notechis and Hoplocephalus snakes [7-9].

Also, the onset of VICC appears to proceed more rapidly following envenoming by Pseudonaja snake bites relative to those of Notechis snakes [14], and MAHA only seems to occur following VICC-associated snakebites [5].

Despite these indirect observations, the exact pathophysiology of venom-associated MAHA remains unclear.


To gain insight into the venom-mediated damage to the endovascular system, in their recent article in Research and Practice in Thrombosis and Haemostasis, Enjeti and colleagues have focused on the role of microvesicles (MVs) in snakebite patients [15].

Microvesicles are shed by outward blebbing of the plasma membrane and typically function to transfer cargo, such as proteins and ribonucleic acids, from a donor cell to an acceptor cell, leading to behavioral changes in the acceptor cell.

Microvesicles may be shed by platelets, endothelial cells, megakaryocytes, and tumor cells, and the diameter of these vesicles ranges from 30 to 1000 nm [16, 17].

Elevated levels of MVs in the circulation are often associated with pathophysiological conditions such as sepsis, diabetes mellitus, cardiovascular diseases, and cancer [18-22], and MVs may even impact progression of disease.

In their study, Enjeti and colleagues primarily studied MVs generated by platelets, red blood cells, and endothelial cells.


Compared to control subjects, platelet MVs were significantly higher in the blood of snakebite patients that did not suffer from MAHA, while paradoxically red blood cell–derived MV levels were enhanced in patients experiencing MAHA [15].

In snakebite patients either suffering from MAHA or not, endothelial MVs were decreased.

These observations led the authors to speculate on the usefulness of red blood cell MVs as a surrogate marker for MAHA.

While the results of the current study are certainly of interest, a number of drawbacks may be identified.


First of all, characterization of the MVs in this study could have been more extensive; flow cytometry was applied to detect MVs, and this method only detects larger MVs.

Thus, the majority of MVs (with a diameter of <400 nmol L−1) may have been missed.

It remains unclear how many of the red blood cell MVs were actually red cell fragments, with fragmentation being a hallmark of MAHA.

Second, while the diagnostic use of MVs in snakebite patients sounds attractive, equipment allowing for high throughput analysis of MVs in snakebite patients is not readily available.

More rapid and precise diagnostic strategies to identify envenomed patients with MAHA could lead to a faster administration of specific treatment modalities.


Finally, the current study failed to answer why envenomation leads to MV generation and if these MVs play a role in MAHA and/or the coagulopathy often seen in snakebite patients.

Furthermore, it is ambiguous whether MAHA is secondary to VICC, and if so, whether fibrin deposition in the microvasculature could contribute to the pathophysiology of MAHA.

While this would be consistent with the formation of schistocytes and the observed thrombocytopenia, the correlation with both platelet and endothelial MVs remains to be identified.

Until further work is performed on the link between MVs and venom-associated MAHA in snakebite patients, a causal role for such MVs in these pathological processes may just be a popping bubble.


We thank the authors Bos and Veersteg for their insightful commentary [1] on our brief report entitled “Circulating Microvesicles in Snakebite Patients with Microangiopathy” recently published in Research and Practice in Thrombosis and Haemostasis [2].

We echo their concern that venom-induced consumption coagulopathy (VICC) is one of the most medically important envenoming syndromes worldwide, particularly in tropical areas [3].

The activation of the clotting pathway by procoagulant venom toxins produced by many Viperid snakes and certain elapids, including Australian elapids, results in consumption of clotting factors leading to high risk of major hemorrhage in the victims [4].

However, the occurrence of VICC and microangiopathic hemolysis (MAHA) is an unintended complication in human envenoming.

In animals, usually meant to be prey for snakes, venom post-synaptic neurotoxins play a more central role by paralyzing the animal [5].

Hence, in vivo animal models for coagulopathy have been difficult to establish because of differential effect of snake venom on different animal plasma [5].


MAHA occurs in a smaller proportion of snake bite victims with VICC and is often associated with thrombocytopenia/anemia [6].

The mechanisms of endothelial damage and precise risk for developing MAHA are poorly understood.

The results from our study provide, for the first time, the investigation of platelet, red cell, and endothelial-derived microvesicles (MV) as measured by flow cytometry in the plasma of snake bite patients recruited from the Australian Snakebite Project (ASP).

Essentially, in this hypothesis-generating study, it was observed that red cell MV are significantly higher in snake bite patients with VICC and MAHA when compared to snake bite patients with VICC and no MAHA (Figure 1).

The mechanisms for production of MV and red cell fragments are likely very different.

We believe that these 200- to 1000-nm MV are distinct from larger red cell fragments of a few microns that have been described from studies using the automated hematology analyzers [7, 8].


More importantly, a markedly elevated red cell MV level detected by flow cytometry may be used as a surrogate marker for MAHA whereas thrombocytopenia alone may not be able to discriminate between those snakebite patients with evolving MAHA and those without.

In spite of the limitations in using flow cytometry, it is easily available and its use in routine diagnostics is widespread.

In fact, it is one of the few techniques extensively investigated for measurement of MV and where standardization recommendations have been published from across three workshops by the International Society on Thrombosis and Haemostasis [9-11].

The MV detected by flow cytometry have also been described to be more relevant for coagulant function in human plasma [12].

As technology advances, the availability of bench-top hematology analyzers with flow-cytometric capacity in many tropical reference centers is not unforeseeable in the near future.


While our results do not resolve the mechanistic basis of MAHA, the finding of low endothelial MV in snake bite victims is intriguing.

In vitro models using endothelial cell culture exposed to snake venom could be one approach to understanding this effect.

Measuring leucocyte-derived MV would be useful in considering the role of snake venom–induced leukocyte damage and its contribution to the coagulopathic process.

Serial sampling could provide valuable information on the time course of the pathophysiological events.

We also envisage that functional studies would add value to measurement of the red cell, endothelial, and platelet “bubbles” (ie, MV) after envenoming as a segue to understanding the “trouble” (ie, pathophysiological process) underpinning VICC and MAHA.


GenBank (1) is a public database of all known nucleotide and protein sequences with supporting bibliographic and biological annotation, built and distributed by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), located on the campus of the US National Institutes of Health (NIH).


NCBI builds GenBank primarily from the direct submission of sequence data from authors.

Another major source of data is bulk submission of EST and other high-throughput data from sequencing centers.

The US Office of Patents and Trademarks (USPTO) also contributes sequence data from issued patents.

The data are supplemented by sequences submitted to other public databases.

Through a long-standing international collaboration with the EMBL Data Library (2) in the UK and the DNA Databank of Japan (DDBJ) (3), data are exchanged daily to ensure that all three sites maintain a comprehensive collection of sequence information.

NCBI makes the data available at no cost over the Internet, by FTP access and by Web text and sequence similarity search services.

NCBI also offers a wide range of WWW retrieval and analysis services which operate on the GenBank data (4).


GenBank continues to grow at an exponential rate.

Over the past 12 months 2.1 million new sequences have been added.

As of Release 113 in August 1999, GenBank contained over 3.4 billion nucleotide bases from 4.6 million different sequences.


Complete genomes (http://www.ncbi.nlm.nih.gov/Entrez/Genome/ org.html ) represent a growing portion of the database, with 14 of the 22 complete genomes now in GenBank deposited over the past 2 years.

Recent additions include Aeropyrum pernix K1, the deep sea archaea bacterium, Pyrococcus abyssi and the nearly complete genome of Caenorhabditis elegans.

There are at least 40 additional microorganism genomes, plus that of Drosophila melanogaster, that are being sequenced.

Many of these are expected to be in the public databases over the coming year.


Historically, GenBank had been doubling in size about every 18 months, but that rate has accelerated to doubling every 15 months due primarily to the enormous growth in data from expressed sequence tags (ESTs).

Over 63% of the sequences in the current GenBank release are ESTs, and current EST projects for human, mouse, rat and other organisms will contribute still more data.


Over 55 000 different species are represented in GenBank and new species are being added at the rate of 1250 per month.

Human sequences constitute 56% of the total sequences (34% of all sequences are human ESTs).

After Homo sapiens, the top species in GenBank in terms of the number of bases include Mus musculus, C.elegans, D.melanogaster and Arabidopsis thaliana.

Database sequences are processed and can be queried using a comprehensive sequence-based taxonomy (http://www. ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html ) developed by NCBI in collaboration with EMBL and DDBJ and with the valuable assistance of external advisors and curators.

The NCBI taxonomy is also covered in a separate article in this issue (4).


Each GenBank entry includes a concise description of the sequence, the scientific name and taxonomy of the source Nucleic Acids Research, 2000, Vol. 28, No. 1 organism, bibliographic references, and a table of features (http://www.ncbi.nlm.nih.gov/collab/FT/index.html ) that identifies coding regions and other sites of biological significance, such as transcription units, repeat regions, sites of mutations or modifications and other sequence features.

Protein translations for coding regions are also in the feature table.


The files in the GenBank distribution have traditionally been divided into 'divisions' that roughly correspond to taxonomic divisions, e.g., bacteria, viruses, primates and rodents.

In recent years divisions have been added as needed for specific initiatives in biology, such as divisions for EST sequences, genome survey sequences and high throughput genomic sequences.

There are currently 16 divisions.

For convenience in file transfer, the larger divisions, e.g., EST and primate, are divided into multiple files when posting the bimonthly GenBank releases on NCBI's FTP site.


ESTs continue to be the major source of new sequence records and genes.

Last year there were 1 765 860 sequences in the EST division of GenBank.

Over the past year the number of ESTs has increased by >70% to the current total of 3 017 997 sequences representing over 180 different organisms.

The top five organisms include: human, with 1 587 029 sequences (53% of the total); mouse, with 696 281 sequences (23%); rat, with 124 350 sequences (4%); nematode, with 100 844 sequences (3%); and the fruit fly, with 86 121 sequences (3%).


ESTs also continue to provide the major source of new gene discoveries.

As part of its daily processing of EST data, NCBI identifies through BLAST searches all homologies for new EST sequences and incorporates that information into the companion dbEST database (5).

In order to organize the EST data in a useful fashion, NCBI maintains the UniGene (http:// www.ncbi.nlm.nih.gov/UniGene/ ) collection of unique human (6), mouse and rat genes.

Additional information about UniGene is included in a separate article in this issue (4).


The STS division of GenBank currently contains >83 000 sequences and includes anonymous STSs based on genomic sequence as well gene-based STSs derived from the 3 ends of genes and ESTs.

These STS records usually include primer sequences, annotations and PCR reaction conditions.


The ultimate purpose for creating high resolution physical maps of the human genome is to create a scaffold for organizing large scale sequencing (7).

Physical maps based on STS landmarks are used to develop so-called 'sequence-ready' clones consisting of overlapping cosmids or BACs.

As the HTG sequence data derived from these clones are submitted to GenBank, STSs become crucial reference points for organizing, presenting and searching the data.

NCBI uses 'electronic PCR' to compare all human sequences with the contents of the STS division of GenBank; this identifies primer-binding sites on the human sequences that may be amplified in a PCR reaction.

This tool permits the assignment of an initial location on the map for sequence data and the association of existing GenBank entries to the new reference sequence.

The electronic PCR tool is also being made publicly available on the Web to enable any researcher with a new human sequence to relate that sequence to existing maps and HTG sequence data.


The Genome Survey Sequences (GSS) division of GenBank has been the fastest growing division in the last year, having increased 4.5-fold to a total of 1 008 904 records with 518 917 227 nucleotides.

GSS records represent 'random' genomic sequences, but are predominantly represented by 'BAC ends' which are single reads from bacterial artificial chromosomes used in a variety of genome sequencing projects, notably that of human (819 918 records), Oryza sativa (49 585) and Fugu rubripes (32 030 records).

The human data is being used (http:// www.ncbi.nlm.nih.gov/genome/clone ) along with the STS records in tiling the BACs used for the Human Genome Project (8).


The high throughput genomic sequences in the HTG division of GenBank are unfinished large-scale genomic records that are in transition to a finished state, after which they will be placed in the appropriate organism division (9).

These records are designated as Phase 0-3 depending on the quality of the data.

Phase 0 records consist of survey sequences generated to characterize clones and may or may not progress to Phase 1.

Phase 1 records contain unfinished sequence, and may consist of unordered, unoriented contigs with gaps.

Phase 2 records contain unfinished sequence as ordered, oriented contigs, with or without gaps.

Phase 3 records consist of finished sequence, with no gaps and may have annotations.

When a HTG record reaches phase 3 it is moved from the HTG division into the appropriate organismic division of GenBank.


It is now clear that a great number of human sequences will remain in the unfinished (HTG) division of GenBank as working draft sequence, while completed sequences will continue to move to the corresponding organismic division (PRI).

Together these two divisions should add some 2000 Mb of new genomic sequences from US-sponsored laboratories within the next year.


By agreement among the collaborative DNA sequence databases, a third identifier was introduced in February 1999 which consolidates the information present in both the gi and accession numbers.

GenBank displays this identifier on the VERSION line, which appears below the NID line in the GenBank flat-file format and is of the form 'Accession.version'.

For example, an entry appearing in the database for the first time has a VERSION number equivalent to the ACCESSION number followed by '.1' to reflect that this is the first version of the sequence in this entry, e.g., The VERSION line also displays the gi number.

If the nucleotide sequence changes, then so will the gi number and the version, but the accession will remain the same.

Although the NID line carries redundant information, this line remains in the file to ensure compatibility with existing programs.


A similar system for tracking changes in the corresponding protein translations was also introduced in February 1999.

Protein sequences now have identification numbers (in the format of three letters followed by five digits, e.g., AAA00001) that do not change, followed by a version number that increases with each subsequent version of the sequence.

This identifier appears as a qualifier for a CDS feature in the FEATURES table portion of a GenBank entry, e.g., /protein_id='AAA00001.1'


Protein sequence translations also currently receive their own unique gi number, which appears as a second qualifier on the CDS feature: /db_xref='PID:g1234567'.

The letter prefix indicates the database of origin for these identifiers (d=DDBJ, e=EMBL, g=GenBank).

Eventually, the gi number will be phased out since the new 'protein_id', complete with the version number, will represent both a unique identifier and a means to identify changes in the sequence.


The data in GenBank, and the collaborating databases EMBL and DDBJ, come from two sources: (i) individual authors who submit data directly to one of the databases, and (ii) bulk submissions from sequencing centers in the form of ESTs, STSs, GSSs or large genomic records (usually sequences from cosmids, BACs or YACs).

Data are exchanged daily with DDBJ and EMBL so that the daily updates from NCBI servers incorporate the most recently available sequence data from all sources.


Virtually all records enter GenBank as direct electronic submissions, with the majority of authors using the BankIt or Sequin programs.

Many journals require authors with sequence data to submit the data to a public database as a condition of publication.


GenBank staff can usually assign an accession number to a sequence submission within 2 working days of receipt, and do so at a rate of several hundred per day.

The accession number serves as confirmation that the sequence has been submitted and allows readers of the article to retrieve the relevant data.


All direct submissions receive a systematic quality assurance review including checking for vector contamination, verifying proper translation of coding regions, and checking for correct taxonomy and bibliographic citations.

A draft of the GenBank record is passed back to the author for review before it enters the database.


Authors have the right to request that their sequences be kept confidential until the time of publication.

In these cases, authors are instructed to inform GenBank staff of the publication date of the article in which the sequence is cited in order to ensure a timely release of the data.

GenBank policy requires that deposited sequence data be made public when the sequence or accession number is published.


Although only the submitting scientist is permitted to modify sequence data or annotations, all users are encouraged to report lags in releasing data or possible errors or omissions to GenBank at update@ncbi.

nlm.nih.gov


Several large-scale sequencing projects are producing megabases of human genomic DNA sequence.

NCBI works closely with sequencing centers to ensure timely incorporation of these data into GenBank for public release.

In parallel, NCBI has developed methods to integrate these sequences with genetic and physical map data and to search the sequences more effectively (e.g., through options in BLAST to mask Alu and other types of repetitive elements).

GenBank offers special batch procedures for large-scale sequencing groups to facilitate data submission, including the program 'fa2htgs' and other tools (10).


About 40% of individual submissions are received through a Web-based data submission tool, BankIt (http://www.ncbi.

nlm.nih.gov/BankIt ).

With BankIt, authors enter sequence information directly into a form, edit as necessary and add biological annotation (e.g., coding regions, mRNA features).

Free-form text boxes allow the submitter to further describe the sequence, without having to learn formatting rules or use restricted vocabularies.

BankIt creates a draft record in GenBank flat-file format for the user to review and revise.

BankIt is the tool of choice for simple submissions, especially when only one or a small number of records is submitted (9).

BankIt can also be used by submitters to update their existing GenBank records.


NCBI has developed a stand-alone multi-platform submission program called Sequin (http://www.ncbi.nlm.nih.gov/Sequin/ index.html ) which can also be linked online to NCBI.

Sequin handles simple sequences (e.g. a cDNA), as well as long sequences and segmented entries, for which BankIt and other Web-based submission tools are not well-suited.

Sequin has convenient editing and complex annotation capabilities and contains a number of built-in validation functions for enhanced quality assurance.


It is also designed to facilitate the submission of sequences from phylogenetic, population and mutation studies, and can incorporate alignment data.

Sequin can be used to edit and update sequence records, as well as to perform sequence analysis.

For example, Sequin can now incorporate any analysis tool available on the Web that accepts FASTA or ASN.1 (Abstract Syntax Notation 1) formatted data as its input.


In addition, Sequin is able to work on large records (e.g., the Escherichia coli genome at 5.6 Mb) and read in all of its annotations via simple tables.

Versions for Macintosh, PC and Unix computers are available via anonymous FTP to 'ncbi.nlm.nih.gov' in the 'sequin' directory.

Once a submission is completed, users can Email it to the address: gb-sub@ncbi.nlm.nih.gov .

Additional information about Sequin can be found through the NCBI home page.


Entrez (http://www.ncbi.nlm.nih.gov/entrez/ ) is an integrated database retrieval system that accesses DNA and protein sequence data, genome mapping data, population sets, the NCBI taxonomy, protein structures from the Molecular Modeling Database, MMDB (11) and MEDLINE references via PubMed.

The DNA and protein sequence data are integrated from a Nucleic Acids Research, 2000, Vol. 28, No. 1 variety of sources and therefore include more sequence data than are available within GenBank alone.

Entrez searching is provided on NCBI's Web site, via the Query Email server (query@ncbi.nlm.nih.gov ), and as a network client that can be downloaded by FTP.

Entrez is also discussed elsewhere in this issue (4).


The most frequent type of analysis performed using GenBank is the search for sequences similar to a query sequence.

NCBI offers the BLAST (http://www.ncbi.nlm.nih.gov/BLAST/ ) family of programs to locate good alignments between a query sequence and database sequences (12,13).

BLAST searching is provided on NCBI's Web site, via an Email server (blast@ncbi.nlm.nih.gov ), and as a set of stand-alone programs distributed by FTP.

BLAST is discussed in more detail in a separate article in this issue (4).


NCBI uses the ASN.1 data format for internal maintenance of GenBank, but distributes the GenBank releases in the traditional flat-file format.

The full GenBank release (issued every 2 months) and the daily updates (which also incorporate sequence data from EMBL and DDBJ) are available by anonymous FTP from 'ncbi.nlm.nih.gov'.

The full release in flat-file format is available as compressed files in the directory, 'genbank'.

A cumulative update file is contained in the sub-directory, 'daily', and a non- cumulative set of updates is contained in 'daily-nc'.

A set of sequence-only files in FASTA format, corresponding to the GenBank database subsets searched by BLAST and including the non-redundant nucleotide and protein databases, is available in the 'blast/db' directory.

Software developers creating their own interfaces or analysis tools for GenBank data are offered the NCBI ToolKit to assist in developing specialized applications.

The ToolKit software can be found in the directory 'toolbox/ ncbi_tools'.


In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources that operate on the data in GenBank and a variety of other biological data made available through NCBI’s web site.

NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser.

Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing, Human MapViewer, Human-Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved Domain Database (CDD).

Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets.

All of the resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov.


The National Center for Biotechnology Information (NCBI) at the National Institutes of Health was created in 1988 to develop information systems for molecular biology.

In addition to maintaining the GenBank [1] nucleic acid sequence database, to which data is submitted directly by the scientific community, NCBI provides data retrieval systems and computational resources for the analysis of GenBank data and the variety of other biological data made available through NCBI.

For this update, the NCBI suite of database resources is grouped into the seven categories given below.

All resources discussed below are available from the NCBI home page at http://www.ncbi.nlm.nih.gov.


Entrez [2] is an integrated database retrieval system that accesses DNA and protein sequences derived from several sources [1,3–6], genome maps, population sets, protein structures from the Molecular Modeling Database (MMDB) [7] and the biomedical literature via PubMed and Online Mendelian Inheritance in Man (OMIM), with embedded links to the NCBI taxonomy.

PubMed includes primarily the 11 million references and abstracts in MEDLINE, with links to the full-text of more than 1100 journals available on the web.

Several new features have been added to Entrez recently including searches for proteins by molecular weight range or by protein domain.

Population sets and ‘ProbeSet’ data from gene-expression experiments are also now accessible from Entrez.

New Entrez formatting options include graphical and text-mode display of PHRAP scores for HTG records and XML output for all GenBank records.

The Books database has been integrated into Entrez allowing the searching of three online scientific textbooks [8–10].


The LocusLink database of official gene names and other gene identifiers [6], was developed at NCBI in conjunction with several international collaborators, and offers a single query interface to curated sequences and descriptive information about genes.

New links from the LocusLink report lead to Proteome Inc.’s Gene Ontology, and NCBI’s Evidence Viewer which shows the alignments between GenBank sequences and the NCBI Human Genome Assembly used to derive alignment-based gene models.

Protein domains from NCBI’s Conserved Domain Database (CDD) that are detected in gene translation products are also now listed in the LocusLink report.


The Basic Local Alignment Search Tool (BLAST) family of programs [11,12] performs sequence-similarity searches, beginning with either a query sequence or a GenBank accession number.

Successful searches return a set of gapped alignments between the query and similar database sequences, with links to the full database records.

Each alignment receives a score and a measure of statistical significance, called the Expectation Value, for judging its quality.

The NCBI BLAST interface has been re-designed and offers several new search options including the specification of an Expectation Value range, rather than a threshold, for reporting alignments, and the specification of a residue range to limit searches to a portion of the query sequence.

XML output is now supported.

A new alignment format, called the ‘Hit Table’, provides a compact, tabular summary of the BLAST results including, for each database hit, the positions of alignment starts and stops, coupled with scores and Expectation Values.

In addition, BLAST can generate a taxonomically organized output that shows the distribution of BLAST hits by organism in three formats.

A particularly powerful feature of the new BLAST interface allows searches to be restricted to a database subset using standard Entrez search strings; the same restrictions may be applied to screen the output of an initially unrestricted search.

These features provide the means to effectively construct a custom database for searching, or to parse the output of a search to include only sequences of interest, respectively.

Along with the revised BLAST interface, NCBI has implemented a standard URL-API which allows complete search specifications, including BLAST parameters and search query, to be contained in the URL posted to the web page.

A ‘GetURL’ button on the BLAST pages allows for the saving of the current parameter set, but URLs for custom searches may also be constructed easily by users.

On the algorithmic side, BLAST now takes into account the amino acid composition of the query sequence in its estimation of statistical significance.

A composition-based statistical treatment, used in conventional protein BLAST searches as well as PSI-BLAST [12] searches, tends to reduce the number of false-positive database hits [13].

A useful BLAST utility, BLAST2Sequences [14], compares two DNA or protein sequences and produces a dot-plot representation of the alignments it reports.

Translated search options, such as blastx, tblastx and tblastn, now extend the program’s range beyond blastn and blastp.

Using a new nucleotide BLAST variant, called MegaBLAST [15], batches of nucleotide sequences can be pasted into a web page or uploaded from a file, and used to search for nearly exact matches in nucleotide databases.

MegaBLAST is up to 10 times faster than BLASTn for such searches.


NCBI has developed a semi-automated system for assembling both finished and unfinished human genome sequence on a regular basis so as to incorporate the most current data.

The NCBI-generated assembly of the human genome may be searched via a specialized variant called Human Genome BLAST using either nucleotide or protein queries.

Human Genome BLAST generates custom ‘Genome view’ of the BLAST hits which is integrated with the Human Genome MapViewer so that the hits can be viewed in the context of a combination of the maps used by the MapViewer, such as maps showing confirmed and predicted gene locations, or EST hits.

Finally, a special database, called the Trace Archive, which contains raw data underlying sequences generated by the various genome projects, may be searched using MegaBLAST.

The Trace Archive contains Whole Genome Shotgun (WGS) reads from the mouse as well as data from rat, human, zebrafish and worm.


BLAST-Link (Blink) is a new resource that displays pre-computed protein BLAST alignments for each protein sequence in the Entrez databases.

Blink allows for the display of subsets of these alignments by taxonomic criteria, by database of origin, relation to a complete genome, membership in a COG [16] or by relation to a 3D structure or conserved protein domain.

Blink links are displayed for protein records in Entrez as well as within LocusLink reports.


UniGene [17] is a system for automatically partitioning GenBank mRNA sequences and EST, into a non-redundant set of gene-oriented clusters with identical 3′ untranslated regions (3′ UTRs).

Six new organisms have been added to UniGene over the last year: barley, rice, Thale cress, maize, frog and wheat.

These organisms join human, mouse, rat, zebrafish and cow, bringing the total number of organisms represented to 11.

The UniGene databases are updated weekly with new EST sequences, and bimonthly with newly characterized sequences and may be downloaded by FTP.


HomoloGene is a database of both curated and calculated gene orthologs and homologs for the human, mouse, rat, zebrafish, cow, frog and fly; the latter two organisms added over the last year.

There are now over 50,000 homology groups for human and mouse.

The current datasets for the calculated orthologs and homologs and Mutually Orthologous Pairs are available via FTP.


The References Sequence (RefSeq) database [6], provides curated reference sequences for mRNAs and proteins from human and other organisms.

A new type of RefSeq, with accession number of the form NG_######, has been created to accommodate curated genomic regions.

Such regions now include the human hair keratin gene cluster on chromosome 17, the human α globin region on chromosome 16, and the human MHC class III complement gene cluster on chromosome 6, among others.


The database of Single Nucleotide Polymorphisms (dbSNP) [18] serves as a repository for both single base nucleotide substitutions and short deletion and insertion polymorphisms and now contains almost 3 million human SNPs.

A new web interface allows flexible searches by gene name and by cross-reference to other databases such as OMIM or the structure databases.

Searches for SNPs lying between two markers and batch retrieval are now also supported.


Electronic PCR (e-PCR) is a tool for locating STSs within a nucleotide sequence by comparing the query against a database of STS sequences and primer pairs.

A new non-redundant database of over 133,000 human and 80,000 non-human STSs called UniSTS is now available for such comparisons.


The Human Genome MapViewer displays NCBI’s current human genome assembly using up to seven parallel chromosomal maps simultaneously.

The maps displayed can be selected from a set of 21, and include cytogenetic maps, such as chromosomal ideograms, sequence-based maps, such as those showing contigs, genes and SNPs, and radiation hybrid maps, such as the G3 and GB4 maps.

Several new maps have recently been added to show ab initio gene models predicted by GenomeScan, EST alignments with links to UniGene clusters and mRNA alignments used to construct gene models.

The MapViewer also offers a tabular view of the data, in addition to the default graphical view, which is convenient for the import of the data into other programs for further analysis, and can be used to download user-selected segments of the NCBI Human Genome Assembly using a ‘Download/View Sequence’ link.

Supported download formats are GenBank and FASTA.

The NCBI Human Genome Assembly data are also available via FTP.

The Human Genome Resources web page collects links to all of NCBI’s human genome-related tools on one page.

A recent addition to these links is the Human BAC Resource, a catalog of large-insert, FISH-mapped clones containing sequence-tagged sites that can be used to integrate cytogenetic, radiation-hybrid, linkage and sequence maps of the human genome and to locate clone distributors.