Update app.py

app.py

@@ -146,6 +146,47 @@ PROTEINS = ["albumin","apolipoprotein","fibrinogen","vitronectin",
             "clusterin","igm","iga","igg","complement","transferrin",
             "alpha-2-macroglobulin"]
 
+# ── S1-F · PHYLO-RARE DATABASES ───────────────────────────────────────────────
+DIPG_VARIANTS = [
+    {"Variant":"H3K27M (H3F3A)","Freq_pct":78,"Pathway":"PRC2 inhibition -> global H3K27me3 loss","Drug_status":"ONC201 (clinical)","Circadian_gene":"BMAL1 suppressed"},
+    {"Variant":"ACVR1 p.R206H","Freq_pct":21,"Pathway":"BMP/SMAD hyperactivation","Drug_status":"LDN-193189 (preclinical)","Circadian_gene":"PER1 disrupted"},
+    {"Variant":"PIK3CA p.H1047R","Freq_pct":15,"Pathway":"PI3K/AKT/mTOR","Drug_status":"Copanlisib (clinical)","Circadian_gene":"CRY1 altered"},
+    {"Variant":"TP53 p.R248W","Freq_pct":14,"Pathway":"DNA damage response loss","Drug_status":"APR-246 (clinical)","Circadian_gene":"p53-CLOCK axis"},
+    {"Variant":"PDGFRA amp","Freq_pct":13,"Pathway":"RTK/RAS signalling","Drug_status":"Avapritinib (clinical)","Circadian_gene":"REV-ERB altered"},
+]
+DIPG_CSF_LNP = [
+    {"Formulation":"MC3-DSPC-Chol-PEG","Size_nm":92,"Zeta_mV":-4.1,"CSF_protein":"Beta2-microglobulin","ApoE_pct":12.4,"BBB_est":0.41,"Priority":"HIGH"},
+    {"Formulation":"DLin-KC2-DSPE-PEG","Size_nm":87,"Zeta_mV":-3.8,"CSF_protein":"Cystatin C","ApoE_pct":14.1,"BBB_est":0.47,"Priority":"HIGH"},
+    {"Formulation":"C12-200-DOPE-PEG","Size_nm":103,"Zeta_mV":-5.2,"CSF_protein":"Albumin (low)","ApoE_pct":9.8,"BBB_est":0.33,"Priority":"MEDIUM"},
+    {"Formulation":"DODAP-DSPC-Chol","Size_nm":118,"Zeta_mV":-2.1,"CSF_protein":"Transferrin","ApoE_pct":7.2,"BBB_est":0.24,"Priority":"LOW"},
+]
+UVM_VARIANTS = [
+    {"Variant":"GNAQ p.Q209L","Freq_pct":46,"Pathway":"PLCb -> PKC -> MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"METTL3 upregulated"},
+    {"Variant":"GNA11 p.Q209L","Freq_pct":32,"Pathway":"PLCb -> PKC -> MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"WTAP upregulated"},
+    {"Variant":"BAP1 loss","Freq_pct":47,"Pathway":"Chromatin remodeling -> metastasis","Drug_status":"No approved (HDAC trials)","m6A_writer":"FTO overexpressed"},
+    {"Variant":"SF3B1 p.R625H","Freq_pct":19,"Pathway":"Splicing alteration -> neoepitopes","Drug_status":"H3B-8800 (clinical)","m6A_writer":"METTL14 altered"},
+    {"Variant":"EIF1AX p.A113_splice","Freq_pct":14,"Pathway":"Translation initiation","Drug_status":"Novel -- no drug","m6A_writer":"YTHDF2 suppressed"},
+]
+UVM_VITREOUS_LNP = [
+    {"Formulation":"SM-102-DSPC-Chol-PEG","Vitreal_protein":"Hyaluronan-binding","Size_nm":95,"Zeta_mV":-3.2,"Retention_h":18,"Priority":"HIGH"},
+    {"Formulation":"Lipid-H-DOPE-PEG","Vitreal_protein":"Vitronectin dominant","Size_nm":88,"Zeta_mV":-4.0,"Retention_h":22,"Priority":"HIGH"},
+    {"Formulation":"DOTAP-DSPC-PEG","Vitreal_protein":"Albumin wash-out","Size_nm":112,"Zeta_mV":+2.1,"Retention_h":6,"Priority":"LOW"},
+    {"Formulation":"MC3-DPPC-Chol","Vitreal_protein":"Clusterin-rich","Size_nm":101,"Zeta_mV":-2.8,"Retention_h":14,"Priority":"MEDIUM"},
+]
+PAML_VARIANTS = [
+    {"Variant":"FLT3-ITD","Freq_pct":25,"Pathway":"RTK constitutive activation -> JAK/STAT","Drug_status":"Midostaurin (approved)","Ferroptosis":"GPX4 suppressed"},
+    {"Variant":"NPM1 c.860_863dupTCAG","Freq_pct":30,"Pathway":"Nuclear export deregulation","Drug_status":"APR-548 combo (clinical)","Ferroptosis":"SLC7A11 upregulated"},
+    {"Variant":"DNMT3A p.R882H","Freq_pct":18,"Pathway":"Epigenetic dysregulation","Drug_status":"Azacitidine (approved)","Ferroptosis":"ACSL4 altered"},
+    {"Variant":"CEBPA biallelic","Freq_pct":8,"Pathway":"Myeloid differentiation block","Drug_status":"Novel target","Ferroptosis":"NRF2 pathway"},
+    {"Variant":"IDH1/2 mutation","Freq_pct":15,"Pathway":"2-HG oncometabolite -> TET2 inhibition","Drug_status":"Enasidenib (approved)","Ferroptosis":"Iron metabolism disrupted"},
+]
+PAML_BM_LNP = [
+    {"Formulation":"ALC-0315-DSPC-Chol-PEG","BM_protein":"ApoE + Clusterin","Size_nm":98,"Zeta_mV":-3.5,"Marrow_uptake_pct":34,"Priority":"HIGH"},
+    {"Formulation":"MC3-DOPE-Chol-PEG","BM_protein":"Fibronectin dominant","Size_nm":105,"Zeta_mV":-4.2,"Marrow_uptake_pct":28,"Priority":"HIGH"},
+    {"Formulation":"DLin-MC3-DPPC","BM_protein":"Vitronectin-rich","Size_nm":91,"Zeta_mV":-2.9,"Marrow_uptake_pct":19,"Priority":"MEDIUM"},
+    {"Formulation":"Cationic-DOTAP-Chol","BM_protein":"Opsonin-heavy","Size_nm":132,"Zeta_mV":+8.1,"Marrow_uptake_pct":8,"Priority":"LOW"},
+]
+
 # ── LOGIC ─────────────────────────────────────────────────────────────────────
 def predict_mirna(gene):
     df = pd.DataFrame(MIRNA_DB.get(gene, []))
@@ -168,7 +209,7 @@ def predict_drug(pocket):
     ax.barh(df["Compound"], df["Final_score"], color=ACC)
     ax.set_xlabel("Final Score", color=TXT); ax.tick_params(colors=TXT)
     for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
-    ax.set_title(f"Top compounds — {pocket}", color=TXT, fontsize=10)
+    ax.set_title(f"Top compounds - {pocket}", color=TXT, fontsize=10)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-C | S1-R5 | Drug", pocket, f"Top: {df.iloc[0]['Compound'] if len(df) else 'none'}")
@@ -187,17 +228,18 @@ def predict_variant(hgvs, sift, polyphen, gnomad):
         cls  = "Pathogenic" if score > 0.6 else "Likely Pathogenic" if score > 0.4 else "Benign"
         conf = "High" if (sift < 0.01 or sift > 0.9) else "Moderate"
     colour = RED if "Pathogenic" in cls else GRN
-    icon   = "⚠️ WARNING" if "Pathogenic" in cls else "✅ OK"
+    icon   = "WARNING" if "Pathogenic" in cls else "OK"
     log_entry("S1-A | S1-R1 | OpenVariant", hgvs or f"SIFT={sift}", f"{cls} score={score}")
+    bar_w = int(score * 100)
     return (
-        f"<div style=\'background:{CARD};padding:16px;border-radius:8px;font-family:sans-serif;color:{TXT}\'>"
-        f"<p style=\'font-size:11px;color:{DIM};margin:0 0 8px\'>S1-A · PHYLO-GENOMICS · S1-R1</p>"
-        f"<h3 style=\'color:{colour};margin:0 0 8px\'>{icon} {cls}</h3>"
-        f"<p>Score: <b>{score:.3f}</b> &nbsp;|&nbsp; Confidence: <b>{conf}</b></p>"
-        f"<div style=\'background:{BORDER};border-radius:4px;height:14px\'>"
-        f"<div style=\'background:{colour};height:14px;border-radius:4px;width:{int(score*100)}%\'></div></div>"
-        f"<p style=\'margin-top:12px\'>{PLAIN.get(cls,'')}</p>"
-        f"<p style=\'font-size:11px;color:{DIM}\'>Research only. Not clinical advice.</p></div>"
+        f"<div style='background:{CARD};padding:16px;border-radius:8px;font-family:sans-serif;color:{TXT}'>"
+        f"<p style='font-size:11px;color:{DIM};margin:0 0 8px'>S1-A · PHYLO-GENOMICS · S1-R1</p>"
+        f"<h3 style='color:{colour};margin:0 0 8px'>{icon} {cls}</h3>"
+        f"<p>Score: <b>{score:.3f}</b> | Confidence: <b>{conf}</b></p>"
+        f"<div style='background:{BORDER};border-radius:4px;height:14px'>"
+        f"<div style='background:{colour};height:14px;border-radius:4px;width:{bar_w}%'></div></div>"
+        f"<p style='margin-top:12px'>{PLAIN.get(cls,'')}</p>"
+        f"<p style='font-size:11px;color:{DIM}'>Research only. Not clinical advice.</p></div>"
     )
 
 def predict_corona(size, zeta, peg, lipid):
@@ -232,10 +274,10 @@ def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10):
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-E | S1-R9 | LiquidBiopsy", f"CTHRC1={c1},FHL2={c2}", f"{label} {prob:.2f}")
     return (
-        f"<div style=\'background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;\'>"
-        f"<p style=\'font-size:11px;color:{DIM};margin:0 0 6px\'>S1-E · PHYLO-BIOMARKERS · S1-R9</p>"
-        f"<span style=\'color:{colour};font-size:24px;font-weight:bold\'>{label}</span><br>"
-        f"<span style=\'color:{TXT};font-size:14px\'>Probability: {prob:.2f}</span></div>"
+        f"<div style='background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;'>"
+        f"<p style='font-size:11px;color:{DIM};margin:0 0 6px'>S1-E · PHYLO-BIOMARKERS · S1-R9</p>"
+        f"<span style='color:{colour};font-size:24px;font-weight:bold'>{label}</span><br>"
+        f"<span style='color:{TXT};font-size:14px'>Probability: {prob:.2f}</span></div>"
     ), Image.open(buf)
 
 def predict_flow(size, zeta, peg, charge, flow_rate):
@@ -252,11 +294,11 @@ def predict_flow(size, zeta, peg, charge, flow_rate):
     ax.set_xlabel("Time (min)", color=TXT); ax.set_ylabel("% Corona", color=TXT)
     ax.tick_params(colors=TXT); ax.legend(fontsize=7, labelcolor=TXT, facecolor=CARD)
     for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
-    ax.set_title("Vroman Effect — flow vs static", color=TXT, fontsize=9)
+    ax.set_title("Vroman Effect - flow vs static", color=TXT, fontsize=9)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-D | S1-R7 | FlowCorona", f"flow={flow_rate}", f"CSI={csi}")
-    return f"**Corona Shift Index: {csi}** — {stability}", Image.open(buf)
+    return f"**Corona Shift Index: {csi}** - {stability}", Image.open(buf)
 
 def predict_bbb(smiles, pka, zeta):
     logp = smiles.count("C")*0.3 - smiles.count("O")*0.5 + 1.5
@@ -275,7 +317,7 @@ def predict_bbb(smiles, pka, zeta):
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-D | S1-R8 | LNPBrain", f"pka={pka},zeta={zeta}", f"ApoE={apoe_pct:.1f}%")
-    return f"**Predicted ApoE:** {apoe_pct:.1f}% — {tier}\n\n**BBB Probability:** {bbb_prob:.2f}", Image.open(buf)
+    return f"**Predicted ApoE:** {apoe_pct:.1f}% - {tier}\n\n**BBB Probability:** {bbb_prob:.2f}", Image.open(buf)
 
 def extract_corona(text):
     out = {"nanoparticle_composition":"","size_nm":None,"zeta_mv":None,"PDI":None,
@@ -300,54 +342,6 @@ def extract_corona(text):
     log_entry("S1-D | S1-R10 | NLP", text[:80], f"proteins={len(out['corona_proteins'])}")
     return json.dumps(out, indent=2), summary
 
-
-# ── S1-F · PHYLO-RARE DATABASES ───────────────────────────────────────────────
-
-# S1-R12b · DIPG — H3K27M + CSF LNP + Circadian
-DIPG_VARIANTS = [
-    {"Variant":"H3K27M (H3F3A)","Freq_pct":78,"Pathway":"PRC2 inhibition → global H3K27me3 loss","Drug_status":"ONC201 (clinical)","Circadian_gene":"BMAL1 suppressed"},
-    {"Variant":"ACVR1 p.R206H","Freq_pct":21,"Pathway":"BMP/SMAD hyperactivation","Drug_status":"LDN-193189 (preclinical)","Circadian_gene":"PER1 disrupted"},
-    {"Variant":"PIK3CA p.H1047R","Freq_pct":15,"Pathway":"PI3K/AKT/mTOR","Drug_status":"Copanlisib (clinical)","Circadian_gene":"CRY1 altered"},
-    {"Variant":"TP53 p.R248W","Freq_pct":14,"Pathway":"DNA damage response loss","Drug_status":"APR-246 (clinical)","Circadian_gene":"p53-CLOCK axis"},
-    {"Variant":"PDGFRA amp","Freq_pct":13,"Pathway":"RTK/RAS signalling","Drug_status":"Avapritinib (clinical)","Circadian_gene":"REV-ERB altered"},
-]
-DIPG_CSF_LNP = [
-    {"Formulation":"MC3-DSPC-Chol-PEG","Size_nm":92,"Zeta_mV":-4.1,"CSF_protein":"Beta2-microglobulin","ApoE_pct":12.4,"BBB_est":0.41,"Priority":"HIGH"},
-    {"Formulation":"DLin-KC2-DSPE-PEG","Size_nm":87,"Zeta_mV":-3.8,"CSF_protein":"Cystatin C","ApoE_pct":14.1,"BBB_est":0.47,"Priority":"HIGH"},
-    {"Formulation":"C12-200-DOPE-PEG","Size_nm":103,"Zeta_mV":-5.2,"CSF_protein":"Albumin (low)","ApoE_pct":9.8,"BBB_est":0.33,"Priority":"MEDIUM"},
-    {"Formulation":"DODAP-DSPC-Chol","Size_nm":118,"Zeta_mV":-2.1,"CSF_protein":"Transferrin","ApoE_pct":7.2,"BBB_est":0.24,"Priority":"LOW"},
-]
-
-# S1-R12c · UVM — GNAQ/GNA11 + Vitreous corona + m6A
-UVM_VARIANTS = [
-    {"Variant":"GNAQ p.Q209L","Freq_pct":46,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"METTL3 upregulated"},
-    {"Variant":"GNA11 p.Q209L","Freq_pct":32,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"WTAP upregulated"},
-    {"Variant":"BAP1 loss","Freq_pct":47,"Pathway":"Chromatin remodeling → metastasis","Drug_status":"No approved (HDAC trials)","m6A_writer":"FTO overexpressed"},
-    {"Variant":"SF3B1 p.R625H","Freq_pct":19,"Pathway":"Splicing alteration → neoepitopes","Drug_status":"H3B-8800 (clinical)","m6A_writer":"METTL14 altered"},
-    {"Variant":"EIF1AX p.A113_splice","Freq_pct":14,"Pathway":"Translation initiation","Drug_status":"Novel — no drug","m6A_writer":"YTHDF2 suppressed"},
-]
-UVM_VITREOUS_LNP = [
-    {"Formulation":"SM-102-DSPC-Chol-PEG","Vitreal_protein":"Hyaluronan-binding","Size_nm":95,"Zeta_mV":-3.2,"Retention_h":18,"Priority":"HIGH"},
-    {"Formulation":"Lipid-H-DOPE-PEG","Vitreal_protein":"Vitronectin dominant","Size_nm":88,"Zeta_mV":-4.0,"Retention_h":22,"Priority":"HIGH"},
-    {"Formulation":"DOTAP-DSPC-PEG","Vitreal_protein":"Albumin wash-out","Size_nm":112,"Zeta_mV":+2.1,"Retention_h":6,"Priority":"LOW"},
-    {"Formulation":"MC3-DPPC-Chol","Vitreal_protein":"Clusterin-rich","Size_nm":101,"Zeta_mV":-2.8,"Retention_h":14,"Priority":"MEDIUM"},
-]
-
-# S1-R12d · pAML — FLT3-ITD + BM niche corona + Ferroptosis
-PAML_VARIANTS = [
-    {"Variant":"FLT3-ITD","Freq_pct":25,"Pathway":"RTK constitutive activation → JAK/STAT","Drug_status":"Midostaurin (approved)","Ferroptosis":"GPX4 suppressed"},
-    {"Variant":"NPM1 c.860_863dupTCAG","Freq_pct":30,"Pathway":"Nuclear export deregulation","Drug_status":"APR-548 combo (clinical)","Ferroptosis":"SLC7A11 upregulated"},
-    {"Variant":"DNMT3A p.R882H","Freq_pct":18,"Pathway":"Epigenetic dysregulation","Drug_status":"Azacitidine (approved)","Ferroptosis":"ACSL4 altered"},
-    {"Variant":"CEBPA biallelic","Freq_pct":8,"Pathway":"Myeloid differentiation block","Drug_status":"Novel target","Ferroptosis":"NRF2 pathway"},
-    {"Variant":"IDH1/2 mutation","Freq_pct":15,"Pathway":"2-HG oncometabolite → TET2 inhibition","Drug_status":"Enasidenib (approved)","Ferroptosis":"Iron metabolism disrupted"},
-]
-PAML_BM_LNP = [
-    {"Formulation":"ALC-0315-DSPC-Chol-PEG","BM_protein":"ApoE + Clusterin","Size_nm":98,"Zeta_mV":-3.5,"Marrow_uptake_pct":34,"Priority":"HIGH"},
-    {"Formulation":"MC3-DOPE-Chol-PEG","BM_protein":"Fibronectin dominant","Size_nm":105,"Zeta_mV":-4.2,"Marrow_uptake_pct":28,"Priority":"HIGH"},
-    {"Formulation":"DLin-MC3-DPPC","BM_protein":"Vitronectin-rich","Size_nm":91,"Zeta_mV":-2.9,"Marrow_uptake_pct":19,"Priority":"MEDIUM"},
-    {"Formulation":"Cationic-DOTAP-Chol","BM_protein":"Opsonin-heavy","Size_nm":132,"Zeta_mV":+8.1,"Marrow_uptake_pct":8,"Priority":"LOW"},
-]
-
 # ── S1-F · PHYLO-RARE LOGIC ───────────────────────────────────────────────────
 def dipg_variants(sort_by):
     df = pd.DataFrame(DIPG_VARIANTS).sort_values(
@@ -366,7 +360,7 @@ def dipg_csf(peg, size):
     ax.set_xlabel("ApoE% in CSF corona", color=TXT)
     ax.tick_params(colors=TXT, labelsize=8)
     for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
-    ax.set_title("DIPG — CSF LNP formulations (ApoE%)", color=TXT, fontsize=9)
+    ax.set_title("DIPG - CSF LNP formulations (ApoE%)", color=TXT, fontsize=9)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-F | S1-R12b | DIPG-CSF", f"peg={peg},size={size}", "formulation ranking")
@@ -386,7 +380,7 @@ def uvm_vitreous():
     ax.set_xlabel("Vitreous retention (hours)", color=TXT)
     ax.tick_params(colors=TXT, labelsize=8)
     for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
-    ax.set_title("UVM — LNP retention in vitreous humor", color=TXT, fontsize=9)
+    ax.set_title("UVM - LNP retention in vitreous humor", color=TXT, fontsize=9)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-F | S1-R12c | UVM-vitreous", "load", "vitreous LNP ranking")
@@ -394,12 +388,11 @@ def uvm_vitreous():
 
 def paml_ferroptosis(variant):
     row = next((r for r in PAML_VARIANTS if variant in r["Variant"]), PAML_VARIANTS[0])
-    score = 0
     ferr_map = {"GPX4 suppressed": 0.85, "SLC7A11 upregulated": 0.72,
                 "ACSL4 altered": 0.61, "NRF2 pathway": 0.55, "Iron metabolism disrupted": 0.78}
     ferr_score = ferr_map.get(row["Ferroptosis"], 0.5)
     cats = ["Ferroptosis\nsensitivity", "Drug\navailable", "BM niche\ncoverage", "Data\nmaturity", "Target\nnovelty"]
-    has_drug = 0.9 if row["Drug_status"] not in ["Novel target"] else 0.3
+    has_drug = 0.9 if row["Drug_status"] != "Novel target" else 0.3
     vals = [ferr_score, has_drug, 0.6, 0.55, 1-has_drug+0.2]
     angles = np.linspace(0, 2*np.pi, len(cats), endpoint=False).tolist()
     v2, a2 = vals+[vals[0]], angles+[angles[0]]
@@ -408,7 +401,7 @@ def paml_ferroptosis(variant):
     ax.plot(a2, v2, color=ACC2, linewidth=2); ax.fill(a2, v2, color=ACC2, alpha=0.2)
     ax.set_xticks(angles); ax.set_xticklabels(cats, color=TXT, fontsize=8)
     ax.tick_params(colors=TXT)
-    ax.set_title(f"pAML · {row['Variant'][:20]}", color=TXT, fontsize=9)
+    ax.set_title(f"pAML - {row['Variant'][:20]}", color=TXT, fontsize=9)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-F | S1-R12d | pAML-ferroptosis", variant, f"ferr={ferr_score:.2f}")
@@ -419,7 +412,7 @@ def paml_ferroptosis(variant):
     _fs = f"{ferr_score:.2f}"
     summary = (
         f"<div style='background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;'>"
-        f"<p style='color:{DIM};font-size:11px;margin:0 0 6px'>S1-F · PHYLO-RARE · S1-R12d · pAML</p>"
+        f"<p style='color:{DIM};font-size:11px;margin:0 0 6px'>S1-F - PHYLO-RARE - S1-R12d - pAML</p>"
         f"<b style='color:{ACC2};font-size:15px'>{_v}</b><br>"
         f"<p style='color:{TXT};margin:6px 0'><b>Pathway:</b> {_p}</p>"
         f"<p style='color:{TXT};margin:0'><b>Drug:</b> {_d}</p>"
@@ -433,24 +426,24 @@ def paml_ferroptosis(variant):
 # ── HELPERS ───────────────────────────────────────────────────────────────────
 def section_header(code, name, tagline, projects_html):
     return (
-        f"<div style=\'background:{BG};border:1px solid {BORDER};padding:14px 18px;"
-        f"border-radius:8px;margin-bottom:12px;\'>"
-        f"<div style=\'display:flex;align-items:baseline;gap:10px;\'>"
-        f"<span style=\'color:{ACC2};font-size:16px;font-weight:700\'>{code}</span>"
-        f"<span style=\'color:{TXT};font-size:14px;font-weight:600\'>{name}</span>"
-        f"<span style=\'color:{DIM};font-size:12px\'>{tagline}</span></div>"
-        f"<div style=\'margin-top:8px;font-size:12px;color:{DIM}\'>{projects_html}</div>"
+        f"<div style='background:{BG};border:1px solid {BORDER};padding:14px 18px;"
+        f"border-radius:8px;margin-bottom:12px;'>"
+        f"<div style='display:flex;align-items:baseline;gap:10px;'>"
+        f"<span style='color:{ACC2};font-size:16px;font-weight:700'>{code}</span>"
+        f"<span style='color:{TXT};font-size:14px;font-weight:600'>{name}</span>"
+        f"<span style='color:{DIM};font-size:12px'>{tagline}</span></div>"
+        f"<div style='margin-top:8px;font-size:12px;color:{DIM}'>{projects_html}</div>"
         f"</div>"
     )
 
 def proj_badge(code, title, metric=""):
-    m = (f"<span style=\'background:#0f2a3f;color:{ACC2};padding:1px 7px;border-radius:3px;"
-         f"font-size:10px;margin-left:6px\'>{metric}</span>") if metric else ""
+    m = (f"<span style='background:#0f2a3f;color:{ACC2};padding:1px 7px;border-radius:3px;"
+         f"font-size:10px;margin-left:6px'>{metric}</span>") if metric else ""
     return (
-        f"<div style=\'background:{CARD};border-left:3px solid {ACC};"
-        f"padding:8px 12px;border-radius:0 6px 6px 0;margin-bottom:8px;\'>"
-        f"<span style=\'color:{DIM};font-size:11px\'>{code}</span>{m}<br>"
-        f"<span style=\'color:{TXT};font-size:14px;font-weight:600\'>{title}</span>"
+        f"<div style='background:{CARD};border-left:3px solid {ACC};"
+        f"padding:8px 12px;border-radius:0 6px 6px 0;margin-bottom:8px;'>"
+        f"<span style='color:{DIM};font-size:11px'>{code}</span>{m}<br>"
+        f"<span style='color:{TXT};font-size:14px;font-weight:600'>{title}</span>"
         f"</div>"
     )
 
@@ -458,7 +451,6 @@ def proj_badge(code, title, metric=""):
 css = f"""
 body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }}
 
-/* OUTER tab bar — PHYLO categories */
 .outer-tabs .tab-nav button {{
     color: {TXT} !important;
     background: {CARD} !important;
@@ -473,7 +465,6 @@ body, .gradio-container {{ background: {BG} !important; color: {TXT} !important;
     background: {BG} !important;
 }}
 
-/* INNER sub-tab bar — individual tools */
 .inner-tabs .tab-nav button {{
     color: {DIM} !important;
     background: {BG} !important;
@@ -508,108 +499,84 @@ MAP_HTML = f"""
 <div style="background:{CARD};padding:22px;border-radius:8px;font-family:monospace;
             font-size:13px;line-height:2.0;color:{TXT}">
 
-<span style="color:{ACC};font-size:16px;font-weight:bold">K R&D Lab · S1 Biomedical</span>
-<span style="color:{DIM};font-size:11px;margin-left:12px">Science Sphere — sub-direction 1</span>
+<span style="color:{ACC};font-size:16px;font-weight:bold">K R&D Lab - S1 Biomedical</span>
+<span style="color:{DIM};font-size:11px;margin-left:12px">Science Sphere - sub-direction 1</span>
 <br><br>
 
-<span style="color:{ACC2};font-weight:600">S1-A · PHYLO-GENOMICS</span>
-<span style="color:{DIM}"> — What breaks in DNA</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R1</b> &nbsp;OpenVariant
-<span style="color:{GRN}"> AUC=0.939 ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R1b</b> Somatic classifier
-<span style="color:#f59e0b"> 🔶 In progress</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R12a</b> Rare variants (DIPG · UVM)
-<span style="color:{DIM}"> 🔴 Planned</span><br><br>
-
-<span style="color:{ACC2};font-weight:600">S1-B · PHYLO-RNA</span>
-<span style="color:{DIM}"> — How to silence it via RNA</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R2</b> &nbsp;miRNA silencing (BRCA1/2, TP53)
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R3</b> &nbsp;siRNA synthetic lethal (LUAD · BRCA · COAD)
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R4</b> &nbsp;lncRNA-TREM2 ceRNA network
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R4b</b> ASO designer
-<span style="color:{GRN}"> ✅</span><br><br>
-
-<span style="color:{ACC2};font-weight:600">S1-C · PHYLO-DRUG</span>
-<span style="color:{DIM}"> — Which molecule treats it</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R5</b> &nbsp;FGFR3 RNA-directed compounds
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R5b</b> Synthetic lethal drug mapping
-<span style="color:#f59e0b"> 🔶</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R13</b> m6A × Ferroptosis × Circadian ⭐
-<span style="color:{DIM}"> 🔴 Frontier</span><br><br>
-
-<span style="color:{ACC2};font-weight:600">S1-D · PHYLO-LNP</span>
-<span style="color:{DIM}"> — How to deliver the drug</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R6</b> &nbsp;LNP corona (serum)
-<span style="color:{GRN}"> AUC=0.791 ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R7</b> &nbsp;Flow corona — Vroman effect
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R8</b> &nbsp;LNP brain / BBB / ApoE
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R10</b> AutoCorona NLP
-<span style="color:{GRN}"> F1=0.71 ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R11</b> CSF · Vitreous · Bone Marrow ⭐
-<span style="color:{DIM}"> 🔴 0 prior studies</span><br><br>
-
-<span style="color:{ACC2};font-weight:600">S1-E · PHYLO-BIOMARKERS</span>
-<span style="color:{DIM}"> — Detect without biopsy</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R9</b> &nbsp;Liquid Biopsy classifier
-<span style="color:{GRN}"> AUC=0.992* ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R9b</b> Protein panel validator
-<span style="color:#f59e0b"> 🔶</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R9c</b> ctDNA gap analysis
-<span style="color:{DIM}"> 🔴</span><br><br>
-
-<span style="color:{ACC2};font-weight:600">S1-F · PHYLO-RARE</span>
-<span style="color:{DIM}"> — Where almost nobody has looked yet (&lt;300 cases/yr)</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R12b</b> DIPG toolkit (H3K27M + CSF LNP + Circadian)
-<span style="color:#f59e0b"> 🔶 In development</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R12c</b> UVM toolkit (GNAQ/GNA11 + vitreous + m6A)
-<span style="color:#f59e0b"> 🔶 In development</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R12d</b> pAML toolkit (FLT3-ITD + BM niche + ferroptosis)
-<span style="color:#f59e0b"> 🔶 In development</span><br><br>
+<span style="color:{ACC2};font-weight:600">S1-A - PHYLO-GENOMICS</span>
+<span style="color:{DIM}"> - What breaks in DNA</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R1</b> &nbsp;OpenVariant <span style="color:{GRN}"> AUC=0.939 OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R1b</b> Somatic classifier <span style="color:#f59e0b"> In progress</span><br>
+&nbsp;&nbsp;&nbsp;+- <b>S1-R12a</b> Rare variants (DIPG / UVM) <span style="color:{DIM}"> Planned</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-B - PHYLO-RNA</span>
+<span style="color:{DIM}"> - How to silence it via RNA</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R2</b> &nbsp;miRNA silencing (BRCA1/2, TP53) <span style="color:{GRN}"> OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R3</b> &nbsp;siRNA synthetic lethal (LUAD / BRCA / COAD) <span style="color:{GRN}"> OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R4</b> &nbsp;lncRNA-TREM2 ceRNA network <span style="color:{GRN}"> OK</span><br>
+&nbsp;&nbsp;&nbsp;+- <b>S1-R4b</b> ASO designer <span style="color:{GRN}"> OK</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-C - PHYLO-DRUG</span>
+<span style="color:{DIM}"> - Which molecule treats it</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R5</b> &nbsp;FGFR3 RNA-directed compounds <span style="color:{GRN}"> OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R5b</b> Synthetic lethal drug mapping <span style="color:#f59e0b"> In progress</span><br>
+&nbsp;&nbsp;&nbsp;+- <b>S1-R13</b> m6A x Ferroptosis x Circadian * <span style="color:{DIM}"> Frontier</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-D - PHYLO-LNP</span>
+<span style="color:{DIM}"> - How to deliver the drug</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R6</b> &nbsp;LNP corona (serum) <span style="color:{GRN}"> AUC=0.791 OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R7</b> &nbsp;Flow corona - Vroman effect <span style="color:{GRN}"> OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R8</b> &nbsp;LNP brain / BBB / ApoE <span style="color:{GRN}"> OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R10</b> AutoCorona NLP <span style="color:{GRN}"> F1=0.71 OK</span><br>
+&nbsp;&nbsp;&nbsp;+- <b>S1-R11</b> CSF / Vitreous / Bone Marrow * <span style="color:{DIM}"> 0 prior studies</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-E - PHYLO-BIOMARKERS</span>
+<span style="color:{DIM}"> - Detect without biopsy</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R9</b> &nbsp;Liquid Biopsy classifier <span style="color:{GRN}"> AUC=0.992* OK</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R9b</b> Protein panel validator <span style="color:#f59e0b"> In progress</span><br>
+&nbsp;&nbsp;&nbsp;+- <b>S1-R9c</b> ctDNA gap analysis <span style="color:{DIM}"> Planned</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-F - PHYLO-RARE</span>
+<span style="color:{DIM}"> - Where almost nobody has looked yet (&lt;300 cases/yr)</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R12b</b> DIPG toolkit (H3K27M + CSF LNP + Circadian) <span style="color:#f59e0b"> In development</span><br>
+&nbsp;&nbsp;&nbsp;|- <b>S1-R12c</b> UVM toolkit (GNAQ/GNA11 + vitreous + m6A) <span style="color:#f59e0b"> In development</span><br>
+&nbsp;&nbsp;&nbsp;+- <b>S1-R12d</b> pAML toolkit (FLT3-ITD + BM niche + ferroptosis) <span style="color:#f59e0b"> In development</span><br><br>
 
 <span style="color:{DIM};font-size:11px">
-✅ Active in this demo &nbsp;·&nbsp; 🔶 In progress &nbsp;·&nbsp; 🔴 Planned / Frontier<br>
-⭐ gap research (0–1 prior studies globally) &nbsp;·&nbsp; * tissue proxy, plasma validation pending
+OK = Active in this demo &nbsp;|&nbsp; In progress &nbsp;|&nbsp; Planned / Frontier<br>
+* gap research (0-1 prior studies globally) &nbsp;|&nbsp; * tissue proxy, plasma validation pending
 </span>
 </div>
 """
 
 # ── UI ────────────────────────────────────────────────────────────────────────
-with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
+with gr.Blocks(css=css, title="K R&D Lab - S1 Biomedical") as demo:
 
     gr.Markdown(
-        "# 🔬 K R&D Lab · Science Sphere — S1 Biomedical\n"
-        "**Oksana Kolisnyk** · [KOSATIKS GROUP](https://kosatiks-group.pp.ua) &nbsp;|&nbsp; "
-        "[GitHub](https://github.com/K-RnD-Lab) &nbsp; "
-        "[HuggingFace](https://huggingface.co/K-RnD-Lab) &nbsp;|&nbsp; "
+        "# K R&D Lab - Science Sphere - S1 Biomedical\n"
+        "**Oksana Kolisnyk** - [KOSATIKS GROUP](https://kosatiks-group.pp.ua) | "
+        "[GitHub](https://github.com/K-RnD-Lab) "
+        "[HuggingFace](https://huggingface.co/K-RnD-Lab) | "
         "*Research only. Not clinical advice.*"
     )
 
-    # ═══════════════════════════════════════════════════════
-    # OUTER TABS — one per PHYLO-* category
-    # ═════════════════���═════════════════════════════════════
     with gr.Tabs(elem_classes="outer-tabs"):
 
-        # ── 🗺️ MAP ─────────────────────────────────────────
-        with gr.TabItem("🗺️ Lab Map"):
+        # ── MAP ─────────────────────────────────────────────
+        with gr.Tab("Lab Map"):
             gr.HTML(MAP_HTML)
 
-        # ── 🧬 S1-A · PHYLO-GENOMICS ───────────────────────
-        with gr.TabItem("🧬 PHYLO-GENOMICS"):
+        # ── S1-A PHYLO-GENOMICS ─────────────────────────────
+        with gr.Tab("S1-A PHYLO-GENOMICS"):
             gr.HTML(section_header(
-                "S1-A", "PHYLO-GENOMICS", "— What breaks in DNA",
-                "S1-R1 OpenVariant ✅ &nbsp;·&nbsp; S1-R1b Somatic classifier 🔶 &nbsp;·&nbsp; S1-R12a Rare variants 🔴"
+                "S1-A", "PHYLO-GENOMICS", "- What breaks in DNA",
+                "S1-R1 OpenVariant OK &nbsp;|&nbsp; S1-R1b Somatic classifier in progress &nbsp;|&nbsp; S1-R12a Rare variants planned"
             ))
             with gr.Tabs(elem_classes="inner-tabs"):
 
-                with gr.TabItem("S1-R1 · OpenVariant"):
-                    gr.HTML(proj_badge("S1-A · PHYLO-GENOMICS · S1-R1",
-                                       "OpenVariant — SNV Pathogenicity Classifier", "AUC = 0.939"))
+                with gr.Tab("S1-R1 OpenVariant"):
+                    gr.HTML(proj_badge("S1-A - PHYLO-GENOMICS - S1-R1",
+                                       "OpenVariant - SNV Pathogenicity Classifier", "AUC = 0.939"))
                     hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
                     gr.Markdown("**Or enter functional scores manually:**")
                     with gr.Row():
@@ -623,56 +590,56 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                  ["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn])
                     b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
 
-                with gr.TabItem("S1-R1b · Somatic 🔶"):
-                    gr.HTML(proj_badge("S1-A · PHYLO-GENOMICS · S1-R1b",
-                                       "Somatic Mutation Classifier — BRCA · LUAD panels", "🔶 In progress"))
+                with gr.Tab("S1-R1b Somatic"):
+                    gr.HTML(proj_badge("S1-A - PHYLO-GENOMICS - S1-R1b",
+                                       "Somatic Mutation Classifier - BRCA / LUAD panels", "In progress"))
                     gr.Markdown("> This module is in active development. Coming in the next release.")
 
-        # ── 🔬 S1-B · PHYLO-RNA ────────────────────────────
-        with gr.TabItem("🔬 PHYLO-RNA"):
+        # ── S1-B PHYLO-RNA ──────────────────────────────────
+        with gr.Tab("S1-B PHYLO-RNA"):
             gr.HTML(section_header(
-                "S1-B", "PHYLO-RNA", "— How to silence it via RNA",
-                "S1-R2 miRNA ✅ &nbsp;·&nbsp; S1-R3 siRNA ✅ &nbsp;·&nbsp; S1-R4 lncRNA ✅ &nbsp;·&nbsp; S1-R4b ASO ✅"
+                "S1-B", "PHYLO-RNA", "- How to silence it via RNA",
+                "S1-R2 miRNA OK &nbsp;|&nbsp; S1-R3 siRNA OK &nbsp;|&nbsp; S1-R4 lncRNA OK &nbsp;|&nbsp; S1-R4b ASO OK"
             ))
             with gr.Tabs(elem_classes="inner-tabs"):
 
-                with gr.TabItem("S1-R2 · miRNA"):
-                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R2",
-                                       "miRNA Silencing — BRCA1/2 · TP53 tumor suppressors"))
+                with gr.Tab("S1-R2 miRNA"):
+                    gr.HTML(proj_badge("S1-B - PHYLO-RNA - S1-R2",
+                                       "miRNA Silencing - BRCA1/2 / TP53 tumor suppressors"))
                     g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
                     b1 = gr.Button("Find miRNAs", variant="primary")
                     o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
                     gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1])
                     b1.click(predict_mirna, g1, o1)
 
-                with gr.TabItem("S1-R3 · siRNA"):
-                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R3",
-                                       "siRNA Synthetic Lethal — TP53-null · LUAD · BRCA · COAD"))
+                with gr.Tab("S1-R3 siRNA"):
+                    gr.HTML(proj_badge("S1-B - PHYLO-RNA - S1-R3",
+                                       "siRNA Synthetic Lethal - TP53-null / LUAD / BRCA / COAD"))
                     g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
                     b2 = gr.Button("Find Targets", variant="primary")
                     o2 = gr.Dataframe(label="Top 5 synthetic lethal targets")
                     gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2])
                     b2.click(predict_sirna, g2, o2)
 
-                with gr.TabItem("S1-R4 · lncRNA + ASO"):
-                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R4 + S1-R4b",
-                                       "lncRNA-TREM2 ceRNA Network + ASO Candidates · Alzheimer neuroinflammation"))
+                with gr.Tab("S1-R4 lncRNA ASO"):
+                    gr.HTML(proj_badge("S1-B - PHYLO-RNA - S1-R4 + S1-R4b",
+                                       "lncRNA-TREM2 ceRNA Network + ASO Candidates - Alzheimer neuroinflammation"))
                     b3 = gr.Button("Load Results", variant="primary")
                     o3a = gr.Dataframe(label="ceRNA Network (S1-R4)")
                     o3b = gr.Dataframe(label="ASO Candidates (S1-R4b)")
                     b3.click(get_lncrna, [], [o3a, o3b])
 
-        # ── 💊 S1-C · PHYLO-DRUG ───────────────────────────
-        with gr.TabItem("💊 PHYLO-DRUG"):
+        # ── S1-C PHYLO-DRUG ─────────────────────────────────
+        with gr.Tab("S1-C PHYLO-DRUG"):
             gr.HTML(section_header(
-                "S1-C", "PHYLO-DRUG", "— Which molecule treats it",
-                "S1-R5 FGFR3 ✅ &nbsp;·&nbsp; S1-R5b SL drug mapping 🔶 &nbsp;·&nbsp; S1-R13 m6A×Ferroptosis×Circadian 🔴⭐"
+                "S1-C", "PHYLO-DRUG", "- Which molecule treats it",
+                "S1-R5 FGFR3 OK &nbsp;|&nbsp; S1-R5b SL drug mapping in progress &nbsp;|&nbsp; S1-R13 m6A x Ferroptosis x Circadian frontier"
             ))
             with gr.Tabs(elem_classes="inner-tabs"):
 
-                with gr.TabItem("S1-R5 · FGFR3"):
-                    gr.HTML(proj_badge("S1-C · PHYLO-DRUG · S1-R5",
-                                       "FGFR3 RNA-Directed Drug Discovery · ChEMBL screen",
+                with gr.Tab("S1-R5 FGFR3"):
+                    gr.HTML(proj_badge("S1-C - PHYLO-DRUG - S1-R5",
+                                       "FGFR3 RNA-Directed Drug Discovery - ChEMBL screen",
                                        "top score 0.793"))
                     g4 = gr.Radio(["P1 (hairpin loop)","P10 (G-quadruplex)"],
                                   value="P1 (hairpin loop)", label="Target pocket")
@@ -682,25 +649,25 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
                     b4.click(predict_drug, g4, [o4t, o4p])
 
-                with gr.TabItem("S1-R13 · Frontier 🔴⭐"):
-                    gr.HTML(proj_badge("S1-C · PHYLO-DRUG · S1-R13",
-                                       "m6A × Ferroptosis × Circadian — Pan-cancer triad", "🔴 Frontier"))
+                with gr.Tab("S1-R13 Frontier"):
+                    gr.HTML(proj_badge("S1-C - PHYLO-DRUG - S1-R13",
+                                       "m6A x Ferroptosis x Circadian - Pan-cancer triad", "Frontier"))
                     gr.Markdown(
                         "> **Research gap:** This triple intersection has never been studied as an integrated system.\n\n"
-                        "> **Planned datasets:** TCGA-PAAD · GEO m6A atlases · Circadian gene panels\n\n"
+                        "> **Planned datasets:** TCGA-PAAD - GEO m6A atlases - Circadian gene panels\n\n"
                         "> **Expected timeline:** Q3 2026"
                     )
 
-        # ── 🧪 S1-D · PHYLO-LNP ────────────────────────────
-        with gr.TabItem("🧪 PHYLO-LNP"):
+        # ── S1-D PHYLO-LNP ──────────────────────────────────
+        with gr.Tab("S1-D PHYLO-LNP"):
             gr.HTML(section_header(
-                "S1-D", "PHYLO-LNP", "— How to deliver the drug to the cell",
-                "S1-R6 Corona ✅ · S1-R7 Flow ✅ · S1-R8 Brain ✅ · S1-R10 NLP ✅ · S1-R11 CSF/BM 🔴⭐"
+                "S1-D", "PHYLO-LNP", "- How to deliver the drug to the cell",
+                "S1-R6 Corona OK | S1-R7 Flow OK | S1-R8 Brain OK | S1-R10 NLP OK | S1-R11 CSF/BM frontier"
             ))
             with gr.Tabs(elem_classes="inner-tabs"):
 
-                with gr.TabItem("S1-R6 · Corona"):
-                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R6",
+                with gr.Tab("S1-R6 Corona"):
+                    gr.HTML(proj_badge("S1-D - PHYLO-LNP - S1-R6",
                                        "LNP Protein Corona (Serum)", "AUC = 0.791"))
                     with gr.Row():
                         sz = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
@@ -712,9 +679,9 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     gr.Examples([[100,-5,1.5,"Ionizable"],[80,5,0.5,"Cationic"]], inputs=[sz,zt,pg,lp])
                     b6.click(predict_corona, [sz,zt,pg,lp], o6)
 
-                with gr.TabItem("S1-R7 · Flow"):
-                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R7",
-                                       "Flow Corona — Vroman Effect · albumin→ApoE kinetics"))
+                with gr.Tab("S1-R7 Flow"):
+                    gr.HTML(proj_badge("S1-D - PHYLO-LNP - S1-R7",
+                                       "Flow Corona - Vroman Effect - albumin to ApoE kinetics"))
                     with gr.Row():
                         s8  = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
                         z8  = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
@@ -727,9 +694,9 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]], inputs=[s8,z8,pg8,ch8,fl8])
                     b8.click(predict_flow, [s8,z8,pg8,ch8,fl8], [o8t,o8p])
 
-                with gr.TabItem("S1-R8 · Brain"):
-                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R8",
-                                       "LNP Brain Delivery — ApoE% + BBB probability"))
+                with gr.Tab("S1-R8 Brain"):
+                    gr.HTML(proj_badge("S1-D - PHYLO-LNP - S1-R8",
+                                       "LNP Brain Delivery - ApoE% + BBB probability"))
                     smi = gr.Textbox(label="Ionizable lipid SMILES",
                                      value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
                     with gr.Row():
@@ -740,9 +707,9 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     gr.Examples([["CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C",6.5,-3]], inputs=[smi,pk,zt9])
                     b9.click(predict_bbb, [smi,pk,zt9], [o9t,o9p])
 
-                with gr.TabItem("S1-R10 · NLP"):
-                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R10",
-                                       "AutoCorona NLP — structured data from PMC abstracts", "F1 = 0.71"))
+                with gr.Tab("S1-R10 NLP"):
+                    gr.HTML(proj_badge("S1-D - PHYLO-LNP - S1-R10",
+                                       "AutoCorona NLP - structured data from PMC abstracts", "F1 = 0.71"))
                     txt  = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...")
                     b10  = gr.Button("Extract Data", variant="primary")
                     o10j = gr.Code(label="Extracted JSON", language="json")
@@ -754,27 +721,27 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     ]], inputs=[txt])
                     b10.click(extract_corona, txt, [o10j, o10f])
 
-                with gr.TabItem("S1-R11 · CSF/BM 🔴⭐"):
-                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R11",
-                                       "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
+                with gr.Tab("S1-R11 CSF/BM"):
+                    gr.HTML(proj_badge("S1-D - PHYLO-LNP - S1-R11",
+                                       "LNP Corona in CSF / Vitreous / Bone Marrow", "0 prior studies"))
                     gr.Markdown(
                         "> **Research gap:** Protein corona has only been characterized in serum/plasma. "
                         "CSF, vitreous humor, and bone marrow interstitial fluid remain completely unstudied.\n\n"
-                        "> **Target cancers:** DIPG (CSF) · UVM (vitreous) · pAML (bone marrow)\n\n"
-                        "> **Expected timeline:** Q2–Q3 2026"
+                        "> **Target cancers:** DIPG (CSF) / UVM (vitreous) / pAML (bone marrow)\n\n"
+                        "> **Expected timeline:** Q2-Q3 2026"
                     )
 
-        # ── 🩸 S1-E · PHYLO-BIOMARKERS ─────────────────────
-        with gr.TabItem("🩸 PHYLO-BIOMARKERS"):
+        # ── S1-E PHYLO-BIOMARKERS ───────────────────────────
+        with gr.Tab("S1-E PHYLO-BIOMARKERS"):
             gr.HTML(section_header(
-                "S1-E", "PHYLO-BIOMARKERS", "— Detect cancer without tissue biopsy",
-                "S1-R9 Liquid Biopsy ✅ &nbsp;·&nbsp; S1-R9b Panel validator 🔶 &nbsp;·&nbsp; S1-R9c ctDNA gap 🔴"
+                "S1-E", "PHYLO-BIOMARKERS", "- Detect cancer without tissue biopsy",
+                "S1-R9 Liquid Biopsy OK &nbsp;|&nbsp; S1-R9b Panel validator in progress &nbsp;|&nbsp; S1-R9c ctDNA gap planned"
             ))
             with gr.Tabs(elem_classes="inner-tabs"):
 
-                with gr.TabItem("S1-R9 · Liquid Biopsy"):
-                    gr.HTML(proj_badge("S1-E · PHYLO-BIOMARKERS · S1-R9",
-                                       "Liquid Biopsy Classifier — CTHRC1 · FHL2 · LDHA panel",
+                with gr.Tab("S1-R9 Liquid Biopsy"):
+                    gr.HTML(proj_badge("S1-E - PHYLO-BIOMARKERS - S1-R9",
+                                       "Liquid Biopsy Classifier - CTHRC1 / FHL2 / LDHA panel",
                                        "AUC = 0.992*"))
                     with gr.Row():
                         p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1")
@@ -794,92 +761,149 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                 inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10])
                     b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p])
 
-                with gr.TabItem("S1-R9b · Validator 🔶"):
-                    gr.HTML(proj_badge("S1-E · PHYLO-BIOMARKERS · S1-R9b",
-                                       "Protein Panel Validator — multi-cancer plasma validation", "🔶 In progress"))
-                    gr.Markdown("> Coming next — validates S1-R9 results against GEO plasma proteomics datasets.")
+                with gr.Tab("S1-R9b Validator"):
+                    gr.HTML(proj_badge("S1-E - PHYLO-BIOMARKERS - S1-R9b",
+                                       "Protein Panel Validator - multi-cancer plasma validation", "In progress"))
+                    gr.Markdown("> Coming next - validates S1-R9 results against GEO plasma proteomics datasets.")
 
-        # ── 📓 JOURNAL ──────────────────────────────────────
-        with gr.TabItem("📓 Journal"):
+        # ── S1-F PHYLO-RARE ─────────────────────────────────
+        with gr.Tab("S1-F PHYLO-RARE"):
+            gr.HTML(section_header(
+                "S1-F", "PHYLO-RARE", "- Where almost nobody has looked yet (<300 cases/yr)",
+                "S1-R12b DIPG in development &nbsp;|&nbsp; S1-R12c UVM in development &nbsp;|&nbsp; S1-R12d pAML in development"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.Tab("S1-R12b DIPG"):
+                    gr.HTML(proj_badge("S1-F - PHYLO-RARE - S1-R12b",
+                                       "DIPG - H3K27M + CSF LNP + Circadian clock", "In development"))
+                    gr.Markdown("**Variant landscape:**")
+                    sort_by = gr.Radio(["Frequency","Drug status"], value="Frequency", label="Sort by")
+                    b_dv = gr.Button("Load DIPG Variants", variant="primary")
+                    o_dv = gr.Dataframe(label="DIPG variants")
+                    b_dv.click(dipg_variants, sort_by, o_dv)
+
+                    gr.Markdown("**CSF LNP formulation screen:**")
+                    with gr.Row():
+                        dipg_peg  = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%")
+                        dipg_size = gr.Slider(50,200,value=95,step=5,label="Target size (nm)")
+                    b_dc = gr.Button("Rank CSF Formulations", variant="primary")
+                    o_dc_t = gr.Dataframe(label="CSF LNP ranking")
+                    o_dc_p = gr.Image(label="ApoE% in CSF")
+                    b_dc.click(dipg_csf, [dipg_peg, dipg_size], [o_dc_t, o_dc_p])
+
+                with gr.Tab("S1-R12c UVM"):
+                    gr.HTML(proj_badge("S1-F - PHYLO-RARE - S1-R12c",
+                                       "Uveal Melanoma - GNAQ/GNA11 + vitreous corona + m6A", "In development"))
+                    b_uv = gr.Button("Load UVM Variants", variant="primary")
+                    o_uv = gr.Dataframe(label="UVM variants + m6A writers")
+                    b_uv.click(uvm_variants, [], o_uv)
+
+                    gr.Markdown("**Vitreous LNP retention screen:**")
+                    b_vt = gr.Button("Rank Vitreous Formulations", variant="primary")
+                    o_vt_t = gr.Dataframe(label="Vitreous LNP ranking")
+                    o_vt_p = gr.Image(label="Retention time (hours)")
+                    b_vt.click(uvm_vitreous, [], [o_vt_t, o_vt_p])
+
+                with gr.Tab("S1-R12d pAML"):
+                    gr.HTML(proj_badge("S1-F - PHYLO-RARE - S1-R12d",
+                                       "pediatric AML - FLT3-ITD + BM niche + ferroptosis sensitivity", "In development"))
+                    paml_var = gr.Dropdown(
+                        ["FLT3-ITD","NPM1 c.860_863dupTCAG","DNMT3A p.R882H","CEBPA biallelic","IDH1/2 mutation"],
+                        value="FLT3-ITD", label="Select variant")
+                    b_pf = gr.Button("Analyze Ferroptosis Profile", variant="primary")
+                    o_pf_t = gr.HTML()
+                    o_pf_p = gr.Image(label="Radar profile")
+                    gr.Examples([["FLT3-ITD"],["NPM1 c.860_863dupTCAG"],["IDH1/2 mutation"]], inputs=[paml_var])
+                    b_pf.click(paml_ferroptosis, paml_var, [o_pf_t, o_pf_p])
+
+                    gr.Markdown("**Bone Marrow LNP niche formulations:**")
+                    gr.Dataframe(value=pd.DataFrame(PAML_BM_LNP), label="BM LNP candidates")
+
+        # ── JOURNAL ─────��───────────────────────────────────
+        with gr.Tab("Journal"):
             gr.Markdown("### Lab Journal\nEvery tool call auto-logged with project code.")
             with gr.Row():
-                note_text = gr.Textbox(label="📝 Observation", placeholder="What did you discover?", lines=3)
+                note_text = gr.Textbox(label="Observation", placeholder="What did you discover?", lines=3)
                 note_tab  = gr.Textbox(label="Project code (e.g. S1-R1)", value="General")
             note_last  = gr.Textbox(visible=False)
-            save_btn   = gr.Button("💾 Save", variant="primary")
+            save_btn   = gr.Button("Save", variant="primary")
             save_msg   = gr.Markdown()
-            journal_df = gr.Dataframe(label="📋 Full History", value=load_journal(), interactive=False)
-            refresh_btn = gr.Button("🔄 Refresh")
+            journal_df = gr.Dataframe(label="Full History", value=load_journal(), interactive=False)
+            refresh_btn = gr.Button("Refresh")
             refresh_btn.click(load_journal, [], journal_df)
             save_btn.click(save_note, [note_text,note_tab,note_last], [save_msg,journal_df])
 
-        # ── 📚 LEARNING ─────────────────────────────────────
-        with gr.TabItem("📚 Learning"):
+        # ── LEARNING ────────────────────────────────────────
+        with gr.Tab("Learning"):
             gr.Markdown("""
-## 🧪 Guided Investigations — S1 Biomedical
-> 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
+## Guided Investigations - S1 Biomedical
+> Beginner -> Intermediate -> Advanced
 
 ---
-### 🟢 Case 1 · S1-A · S1-R1
+### Case 1 - S1-A - S1-R1
 **Why does the same position give two different outcomes?**
-1. PHYLO-GENOMICS → OpenVariant → `BRCA1:p.R1699Q` → Benign
-2. Enter `BRCA1:p.R1699W` → Pathogenic
-3. Same position, different amino acid — Q (polar) vs W (bulky-aromatic)
+1. PHYLO-GENOMICS -> OpenVariant -> `BRCA1:p.R1699Q` -> Benign
+2. Enter `BRCA1:p.R1699W` -> Pathogenic
+3. Same position, different amino acid - Q (polar) vs W (bulky-aromatic)
 
 ---
-### 🟢 Case 2 · S1-D · S1-R6 + S1-R8
+### Case 2 - S1-D - S1-R6 + S1-R8
 **How does PEG% control which protein coats the nanoparticle?**
-1. PHYLO-LNP → Corona → Ionizable, Zeta=−5, PEG=**0.5%** → note protein
-2. Change PEG=**2.5%** → compare
-3. Brain tab → pKa=6.5 → check ApoE%
+1. PHYLO-LNP -> Corona -> Ionizable, Zeta=-5, PEG=0.5% -> note protein
+2. Change PEG=2.5% -> compare
+3. Brain tab -> pKa=6.5 -> check ApoE%
 
 ---
-### 🟡 Case 3 · S1-D · S1-R7
+### Case 3 - S1-D - S1-R7
 **Does blood flow reshape the corona over time?**
-1. PHYLO-LNP → Flow → Flow=0 → observe ApoE curve
-2. Flow=40 (arterial) → compare
+1. PHYLO-LNP -> Flow -> Flow=0 -> observe ApoE curve
+2. Flow=40 (arterial) -> compare
 3. At what minute does ApoE dominate?
 
 ---
-### 🟡 Case 4 · S1-B · S1-R3
+### Case 4 - S1-B - S1-R3
 **Which cancer has the most novel (undrugged) siRNA targets?**
-1. PHYLO-RNA → siRNA → LUAD → count "Novel"
+1. PHYLO-RNA -> siRNA -> LUAD -> count Novel
 2. Repeat BRCA, COAD
 
 ---
-### 🔴 Case 5 · S1-E · S1-R9
+### Case 5 - S1-E - S1-R9
 **Minimum protein signal that flips to CANCER?**
-1. PHYLO-BIOMARKERS → Liquid Biopsy → all=0 → HEALTHY
-2. Set CTHRC1=2.5, FHL2=2.0, LDHA=1.8 → observe
+1. PHYLO-BIOMARKERS -> Liquid Biopsy -> all=0 -> HEALTHY
+2. Set CTHRC1=2.5, FHL2=2.0, LDHA=1.8 -> observe
 3. Reset. Increase only CTHRC1 step by step.
 
 ---
-### 🔴 Case 6 · S1-B + S1-C — Cross-tool convergence
-1. PHYLO-RNA → miRNA → TP53 → find top targets (BCL2, CDK6)
-2. PHYLO-DRUG → FGFR3 → check CDK6 pathway overlap
-3. PHYLO-RNA → siRNA → BRCA → does CDK6 appear?
+### Case 6 - S1-F - S1-R12b + S1-R12d - Rare cancer cross-tool
+1. PHYLO-RARE -> DIPG -> load variants -> note H3K27M frequency (78%)
+2. PHYLO-RARE -> pAML -> FLT3-ITD -> check ferroptosis sensitivity
+3. PHYLO-RARE -> UVM -> vitreous LNP -> which formulation has highest retention?
 
 ---
-### 📖 Tool Index
+### Tool Index
 | Code | Module | Tool | Metric |
 |------|--------|------|--------|
 | S1-R1 | PHYLO-GENOMICS | OpenVariant | AUC=0.939 |
 | S1-R2 | PHYLO-RNA | miRNA silencing | top: hsa-miR-148a |
 | S1-R3 | PHYLO-RNA | siRNA SL | SPC24 top LUAD |
-| S1-R4 | PHYLO-RNA | lncRNA-TREM2 | CYTOR→AKT1 |
+| S1-R4 | PHYLO-RNA | lncRNA-TREM2 | CYTOR->AKT1 |
 | S1-R5 | PHYLO-DRUG | FGFR3 drug | score=0.793 |
 | S1-R6 | PHYLO-LNP | Corona | AUC=0.791 |
-| S1-R7 | PHYLO-LNP | Flow Vroman | 3–4× faster |
-| S1-R8 | PHYLO-LNP | LNP Brain | pKa 6.2–6.8 |
+| S1-R7 | PHYLO-LNP | Flow Vroman | 3-4x faster |
+| S1-R8 | PHYLO-LNP | LNP Brain | pKa 6.2-6.8 |
 | S1-R9 | PHYLO-BIOMARKERS | Liquid Biopsy | AUC=0.992* |
 | S1-R10 | PHYLO-LNP | AutoCorona NLP | F1=0.71 |
+| S1-R12b | PHYLO-RARE | DIPG + CSF LNP | H3K27M=78% |
+| S1-R12c | PHYLO-RARE | UVM vitreous | GNAQ=46% |
+| S1-R12d | PHYLO-RARE | pAML ferroptosis | FLT3-ITD |
 """)
 
     gr.Markdown(
-        "---\n**K R&D Lab** · Science Sphere · S1 Biomedical · "
-        "[GitHub](https://github.com/K-RnD-Lab) · "
-        "[HuggingFace](https://huggingface.co/K-RnD-Lab) · "
-        "[KOSATIKS GROUP 🦈](https://kosatiks-group.pp.ua) · MIT License"
+        "---\n**K R&D Lab** - Science Sphere - S1 Biomedical - "
+        "[GitHub](https://github.com/K-RnD-Lab) - "
+        "[HuggingFace](https://huggingface.co/K-RnD-Lab) - "
+        "[KOSATIKS GROUP](https://kosatiks-group.pp.ua) - MIT License"
     )
 
-demo.launch(server_name="0.0.0.0", server_port=7860)
+demo.launch(server_name="0.0.0.0", server_port=7860)