Update app.py

app.py

@@ -15,6 +15,9 @@ CARD = "#1e293b"
 ACC  = "#f97316"
 ACC2 = "#38bdf8"
 TXT  = "#f1f5f9"
+GRN  = "#22c55e"
+RED  = "#ef4444"
+DIM  = "#64748b"
 
 LOG_PATH = Path("/tmp/lab_journal.csv")
 
@@ -25,13 +28,9 @@ def log_entry(tab, inputs, result, note=""):
             w = csv.DictWriter(f, fieldnames=["timestamp","tab","inputs","result","note"])
             if write_header:
                 w.writeheader()
-            w.writerow({
-                "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
-                "tab": tab,
-                "inputs": str(inputs),
-                "result": str(result)[:200],
-                "note": note
-            })
+            w.writerow({"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
+                        "tab": tab, "inputs": str(inputs),
+                        "result": str(result)[:200], "note": note})
     except Exception:
         pass
 
@@ -47,6 +46,7 @@ def save_note(note, tab, last_result):
     log_entry(tab, "", last_result, note)
     return "✅ Saved!", load_journal()
 
+# ── DATABASES ─────────────────────────────────────────────────────────────────
 MIRNA_DB = {
     "BRCA2": [
         {"miRNA":"hsa-miR-148a-3p","log2FC":-0.70,"padj":0.013,"targets":"DNMT1, AKT2","pathway":"Epigenetic reprogramming"},
@@ -70,7 +70,6 @@ MIRNA_DB = {
         {"miRNA":"hsa-miR-215-5p","log2FC":-0.51,"padj":0.038,"targets":"DTL, DHFR","pathway":"DNA damage response"},
     ],
 }
-
 SIRNA_DB = {
     "LUAD": [
         {"Gene":"SPC24","dCERES":-0.175,"log2FC":1.13,"Drug_status":"Novel","siRNA":"GCAGCUGAAGAAACUGAAU"},
@@ -94,7 +93,6 @@ SIRNA_DB = {
         {"Gene":"PKMYT1","dCERES":-0.122,"log2FC":1.07,"Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"},
     ],
 }
-
 CERNA = [
     {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p","target":"AKT1","pathway":"TREM2 core signaling"},
     {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p","target":"NFKB1","pathway":"Neuroinflammation"},
@@ -109,7 +107,6 @@ ASO = [
     {"lncRNA":"LINC00847","position":89,"accessibility":0.598,"GC_pct":56,"Tm":48.3,"priority":"MEDIUM"},
     {"lncRNA":"ZFAS1","position":312,"accessibility":0.571,"GC_pct":48,"Tm":45.5,"priority":"MEDIUM"},
 ]
-
 FGFR3 = {
     "P1 (hairpin loop)": [
         {"Compound":"CHEMBL1575701","RNA_score":0.809,"Toxicity":0.01,"Final_score":0.793},
@@ -126,7 +123,6 @@ FGFR3 = {
         {"Compound":"Berberine","RNA_score":0.735,"Toxicity":3.2,"Final_score":0.708},
     ],
 }
-
 VARIANT_DB = {
     "BRCA1:p.R1699Q": {"score":0.03,"cls":"Benign","conf":"High"},
     "BRCA1:p.R1699W": {"score":0.97,"cls":"Pathogenic","conf":"High"},
@@ -137,10 +133,10 @@ VARIANT_DB = {
     "ALK:p.F1174L":   {"score":0.94,"cls":"Pathogenic","conf":"High"},
 }
 PLAIN = {
-    "Pathogenic":       "This variant is likely to cause disease. Clinical follow-up is strongly recommended.",
-    "Likely Pathogenic":"This variant is probably harmful. Discuss with your doctor.",
-    "Benign":           "This variant is likely harmless. Common in the general population.",
-    "Likely Benign":    "This variant is probably harmless. No strong reason for concern.",
+    "Pathogenic":        "This variant is likely to cause disease. Clinical follow-up is strongly recommended.",
+    "Likely Pathogenic": "This variant is probably harmful. Discuss with your doctor.",
+    "Benign":            "This variant is likely harmless. Common in the general population.",
+    "Likely Benign":     "This variant is probably harmless. No strong reason for concern.",
 }
 BM_W = {
     "CTHRC1":0.18,"FHL2":0.15,"LDHA":0.14,"P4HA1":0.13,
@@ -151,18 +147,19 @@ PROTEINS = ["albumin","apolipoprotein","fibrinogen","vitronectin",
             "clusterin","igm","iga","igg","complement","transferrin",
             "alpha-2-macroglobulin"]
 
+# ── LOGIC ─────────────────────────────────────────────────────────────────────
 def predict_mirna(gene):
     df = pd.DataFrame(MIRNA_DB.get(gene, []))
-    log_entry("BRCA2 miRNA", gene, f"Found {len(df)} miRNAs for {gene}")
+    log_entry("S1-B | S1-R2 | miRNA", gene, f"{len(df)} miRNAs")
     return df
 
 def predict_sirna(cancer):
     df = pd.DataFrame(SIRNA_DB.get(cancer, []))
-    log_entry("TP53 siRNA", cancer, f"Found {len(df)} targets for {cancer}")
+    log_entry("S1-B | S1-R3 | siRNA", cancer, f"{len(df)} targets")
     return df
 
 def get_lncrna():
-    log_entry("lncRNA-TREM2", "load", "ceRNA+ASO tables")
+    log_entry("S1-B | S1-R4 | lncRNA", "load", "ceRNA+ASO")
     return pd.DataFrame(CERNA), pd.DataFrame(ASO)
 
 def predict_drug(pocket):
@@ -172,15 +169,11 @@ 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("#334155")
+    for sp in ax.spines.values(): sp.set_edgecolor("#334155")
     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("FGFR3 Drug", pocket, f"Top: {df.iloc[0]['Compound'] if len(df) else 'none'}")
+    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'}")
     return df, Image.open(buf)
 
 def predict_variant(hgvs, sift, polyphen, gnomad):
@@ -190,27 +183,26 @@ def predict_variant(hgvs, sift, polyphen, gnomad):
         cls, conf, score = r["cls"], r["conf"], r["score"]
     else:
         score = 0.0
-        if sift < 0.05:      score += 0.4
-        if polyphen > 0.85:  score += 0.35
-        if gnomad < 0.0001:  score += 0.25
+        if sift < 0.05:     score += 0.4
+        if polyphen > 0.85: score += 0.35
+        if gnomad < 0.0001: score += 0.25
         score = round(score, 3)
-        cls  = ("Pathogenic" if score > 0.6 else
-                "Likely Pathogenic" if score > 0.4 else "Benign")
+        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 = "#ef4444" if "Pathogenic" in cls else "#22c55e"
+    colour = RED if "Pathogenic" in cls else GRN
     icon   = "⚠️ WARNING" if "Pathogenic" in cls else "✅ OK"
-    bar_w  = int(score * 100)
     explanation = PLAIN.get(cls, "")
-    log_entry("OpenVariant", hgvs or f"SIFT={sift}", f"{cls} score={score}")
+    log_entry("S1-A | S1-R1 | OpenVariant", hgvs or f"SIFT={sift}", f"{cls} score={score}")
     return (
-        f"<div style='background:{CARD};padding:16px;border-radius:8px;"
-        f"font-family:sans-serif;color:{TXT}'>"
-        f"<h3 style='color:{colour}'>{icon} {cls}</h3>"
+        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\'>"
+        f"S1 · Biomedical &nbsp;›&nbsp; S1-A · PHYLO-GENOMICS &nbsp;›&nbsp; S1-R1 · OpenVariant</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:#334155;border-radius:4px;height:16px'>"
-        f"<div style='background:{colour};height:16px;border-radius:4px;width:{bar_w}%'></div></div>"
-        f"<p style='margin-top:12px'>{explanation}</p>"
-        f"<p style='font-size:11px;color:#64748b'>Research only. Not clinical.</p>"
+        f"<div style=\'background:#334155;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\'>{explanation}</p>"
+        f"<p style=\'font-size:11px;color:{DIM}\'>Research only. Not clinical advice.</p>"
         f"</div>"
     )
 
@@ -221,14 +213,12 @@ def predict_corona(size, zeta, peg, lipid):
     if abs(zeta) < 10: score += 1
     if peg > 1.5:      score += 2
     if size < 100:     score += 1
-    proteins = ["ApoE","Albumin","Fibrinogen","Vitronectin","ApoA-I"]
-    dominant = proteins[min(score, 4)]
-    efficacy = ("High" if score >= 4 else "Medium" if score >= 2 else "Low")
-    log_entry("LNP Corona", f"size={size},zeta={zeta},peg={peg},lipid={lipid}",
-              f"dominant={dominant},efficacy={efficacy}")
+    dominant = ["ApoE","Albumin","Fibrinogen","Vitronectin","ApoA-I"][min(score, 4)]
+    efficacy = "High" if score >= 4 else "Medium" if score >= 2 else "Low"
+    log_entry("S1-D | S1-R6 | Corona", f"size={size},peg={peg}", f"dominant={dominant}")
     return (f"**Dominant corona protein:** {dominant}\n\n"
-            f"**Predicted efficacy class:** {efficacy}\n\n"
-            f"**Composite score:** {score}/6")
+            f"**Predicted efficacy:** {efficacy}\n\n"
+            f"**Score:** {score}/6")
 
 def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10):
     vals    = [c1,c2,c3,c4,c5,c6,c7,c8,c9,c10]
@@ -237,209 +227,339 @@ def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10):
     raw     = sum(v*w for v,w in zip(vals, weights))
     prob    = 1 / (1 + np.exp(-raw * 2))
     label   = "CANCER" if prob > 0.5 else "HEALTHY"
-    colour  = "#ef4444" if prob > 0.5 else "#22c55e"
+    colour  = RED if prob > 0.5 else GRN
     contribs = [v*w for v,w in zip(vals, weights)]
-    cols     = [ACC if c > 0 else ACC2 for c in contribs]
     fig, ax  = plt.subplots(figsize=(6, 3.5), facecolor=CARD)
     ax.set_facecolor(CARD)
-    ax.barh(names, contribs, color=cols)
+    ax.barh(names, contribs, color=[ACC if c > 0 else ACC2 for c in contribs])
     ax.axvline(0, color=TXT, linewidth=0.8)
     ax.set_xlabel("Contribution to cancer score", color=TXT)
     ax.tick_params(colors=TXT, labelsize=8)
-    for sp in ax.spines.values():
-        sp.set_edgecolor("#334155")
+    for sp in ax.spines.values(): sp.set_edgecolor("#334155")
     ax.set_title("Protein contributions", 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("Liquid Biopsy", f"CTHRC1={c1},FHL2={c2}...", f"{label} prob={prob:.2f}")
+    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:12px;border-radius:8px;"
-        f"color:{colour};font-size:20px;font-family:sans-serif'>"
-        f"<b>{label}</b><br>"
-        f"<span style='color:{TXT};font-size:14px'>Probability: {prob:.2f}</span></div>"
+        f"<div style=\'background:{CARD};padding:12px;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:22px;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):
-    csi = ((flow_rate/40)*0.6 + (peg/5)*0.2 +
-           (1 if charge == "Cationic" else 0)*0.2)
-    csi = round(min(csi, 1.0), 3)
-    stability = ("High remodeling" if csi > 0.6 else
-                 "Medium" if csi > 0.3 else "Stable")
+    csi = round(min((flow_rate/40)*0.6 + (peg/5)*0.2 + (1 if charge=="Cationic" else 0)*0.2, 1.0), 3)
+    stability = "High remodeling" if csi > 0.6 else "Medium" if csi > 0.3 else "Stable"
     t  = np.linspace(0, 60, 200)
     kf = 0.03 * (1 + flow_rate/40)
     ks = 0.038 * (1 + flow_rate/40)
     fig, ax = plt.subplots(figsize=(6, 3.5), facecolor=CARD)
     ax.set_facecolor(CARD)
     ax.plot(t, 60*np.exp(-0.03*t)+20, color="#60a5fa", ls="--", label="Albumin (static)")
-    ax.plot(t, 60*np.exp(-kf*t)+10,   color="#60a5fa",           label="Albumin (flow)")
+    ax.plot(t, 60*np.exp(-kf*t)+10,   color="#60a5fa",          label="Albumin (flow)")
     ax.plot(t, 14*(1-np.exp(-0.038*t))+5, color=ACC, ls="--",   label="ApoE (static)")
     ax.plot(t, 20*(1-np.exp(-ks*t))+5,    color=ACC,             label="ApoE (flow)")
-    ax.set_xlabel("Time (min)", color=TXT)
-    ax.set_ylabel("% Corona",   color=TXT)
+    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("#334155")
-    ax.set_title("Vroman Effect", color=TXT, fontsize=9)
+    for sp in ax.spines.values(): sp.set_edgecolor("#334155")
+    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("Flow Corona", f"flow={flow_rate},charge={charge}", f"CSI={csi},{stability}")
+    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)
 
 def predict_bbb(smiles, pka, zeta):
     logp     = smiles.count("C")*0.3 - smiles.count("O")*0.5 + 1.5
     apoe_pct = max(0, min(40, (7.0-pka)*8 + abs(zeta)*0.5 + logp*0.8))
     bbb_prob = min(0.95, apoe_pct/30)
-    tier     = ("HIGH (>20%)" if apoe_pct > 20 else
-                "MEDIUM (10-20%)" if apoe_pct > 10 else "LOW (<10%)")
-    cats   = ["ApoE%","BBB","logP","pKa fit","Zeta"]
-    vals   = [apoe_pct/40, bbb_prob, min(logp/5,1),
-              (7-abs(pka-6.5))/7, (10-abs(zeta))/10]
-    angles = np.linspace(0, 2*np.pi, len(cats), endpoint=False).tolist()
-    v2, a2 = vals+[vals[0]], angles+[angles[0]]
-    fig, ax = plt.subplots(figsize=(5, 4), subplot_kw={"polar":True}, facecolor=CARD)
+    tier     = "HIGH (>20%)" if apoe_pct > 20 else "MEDIUM (10-20%)" if apoe_pct > 10 else "LOW (<10%)"
+    cats     = ["ApoE%","BBB","logP","pKa fit","Zeta"]
+    vals     = [apoe_pct/40, bbb_prob, min(logp/5,1), (7-abs(pka-6.5))/7, (10-abs(zeta))/10]
+    angles   = np.linspace(0, 2*np.pi, len(cats), endpoint=False).tolist()
+    v2, a2   = vals+[vals[0]], angles+[angles[0]]
+    fig, ax  = plt.subplots(figsize=(5, 4), subplot_kw={"polar":True}, facecolor=CARD)
     ax.set_facecolor(CARD)
-    ax.plot(a2, v2, color=ACC, linewidth=2)
-    ax.fill(a2, v2, color=ACC, alpha=0.2)
-    ax.set_xticks(angles)
-    ax.set_xticklabels(cats, color=TXT, fontsize=8)
+    ax.plot(a2, v2, color=ACC, linewidth=2); ax.fill(a2, v2, color=ACC, alpha=0.2)
+    ax.set_xticks(angles); ax.set_xticklabels(cats, color=TXT, fontsize=8)
     ax.tick_params(colors=TXT)
     plt.tight_layout()
-    buf = BytesIO()
-    plt.savefig(buf, format="png", dpi=120, facecolor=CARD)
-    plt.close()
-    buf.seek(0)
-    log_entry("LNP Brain", f"pka={pka},zeta={zeta}", f"ApoE={apoe_pct:.1f}%,BBB={bbb_prob:.2f}")
-    return (f"**Predicted ApoE:** {apoe_pct:.1f}% — {tier}\n\n"
-            f"**BBB Probability:** {bbb_prob:.2f}"), Image.open(buf)
+    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)
 
 def extract_corona(text):
-    out = {
-        "nanoparticle_composition": "",
-        "size_nm": None, "zeta_mv": None, "PDI": None,
-        "protein_source": "", "corona_proteins": [], "confidence": {}
-    }
+    out = {"nanoparticle_composition":"","size_nm":None,"zeta_mv":None,"PDI":None,
+           "protein_source":"","corona_proteins":[],"confidence":{}}
     m = re.search(r"(\d+\.?\d*)\s*(?:nm|nanometer)", text, re.I)
-    if m:
-        out["size_nm"] = float(m.group(1))
-        out["confidence"]["size_nm"] = "HIGH"
+    if m: out["size_nm"] = float(m.group(1)); out["confidence"]["size_nm"] = "HIGH"
     m = re.search(r"([+-]?\d+\.?\d*)\s*mV", text, re.I)
-    if m:
-        out["zeta_mv"] = float(m.group(1))
-        out["confidence"]["zeta_mv"] = "HIGH"
+    if m: out["zeta_mv"] = float(m.group(1)); out["confidence"]["zeta_mv"] = "HIGH"
     m = re.search(r"PDI\s*[=:of]*\s*(\d+\.?\d*)", text, re.I)
-    if m:
-        out["PDI"] = float(m.group(1))
-        out["confidence"]["PDI"] = "HIGH"
+    if m: out["PDI"] = float(m.group(1)); out["confidence"]["PDI"] = "HIGH"
     for src in ["human plasma","human serum","fetal bovine serum","FBS","PBS"]:
         if src.lower() in text.lower():
-            out["protein_source"] = src
-            out["confidence"]["protein_source"] = "HIGH"
-            break
-    out["corona_proteins"] = [
-        {"name": p, "confidence": "MEDIUM"} for p in PROTEINS if p in text.lower()
-    ]
+            out["protein_source"] = src; out["confidence"]["protein_source"] = "HIGH"; break
+    out["corona_proteins"] = [{"name":p,"confidence":"MEDIUM"} for p in PROTEINS if p in text.lower()]
     for lip in ["DSPC","DOPE","MC3","DLin","cholesterol","PEG","DOTAP"]:
-        if lip in text:
-            out["nanoparticle_composition"] += lip + " "
+        if lip in text: out["nanoparticle_composition"] += lip + " "
     out["nanoparticle_composition"] = out["nanoparticle_composition"].strip()
     flags = []
     if not out["size_nm"]:         flags.append("size_nm not found")
     if not out["zeta_mv"]:         flags.append("zeta_mv not found")
     if not out["corona_proteins"]: flags.append("no proteins detected")
     summary = "All key fields extracted" if not flags else " | ".join(flags)
-    log_entry("AutoCorona NLP", text[:80]+"...",
-              f"proteins={len(out['corona_proteins'])},{summary}")
+    log_entry("S1-D | S1-R10 | NLP", text[:80], f"proteins={len(out['corona_proteins'])}")
     return json.dumps(out, indent=2), summary
 
+# ── SECTION BADGE HELPER ──────────────────────────────────────────────────────
+def badge(sphere_code, sphere_name, project_code, project_name, description, metric=""):
+    metric_html = (f"<span style=\'background:#1e3a5f;color:{ACC2};padding:2px 8px;"
+                   f"border-radius:4px;font-size:11px;margin-left:8px\'>{metric}</span>"
+                   if metric else "")
+    return (
+        f"<div style=\'background:{CARD};border-left:3px solid {ACC};"
+        f"padding:10px 14px;border-radius:0 6px 6px 0;margin-bottom:6px;\'>"
+        f"<span style=\'color:{DIM};font-size:11px\'>S1 · Biomedical &nbsp;›&nbsp; "
+        f"{sphere_code} · {sphere_name} &nbsp;›&nbsp; {project_code}</span>{metric_html}<br>"
+        f"<span style=\'color:{TXT};font-weight:bold;font-size:15px\'>{project_name}</span><br>"
+        f"<span style=\'color:{DIM};font-size:12px\'>{description}</span>"
+        f"</div>"
+    )
+
+# ── CSS ───────────────────────────────────────────────────────────────────────
 css = (
     f"body,.gradio-container{{background:{BG}!important;color:{TXT}!important}}"
-    f".tab-nav button{{color:{TXT}!important;background:{CARD}!important}}"
+    f".tab-nav button{{color:{TXT}!important;background:{CARD}!important;font-size:13px!important}}"
     f".tab-nav button.selected{{border-bottom:2px solid {ACC}!important;color:{ACC}!important}}"
     f"h1,h2,h3{{color:{ACC}!important}}"
     f".gr-button-primary{{background:{ACC}!important;border:none!important}}"
     f"footer{{display:none!important}}"
+    # section divider tabs look different
+    f".tab-nav button[data-testid*='divider']{{color:{DIM}!important;cursor:default!important;"
+    f"border-bottom:1px solid #334155!important;font-size:11px!important}}"
 )
 
-LEARNING_CASES = """
-## 🧪 Top 5 Guided Investigations
-### Case 1 — Beginner 🟢
-**Question:** Why is the same gene position benign vs pathogenic?
-1. OpenVariant → enter `BRCA1:p.R1699Q` → Benign
-2. Enter `BRCA1:p.R1699W` → Pathogenic
-3. Same position, different amino acid — what changed?
-**Key concept:** Amino acid polarity determines protein folding impact.
+# ── CONTENT STRINGS ───────────────────────────────────────────────────────────
+MAP_HTML = f"""
+<div style="background:{CARD};padding:20px;border-radius:8px;font-family:monospace;
+            font-size:13px;line-height:1.9;color:{TXT}">
+
+<span style="color:{ACC};font-size:15px;font-weight:bold">K R&D Lab · S1 Biomedical</span>
+<span style="color:{DIM};font-size:11px"> — Science Sphere, sub-direction 1</span>
+<br><br>
+
+<span style="color:{ACC2}">S1-A · PHYLO-GENOMICS</span> &nbsp;<span style="color:{DIM}">← What breaks in DNA</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R1</b> &nbsp;OpenVariant &nbsp;<span style="color:{GRN}">AUC=0.939 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R1b</b> Somatic classifier &nbsp;<span style="color:#f59e0b">🔶 In progress</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R12a</b> Rare variants (DIPG · UVM) &nbsp;<span style="color:{DIM}">🔴 Planned</span><br>
+<br>
+
+<span style="color:{ACC2}">S1-B · PHYLO-RNA</span> &nbsp;<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) &nbsp;<span style="color:{GRN}">✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R3</b> &nbsp;siRNA synthetic lethal (20+ cancers) &nbsp;<span style="color:{GRN}">✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R4</b> &nbsp;lncRNA-TREM2 ceRNA network &nbsp;<span style="color:{GRN}">✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R4b</b> ASO designer &nbsp;<span style="color:{GRN}">✅</span><br>
+<br>
+
+<span style="color:{ACC2}">S1-C · PHYLO-DRUG</span> &nbsp;<span style="color:{DIM}">← Which molecule treats it</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R5</b> &nbsp;FGFR3 RNA-directed compounds &nbsp;<span style="color:{GRN}">✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R5b</b> Synthetic lethal drug mapping &nbsp;<span style="color:#f59e0b">🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R13</b> m6A × Ferroptosis × Circadian ⭐ &nbsp;<span style="color:{DIM}">🔴 Frontier</span><br>
+<br>
+
+<span style="color:{ACC2}">S1-D · PHYLO-LNP</span> &nbsp;<span style="color:{DIM}">← How to deliver the drug</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R6</b> &nbsp;LNP corona (serum) &nbsp;<span style="color:{GRN}">AUC=0.791 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R7</b> &nbsp;Flow corona — Vroman effect &nbsp;<span style="color:{GRN}">✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R8</b> &nbsp;LNP brain / BBB / ApoE &nbsp;<span style="color:{GRN}">✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R10</b> AutoCorona NLP &nbsp;<span style="color:{GRN}">F1=0.71 ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R11</b> CSF · Vitreous · Bone Marrow ⭐ &nbsp;<span style="color:{DIM}">🔴 0 prior studies</span><br>
+<br>
+
+<span style="color:{ACC2}">S1-E · PHYLO-BIOMARKERS</span> &nbsp;<span style="color:{DIM}">← Detect without biopsy</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R9</b> &nbsp;Liquid Biopsy classifier &nbsp;<span style="color:{GRN}">AUC=0.992* ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R9b</b> Protein panel validator &nbsp;<span style="color:#f59e0b">🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R9c</b> ctDNA gap analysis &nbsp;<span style="color:{DIM}">🔴</span><br>
+<br>
+
+<span style="color:{ACC2}">S1-F · PHYLO-RARE</span> &nbsp;<span style="color:{DIM}">← Where nobody looked yet</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R12b</b> DIPG toolkit (H3K27M) &nbsp;<span style="color:{DIM}">🔴</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R12c</b> UVM toolkit (GNAQ/GNA11) &nbsp;<span style="color:{DIM}">🔴</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R12d</b> pAML toolkit (FLT3-ITD) &nbsp;<span style="color:{DIM}">🔴</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
+</span>
+</div>
+"""
+
+LEARNING_CASES = f"""
+## 🧪 Guided Investigations — S1 Biomedical
+
+> Progress through levels: 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
+
 ---
-### Case 2 — Beginner 🟢
-**Question:** How does PEG% change what protein sticks to LNPs?
-1. LNP Corona → Ionizable, Zeta=-5, Size=100, PEG=0.5% → note protein
-2. PEG=2.5% → compare
-3. LNP Brain → pKa=6.5 → compare ApoE%
-**Key concept:** More PEG → less Fibrinogen, more ApoE.
+
+### 🟢 Case 1 · S1-A · S1-R1 — Variant Pathogenicity
+**Why does the same position give two different outcomes?**
+
+1. **OpenVariant** → `BRCA1:p.R1699Q` → Benign
+2. **OpenVariant** → `BRCA1:p.R1699W` → Pathogenic
+3. Same position (R1699), different amino acid (Q vs W). What changed?
+
+*Key concept: Amino acid polarity determines BRCT domain folding. Q is polar-uncharged; W is bulky-aromatic.*
+
 ---
-### Case 3 — Intermediate 🟡
-**Question:** Does blood flow change corona composition?
-1. Flow Corona → Flow=0, Ionizable
-2. Flow=40 (arterial) → compare ApoE curve
-3. At what minute does ApoE plateau?
-**Key concept:** Vroman effect — albumin displaced by ApoE under flow.
+
+### 🟢 Case 2 · S1-D · S1-R6 + S1-R8 — PEG and Brain Delivery
+**How does PEG% change which protein coats your nanoparticle?**
+
+1. **LNP Corona** → Ionizable, Zeta=−5, Size=100, **PEG=0.5%** → note protein
+2. **PEG=2.5%** → compare dominant protein
+3. **LNP Brain** → pKa=6.5 → check ApoE%
+
+*Key concept: More PEG → steric shielding → less Fibrinogen → more ApoE → better BBB crossing.*
+
 ---
-### Case 4 — Intermediate 🟡
-**Question:** Which cancer has the most novel siRNA targets?
-1. TP53 siRNA → LUAD → count "Novel"
-2. Repeat BRCA, COAD
-3. Pick one Novel gene → Google: "[gene] cancer therapeutic target"
+
+### 🟡 Case 3 · S1-D · S1-R7 — Vroman Effect Under Flow
+**Does blood flow speed reshape the corona over time?**
+
+1. **Flow Corona** → Flow=0, Ionizable → observe ApoE plateau minute
+2. **Flow=40** (arterial) → compare same curve
+3. At what minute does ApoE dominate under arterial flow?
+
+*Key concept: Albumin adsorbs first (abundance), then displaced by ApoE (affinity). Flow accelerates exchange 3–4×.*
+
 ---
-### Case 5 — Advanced 🔴
-**Question:** Can you identify cancer from protein levels?
-1. Liquid Biopsy → all sliders=0 → HEALTHY
+
+### 🟡 Case 4 · S1-B · S1-R3 — Novel siRNA Targets
+**Which cancer type has the most undrugged therapeutic targets?**
+
+1. **TP53 siRNA** → LUAD → count Drug_status = "Novel"
+2. Repeat for BRCA, COAD
+3. Pick one Novel gene → search: `[gene name] cancer PubMed`
+
+*Key concept: "Novel" = no approved or clinical drug yet — highest opportunity for new therapeutic development.*
+
+---
+
+### 🔴 Case 5 · S1-E · S1-R9 — Cancer Detection Threshold
+**What is the minimum signal that flips diagnosis to CANCER?**
+
+1. **Liquid Biopsy** → all sliders = 0 → HEALTHY
 2. Set CTHRC1=2.5, FHL2=2.0, LDHA=1.8 → observe
-3. Find minimum CTHRC1 that tips to CANCER
-**Key concept:** CTHRC1 weight (0.18) dominates the score.
+3. Reset all. Increase only CTHRC1 step by step. At what value does it tip?
+
+*Key concept: CTHRC1 weight=0.18 is the dominant feature. One protein can outweigh a full panel.*
+
+---
+
+### 🔴 Case 6 · S1-B + S1-C · Cross-tool — Convergent Evidence
+**Is there target overlap between RNA silencing and drug discovery?**
+
+1. **miRNA** → gene=TP53 → find top silenced targets (BCL2, CDK6)
+2. **FGFR3 Drug** → P1 → find if CDK6 pathway appears in compound targets
+3. **siRNA** → BRCA → does CDK6 appear in synthetic lethal list?
+
+*Key concept: Convergence across S1-B and S1-C = higher-confidence therapeutic hypothesis. This is how real pipelines are built.*
 """
 
-with gr.Blocks(css=css, title="K R&D Lab") as demo:
+# ── BLOCKS UI ─────────────────────────────────────────────────────────────────
+with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 
     gr.Markdown(
-        "# 🧬 K R&D Lab — Computational Biology Suite\n"
-        "**Oksana Kolisnyk** · ML Engineer · "
-        "[KOSATIKS GROUP](https://kosatiks-group.pp.ua)\n"
-        "> 10 open-source tools + lab journal."
+        "# 🔬 K R&D Lab · Science Sphere\n"
+        "**S1 Biomedical** — 10 active projects · "
+        "[Oksana Kolisnyk](https://kosatiks-group.pp.ua) · KOSATIKS GROUP\n"
+        "> Research only. Not clinical advice. &nbsp;|&nbsp; "
+        "[GitHub](https://github.com/K-RnD-Lab) &nbsp; "
+        "[HuggingFace](https://huggingface.co/K-RnD-Lab)"
     )
 
     with gr.Tabs():
 
-        with gr.TabItem("🧬 BRCA2 miRNA"):
-            gr.Markdown("### Tumor Suppressor miRNAs")
+        # ── MAP ───────────────────────────────────────────────────────────────
+        with gr.TabItem("🗺️ Lab Map"):
+            gr.HTML(MAP_HTML)
+
+        # ── S1-A · GENOMICS ───────────────────────────────────────────────────
+        with gr.TabItem("── S1-A · GENOMICS ──"):
+            gr.HTML(
+                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
+                f"<b style=\'color:{ACC2}\'>S1-A · PHYLO-GENOMICS</b>"
+                f"<span style=\'color:{DIM};font-size:12px\'> — What breaks in DNA</span><br>"
+                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R1 (active) · S1-R1b (in progress) · S1-R12a (planned)</span>"
+                f"</div>"
+            )
+
+        with gr.TabItem("S1-R1 · OpenVariant"):
+            gr.HTML(badge("S1-A","PHYLO-GENOMICS","S1-R1","OpenVariant",
+                          "SNV pathogenicity classifier · ClinVar 2026","AUC = 0.939"))
+            hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
+            gr.Markdown("**Or enter functional scores manually:**")
+            with gr.Row():
+                sift = gr.Slider(0,1,value=0.5,step=0.01,label="SIFT (0=damaging)")
+                pp   = gr.Slider(0,1,value=0.5,step=0.01,label="PolyPhen-2")
+                gn   = gr.Slider(0,0.01,value=0.001,step=0.0001,label="gnomAD AF")
+            b5 = gr.Button("Predict Pathogenicity", variant="primary")
+            o5 = gr.HTML()
+            gr.Examples([["BRCA1:p.R1699Q",0.82,0.05,0.0012],
+                         ["TP53:p.R248W",0.00,1.00,0.0],
+                         ["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn])
+            b5.click(predict_variant, [hgvs,sift,pp,gn], o5)
+
+        # ── S1-B · RNA THERAPEUTICS ───────────────────────────────────────────
+        with gr.TabItem("── S1-B · RNA THER ──"):
+            gr.HTML(
+                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
+                f"<b style=\'color:{ACC2}\'>S1-B · PHYLO-RNA</b>"
+                f"<span style=\'color:{DIM};font-size:12px\'> — How to silence it via RNA</span><br>"
+                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R2 · S1-R3 · S1-R4 · S1-R4b (all active)</span>"
+                f"</div>"
+            )
+
+        with gr.TabItem("S1-R2 · miRNA"):
+            gr.HTML(badge("S1-B","PHYLO-RNA","S1-R2","miRNA Silencing",
+                          "Downregulated miRNAs in tumor suppressor knockouts · BRCA1/2 · TP53"))
             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"],["TP53"]], inputs=[g1])
             b1.click(predict_mirna, g1, o1)
 
-        with gr.TabItem("💉 TP53 siRNA"):
-            gr.Markdown("### Synthetic Lethal siRNA Targets")
+        with gr.TabItem("S1-R3 · siRNA"):
+            gr.HTML(badge("S1-B","PHYLO-RNA","S1-R3","siRNA Synthetic Lethal",
+                          "TP53-null cancer — novel undrugged SL targets · 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 siRNA targets")
-            gr.Examples([["LUAD"],["BRCA"]], inputs=[g2])
+            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("🧠 lncRNA-TREM2"):
-            gr.Markdown("### lncRNA Networks in Alzheimer's")
+        with gr.TabItem("S1-R4 · lncRNA"):
+            gr.HTML(badge("S1-B","PHYLO-RNA","S1-R4","lncRNA-TREM2 Network",
+                          "ceRNA axis in Alzheimer neuroinflammation · CYTOR→miR-138-5p→AKT1"))
             b3 = gr.Button("Load Results", variant="primary")
             o3a = gr.Dataframe(label="ceRNA Network")
-            o3b = gr.Dataframe(label="ASO Candidates")
+            o3b = gr.Dataframe(label="ASO Candidates (S1-R4b)")
             b3.click(get_lncrna, [], [o3a, o3b])
 
-        with gr.TabItem("💊 FGFR3 Drug"):
-            gr.Markdown("### RNA-Directed Drug Discovery: FGFR3")
+        # ── S1-C · DRUG DISCOVERY ─────────────────────────────────────────────
+        with gr.TabItem("── S1-C · DRUG ──"):
+            gr.HTML(
+                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
+                f"<b style=\'color:{ACC2}\'>S1-C · PHYLO-DRUG</b>"
+                f"<span style=\'color:{DIM};font-size:12px\'> — Which molecule treats it</span><br>"
+                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R5 (active) · S1-R5b (in progress) · S1-R13 (frontier)</span>"
+                f"</div>"
+            )
+
+        with gr.TabItem("S1-R5 · Drug"):
+            gr.HTML(badge("S1-C","PHYLO-DRUG","S1-R5","FGFR3 RNA-Directed Drug Discovery",
+                          "Small molecules binding FGFR3 RNA structural pockets · ChEMBL screen",
+                          "top score 0.793"))
             g4 = gr.Radio(["P1 (hairpin loop)","P10 (G-quadruplex)"],
                           value="P1 (hairpin loop)", label="Target pocket")
             b4 = gr.Button("Screen Compounds", variant="primary")
@@ -448,94 +568,61 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
             gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
             b4.click(predict_drug, g4, [o4t, o4p])
 
-        with gr.TabItem("🔬 OpenVariant"):
-            gr.Markdown("### OpenVariant — Pathogenicity Classifier\nAUC=0.939 on ClinVar 2026.")
-            hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
-            gr.Markdown("**Or enter scores manually:**")
-            with gr.Row():
-                sift = gr.Slider(0, 1, value=0.5,     step=0.01,   label="SIFT (0=damaging)")
-                pp   = gr.Slider(0, 1, value=0.5,     step=0.01,   label="PolyPhen-2")
-                gn   = gr.Slider(0, 0.01, value=0.001, step=0.0001, label="gnomAD AF")
-            b5 = gr.Button("Predict Pathogenicity", variant="primary")
-            o5 = gr.HTML(label="Result")
-            gr.Examples(
-                [["BRCA1:p.R1699Q", 0.82, 0.05, 0.0012],
-                 ["TP53:p.R248W",   0.00, 1.00, 0.0],
-                 ["BRCA2:p.D2723A", 0.01, 0.98, 0.0]],
-                inputs=[hgvs, sift, pp, gn])
-            b5.click(predict_variant, [hgvs, sift, pp, gn], o5)
-
-        with gr.TabItem("🧪 LNP Corona"):
-            gr.Markdown("### LNP Protein Corona Prediction")
+        # ── S1-D · LNP DELIVERY ───────────────────────────────────────────────
+        with gr.TabItem("── S1-D · LNP ──"):
+            gr.HTML(
+                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
+                f"<b style=\'color:{ACC2}\'>S1-D · PHYLO-LNP</b>"
+                f"<span style=\'color:{DIM};font-size:12px\'> — How to deliver the drug to the cell</span><br>"
+                f"<span style=\'color:{TXT};font-size:13px\'>Active: S1-R6 · S1-R7 · S1-R8 · S1-R10 &nbsp;|&nbsp; "
+                f"Frontier: S1-R11 (CSF/Vitreous/Bone Marrow — 0 prior studies)</span>"
+                f"</div>"
+            )
+
+        with gr.TabItem("S1-R6 · Corona"):
+            gr.HTML(badge("S1-D","PHYLO-LNP","S1-R6","LNP Protein Corona (Serum)",
+                          "Dominant corona protein from formulation parameters","AUC = 0.791"))
             with gr.Row():
-                sz = gr.Slider(50, 300, value=100, step=1,   label="Size (nm)")
-                zt = gr.Slider(-40, 10, value=-5,  step=1,   label="Zeta (mV)")
+                sz = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
+                zt = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
             with gr.Row():
-                pg = gr.Slider(0, 5, value=1.5,    step=0.1, label="PEG mol%")
-                lp = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],
-                                 value="Ionizable", label="Lipid type")
-            b6 = gr.Button("Predict", variant="primary")
-            o6 = gr.Markdown()
+                pg = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%")
+                lp = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Lipid type")
+            b6 = gr.Button("Predict", variant="primary"); o6 = gr.Markdown()
             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("🩸 Liquid Biopsy"):
-            gr.Markdown("### Protein Corona Cancer Diagnostics\nClassify cancer vs healthy.")
-            with gr.Row():
-                p1  = gr.Slider(-3, 3, value=0, step=0.1, label="CTHRC1")
-                p2  = gr.Slider(-3, 3, value=0, step=0.1, label="FHL2")
-                p3  = gr.Slider(-3, 3, value=0, step=0.1, label="LDHA")
-                p4  = gr.Slider(-3, 3, value=0, step=0.1, label="P4HA1")
-                p5  = gr.Slider(-3, 3, value=0, step=0.1, label="SERPINH1")
-            with gr.Row():
-                p6  = gr.Slider(-3, 3, value=0, step=0.1, label="ABCA8")
-                p7  = gr.Slider(-3, 3, value=0, step=0.1, label="CA4")
-                p8  = gr.Slider(-3, 3, value=0, step=0.1, label="CKB")
-                p9  = gr.Slider(-3, 3, value=0, step=0.1, label="NNMT")
-                p10 = gr.Slider(-3, 3, value=0, step=0.1, label="CACNA2D2")
-            b7  = gr.Button("Classify", variant="primary")
-            o7t = gr.HTML()
-            o7p = gr.Image(label="Feature contributions")
-            gr.Examples(
-                [[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],
-                 [0,0,0,0,0,0,0,0,0,0]],
-                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("🌊 Flow Corona"):
-            gr.Markdown("### Corona Remodeling Under Blood Flow")
+        with gr.TabItem("S1-R7 · Flow"):
+            gr.HTML(badge("S1-D","PHYLO-LNP","S1-R7","Flow Corona — Vroman Effect",
+                          "Blood flow reshapes LNP corona over time · albumin→ApoE exchange 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)")
-                pg8 = gr.Slider(0, 5,    value=1.5, step=0.1, label="PEG mol%")
+                s8  = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
+                z8  = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
+                pg8 = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%")
             with gr.Row():
-                ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],
-                                  value="Ionizable", label="Charge type")
-                fl8 = gr.Slider(0, 40, value=20, step=1, label="Flow rate cm/s (aorta=40)")
-            b8  = gr.Button("Model Vroman Effect", variant="primary")
-            o8t = gr.Markdown()
-            o8p = gr.Image(label="Kinetics plot")
-            gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]],
-                        inputs=[s8,z8,pg8,ch8,fl8])
+                ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Charge")
+                fl8 = gr.Slider(0,40,value=20,step=1,label="Flow rate cm/s (aorta=40)")
+            b8 = gr.Button("Model Vroman Effect", variant="primary")
+            o8t = gr.Markdown(); o8p = gr.Image(label="Kinetics")
+            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("🧠 LNP Brain"):
-            gr.Markdown("### LNP Brain Delivery Predictor")
-            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.TabItem("S1-R8 · Brain"):
+            gr.HTML(badge("S1-D","PHYLO-LNP","S1-R8","LNP Brain Delivery",
+                          "ApoE corona % and blood-brain barrier crossing 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():
-                pk  = gr.Slider(4, 8,    value=6.5, step=0.1, label="pKa")
-                zt9 = gr.Slider(-20, 10, value=-3,  step=1,   label="Zeta (mV)")
-            b9  = gr.Button("Predict BBB Crossing", variant="primary")
-            o9t = gr.Markdown()
-            o9p = gr.Image(label="Radar profile")
-            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("📄 AutoCorona NLP"):
-            gr.Markdown("### AutoCorona NLP Extraction\nPaste any paper abstract.")
-            txt  = gr.Textbox(lines=6, label="Paper abstract", placeholder="Paste text here...")
+                pk  = gr.Slider(4,8,value=6.5,step=0.1,label="pKa")
+                zt9 = gr.Slider(-20,10,value=-3,step=1,label="Zeta (mV)")
+            b9 = gr.Button("Predict BBB Crossing", variant="primary")
+            o9t = gr.Markdown(); o9p = gr.Image(label="Radar profile")
+            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(badge("S1-D","PHYLO-LNP","S1-R10","AutoCorona NLP",
+                          "Extract structured LNP data from PubMed/PMC abstracts","F1 = 0.71"))
+            txt  = gr.Textbox(lines=6,label="Paper abstract",placeholder="Paste abstract text here...")
             b10  = gr.Button("Extract Data", variant="primary")
             o10j = gr.Code(label="Extracted JSON", language="json")
             o10f = gr.Textbox(label="Validation flags")
@@ -546,55 +633,82 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
             ]], inputs=[txt])
             b10.click(extract_corona, txt, [o10j, o10f])
 
-        with gr.TabItem("📓 Lab Journal"):
-            gr.Markdown("### Your Research Log\nEvery query is auto-saved.")
+        # ── S1-E · BIOMARKERS ─────────────────────────────────────────────────
+        with gr.TabItem("── S1-E · BIO ──"):
+            gr.HTML(
+                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
+                f"<b style=\'color:{ACC2}\'>S1-E · PHYLO-BIOMARKERS</b>"
+                f"<span style=\'color:{DIM};font-size:12px\'> — Detect cancer without tissue biopsy</span><br>"
+                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R9 (active) · S1-R9b (in progress) · S1-R9c (planned)</span>"
+                f"</div>"
+            )
+
+        with gr.TabItem("S1-R9 · Biopsy"):
+            gr.HTML(badge("S1-E","PHYLO-BIOMARKERS","S1-R9","Liquid Biopsy Classifier",
+                          "CTHRC1 · FHL2 · LDHA panel · tissue proteomics proxy","AUC = 0.992*"))
             with gr.Row():
-                note_text = gr.Textbox(
-                    label="📝 Add observation / conclusion",
-                    placeholder="What did you discover? What's your next question?",
-                    lines=3)
-                note_tab = gr.Textbox(label="Which tool?", value="General")
-            note_last = gr.Textbox(label="Result to annotate", visible=False)
-            save_btn  = gr.Button("💾 Save Observation", variant="primary")
-            save_msg  = gr.Markdown()
-            journal_df = gr.Dataframe(
-                label="📋 Full History",
-                value=load_journal(),
-                interactive=False)
+                p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1")
+                p2=gr.Slider(-3,3,value=0,step=0.1,label="FHL2")
+                p3=gr.Slider(-3,3,value=0,step=0.1,label="LDHA")
+                p4=gr.Slider(-3,3,value=0,step=0.1,label="P4HA1")
+                p5=gr.Slider(-3,3,value=0,step=0.1,label="SERPINH1")
+            with gr.Row():
+                p6=gr.Slider(-3,3,value=0,step=0.1,label="ABCA8")
+                p7=gr.Slider(-3,3,value=0,step=0.1,label="CA4")
+                p8=gr.Slider(-3,3,value=0,step=0.1,label="CKB")
+                p9=gr.Slider(-3,3,value=0,step=0.1,label="NNMT")
+                p10=gr.Slider(-3,3,value=0,step=0.1,label="CACNA2D2")
+            b7=gr.Button("Classify", variant="primary")
+            o7t=gr.HTML(); o7p=gr.Image(label="Feature contributions")
+            gr.Examples([[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],[0,0,0,0,0,0,0,0,0,0]],
+                        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])
+
+        # ── LAB ───────────────────────────────────────────────────────────────
+        with gr.TabItem("📓 Journal"):
+            gr.Markdown("### Lab Journal · Auto-logged by project code\nEvery tool call is saved with its S1-X · S1-RX label.")
+            with gr.Row():
+                note_text = gr.Textbox(label="📝 Observation / conclusion",
+                                       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_msg   = gr.Markdown()
+            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])
-            gr.Markdown("📥 Log saved as `lab_journal.csv` in the app folder.")
+            save_btn.click(save_note, [note_text,note_tab,note_last], [save_msg,journal_df])
 
-        with gr.TabItem("📚 Learning Mode"):
+        with gr.TabItem("📚 Learning"):
             gr.Markdown(LEARNING_CASES)
-            gr.Markdown("---\n### 📖 Quick Reference")
-            gr.Markdown("""
-| Tool | Predicts | Key input |
-|------|----------|-----------|
-| OpenVariant | Pathogenic/Benign | Gene mutation |
-| LNP Corona | Dominant protein | Formulation |
-| Flow Corona | Vroman kinetics | Flow rate |
-| LNP Brain | ApoE% + BBB prob | pKa + zeta |
-| Liquid Biopsy | Cancer/Healthy | Protein z-scores |
-| BRCA2 miRNA | Downregulated miRNAs | Gene name |
-| TP53 siRNA | Synthetic lethal targets | Cancer type |
-| lncRNA-TREM2 | ceRNA + ASOs | — |
-| FGFR3 Drug | Small molecules | Pocket type |
-| AutoCorona NLP | Structured data | Abstract text |
-""")
-            gr.Markdown("""
+            gr.Markdown("""---
+### 📖 Quick Reference — S1 Active Tools
+
+| Code | Sub-dir | Tool | Key metric |
+|------|---------|------|------------|
+| S1-R1 | S1-A | OpenVariant | AUC=0.939 |
+| S1-R2 | S1-B | miRNA silencing | hsa-miR-148a-3p top hit |
+| S1-R3 | S1-B | siRNA SL targets | SPC24 top in LUAD |
+| S1-R4 | S1-B | lncRNA-TREM2 | CYTOR→AKT1 |
+| S1-R5 | S1-C | FGFR3 drug | score=0.793 |
+| S1-R6 | S1-D | LNP corona | AUC=0.791 |
+| S1-R7 | S1-D | Flow corona | 3–4× ApoE acceleration |
+| S1-R8 | S1-D | LNP brain | pKa 6.2–6.8 optimal |
+| S1-R9 | S1-E | Liquid biopsy | AUC=0.992* |
+| S1-R10 | S1-D | AutoCorona NLP | F1=0.71 |
+
 ### 🔗 Resources
-- [PubMed](https://pubmed.ncbi.nlm.nih.gov)
-- [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar/)
-- [UniProt](https://www.uniprot.org)
-- [ChEMBL](https://www.ebi.ac.uk/chembl/)
+[PubMed](https://pubmed.ncbi.nlm.nih.gov) · [ClinVar](https://ncbi.nlm.nih.gov/clinvar/) · 
+[miRBase](https://mirbase.org) · [ChEMBL](https://ebi.ac.uk/chembl/) · 
+[DepMap](https://depmap.org) · [UniProt](https://uniprot.org)
 """)
 
     gr.Markdown(
-        "---\n**K R&D Lab** | Research only — not clinical | "
-        "[GitHub](https://github.com/TEZv/K-RnD-Lab-PHYLO-03_2026) | "
-        "[KOSATIKS GROUP 🦈](https://kosatiks-group.pp.ua)"
+        f"---\n"
+        f"**K R&D Lab** · Science Sphere · S1 Biomedical · "
+        f"[GitHub](https://github.com/K-RnD-Lab) · "
+        f"[HuggingFace](https://huggingface.co/K-RnD-Lab) · "
+        f"[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)