import pandas as pd
ann_rows = [{'gene': 'EVS', 'name': '—', 'summary': '—'}, {'gene': 'MIRNAS', 'name': 'microRNA 3685', 'summary': 'microRNAs (miRNAs) are short (20-24 nt) non-coding RNAs that are involved in post-transcriptional regulation of gene exp'}]
pd.DataFrame(ann_rows)

print('No GO:BP enrichment data available')

print('No STRING PPI data available')

pw_rows = [{'gene': 'EVS', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'MIRNAS', 'n_pathways': 0, 'top_pathway': '—'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('EV-Mediated Epigenetic Reprogramming', 0.665), ('EV-Mediated Epigenetic Reprogramming', 0.661)]
titles = [h[0] for h in hyp_data][::-1]
scores = [h[1] for h in hyp_data][::-1]
fig, ax = plt.subplots(figsize=(10, max(8, len(titles)*0.4)))
colors = ['#ef5350' if s >= 0.6 else '#ffa726' if s >= 0.5 else '#66bb6a' for s in scores]
ax.barh(range(len(titles)), scores, color=colors)
ax.set_yticks(range(len(titles))); ax.set_yticklabels(titles, fontsize=7)
ax.set_xlabel('Composite Score'); ax.set_title('Extracellular vesicle biomarkers for early AD detection')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['EV-Mediated Epigenetic Reprogramming', 'EV-Mediated Epigenetic Reprogramming']
matrix = np.array([[0.9, 0.7, 0.8, 0.8, 0.4, 0.7, 0.7, 0.7, 0.6], [0.9, 0.7, 0.8, 0.8, 0.0, 0.7, 0.7, 0.7, 0.6]])
dims = ['novelty_score', 'feasibility_score', 'impact_score', 'mechanistic_plausibility_score', 'clinical_relevance_score', 'data_availability_score', 'reproducibility_score', 'druggability_score', 'safety_profile_score']
if matrix.size:
    fig, ax = plt.subplots(figsize=(10, 5))
    im = ax.imshow(matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1)
    ax.set_xticks(range(len(dims)))
    ax.set_xticklabels([d.replace('_score','').replace('_',' ').title() for d in dims],
                       rotation=45, ha='right', fontsize=8)
    ax.set_yticks(range(len(labels))); ax.set_yticklabels(labels, fontsize=7)
    ax.set_title('Score dimensions — hypotheses')
    plt.colorbar(im, ax=ax, shrink=0.8)
    plt.tight_layout(); plt.show()
else:
    print('No score data available')

lit_data = [{'year': '2022', 'journal': 'Int J Mol Sci', 'title': 'Circulating Small EVs miRNAs as Predictors of Pathological Response to Neo-Adjuv', 'pmid': '36293478'}, {'year': '2022', 'journal': 'Semin Cancer Biol', 'title': 'miRNAs mediated drug resistance in hematological malignancies.', 'pmid': '33757848'}, {'year': '2022', 'journal': 'Life (Basel)', 'title': 'EVs-miRNA: The New Molecular Markers for Chronic Respiratory Diseases.', 'pmid': '36294979'}, {'year': '2023', 'journal': 'Hum Cell', 'title': 'The role of miRNAs from mesenchymal stem/stromal cells-derived extracellular ves', 'pmid': '36261702'}, {'year': '2022', 'journal': 'J Inflamm Res', 'title': 'The Application of Extracellular Vesicles Mediated miRNAs in Osteoarthritis: Cur', 'pmid': '35479833'}]
if lit_data:
    df = pd.DataFrame(lit_data)
    print(f'{len(lit_data)} PubMed results')
    display(df)
else:
    print('No PubMed results')

print('No PubMed results for hypothesis h-test-8d254307')

edge_data = [{'source': 'amyloid_beta', 'relation': 'associated_with', 'target': 'extracellular_vesicles', 'strength': 0.8}, {'source': 'tau', 'relation': 'associated_with', 'target': 'extracellular_vesicles', 'strength': 0.75}, {'source': 'extracellular_vesicles', 'relation': 'biomarker_for', 'target': "Alzheimer's disease", 'strength': 0.7}]
if edge_data:
    pd.DataFrame(edge_data).head(25)
else:
    print('No KG edge data available')