Update app.py

app.py

@@ -593,6 +593,8 @@ def plot_corona():
 # ========== CSS ==========
 css = f"""
 body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }}
+
+/* OUTER tab bar — PHYLO categories (S1-A, S1-B, ...) */
 .outer-tabs .tab-nav button {{
     color: {TXT} !important;
     background: {CARD} !important;
@@ -606,6 +608,8 @@ body, .gradio-container {{ background: {BG} !important; color: {TXT} !important;
     color: {ACC} !important;
     background: {BG} !important;
 }}
+
+/* INNER sub-tab bar — R1, R2, R3 (напрямки) */
 .inner-tabs .tab-nav button {{
     color: {DIM} !important;
     background: {BG} !important;
@@ -629,6 +633,20 @@ body, .gradio-container {{ background: {BG} !important; color: {TXT} !important;
     border-radius: 0 6px 6px 6px !important;
     padding: 14px !important;
 }}
+
+/* Третій рівень вкладок — a, b (експерименти) */
+.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; }}
@@ -665,11 +683,15 @@ MAP_HTML = f"""
 &nbsp;&nbsp;&nbsp;├─ <b>S1-F · R1a</b> DIPG toolkit (H3K27M + CSF LNP + Circadian) <span style="color:#f59e0b"> 🔶</span><br>
 &nbsp;&nbsp;&nbsp;├─ <b>S1-F · R2a</b> UVM toolkit (GNAQ/GNA11 + vitreous + m6A) <span style="color:#f59e0b"> 🔶</span><br>
 &nbsp;&nbsp;&nbsp;└─ <b>S1-F · R3a</b> pAML toolkit (FLT3-ITD + BM niche + ferroptosis) <span style="color:#f59e0b"> 🔶</span><br><br>
+<span style="color:{ACC2};font-weight:600">S1-G · PHYLO-SIM</span> — 3D Models & Simulations<br>
+&nbsp;&nbsp;&nbsp;├─ <b>Nanoparticle</b> Interactive 3D model <span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>DNA Helix</b> Double helix visualization <span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>Protein Corona</b> Schematic corona <span style="color:{GRN}"> ✅</span><br><br>
 <span style="color:{DIM};font-size:11px">✅ Active · 🔶 In progress · 🔴 Planned</span>
 </div>
 """
 
-# ========== ІНТЕРФЕЙС ==========
+# ========== ІНТЕРФЕЙС З ТРЬОМА РІВНЯМИ ВКЛАДОК ==========
 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"
@@ -683,202 +705,330 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
             gr.HTML(MAP_HTML)
 
         # === S1-A · PHYLO-GENOMICS ===
-        with gr.TabItem("S1-A · 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)
-
-        with gr.TabItem("S1-A · R1b · Somatic Classifier 🔶"):
-            gr.HTML(proj_badge("S1-A · R1b", "Somatic Mutation Classifier", "🔶 In progress"))
-            gr.Markdown("> This module is in active development. Coming in the next release.")
+        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"):
+                with gr.TabItem("R1 · Variant classification"):
+                    with gr.Tabs(elem_classes="sub-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", "🔶 In progress"))
+                            gr.Markdown("> This module is in active development. Coming in the next release.")
 
         # === S1-B · PHYLO-RNA ===
-        with gr.TabItem("S1-B · R1a · BRCA2 miRNA"):
-            gr.HTML(proj_badge("S1-B · R1a", "miRNA Silencing — BRCA1/2 · TP53"))
-            g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
-            b1 = gr.Button("Find miRNAs", variant="primary")
-            o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
-            gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1])
-            b1.click(predict_mirna, [g1], o1)
-
-        with gr.TabItem("S1-B · 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)
-
-        with gr.TabItem("S1-B · R3a · lncRNA-TREM2"):
-            gr.HTML(proj_badge("S1-B · R3a", "lncRNA-TREM2 ceRNA Network"))
-            b3 = gr.Button("Load Network", variant="primary")
-            o3 = gr.Dataframe(label="ceRNA Network")
-            b3.click(get_lncrna, [], o3)
-
-        with gr.TabItem("S1-B · R3b · ASO Designer"):
-            gr.HTML(proj_badge("S1-B · R3b", "ASO Candidates"))
-            b4 = gr.Button("Show ASOs", variant="primary")
-            o4 = gr.Dataframe(label="ASO Candidates")
-            b4.click(get_aso, [], o4)
+        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 — BRCA1/2 · TP53"))
+                            g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
+                            b1 = gr.Button("Find miRNAs", variant="primary")
+                            o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
+                            gr.Examples([["BRCA2"],["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)
 
         # === S1-C · PHYLO-DRUG ===
-        # Розкоментовано проблемну вкладку "оце" - FGFR3
-        with gr.TabItem("S1-C · 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_drug = 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_drug.click(predict_drug, [g4], [o4t, o4p])
-
-        with gr.TabItem("S1-C · R1b · SL Drug Mapping 🔶"):
-            gr.HTML(proj_badge("S1-C · R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress"))
-            gr.Markdown("> Coming soon.")
-
-        with gr.TabItem("S1-C · R2a · Frontier 🔴"):
-            gr.HTML(proj_badge("S1-C · R2a", "m6A × Ferroptosis × Circadian", "🔴 Frontier"))
-            gr.Markdown("> Planned for Q3 2026")
+        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_drug = 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_drug.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"
+                            )
 
         # === S1-D · PHYLO-LNP ===
-        with gr.TabItem("S1-D · R1a · LNP Corona"):
-            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)
-
-        # Розкоментовано проблемну вкладку "оце" - Flow Corona
-        with gr.TabItem("S1-D · 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")
-            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])
-
-        # Розкоментовано проблемну вкладку "оце" - LNP Brain
-        with gr.TabItem("S1-D · R3a · LNP Brain"):
-            gr.HTML(proj_badge("S1-D · R3a", "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])
-
-        # Розкоментовано проблемну вкладку "оце" - AutoCorona NLP
-        with gr.TabItem("S1-D · R4a · AutoCorona NLP"):
-            gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP — from 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-D · R5a · CSF/BM 🔴"):
-            gr.HTML(proj_badge("S1-D · R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
-            gr.Markdown("> Planned for Q2–Q3 2026")
+        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"
+                            )
+
         # === S1-E · PHYLO-BIOMARKERS ===
-        # Розкоментовано проблемну вкладку "оце" - Liquid Biopsy
-        with gr.TabItem("S1-E · 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("S1-E · R1b · Validator 🔶"):
-            gr.HTML(proj_badge("S1-E · R1b", "Protein Panel Validator", "🔶 In progress"))
-            gr.Markdown("> Coming next.")
+        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.")
 
         # === S1-F · PHYLO-RARE ===
-        # Розкоментовано проблемну вкладку "оце" - DIPG
-        with gr.TabItem("S1-F · R1a · DIPG Toolkit"):
-            gr.HTML(proj_badge("S1-F · R1a", "DIPG: H3K27M + CSF LNP + Circadian", "PBTA"))
-            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")
-            b_dv.click(dipg_variants, [sort_d], o_dv)
-            gr.Markdown("---")
-            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])
-
-        # Розкоментовано проблемну вкладку "оце" - UVM
-        with gr.TabItem("S1-F · R2a · UVM Toolkit"):
-            gr.HTML(proj_badge("S1-F · R2a", "UVM: GNAQ/GNA11 + vitreous + m6A", "TCGA-UVM"))
-            b_uv = gr.Button("Load UVM Variants", variant="primary")
-            o_uv = gr.Dataframe(label="GNAQ/GNA11 map")
-            b_uv.click(uvm_variants, [], o_uv)
-            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])
-
-        # Розкоментовано проблемну вкладку "оце" - pAML
-        with gr.TabItem("S1-F · R3a · pAML Toolkit"):
-            gr.HTML(proj_badge("S1-F · R3a", "pAML: FLT3-ITD + BM niche + ferroptosis", "TARGET-AML"))
-            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("🧠 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 |"
+                                    )
 
         # === S1-G · 3D Lab (НОВА ВКЛАДКА) ===
         with gr.TabItem("🧊 3D Lab"):