Upload app_v5.py

app_v5.py

@@ -0,0 +1,880 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import json, re, csv
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from io import BytesIO
+from PIL import Image
+from datetime import datetime
+from pathlib import Path
+
+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")
+
+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()
+
+# ── DATABASES ─────────────────────────────────────────────────────────────────
+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"]
+
+# ── LOGIC ─────────────────────────────────────────────────────────────────────
+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
+
+
+# ── S1-F · PHYLO-RARE DATABASES ───────────────────────────────────────────────
+
+# S1-R12b · DIPG — H3K27M + CSF LNP + Circadian
+DIPG_VARIANTS = [
+    {"Variant":"H3K27M (H3F3A)","Freq_pct":78,"Pathway":"PRC2 inhibition → global H3K27me3 loss","Drug_status":"ONC201 (clinical)","Circadian_gene":"BMAL1 suppressed"},
+    {"Variant":"ACVR1 p.R206H","Freq_pct":21,"Pathway":"BMP/SMAD hyperactivation","Drug_status":"LDN-193189 (preclinical)","Circadian_gene":"PER1 disrupted"},
+    {"Variant":"PIK3CA p.H1047R","Freq_pct":15,"Pathway":"PI3K/AKT/mTOR","Drug_status":"Copanlisib (clinical)","Circadian_gene":"CRY1 altered"},
+    {"Variant":"TP53 p.R248W","Freq_pct":14,"Pathway":"DNA damage response loss","Drug_status":"APR-246 (clinical)","Circadian_gene":"p53-CLOCK axis"},
+    {"Variant":"PDGFRA amp","Freq_pct":13,"Pathway":"RTK/RAS signalling","Drug_status":"Avapritinib (clinical)","Circadian_gene":"REV-ERB altered"},
+]
+DIPG_CSF_LNP = [
+    {"Formulation":"MC3-DSPC-Chol-PEG","Size_nm":92,"Zeta_mV":-4.1,"CSF_protein":"Beta2-microglobulin","ApoE_pct":12.4,"BBB_est":0.41,"Priority":"HIGH"},
+    {"Formulation":"DLin-KC2-DSPE-PEG","Size_nm":87,"Zeta_mV":-3.8,"CSF_protein":"Cystatin C","ApoE_pct":14.1,"BBB_est":0.47,"Priority":"HIGH"},
+    {"Formulation":"C12-200-DOPE-PEG","Size_nm":103,"Zeta_mV":-5.2,"CSF_protein":"Albumin (low)","ApoE_pct":9.8,"BBB_est":0.33,"Priority":"MEDIUM"},
+    {"Formulation":"DODAP-DSPC-Chol","Size_nm":118,"Zeta_mV":-2.1,"CSF_protein":"Transferrin","ApoE_pct":7.2,"BBB_est":0.24,"Priority":"LOW"},
+]
+
+# S1-R12c · UVM — GNAQ/GNA11 + Vitreous corona + m6A
+UVM_VARIANTS = [
+    {"Variant":"GNAQ p.Q209L","Freq_pct":46,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"METTL3 upregulated"},
+    {"Variant":"GNA11 p.Q209L","Freq_pct":32,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"WTAP upregulated"},
+    {"Variant":"BAP1 loss","Freq_pct":47,"Pathway":"Chromatin remodeling → metastasis","Drug_status":"No approved (HDAC trials)","m6A_writer":"FTO overexpressed"},
+    {"Variant":"SF3B1 p.R625H","Freq_pct":19,"Pathway":"Splicing alteration → neoepitopes","Drug_status":"H3B-8800 (clinical)","m6A_writer":"METTL14 altered"},
+    {"Variant":"EIF1AX p.A113_splice","Freq_pct":14,"Pathway":"Translation initiation","Drug_status":"Novel — no drug","m6A_writer":"YTHDF2 suppressed"},
+]
+UVM_VITREOUS_LNP = [
+    {"Formulation":"SM-102-DSPC-Chol-PEG","Vitreal_protein":"Hyaluronan-binding","Size_nm":95,"Zeta_mV":-3.2,"Retention_h":18,"Priority":"HIGH"},
+    {"Formulation":"Lipid-H-DOPE-PEG","Vitreal_protein":"Vitronectin dominant","Size_nm":88,"Zeta_mV":-4.0,"Retention_h":22,"Priority":"HIGH"},
+    {"Formulation":"DOTAP-DSPC-PEG","Vitreal_protein":"Albumin wash-out","Size_nm":112,"Zeta_mV":+2.1,"Retention_h":6,"Priority":"LOW"},
+    {"Formulation":"MC3-DPPC-Chol","Vitreal_protein":"Clusterin-rich","Size_nm":101,"Zeta_mV":-2.8,"Retention_h":14,"Priority":"MEDIUM"},
+]
+
+# S1-R12d · pAML — FLT3-ITD + BM niche corona + Ferroptosis
+PAML_VARIANTS = [
+    {"Variant":"FLT3-ITD","Freq_pct":25,"Pathway":"RTK constitutive activation → JAK/STAT","Drug_status":"Midostaurin (approved)","Ferroptosis":"GPX4 suppressed"},
+    {"Variant":"NPM1 c.860_863dupTCAG","Freq_pct":30,"Pathway":"Nuclear export deregulation","Drug_status":"APR-548 combo (clinical)","Ferroptosis":"SLC7A11 upregulated"},
+    {"Variant":"DNMT3A p.R882H","Freq_pct":18,"Pathway":"Epigenetic dysregulation","Drug_status":"Azacitidine (approved)","Ferroptosis":"ACSL4 altered"},
+    {"Variant":"CEBPA biallelic","Freq_pct":8,"Pathway":"Myeloid differentiation block","Drug_status":"Novel target","Ferroptosis":"NRF2 pathway"},
+    {"Variant":"IDH1/2 mutation","Freq_pct":15,"Pathway":"2-HG oncometabolite → TET2 inhibition","Drug_status":"Enasidenib (approved)","Ferroptosis":"Iron metabolism disrupted"},
+]
+PAML_BM_LNP = [
+    {"Formulation":"ALC-0315-DSPC-Chol-PEG","BM_protein":"ApoE + Clusterin","Size_nm":98,"Zeta_mV":-3.5,"Marrow_uptake_pct":34,"Priority":"HIGH"},
+    {"Formulation":"MC3-DOPE-Chol-PEG","BM_protein":"Fibronectin dominant","Size_nm":105,"Zeta_mV":-4.2,"Marrow_uptake_pct":28,"Priority":"HIGH"},
+    {"Formulation":"DLin-MC3-DPPC","BM_protein":"Vitronectin-rich","Size_nm":91,"Zeta_mV":-2.9,"Marrow_uptake_pct":19,"Priority":"MEDIUM"},
+    {"Formulation":"Cationic-DOTAP-Chol","BM_protein":"Opsonin-heavy","Size_nm":132,"Zeta_mV":+8.1,"Marrow_uptake_pct":8,"Priority":"LOW"},
+]
+
+# ── S1-F · PHYLO-RARE LOGIC ───────────────────────────────────────────────────
+def dipg_variants(sort_by):
+    df = pd.DataFrame(DIPG_VARIANTS).sort_values(
+        "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}")
+    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\'>{row[\'Variant\']}</b><br>"
+        f"<p style=\'color:{TXT};margin:6px 0\'><b>Pathway:</b> {row[\'Pathway\']}</p>"
+        f"<p style=\'color:{TXT};margin:0\'><b>Drug:</b> {row[\'Drug_status\']}</p>"
+        f"<p style=\'color:{TXT};margin:6px 0\'><b>Ferroptosis link:</b> {row[\'Ferroptosis\']}</p>"
+        f"<p style=\'color:{TXT}\'><b>Ferroptosis sensitivity score:</b> "
+        f"<span style=\'color:{ACC};font-size:18px\'>{ferr_score:.2f}</span></p>"
+        f"<p style=\'font-size:11px;color:{DIM}\'>Research only. Not clinical advice.</p></div>"
+    )
+    return summary, Image.open(buf)
+
+# ── HELPERS ───────────────────────────────────────────────────────────────────
+def section_header(code, name, tagline, projects_html):
+    return (
+        f"<div style=\'background:{BG};border:1px solid {BORDER};padding:14px 18px;"
+        f"border-radius:8px;margin-bottom:12px;\'>"
+        f"<div style=\'display:flex;align-items:baseline;gap:10px;\'>"
+        f"<span style=\'color:{ACC2};font-size:16px;font-weight:700\'>{code}</span>"
+        f"<span style=\'color:{TXT};font-size:14px;font-weight:600\'>{name}</span>"
+        f"<span style=\'color:{DIM};font-size:12px\'>{tagline}</span></div>"
+        f"<div style=\'margin-top:8px;font-size:12px;color:{DIM}\'>{projects_html}</div>"
+        f"</div>"
+    )
+
+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 — individual tools */
+.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;
+}}
+
+h1, h2, h3 {{ color: {ACC} !important; }}
+.gr-button-primary {{ background: {ACC} !important; border: none !important; }}
+footer {{ display: none !important; }}
+"""
+
+# ── LAB 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-R1</b> &nbsp;OpenVariant
+<span style="color:{GRN}"> AUC=0.939 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R1b</b> Somatic classifier
+<span style="color:#f59e0b"> 🔶 In progress</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R12a</b> Rare variants (DIPG · UVM)
+<span style="color:{DIM}"> 🔴 Planned</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-B · PHYLO-RNA</span>
+<span style="color:{DIM}"> — How to silence it via RNA</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R2</b> &nbsp;miRNA silencing (BRCA1/2, TP53)
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R3</b> &nbsp;siRNA synthetic lethal (LUAD · BRCA · COAD)
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R4</b> &nbsp;lncRNA-TREM2 ceRNA network
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R4b</b> ASO designer
+<span style="color:{GRN}"> ✅</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-C · PHYLO-DRUG</span>
+<span style="color:{DIM}"> — Which molecule treats it</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R5</b> &nbsp;FGFR3 RNA-directed compounds
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R5b</b> Synthetic lethal drug mapping
+<span style="color:#f59e0b"> 🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R13</b> m6A × Ferroptosis × Circadian ⭐
+<span style="color:{DIM}"> 🔴 Frontier</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-D · PHYLO-LNP</span>
+<span style="color:{DIM}"> — How to deliver the drug</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R6</b> &nbsp;LNP corona (serum)
+<span style="color:{GRN}"> AUC=0.791 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R7</b> &nbsp;Flow corona — Vroman effect
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R8</b> &nbsp;LNP brain / BBB / ApoE
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R10</b> AutoCorona NLP
+<span style="color:{GRN}"> F1=0.71 ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R11</b> CSF · Vitreous · Bone Marrow ⭐
+<span style="color:{DIM}"> 🔴 0 prior studies</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-E · PHYLO-BIOMARKERS</span>
+<span style="color:{DIM}"> — Detect without biopsy</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R9</b> &nbsp;Liquid Biopsy classifier
+<span style="color:{GRN}"> AUC=0.992* ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R9b</b> Protein panel validator
+<span style="color:#f59e0b"> 🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R9c</b> ctDNA gap analysis
+<span style="color:{DIM}"> 🔴</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-F · PHYLO-RARE</span>
+<span style="color:{DIM}"> — Where almost nobody has looked yet (&lt;300 cases/yr)</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R12b</b> DIPG toolkit (H3K27M + CSF LNP + Circadian)
+<span style="color:#f59e0b"> 🔶 In development</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R12c</b> UVM toolkit (GNAQ/GNA11 + vitreous + m6A)
+<span style="color:#f59e0b"> 🔶 In development</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R12d</b> pAML toolkit (FLT3-ITD + BM niche + ferroptosis)
+<span style="color:#f59e0b"> 🔶 In development</span><br><br>
+
+<span style="color:{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>
+"""
+
+# ── 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.*"
+    )
+
+    # ═══════════════════════════════════════════════════════
+    # OUTER TABS — one per PHYLO-* category
+    # ═══════════════════════════════════════════════════════
+    with gr.Tabs(elem_classes="outer-tabs"):
+
+        # ── 🗺️ MAP ─────────────────────────────────────────
+        with gr.TabItem("🗺️ Lab Map"):
+            gr.HTML(MAP_HTML)
+
+        # ── 🧬 S1-A · PHYLO-GENOMICS ───────────────────────
+        with gr.TabItem("🧬 PHYLO-GENOMICS"):
+            gr.HTML(section_header(
+                "S1-A", "PHYLO-GENOMICS", "— What breaks in DNA",
+                "S1-R1 OpenVariant ✅ &nbsp;·&nbsp; S1-R1b Somatic classifier 🔶 &nbsp;·&nbsp; S1-R12a Rare variants 🔴"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R1 · OpenVariant"):
+                    gr.HTML(proj_badge("S1-A · PHYLO-GENOMICS · S1-R1",
+                                       "OpenVariant — SNV Pathogenicity Classifier", "AUC = 0.939"))
+                    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])
+                    b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
+
+                with gr.TabItem("S1-R1b · Somatic 🔶"):
+                    gr.HTML(proj_badge("S1-A · PHYLO-GENOMICS · S1-R1b",
+                                       "Somatic Mutation Classifier — BRCA · LUAD panels", "🔶 In progress"))
+                    gr.Markdown("> This module is in active development. Coming in the next release.")
+
+        # ── 🔬 S1-B · PHYLO-RNA ────────────────────────────
+        with gr.TabItem("🔬 PHYLO-RNA"):
+            gr.HTML(section_header(
+                "S1-B", "PHYLO-RNA", "— How to silence it via RNA",
+                "S1-R2 miRNA ✅ &nbsp;·&nbsp; S1-R3 siRNA ✅ &nbsp;·&nbsp; S1-R4 lncRNA ✅ &nbsp;·&nbsp; S1-R4b ASO ✅"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R2 · miRNA"):
+                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R2",
+                                       "miRNA Silencing — BRCA1/2 · TP53 tumor suppressors"))
+                    g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
+                    b1 = gr.Button("Find miRNAs", variant="primary")
+                    o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
+                    gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1])
+                    b1.click(predict_mirna, g1, o1)
+
+                with gr.TabItem("S1-R3 · siRNA"):
+                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R3",
+                                       "siRNA Synthetic Lethal — TP53-null · LUAD · BRCA · COAD"))
+                    g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
+                    b2 = gr.Button("Find Targets", variant="primary")
+                    o2 = gr.Dataframe(label="Top 5 synthetic lethal targets")
+                    gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2])
+                    b2.click(predict_sirna, g2, o2)
+
+                with gr.TabItem("S1-R4 · lncRNA + ASO"):
+                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R4 + S1-R4b",
+                                       "lncRNA-TREM2 ceRNA Network + ASO Candidates · Alzheimer neuroinflammation"))
+                    b3 = gr.Button("Load Results", variant="primary")
+                    o3a = gr.Dataframe(label="ceRNA Network (S1-R4)")
+                    o3b = gr.Dataframe(label="ASO Candidates (S1-R4b)")
+                    b3.click(get_lncrna, [], [o3a, o3b])
+
+        # ── 💊 S1-C · PHYLO-DRUG ───────────────────────────
+        with gr.TabItem("💊 PHYLO-DRUG"):
+            gr.HTML(section_header(
+                "S1-C", "PHYLO-DRUG", "— Which molecule treats it",
+                "S1-R5 FGFR3 ✅ &nbsp;·&nbsp; S1-R5b SL drug mapping 🔶 &nbsp;·&nbsp; S1-R13 m6A×Ferroptosis×Circadian 🔴⭐"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R5 · FGFR3"):
+                    gr.HTML(proj_badge("S1-C · PHYLO-DRUG · S1-R5",
+                                       "FGFR3 RNA-Directed Drug Discovery · ChEMBL screen",
+                                       "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("S1-R13 · Frontier 🔴⭐"):
+                    gr.HTML(proj_badge("S1-C · PHYLO-DRUG · S1-R13",
+                                       "m6A × Ferroptosis × Circadian — Pan-cancer triad", "🔴 Frontier"))
+                    gr.Markdown(
+                        "> **Research gap:** This triple intersection has never been studied as an integrated system.\n\n"
+                        "> **Planned datasets:** TCGA-PAAD · GEO m6A atlases · Circadian gene panels\n\n"
+                        "> **Expected timeline:** Q3 2026"
+                    )
+
+        # ── 🧪 S1-D · PHYLO-LNP ────────────────────────────
+        with gr.TabItem("🧪 PHYLO-LNP"):
+            gr.HTML(section_header(
+                "S1-D", "PHYLO-LNP", "— How to deliver the drug to the cell",
+                "S1-R6 Corona ✅ · S1-R7 Flow ✅ · S1-R8 Brain ✅ · S1-R10 NLP ✅ · S1-R11 CSF/BM 🔴⭐"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R6 · Corona"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R6",
+                                       "LNP Protein Corona (Serum)", "AUC = 0.791"))
+                    with gr.Row():
+                        sz = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
+                        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)
+
+                with gr.TabItem("S1-R7 · Flow"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R7",
+                                       "Flow Corona — Vroman Effect · albumin→ApoE kinetics"))
+                    with gr.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])
+
+                with gr.TabItem("S1-R8 · Brain"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R8",
+                                       "LNP Brain Delivery — ApoE% + BBB probability"))
+                    smi = gr.Textbox(label="Ionizable lipid SMILES",
+                                     value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
+                    with gr.Row():
+                        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(proj_badge("S1-D · PHYLO-LNP · S1-R10",
+                                       "AutoCorona NLP — structured data from PMC abstracts", "F1 = 0.71"))
+                    txt  = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...")
+                    b10  = gr.Button("Extract Data", variant="primary")
+                    o10j = gr.Code(label="Extracted JSON", language="json")
+                    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])
+
+                with gr.TabItem("S1-R11 · CSF/BM 🔴⭐"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R11",
+                                       "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
+                    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"
+                    )
+
+        # ── 🩸 S1-E · PHYLO-BIOMARKERS ─────────────────────
+        with gr.TabItem("🩸 PHYLO-BIOMARKERS"):
+            gr.HTML(section_header(
+                "S1-E", "PHYLO-BIOMARKERS", "— Detect cancer without tissue biopsy",
+                "S1-R9 Liquid Biopsy ✅ &nbsp;·&nbsp; S1-R9b Panel validator 🔶 &nbsp;·&nbsp; S1-R9c ctDNA gap 🔴"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R9 · Liquid Biopsy"):
+                    gr.HTML(proj_badge("S1-E · PHYLO-BIOMARKERS · S1-R9",
+                                       "Liquid Biopsy Classifier — CTHRC1 · FHL2 · LDHA panel",
+                                       "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("S1-R9b · Validator 🔶"):
+                    gr.HTML(proj_badge("S1-E · PHYLO-BIOMARKERS · S1-R9b",
+                                       "Protein Panel Validator — multi-cancer plasma validation", "🔶 In progress"))
+                    gr.Markdown("> Coming next — validates S1-R9 results against GEO plasma proteomics datasets.")
+
+        # ── 📓 JOURNAL ──────────────────────────────────────
+        with gr.TabItem("📓 Journal"):
+            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-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])
+
+        # ── 📚 LEARNING ─────────────────────────────────────
+        with gr.TabItem("📚 Learning"):
+            gr.Markdown("""
+## 🧪 Guided Investigations — S1 Biomedical
+> 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
+
+---
+### 🟢 Case 1 · S1-A · S1-R1
+**Why does the same position give two different outcomes?**
+1. PHYLO-GENOMICS → OpenVariant → `BRCA1:p.R1699Q` → Benign
+2. Enter `BRCA1:p.R1699W` → Pathogenic
+3. Same position, different amino acid — Q (polar) vs W (bulky-aromatic)
+
+---
+### 🟢 Case 2 · S1-D · S1-R6 + S1-R8
+**How does PEG% control which protein coats the nanoparticle?**
+1. PHYLO-LNP → Corona → Ionizable, Zeta=−5, PEG=**0.5%** → note protein
+2. Change PEG=**2.5%** → compare
+3. Brain tab → pKa=6.5 → check ApoE%
+
+---
+### 🟡 Case 3 · S1-D · S1-R7
+**Does blood flow reshape the corona over time?**
+1. PHYLO-LNP → Flow → Flow=0 → observe ApoE curve
+2. Flow=40 (arterial) → compare
+3. At what minute does ApoE dominate?
+
+---
+### 🟡 Case 4 · S1-B · S1-R3
+**Which cancer has the most novel (undrugged) siRNA targets?**
+1. PHYLO-RNA → siRNA → LUAD → count "Novel"
+2. Repeat BRCA, COAD
+
+---
+### 🔴 Case 5 · S1-E · S1-R9
+**Minimum protein signal that flips to CANCER?**
+1. PHYLO-BIOMARKERS → Liquid Biopsy → all=0 → HEALTHY
+2. Set CTHRC1=2.5, FHL2=2.0, LDHA=1.8 → observe
+3. Reset. Increase only CTHRC1 step by step.
+
+---
+### 🔴 Case 6 · S1-B + S1-C — Cross-tool convergence
+1. PHYLO-RNA → miRNA → TP53 → find top targets (BCL2, CDK6)
+2. PHYLO-DRUG → FGFR3 → check CDK6 pathway overlap
+3. PHYLO-RNA → siRNA → BRCA → does CDK6 appear?
+
+---
+### 📖 Tool Index
+| Code | Module | Tool | Metric |
+|------|--------|------|--------|
+| S1-R1 | PHYLO-GENOMICS | OpenVariant | AUC=0.939 |
+| S1-R2 | PHYLO-RNA | miRNA silencing | top: hsa-miR-148a |
+| S1-R3 | PHYLO-RNA | siRNA SL | SPC24 top LUAD |
+| S1-R4 | PHYLO-RNA | lncRNA-TREM2 | CYTOR→AKT1 |
+| S1-R5 | PHYLO-DRUG | FGFR3 drug | score=0.793 |
+| S1-R6 | PHYLO-LNP | Corona | AUC=0.791 |
+| S1-R7 | PHYLO-LNP | Flow Vroman | 3–4× faster |
+| S1-R8 | PHYLO-LNP | LNP Brain | pKa 6.2–6.8 |
+| S1-R9 | PHYLO-BIOMARKERS | Liquid Biopsy | AUC=0.992* |
+| S1-R10 | 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.launch(server_name="0.0.0.0", server_port=7860)