Update app.py

app.py

@@ -9,1021 +9,25 @@ from io import BytesIO
 from PIL import Image
 from datetime import datetime
 from pathlib import Path
-import os
 
-# Вимкнення телеметрії Gradio (щоб уникнути непотрібних запитів)
-os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+# ... (тут ваші кольори, логування, бази даних та функції – без змін)
+
+with gr.Blocks(title="K R&D Lab") as demo:
+    gr.Markdown("# K R&D Lab")
+    with gr.Tabs():
+        with gr.TabItem("S1-A · R1a · OpenVariant"):
+            hgvs = gr.Textbox(label="HGVS")
+            sift = gr.Slider(0,1,value=0.5,label="SIFT")
+            pp = gr.Slider(0,1,value=0.5,label="PolyPhen")
+            gn = gr.Slider(0,0.01,value=0.001,label="gnomAD")
+            btn = gr.Button("Predict")
+            out = gr.HTML()
+            btn.click(predict_variant, [hgvs,sift,pp,gn], out)
+        with gr.TabItem("S1-B · R1a · miRNA"):
+            gene = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
+            btn2 = gr.Button("Find")
+            out2 = gr.Dataframe()
+            btn2.click(predict_mirna, [gene], out2)
+        # ... інші вкладки аналогічно, але без вкладених Tabs
 
-# Кольори (ваші)
-BG   = "#0f172a"
-CARD = "#1e293b"
-ACC  = "#f97316"
-ACC2 = "#38bdf8"
-TXT  = "#f1f5f9"
-GRN  = "#22c55e"
-RED  = "#ef4444"
-DIM  = "#8e9bae"
-BORDER = "#334155"
-
-LOG_PATH = Path("/tmp/lab_journal.csv")
-
-# ---------- Логування ----------
-def log_entry(tab, inputs, result, note=""):
-    try:
-        write_header = not LOG_PATH.exists()
-        with open(LOG_PATH, "a", newline="", encoding="utf-8") as f:
-            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})
-    except Exception: pass
-
-def load_journal():
-    try:
-        if not LOG_PATH.exists():
-            return pd.DataFrame(columns=["timestamp","tab","inputs","result","note"])
-        return pd.read_csv(LOG_PATH)
-    except Exception:
-        return pd.DataFrame(columns=["timestamp","tab","inputs","result","note"])
-
-def save_note(note, tab, last_result):
-    log_entry(tab, "", last_result, note)
-    return "✅ Saved!", load_journal()
-
-# ---------- Бази даних (ваші) ----------
-MIRNA_DB = {
-    "BRCA2": [
-        {"miRNA":"hsa-miR-148a-3p","log2FC":-0.70,"padj":0.013,"targets":"DNMT1, AKT2","pathway":"Epigenetic reprogramming"},
-        {"miRNA":"hsa-miR-30e-5p","log2FC":-0.49,"padj":0.032,"targets":"MYC, KRAS","pathway":"Oncogene suppression"},
-        {"miRNA":"hsa-miR-551b-3p","log2FC":-0.59,"padj":0.048,"targets":"SMAD4, CDK6","pathway":"TGF-beta / CDK4/6"},
-        {"miRNA":"hsa-miR-22-3p","log2FC":-0.43,"padj":0.041,"targets":"HIF1A, PTEN","pathway":"Hypoxia / PI3K"},
-        {"miRNA":"hsa-miR-200c-3p","log2FC":-0.38,"padj":0.044,"targets":"ZEB1, ZEB2","pathway":"EMT suppression"},
-    ],
-    "BRCA1": [
-        {"miRNA":"hsa-miR-155-5p","log2FC":-0.81,"padj":0.008,"targets":"SHIP1, SOCS1","pathway":"Immune evasion"},
-        {"miRNA":"hsa-miR-146a-5p","log2FC":-0.65,"padj":0.019,"targets":"TRAF6, IRAK1","pathway":"NF-kB signalling"},
-        {"miRNA":"hsa-miR-21-5p","log2FC":-0.55,"padj":0.027,"targets":"PTEN, PDCD4","pathway":"Apoptosis"},
-        {"miRNA":"hsa-miR-17-5p","log2FC":-0.47,"padj":0.036,"targets":"RB1, E2F1","pathway":"Cell cycle"},
-        {"miRNA":"hsa-miR-34a-5p","log2FC":-0.41,"padj":0.049,"targets":"BCL2, CDK6","pathway":"p53 axis"},
-    ],
-    "TP53": [
-        {"miRNA":"hsa-miR-34a-5p","log2FC":-1.10,"padj":0.001,"targets":"BCL2, CDK6","pathway":"p53-miR-34 axis"},
-        {"miRNA":"hsa-miR-192-5p","log2FC":-0.90,"padj":0.005,"targets":"MDM2, DHFR","pathway":"p53 feedback"},
-        {"miRNA":"hsa-miR-145-5p","log2FC":-0.75,"padj":0.012,"targets":"MYC, EGFR","pathway":"Growth suppression"},
-        {"miRNA":"hsa-miR-107","log2FC":-0.62,"padj":0.023,"targets":"CDK6, HIF1B","pathway":"Hypoxia / cell cycle"},
-        {"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"},
-        {"Gene":"BUB1B","dCERES":-0.119,"log2FC":1.12,"Drug_status":"Novel","siRNA":"CCAAAGAGCUGAAGAACAU"},
-        {"Gene":"CDC45","dCERES":-0.144,"log2FC":1.26,"Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"},
-        {"Gene":"PLK1","dCERES":-0.239,"log2FC":1.03,"Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"},
-        {"Gene":"CDK1","dCERES":-0.201,"log2FC":1.00,"Drug_status":"Clinical","siRNA":"GCAGAAGCACUGAAGAUUU"},
-    ],
-    "BRCA": [
-        {"Gene":"AURKA","dCERES":-0.165,"log2FC":1.20,"Drug_status":"Clinical","siRNA":"GCACUGAAGAUGCAGAAUU"},
-        {"Gene":"AURKB","dCERES":-0.140,"log2FC":1.15,"Drug_status":"Clinical","siRNA":"CCUGAAGACGCUCAAGGUU"},
-        {"Gene":"CENPW","dCERES":-0.125,"log2FC":0.95,"Drug_status":"Novel","siRNA":"GCAGAAGCACUGAAGAUUU"},
-        {"Gene":"RFC2","dCERES":-0.136,"log2FC":0.50,"Drug_status":"Novel","siRNA":"GCAAGAUGCAGAAGCACUU"},
-        {"Gene":"TYMS","dCERES":-0.131,"log2FC":0.72,"Drug_status":"Approved","siRNA":"GGACGCUCAAGAUGCAGAU"},
-    ],
-    "COAD": [
-        {"Gene":"KRAS","dCERES":-0.210,"log2FC":0.80,"Drug_status":"Clinical","siRNA":"GCUGGAGCUGGUGGUAGUU"},
-        {"Gene":"WEE1","dCERES":-0.180,"log2FC":1.05,"Drug_status":"Clinical","siRNA":"GCAGCUGAAGAAACUGAAU"},
-        {"Gene":"CHEK1","dCERES":-0.155,"log2FC":0.90,"Drug_status":"Clinical","siRNA":"CCAAAGAGCUGAAGAACAU"},
-        {"Gene":"RFC2","dCERES":-0.130,"log2FC":0.55,"Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"},
-        {"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"},
-    {"lncRNA":"GAS5","miRNA":"hsa-miR-21-5p","target":"PTEN","pathway":"Neuroinflammation"},
-    {"lncRNA":"GAS5","miRNA":"hsa-miR-222-3p","target":"IL1B","pathway":"Neuroinflammation"},
-    {"lncRNA":"HOTAIRM1","miRNA":"hsa-miR-9-5p","target":"TREM2","pathway":"Direct TREM2 regulation"},
-]
-
-ASO = [
-    {"lncRNA":"GAS5","position":119,"accessibility":0.653,"GC_pct":50,"Tm":47.2,"priority":"HIGH"},
-    {"lncRNA":"CYTOR","position":507,"accessibility":0.653,"GC_pct":50,"Tm":46.8,"priority":"HIGH"},
-    {"lncRNA":"HOTAIRM1","position":234,"accessibility":0.621,"GC_pct":44,"Tm":44.1,"priority":"MEDIUM"},
-    {"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},
-        {"Compound":"CHEMBL15727","RNA_score":0.805,"Toxicity":0.00,"Final_score":0.789},
-        {"Compound":"Thioguanine","RNA_score":0.888,"Toxicity":32.5,"Final_score":0.742},
-        {"Compound":"Deazaguanine","RNA_score":0.888,"Toxicity":35.0,"Final_score":0.735},
-        {"Compound":"CHEMBL441","RNA_score":0.775,"Toxicity":5.2,"Final_score":0.721},
-    ],
-    "P10 (G-quadruplex)": [
-        {"Compound":"CHEMBL15727","RNA_score":0.805,"Toxicity":0.00,"Final_score":0.789},
-        {"Compound":"CHEMBL5411515","RNA_score":0.945,"Toxicity":37.1,"Final_score":0.761},
-        {"Compound":"CHEMBL90","RNA_score":0.760,"Toxicity":2.1,"Final_score":0.745},
-        {"Compound":"CHEMBL102","RNA_score":0.748,"Toxicity":8.4,"Final_score":0.712},
-        {"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"},
-    "BRCA2:p.D2723A": {"score":0.999,"cls":"Pathogenic","conf":"High"},
-    "TP53:p.R248W":   {"score":0.998,"cls":"Pathogenic","conf":"High"},
-    "TP53:p.R248Q":   {"score":0.995,"cls":"Pathogenic","conf":"High"},
-    "EGFR:p.L858R":   {"score":0.96,"cls":"Pathogenic","conf":"High"},
-    "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.",
-}
-
-BM_W = {
-    "CTHRC1":0.18,"FHL2":0.15,"LDHA":0.14,"P4HA1":0.13,
-    "SERPINH1":0.12,"ABCA8":-0.11,"CA4":-0.10,"CKB":-0.09,
-    "NNMT":0.08,"CACNA2D2":-0.07
-}
-
-PROTEINS = ["albumin","apolipoprotein","fibrinogen","vitronectin",
-            "clusterin","igm","iga","igg","complement","transferrin",
-            "alpha-2-macroglobulin"]
-
-# ---------- Дані для рідкісних раків ----------
-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":"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"},
-]
-
-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"},
-]
-
-# ---------- Функції логіки (ваші, без змін) ----------
-def predict_mirna(gene):
-    df = pd.DataFrame(MIRNA_DB.get(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("S1-B | S1-R3 | siRNA", cancer, f"{len(df)} targets")
-    return df
-
-def get_lncrna():
-    log_entry("S1-B | S1-R4 | lncRNA", "load", "ceRNA+ASO")
-    return pd.DataFrame(CERNA), pd.DataFrame(ASO)
-
-def predict_drug(pocket):
-    df = pd.DataFrame(FGFR3.get(pocket, []))
-    fig, ax = plt.subplots(figsize=(6, 4), facecolor=CARD)
-    ax.set_facecolor(CARD)
-    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)
-    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'}")
-    return df, Image.open(buf)
-
-def predict_variant(hgvs, sift, polyphen, gnomad):
-    hgvs = hgvs.strip()
-    if hgvs in VARIANT_DB:
-        r = VARIANT_DB[hgvs]; 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
-        score = round(score, 3)
-        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"
-    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;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>"
-    )
-
-def predict_corona(size, zeta, peg, lipid):
-    score = 0
-    if lipid == "Ionizable": score += 2
-    elif lipid == "Cationic": score += 1
-    if abs(zeta) < 10: score += 1
-    if peg > 1.5:      score += 2
-    if size < 100:     score += 1
-    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**Predicted efficacy:** {efficacy}\n\n**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]
-    names, weights = list(BM_W.keys()), list(BM_W.values())
-    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 = RED if prob > 0.5 else GRN
-    contribs = [v*w for v,w in zip(vals, weights)]
-    fig, ax = plt.subplots(figsize=(6, 3.5), facecolor=CARD)
-    ax.set_facecolor(CARD)
-    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(BORDER)
-    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("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>"
-    ), Image.open(buf)
-
-def predict_flow(size, zeta, peg, charge, flow_rate):
-    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, ks = 0.03*(1+flow_rate/40), 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, 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.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)
-    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)
-
-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)
-    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.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("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":{}}
-    for pat, key in [(r"(\d+\.?\d*)\s*(?:nm|nanometer)","size_nm"),
-                     (r"([+-]?\d+\.?\d*)\s*mV","zeta_mv"),
-                     (r"PDI\s*[=:of]*\s*(\d+\.?\d*)","PDI")]:
-        m = re.search(pat, text, re.I)
-        if m: out[key] = float(m.group(1)); out["confidence"][key] = "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()]
-    for lip in ["DSPC","DOPE","MC3","DLin","cholesterol","PEG","DOTAP"]:
-        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("S1-D | S1-R10 | NLP", text[:80], f"proteins={len(out['corona_proteins'])}")
-    return json.dumps(out, indent=2), summary
-
-# ---------- Функції для рідкісних раків ----------
-def dipg_variants(sort_by):
-    df = pd.DataFrame(DIPG_VARIANTS).sort_values(
-        "Freq_pct" if sort_by == "Frequency" else "Drug_status", ascending=False)
-    log_entry("S1-F | S1-R12b | DIPG-variants", sort_by, f"{len(df)} variants")
-    return df
-
-def dipg_csf(peg, size):
-    df = pd.DataFrame(DIPG_CSF_LNP)
-    df["Score"] = df["ApoE_pct"]/40 + df["BBB_est"] - abs(df["Size_nm"]-size)/200
-    df = df.sort_values("Score", ascending=False)
-    fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD)
-    ax.set_facecolor(CARD)
-    colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]]
-    ax.barh(df["Formulation"], df["ApoE_pct"], color=colors)
-    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)
-    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")
-    return df[["Formulation","Size_nm","Zeta_mV","ApoE_pct","BBB_est","Priority"]], Image.open(buf)
-
-def uvm_variants():
-    df = pd.DataFrame(UVM_VARIANTS)
-    log_entry("S1-F | S1-R12c | UVM-variants", "load", f"{len(df)} variants")
-    return df
-
-def uvm_vitreous():
-    df = pd.DataFrame(UVM_VITREOUS_LNP)
-    fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD)
-    ax.set_facecolor(CARD)
-    colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]]
-    ax.barh(df["Formulation"], df["Retention_h"], color=colors)
-    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)
-    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")
-    return df, Image.open(buf)
-
-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
-    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]]
-    fig, ax = plt.subplots(figsize=(5, 4), subplot_kw={"polar":True}, facecolor=CARD)
-    ax.set_facecolor(CARD)
-    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)
-    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}")
-    _v  = row["Variant"]
-    _p  = row["Pathway"]
-    _d  = row["Drug_status"]
-    _f  = row["Ferroptosis"]
-    _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"<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>"
-        f"<p style='color:{TXT};margin:6px 0'><b>Ferroptosis link:</b> {_f}</p>"
-        f"<p style='color:{TXT}'><b>Ferroptosis sensitivity score:</b> "
-        f"<span style='color:{ACC};font-size:18px'>{_fs}</span></p>"
-        f"<p style='font-size:11px;color:{DIM}'>Research only. Not clinical advice.</p></div>"
-    )
-    return summary, Image.open(buf)
-
-# ---------- Хелпери для UI ----------
-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>"
-    )
-
-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 ""
-    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>"
-    )
-
-# ---------- CSS ----------
-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;
-    font-size: 13px !important;
-    font-weight: 600 !important;
-    padding: 8px 16px !important;
-    border-radius: 6px 6px 0 0 !important;
-}}
-.outer-tabs .tab-nav button.selected {{
-    border-bottom: 3px solid {ACC} !important;
-    color: {ACC} !important;
-    background: {BG} !important;
-}}
-
-/* INNER sub-tab bar — R1, R2, etc. */
-.inner-tabs .tab-nav button {{
-    color: {DIM} !important;
-    background: {BG} !important;
-    font-size: 12px !important;
-    font-weight: 500 !important;
-    padding: 5px 12px !important;
-    border-radius: 4px 4px 0 0 !important;
-    border: 1px solid {BORDER} !important;
-    border-bottom: none !important;
-    margin-right: 3px !important;
-}}
-.inner-tabs .tab-nav button.selected {{
-    color: {ACC2} !important;
-    background: {CARD} !important;
-    border-color: {ACC2} !important;
-    border-bottom: none !important;
-}}
-.inner-tabs > .tabitem {{
-    background: {CARD} !important;
-    border: 1px solid {BORDER} !important;
-    border-radius: 0 6px 6px 6px !important;
-    padding: 14px !important;
-}}
-
-/* Третій рівень вкладок (a, b) */
-.sub-sub-tabs .tab-nav button {{
-    color: {DIM} !important;
-    background: {CARD} !important;
-    font-size: 11px !important;
-    padding: 3px 8px !important;
-    border-radius: 3px 3px 0 0 !important;
-}}
-.sub-sub-tabs .tab-nav button.selected {{
-    color: {ACC} !important;
-    background: {BG} !important;
-}}
-
-h1, h2, h3 {{ color: {ACC} !important; }}
-.gr-button-primary {{ background: {ACC} !important; border: none !important; }}
-footer {{ display: none !important; }}
-"""
-
-# ---------- MAP HTML ----------
-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>
-<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-A · R1a</b> &nbsp;OpenVariant
-<span style="color:{GRN}"> AUC=0.939 ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-A · R1b</b> Somatic classifier
-<span style="color:#f59e0b"> 🔶 In progress</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-A · R2a</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-B · R1a</b> &nbsp;miRNA silencing (BRCA2)
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-B · R2a</b> &nbsp;siRNA synthetic lethal (LUAD · BRCA · COAD)
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-B · R3a</b> &nbsp;lncRNA-TREM2 ceRNA network
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-B · R3b</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-C · R1a</b> &nbsp;FGFR3 RNA-directed compounds
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-C · R1b</b> Synthetic lethal drug mapping
-<span style="color:#f59e0b"> 🔶</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-C · R2a</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-D · R1a</b> &nbsp;LNP corona (serum)
-<span style="color:{GRN}"> AUC=0.791 ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-D · R2a</b> &nbsp;Flow corona — Vroman effect
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-D · R3a</b> &nbsp;LNP brain / BBB / ApoE
-<span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-D · R4a</b> AutoCorona NLP
-<span style="color:{GRN}"> F1=0.71 ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-D · R5a</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-E · R1a</b> &nbsp;Liquid Biopsy classifier
-<span style="color:{GRN}"> AUC=0.992* ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-E · R1b</b> Protein panel validator
-<span style="color:#f59e0b"> 🔶</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-F · R1a</b> DIPG toolkit (H3K27M + CSF LNP + Circadian)
-<span style="color:#f59e0b"> 🔶 In development</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-F · R2a</b> UVM toolkit (GNAQ/GNA11 + vitreous + m6A)
-<span style="color:#f59e0b"> 🔶 In development</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-F · R3a</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
-</span>
-</div>
-"""
-
-# ---------- ПОБУДОВА UI ----------
-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; "
-        "*Research only. Not clinical advice.*"
-    )
-
-    # Основний рівень вкладок (категорії)
-    with gr.Tabs(elem_classes="outer-tabs") as outer_tabs:
-        # 1. Карта лабораторії
-        with gr.TabItem("🗺️ Lab Map"):
-            gr.HTML(MAP_HTML)
-
-        # 2. S1-A · PHYLO-GENOMICS
-        with gr.TabItem("🧬 PHYLO-GENOMICS"):
-            gr.HTML(section_header(
-                "S1-A", "PHYLO-GENOMICS", "— What breaks in DNA",
-                "R1a OpenVariant ✅ &nbsp;·&nbsp; R1b Somatic classifier 🔶"
-            ))
-            with gr.Tabs(elem_classes="inner-tabs") as inner_tabs:
-                # R1 · Variant classification
-                with gr.TabItem("R1 · Variant classification"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs") as sub_tabs:
-                        # R1a · OpenVariant
-                        with gr.TabItem("R1a · OpenVariant"):
-                            gr.HTML(proj_badge("S1-A · R1a", "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():
-                                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")
-                            b_v = gr.Button("Predict Pathogenicity", variant="primary")
-                            o_v = 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], cache_examples=False)
-                            b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
-                        # R1b · Somatic Classifier (в розробці)
-                        with gr.TabItem("R1b · Somatic Classifier 🔶"):
-                            gr.HTML(proj_badge("S1-A · R1b", "Somatic Mutation Classifier — BRCA · LUAD panels", "🔶 In progress"))
-                            gr.Markdown("> This module is in active development. Coming in the next release.")
-
-        # 3. S1-B · PHYLO-RNA
-        with gr.TabItem("🔬 PHYLO-RNA"):
-            gr.HTML(section_header(
-                "S1-B", "PHYLO-RNA", "— How to silence it via RNA",
-                "R1a miRNA ✅ &nbsp;·&nbsp; R2a siRNA ✅ &nbsp;·&nbsp; R3a lncRNA ✅ &nbsp;·&nbsp; R3b ASO ✅"
-            ))
-            with gr.Tabs(elem_classes="inner-tabs"):
-                # R1 · miRNA silencing
-                with gr.TabItem("R1 · miRNA silencing"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R1a · BRCA2 miRNA"):
-                            gr.HTML(proj_badge("S1-B · R1a", "miRNA Silencing — BRCA2"))
-                            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)
-                # R2 · siRNA SL
-                with gr.TabItem("R2 · siRNA SL"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R2a · TP53 siRNA"):
-                            gr.HTML(proj_badge("S1-B · R2a", "siRNA Synthetic Lethal — TP53-null"))
-                            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], cache_examples=False)
-                            b2.click(predict_sirna, [g2], o2)
-                # R3 · lncRNA + ASO
-                with gr.TabItem("R3 · lncRNA + ASO"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R3a · lncRNA-TREM2"):
-                            gr.HTML(proj_badge("S1-B · R3a", "lncRNA-TREM2 ceRNA Network"))
-                            b3a = gr.Button("Load ceRNA", variant="primary")
-                            o3a = gr.Dataframe(label="ceRNA Network (R3a)")
-                            b3a.click(lambda: pd.DataFrame(CERNA), [], o3a)
-                        with gr.TabItem("R3b · ASO Designer"):
-                            gr.HTML(proj_badge("S1-B · R3b", "ASO Designer"))
-                            b3b = gr.Button("Load ASO Candidates", variant="primary")
-                            o3b = gr.Dataframe(label="ASO Candidates (R3b)")
-                            b3b.click(lambda: pd.DataFrame(ASO), [], o3b)
-
-        # 4. S1-C · PHYLO-DRUG
-        with gr.TabItem("💊 PHYLO-DRUG"):
-            gr.HTML(section_header(
-                "S1-C", "PHYLO-DRUG", "— Which molecule treats it",
-                "R1a FGFR3 ✅ &nbsp;·&nbsp; R1b SL drug mapping 🔶 &nbsp;·&nbsp; R2a m6A×Ferroptosis×Circadian 🔴⭐"
-            ))
-            with gr.Tabs(elem_classes="inner-tabs"):
-                # R1 · RNA-directed drug
-                with gr.TabItem("R1 · RNA-directed drug"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R1a · FGFR3 RNA Drug"):
-                            gr.HTML(proj_badge("S1-C · R1a", "FGFR3 RNA-Directed Drug Discovery", "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")
-                            o4t = gr.Dataframe(label="Top 5 candidates")
-                            o4p = gr.Image(label="Binding scores")
-                            gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
-                            b4.click(predict_drug, [g4], [o4t, o4p])
-                        with gr.TabItem("R1b · SL Drug Mapping 🔶"):
-                            gr.HTML(proj_badge("S1-C · R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress"))
-                            gr.Markdown("> In development. Coming soon.")
-                # R2 · Frontier
-                with gr.TabItem("R2 · Frontier"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R2a · m6A×Ferroptosis×Circadian 🔴⭐"):
-                            gr.HTML(proj_badge("S1-C · R2a", "m6A × Ferroptosis × Circadian", "🔴 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"
-                                "> **Expected timeline:** Q3 2026"
-                            )
-
-        # 5. S1-D · PHYLO-LNP
-        with gr.TabItem("🧪 PHYLO-LNP"):
-            gr.HTML(section_header(
-                "S1-D", "PHYLO-LNP", "— How to deliver the drug",
-                "R1a Corona ✅ · R2a Flow ✅ · R3a Brain ✅ · R4a NLP ✅ · R5a CSF/BM 🔴⭐"
-            ))
-            with gr.Tabs(elem_classes="inner-tabs"):
-                # R1 · Serum corona
-                with gr.TabItem("R1 · Serum corona"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R1a · LNP Corona ML"):
-                            gr.HTML(proj_badge("S1-D · R1a", "LNP Protein Corona (Serum)", "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)")
-                            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()
-                            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)
-                # R2 · Flow corona
-                with gr.TabItem("R2 · Flow corona"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R2a · Flow Corona"):
-                            gr.HTML(proj_badge("S1-D · R2a", "Flow Corona — Vroman Effect"))
-                            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%")
-                            with gr.Row():
-                                ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Charge")
-                                fl8 = gr.Slider(0,40,value=20,step=1,label="Flow 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])
-                # R3 · Brain BBB
-                with gr.TabItem("R3 · Brain BBB"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R3a · LNP Brain"):
-                            gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery"))
-                            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])
-                # R4 · NLP
-                with gr.TabItem("R4 · NLP"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R4a · AutoCorona NLP"):
-                            gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP", "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")
-                            o10f = gr.Textbox(label="Validation flags")
-                            gr.Examples([[
-                                "LNPs composed of MC3, DSPC, Cholesterol (50:10:40 mol%) with 1.5% PEG-DMG. "
-                                "Hydrodynamic diameter was 98 nm, zeta potential -3.2 mV, PDI 0.12. "
-                                "Incubated in human plasma. Corona: albumin, apolipoprotein E, fibrinogen."
-                            ]], inputs=[txt])
-                            b10.click(extract_corona, txt, [o10j, o10f])
-                # R5 · Exotic fluids
-                with gr.TabItem("R5 · Exotic fluids 🔴⭐"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R5a · CSF/Vitreous/BM"):
-                            gr.HTML(proj_badge("S1-D · R5a", "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"
-                            )
-
-        # 6. S1-E · PHYLO-BIOMARKERS
-        with gr.TabItem("🩸 PHYLO-BIOMARKERS"):
-            gr.HTML(section_header(
-                "S1-E", "PHYLO-BIOMARKERS", "— Detect without biopsy",
-                "R1a Liquid Biopsy ✅ &nbsp;·&nbsp; R1b Protein validator 🔶"
-            ))
-            with gr.Tabs(elem_classes="inner-tabs"):
-                with gr.TabItem("R1 · Liquid biopsy"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R1a · Liquid Biopsy"):
-                            gr.HTML(proj_badge("S1-E · R1a", "Liquid Biopsy Classifier", "AUC=0.992*"))
-                            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]*10],
-                                        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("R1b · Protein Validator 🔶"):
-                            gr.HTML(proj_badge("S1-E · R1b", "Protein Panel Validator", "🔶 In progress"))
-                            gr.Markdown("> Coming next — validates R1a results against GEO plasma proteomics datasets.")
-
-        # 7. S1-F · PHYLO-RARE
-        with gr.TabItem("🧠 PHYLO-RARE"):
-            gr.HTML(section_header(
-                "S1-F", "PHYLO-RARE", "— Where almost nobody has looked yet",
-                "<b style='color:#ef4444'>⚠️ <300 cases/yr · <5% survival · 0–1 prior studies per gap</b><br>"
-                "R1a DIPG 🔶 &nbsp;·&nbsp; R2a UVM 🔶 &nbsp;·&nbsp; R3a pAML 🔶"
-            ))
-            with gr.Tabs(elem_classes="inner-tabs"):
-                # R1 · DIPG
-                with gr.TabItem("R1 · DIPG"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R1a · DIPG Toolkit"):
-                            gr.HTML(proj_badge("S1-F · R1a", "DIPG Toolkit", "PBTA · GSE126319"))
-                            gr.Markdown(
-                                "> **Why DIPG?** Diffuse Intrinsic Pontine Glioma — median survival 9–11 months. "
-                                "H3K27M oncohistone in **78%** cases. "
-                                "CSF delivery is the only viable route past the brainstem BBB. "
-                                "Circadian disruption (BMAL1 suppression) newly linked — **0 prior LNP studies**."
-                            )
-                            with gr.Tabs():
-                                with gr.TabItem("Variants"):
-                                    sort_d = gr.Radio(["Frequency", "Drug status"], value="Frequency", label="Sort by")
-                                    b_dv   = gr.Button("Load DIPG Variants", variant="primary")
-                                    o_dv   = gr.Dataframe(label="H3K27M co-mutations · PBTA/GSE126319")
-                                    b_dv.click(dipg_variants, [sort_d], o_dv)
-                                with gr.TabItem("CSF LNP"):
-                                    with gr.Row():
-                                        d_peg  = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%")
-                                        d_size = gr.Slider(60, 150, value=90, step=5, label="Target size (nm)")
-                                    b_dc  = gr.Button("Rank CSF Formulations", variant="primary")
-                                    o_dct = gr.Dataframe(label="CSF LNP ranking")
-                                    o_dcp = gr.Image(label="ApoE% in CSF corona")
-                                    b_dc.click(dipg_csf, [d_peg, d_size], [o_dct, o_dcp])
-                                with gr.TabItem("Research Gap"):
-                                    gr.Markdown(
-                                        "**Data:** PBTA (n=240) · GSE126319 (n=28) · GTEx circadian genes\n\n"
-                                        "| Layer | Known | This study gap |\n"
-                                        "|-------|-------|----------------|\n"
-                                        "| Genomics | H3K27M freq=78% | H3K27M × BMAL1/CLOCK |\n"
-                                        "| Delivery | CED convection | LNP corona **in CSF** |\n"
-                                        "| Biology | PRC2 inhibition | Ferroptosis in H3K27M+ DIPG |"
-                                    )
-                # R2 · UVM
-                with gr.TabItem("R2 · UVM"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R2a · UVM Toolkit"):
-                            gr.HTML(proj_badge("S1-F · R2a", "UVM Toolkit", "TCGA-UVM n=80"))
-                            gr.Markdown(
-                                "> **Why UVM?** Uveal Melanoma — metastatic 5-yr survival **15%**. "
-                                "GNAQ/GNA11 mutations in 78% cases. "
-                                "Vitreous humor protein corona has **never been profiled**. "
-                                "METTL3/WTAP upregulated in GNAQ+ tumors — 0 therapeutic studies."
-                            )
-                            with gr.Tabs():
-                                with gr.TabItem("Variants + m6A"):
-                                    b_uv = gr.Button("Load UVM Variants", variant="primary")
-                                    o_uv = gr.Dataframe(label="GNAQ/GNA11 map · TCGA-UVM")
-                                    b_uv.click(uvm_variants, [], o_uv)
-                                with gr.TabItem("Vitreous LNP"):
-                                    b_uw  = gr.Button("Rank Vitreous Formulations", variant="primary")
-                                    o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking")
-                                    o_uwp = gr.Image(label="Retention (hours)")
-                                    b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp])
-                                with gr.TabItem("Research Gap"):
-                                    gr.Markdown(
-                                        "**Data:** TCGA-UVM (n=80) · GEO m6A atlases · Vitreous proteomics\n\n"
-                                        "| Layer | Known | This study gap |\n"
-                                        "|-------|-------|----------------|\n"
-                                        "| Genomics | GNAQ/GNA11 mutations | m6A landscape GNAQ+ vs GNA11+ |\n"
-                                        "| Delivery | Intravitreal injection | LNP corona **in vitreous humor** |\n"
-                                        "| Biology | PLCβ→PKC→MAPK | GNAQ × METTL3 × YTHDF2 axis |"
-                                    )
-                # R3 · pAML
-                with gr.TabItem("R3 · pAML"):
-                    with gr.Tabs(elem_classes="sub-sub-tabs"):
-                        with gr.TabItem("R3a · pAML Toolkit"):
-                            gr.HTML(proj_badge("S1-F · R3a", "pAML Toolkit", "TARGET-AML n≈197"))
-                            gr.Markdown(
-                                "> **Why pAML?** Pediatric AML — relapse OS **<30%**. "
-                                "FLT3-ITD in 25% cases. "
-                                "Bone marrow niche LNP corona: **never studied**. "
-                                "Ferroptosis–FLT3 intersection: 0 prior studies (FerrDb v2 confirmed)."
-                            )
-                            with gr.Tabs():
-                                with gr.TabItem("Ferroptosis Explorer"):
-                                    var_sel = 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_pft = gr.HTML()
-                                    o_pfp = gr.Image(label="Target radar")
-                                    b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp])
-                                with gr.TabItem("BM Niche LNP"):
-                                    gr.Dataframe(
-                                        value=pd.DataFrame(PAML_BM_LNP),
-                                        label="Bone marrow niche LNP candidates · TARGET-AML context"
-                                    )
-                                with gr.TabItem("Research Gap"):
-                                    gr.Markdown(
-                                        "**Data:** TARGET-AML (n=197) · BeatAML · FerrDb v2\n\n"
-                                        "| Layer | Known | This study gap |\n"
-                                        "|-------|-------|----------------|\n"
-                                        "| Genomics | FLT3-ITD → Midostaurin | FLT3-ITD × GPX4/SLC7A11 |\n"
-                                        "| Delivery | Liposomal daunorubicin | LNP corona **in bone marrow** |\n"
-                                        "| Biology | Midostaurin inhibits FLT3 | Ferroptosis SL + FLT3i |"
-                                    )
-
-        # 8. Journal
-        with gr.TabItem("📓 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_tab  = gr.Textbox(label="Project code (e.g. S1-A-R1a)", 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])
-
-        # 9. Learning
-        with gr.TabItem("📚 Learning"):
-            gr.Markdown("""
-## 🧪 Guided Investigations — S1 Biomedical
-> 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
-
----
-### 🟢 Case 1 · S1-A · R1a
-**Why does the same position give two different outcomes?**
-1. PHYLO-GENOMICS → R1a · 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 · R1a + R3a
-**How does PEG% control which protein coats the nanoparticle?**
-1. PHYLO-LNP → R1a · Corona → Ionizable, Zeta=−5, PEG=**0.5%** → note protein
-2. Change PEG=**2.5%** → compare
-3. R3a · Brain → pKa=6.5 → check ApoE%
-
----
-### 🟡 Case 3 · S1-D · R2a
-**Does blood flow reshape the corona over time?**
-1. PHYLO-LNP → R2a · Flow → Flow=0 → observe ApoE curve
-2. Flow=40 (arterial) → compare
-3. At what minute does ApoE dominate?
-
----
-### 🟡 Case 4 · S1-B · R2a
-**Which cancer has the most novel (undrugged) siRNA targets?**
-1. PHYLO-RNA → R2a · siRNA → LUAD → count "Novel"
-2. Repeat BRCA, COAD
-
----
-### 🔴 Case 5 · S1-E · R1a
-**Minimum protein signal that flips to CANCER?**
-1. PHYLO-BIOMARKERS → R1a · 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 · Cross-tool convergence
-1. PHYLO-RNA → R1a · miRNA → TP53 → find top targets (BCL2, CDK6)
-2. PHYLO-DRUG → R1a · FGFR3 → check CDK6 pathway overlap
-3. PHYLO-RNA → R2a · siRNA → BRCA → does CDK6 appear?
-
----
-### 📖 Tool Index
-| Code | Module | Tool | Metric |
-|------|--------|------|--------|
-| S1-A·R1a | PHYLO-GENOMICS | OpenVariant | AUC=0.939 |
-| S1-B·R1a | PHYLO-RNA | miRNA silencing | top: hsa-miR-148a |
-| S1-B·R2a | PHYLO-RNA | siRNA SL | SPC24 top LUAD |
-| S1-B·R3a | PHYLO-RNA | lncRNA-TREM2 | CYTOR→AKT1 |
-| S1-C·R1a | PHYLO-DRUG | FGFR3 drug | score=0.793 |
-| S1-D·R1a | PHYLO-LNP | Corona | AUC=0.791 |
-| S1-D·R2a | PHYLO-LNP | Flow Vroman | 3–4× faster |
-| S1-D·R3a | PHYLO-LNP | LNP Brain | pKa 6.2–6.8 |
-| S1-E·R1a | PHYLO-BIOMARKERS | Liquid Biopsy | AUC=0.992* |
-| S1-D·R4a | PHYLO-LNP | AutoCorona NLP | F1=0.71 |
-""")
-
-    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"
-    )
-
-demo.queue()
 demo.launch()