import pandas as pd
ann_rows = [{'gene': 'ACSL4', 'name': 'acyl-CoA synthetase long chain family member 4', 'protein_class': "['Disease related genes', 'Enzymes', 'Human disease related ", 'disease_involvement': "['Alport syndrome', 'Deafness', 'Disease variant', 'Elliptocytosis', 'Hereditary"}, {'gene': 'ALOX15', 'name': 'arachidonate 15-lipoxygenase', 'protein_class': "['Disease related genes', 'Enzymes', 'Metabolic proteins', '", 'disease_involvement': '—'}, {'gene': 'APOE', 'name': 'apolipoprotein E', 'protein_class': "['Cancer-related genes', 'Candidate cardiovascular disease g", 'disease_involvement': "['Alzheimer disease', 'Amyloidosis', 'Cancer-related genes', 'Disease variant', "}, {'gene': 'CLDN5', 'name': 'claudin 5', 'protein_class': "['Predicted membrane proteins', 'Transporters']", 'disease_involvement': '—'}, {'gene': 'GENES', 'name': 'MIR142 host genes', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'LPCAT3', 'name': 'lysophosphatidylcholine acyltransferase 3', 'protein_class': "['Enzymes', 'Metabolic proteins', 'Predicted membrane protei", 'disease_involvement': '—'}, {'gene': 'MAPT', 'name': 'microtubule associated protein tau', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'PARP1', 'name': 'poly(ADP-ribose) polymerase 1', 'protein_class': "['Cancer-related genes', 'Enzymes', 'FDA approved drug targe", 'disease_involvement': "['Cancer-related genes', 'FDA approved drug targets']"}, {'gene': 'PPARA', 'name': 'peroxisome proliferator activated receptor alpha', 'protein_class': "['FDA approved drug targets', 'Nuclear receptors', 'Predicte", 'disease_involvement': "['FDA approved drug targets']"}, {'gene': 'REGIONAL', 'name': 'dihydrouridine synthase 2', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'SIRT3', 'name': 'sirtuin 3', 'protein_class': "['Enzymes', 'Metabolic proteins', 'Predicted intracellular p", 'disease_involvement': '—'}, {'gene': 'SLC16A1', 'name': 'solute carrier family 16 member 1', 'protein_class': "['Disease related genes', 'Human disease related genes', 'Me", 'disease_involvement': "['Disease variant']"}, {'gene': 'TREM2', 'name': 'triggering receptor expressed on myeloid cells 2', 'protein_class': "['Disease related genes', 'Human disease related genes', 'Po", 'disease_involvement': "['Alzheimer disease', 'Amyloidosis', 'Disease variant', 'Neurodegeneration']"}, {'gene': 'VULNERABILITY', 'name': 'TM2 domain containing 1', 'protein_class': '—', 'disease_involvement': '—'}]
pd.DataFrame(ann_rows)

go_bp = [{'rank': 1, 'term': 'Microglial Cell Activation (GO:0001774)', 'p_value': 2.676276080527817e-13, 'odds_ratio': 1109.6666666666667, 'genes': ['APP', 'TYROBP', 'TREM2', 'MAPT', 'CLU']}, {'rank': 2, 'term': 'Astrocyte Activation (GO:0048143)', 'p_value': 2.4448023088239226e-11, 'odds_ratio': 1427.2142857142858, 'genes': ['APP', 'TREM2', 'MAPT', 'PSEN1']}, {'rank': 3, 'term': 'Astrocyte Development (GO:0014002)', 'p_value': 6.736219018143865e-11, 'odds_ratio': 1037.8181818181818, 'genes': ['APP', 'TREM2', 'MAPT', 'PSEN1']}, {'rank': 4, 'term': 'Regulation Of Amyloid Fibril Formation (GO:1905906)', 'p_value': 6.736219018143865e-11, 'odds_ratio': 1037.8181818181818, 'genes': ['APP', 'TREM2', 'PSEN1', 'CLU']}, {'rank': 5, 'term': 'Positive Regulation Of Supramolecular Fiber Organization (GO:1902905)', 'p_value': 1.0354136710554012e-09, 'odds_ratio': 182.21611721611723, 'genes': ['APP', 'MAPT', 'APOE', 'PSEN1', 'CLU']}, {'rank': 6, 'term': 'Macrophage Activation (GO:0042116)', 'p_value': 2.5696934344337086e-09, 'odds_ratio': 367.889400921659, 'genes': ['APP', 'TREM2', 'MAPT', 'CLU']}, {'rank': 7, 'term': 'Negative Regulation Of Long-Term Synaptic Potentiation (GO:1900272)', 'p_value': 6.919481814980666e-09, 'odds_ratio': 1498.8, 'genes': ['APP', 'TYROBP', 'APOE']}, {'rank': 8, 'term': 'Memory (GO:0007613)', 'p_value': 2.0688966159515193e-08, 'odds_ratio': 210.95238095238096, 'genes': ['TREM2', 'MAPT', 'APOE', 'PSEN1']}, {'rank': 9, 'term': 'Positive Regulation Of ERK1 And ERK2 Cascade (GO:0070374)', 'p_value': 2.4015108691702915e-08, 'odds_ratio': 94.89942528735632, 'genes': ['PDGFRB', 'APP', 'PDGFRA', 'TREM2', 'APOE']}, {'rank': 10, 'term': 'Regulation Of Amyloid-Beta Clearance (GO:1900221)', 'p_value': 5.610477788412988e-08, 'odds_ratio': 624.28125, 'genes': ['TREM2', 'APOE', 'CLU']}]
go_df = pd.DataFrame(go_bp)[['term','p_value','odds_ratio','genes']]
go_df['p_value'] = go_df['p_value'].apply(lambda p: f'{p:.2e}')
go_df['odds_ratio'] = go_df['odds_ratio'].round(1)
go_df['term'] = go_df['term'].str[:60]
go_df['n_hits'] = go_df['genes'].apply(len)
go_df['genes'] = go_df['genes'].apply(lambda g: ', '.join(g))
go_df[['term','n_hits','p_value','odds_ratio','genes']]

import matplotlib.pyplot as plt
import numpy as np

go_bp = [{'rank': 1, 'term': 'Microglial Cell Activation (GO:0001774)', 'p_value': 2.676276080527817e-13, 'odds_ratio': 1109.6666666666667, 'genes': ['APP', 'TYROBP', 'TREM2', 'MAPT', 'CLU']}, {'rank': 2, 'term': 'Astrocyte Activation (GO:0048143)', 'p_value': 2.4448023088239226e-11, 'odds_ratio': 1427.2142857142858, 'genes': ['APP', 'TREM2', 'MAPT', 'PSEN1']}, {'rank': 3, 'term': 'Astrocyte Development (GO:0014002)', 'p_value': 6.736219018143865e-11, 'odds_ratio': 1037.8181818181818, 'genes': ['APP', 'TREM2', 'MAPT', 'PSEN1']}, {'rank': 4, 'term': 'Regulation Of Amyloid Fibril Formation (GO:1905906)', 'p_value': 6.736219018143865e-11, 'odds_ratio': 1037.8181818181818, 'genes': ['APP', 'TREM2', 'PSEN1', 'CLU']}, {'rank': 5, 'term': 'Positive Regulation Of Supramolecular Fiber Organization (GO:1902905)', 'p_value': 1.0354136710554012e-09, 'odds_ratio': 182.21611721611723, 'genes': ['APP', 'MAPT', 'APOE', 'PSEN1', 'CLU']}, {'rank': 6, 'term': 'Macrophage Activation (GO:0042116)', 'p_value': 2.5696934344337086e-09, 'odds_ratio': 367.889400921659, 'genes': ['APP', 'TREM2', 'MAPT', 'CLU']}, {'rank': 7, 'term': 'Negative Regulation Of Long-Term Synaptic Potentiation (GO:1900272)', 'p_value': 6.919481814980666e-09, 'odds_ratio': 1498.8, 'genes': ['APP', 'TYROBP', 'APOE']}, {'rank': 8, 'term': 'Memory (GO:0007613)', 'p_value': 2.0688966159515193e-08, 'odds_ratio': 210.95238095238096, 'genes': ['TREM2', 'MAPT', 'APOE', 'PSEN1']}]
terms = [t['term'][:45] for t in go_bp][::-1]
neglogp = [-np.log10(max(t['p_value'], 1e-300)) for t in go_bp][::-1]
fig, ax = plt.subplots(figsize=(9, 4.5))
ax.barh(terms, neglogp, color='#4fc3f7')
ax.set_xlabel('-log10(p-value)')
ax.set_title('Top GO:BP enrichment (Enrichr)')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

ppi = [{'protein1': 'APOE', 'protein2': 'MAPT', 'score': 0.879, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.57, 'dscore': 0, 'tscore': 0.731}, {'protein1': 'APOE', 'protein2': 'TREM2', 'score': 0.986, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.514, 'dscore': 0, 'tscore': 0.974}, {'protein1': 'APOE', 'protein2': 'CLU', 'score': 0.991, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.971}, {'protein1': 'APOE', 'protein2': 'APP', 'score': 0.995, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.621, 'dscore': 0.72, 'tscore': 0.96}, {'protein1': 'PDGFRA', 'protein2': 'PDGFRB', 'score': 0.969, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0.9, 'tscore': 0.477}, {'protein1': 'TYROBP', 'protein2': 'TREM2', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.526, 'dscore': 0.8, 'tscore': 0.982}, {'protein1': 'APP', 'protein2': 'TREM2', 'score': 0.491, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.359, 'dscore': 0, 'tscore': 0.239}, {'protein1': 'APP', 'protein2': 'MAPT', 'score': 0.886, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.57, 'dscore': 0, 'tscore': 0.746}, {'protein1': 'APP', 'protein2': 'CLU', 'score': 0.972, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.483, 'dscore': 0.72, 'tscore': 0.827}, {'protein1': 'APP', 'protein2': 'PSEN1', 'score': 0.975, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.946, 'dscore': 0, 'tscore': 0.571}, {'protein1': 'CLU', 'protein2': 'TREM2', 'score': 0.954, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.938}]
ppi_df = pd.DataFrame(ppi).sort_values('score', ascending=False)
display_cols = [c for c in ['protein1','protein2','score','escore','tscore'] if c in ppi_df.columns]
print(f'{len(ppi_df)} STRING edges')
ppi_df[display_cols].head(20)

import math
ppi = [{'protein1': 'APOE', 'protein2': 'MAPT', 'score': 0.879, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.57, 'dscore': 0, 'tscore': 0.731}, {'protein1': 'APOE', 'protein2': 'TREM2', 'score': 0.986, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.514, 'dscore': 0, 'tscore': 0.974}, {'protein1': 'APOE', 'protein2': 'CLU', 'score': 0.991, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.971}, {'protein1': 'APOE', 'protein2': 'APP', 'score': 0.995, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.621, 'dscore': 0.72, 'tscore': 0.96}, {'protein1': 'PDGFRA', 'protein2': 'PDGFRB', 'score': 0.969, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0.9, 'tscore': 0.477}, {'protein1': 'TYROBP', 'protein2': 'TREM2', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.526, 'dscore': 0.8, 'tscore': 0.982}, {'protein1': 'APP', 'protein2': 'TREM2', 'score': 0.491, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.359, 'dscore': 0, 'tscore': 0.239}, {'protein1': 'APP', 'protein2': 'MAPT', 'score': 0.886, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.57, 'dscore': 0, 'tscore': 0.746}, {'protein1': 'APP', 'protein2': 'CLU', 'score': 0.972, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.483, 'dscore': 0.72, 'tscore': 0.827}, {'protein1': 'APP', 'protein2': 'PSEN1', 'score': 0.975, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.946, 'dscore': 0, 'tscore': 0.571}, {'protein1': 'CLU', 'protein2': 'TREM2', 'score': 0.954, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.938}]
if ppi:
    nodes = sorted({p for e in ppi for p in (e['protein1'], e['protein2'])})
    n = len(nodes)
    pos = {n_: (math.cos(2*math.pi*i/n), math.sin(2*math.pi*i/n)) for i, n_ in enumerate(nodes)}
    fig, ax = plt.subplots(figsize=(7, 7))
    for e in ppi:
        x1,y1 = pos[e['protein1']]; x2,y2 = pos[e['protein2']]
        ax.plot([x1,x2],[y1,y2], color='#888', alpha=0.3+0.5*e.get('score',0))
    for name,(x,y) in pos.items():
        ax.scatter([x],[y], s=450, color='#ffd54f', edgecolors='#333', zorder=3)
        ax.annotate(name, (x,y), ha='center', va='center', fontsize=8, fontweight='bold', zorder=4)
    ax.set_aspect('equal'); ax.axis('off')
    ax.set_title(f'STRING PPI network ({len(ppi)} edges)')
    plt.tight_layout(); plt.show()

pw_rows = [{'gene': 'ACSL4', 'n_pathways': 2, 'top_pathway': 'Intracellular metabolism of fatty acids regulates insulin secretion'}, {'gene': 'ALOX15', 'n_pathways': 8, 'top_pathway': 'Synthesis of Leukotrienes (LT) and Eoxins (EX)'}, {'gene': 'APOE', 'n_pathways': 8, 'top_pathway': 'Nuclear signaling by ERBB4'}, {'gene': 'CLDN5', 'n_pathways': 2, 'top_pathway': 'Tight junction interactions'}, {'gene': 'GENES', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'LPCAT3', 'n_pathways': 3, 'top_pathway': 'Acyl chain remodelling of PC'}, {'gene': 'MAPT', 'n_pathways': 3, 'top_pathway': 'Caspase-mediated cleavage of cytoskeletal proteins'}, {'gene': 'PARP1', 'n_pathways': 8, 'top_pathway': 'POLB-Dependent Long Patch Base Excision Repair'}, {'gene': 'PPARA', 'n_pathways': 8, 'top_pathway': 'BMAL1:CLOCK,NPAS2 activates circadian expression'}, {'gene': 'REGIONAL', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'SIRT3', 'n_pathways': 5, 'top_pathway': 'Transcriptional activation of mitochondrial biogenesis'}, {'gene': 'SLC16A1', 'n_pathways': 4, 'top_pathway': 'Basigin interactions'}, {'gene': 'TREM2', 'n_pathways': 4, 'top_pathway': 'Immunoregulatory interactions between a Lymphoid and a non-Lymphoid ce'}, {'gene': 'VULNERABILITY', 'n_pathways': 0, 'top_pathway': '—'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('ACSL4-Mediated Neuroinflammatory Amplification in Disea', 0), ('ACSL4-Driven Ferroptotic Priming in Disease-Associated ', 0.869), ('ACSL4-Ferroptotic Priming in Stressed Oligodendrocytes ', 0.801), ('40 Hz Gamma Entrainment Gates ACSL4-Mediated Ferroptoti', 0.801), ('Microglial TREM2-SYK Pathway Enhancement', 0.798), ('ACSL4-Driven Ferroptotic Priming in Disease-Associated ', 0.779), ('LPCAT3-Mediated Lands Cycle Remodeling as the Primary F', 0.779), ('ALOX15-Driven Enzymatic Ferroptosis in AD Oligodendrocy', 0.777), ('LPCAT3-Mediated Lands Cycle Amplification of Ferroptoti', 0.776), ('LPCAT3-Mediated Lands Cycle Amplification of Ferroptoti', 0.77), ('SIRT3-Mediated Mitochondrial Deacetylation Failure with', 0.738), ('Selective Tau Kinase Inhibition in Vulnerable Neuronal ', 0.676), ('Astrocyte MCT1/MCT4 Ratio Disruption with Metabolic Unc', 0.668), ('Astrocyte APOE4-Specific Lipid Metabolism Correction', 0.651), ('Cell-Type Specific Metabolic Reprogramming', 0.643), ('Disease-Associated Microglia Metabolic Reprogramming', 0.631), ('Spatially-Targeted Regional Vulnerability Prevention', 0.616), ('Vascular-Glial Interface Restoration', 0.566), ('Oligodendrocyte DNA Repair Enhancement', 0.55)]
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('Cell type vulnerability in Alzheimers Disease (SEA-AD transcriptomic data)')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['ACSL4-Mediated Neuroinflammatory Amplifi', 'ACSL4-Driven Ferroptotic Priming in Dise', 'ACSL4-Ferroptotic Priming in Stressed Ol', '40 Hz Gamma Entrainment Gates ACSL4-Medi', 'Microglial TREM2-SYK Pathway Enhancement', 'ACSL4-Driven Ferroptotic Priming in Dise', 'LPCAT3-Mediated Lands Cycle Remodeling a', 'ALOX15-Driven Enzymatic Ferroptosis in A', 'LPCAT3-Mediated Lands Cycle Amplificatio', 'LPCAT3-Mediated Lands Cycle Amplificatio']
matrix = np.array([[0, 0, 0, 0.84, 0.36, 0, 0.82, 0, 0.48], [0.85, 0.75, 0.85, 0.84, 0.36, 0, 0.82, 0, 0.48], [0.56, 0.6, 0, 0.74, 0.36, 0, 0.72, 0, 0.42], [0.63, 0.5, 0, 0.52, 0.36, 0, 0.72, 0, 0.61], [0.6, 0.7, 0.8, 0.8, 0.81, 0.7, 0.7, 0.8, 0.6], [0, 0.6, 0, 0.73, 0.36, 0, 0.88, 0, 0.44], [0.58, 0.58, 0, 0.78, 0.36, 0, 0.85, 0, 0.46], [0.62, 0, 0, 0.75, 0.36, 0, 0.88, 0, 0], [0.52, 0.58, 0, 0.77, 0.36, 0, 0.85, 0, 0.46], [0.51, 0, 0, 0.77, 0.36, 0, 0.85, 0, 0]])
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 — top hypotheses')
    plt.colorbar(im, ax=ax, shrink=0.8)
    plt.tight_layout(); plt.show()
else:
    print('No score data available')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
if lit_data:
    df = pd.DataFrame(lit_data)
    print(f'{len(lit_data)} PubMed results')
    display(df)
else:
    print('No PubMed results')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
if lit_data:
    df = pd.DataFrame(lit_data)
    print(f'{len(lit_data)} PubMed results')
    display(df)
else:
    print('No PubMed results')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
if lit_data:
    df = pd.DataFrame(lit_data)
    print(f'{len(lit_data)} PubMed results')
    display(df)
else:
    print('No PubMed results')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
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-48858e2a')

print('No PubMed results for hypothesis h-var-f96e38ec20')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
if lit_data:
    df = pd.DataFrame(lit_data)
    print(f'{len(lit_data)} PubMed results')
    display(df)
else:
    print('No PubMed results')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
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-var-c56b26facf')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
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-seaad-v4-5a7a4079')

print('No PubMed results for hypothesis h-3fdee932')

print('No PubMed results for hypothesis h-seaad-v4-29e81bbc')

lit_data = [{'year': '2025', 'journal': 'Phytomedicine', 'title': 'Integrating metagenomics, lipidomics and proteomics to explore the effect and me', 'pmid': '41014671'}]
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-b34120a1')

print('No PubMed results for hypothesis h-2531ed61')

print('No PubMed results for hypothesis h-80ff3fd6')

print('No PubMed results for hypothesis h-6cfb4671')

print('No PubMed results for hypothesis h-43ec636e')