#!/usr/bin/env python3 """ Simple test for literature-preclinical skill. Mock test: Validates experiment extraction logic (Steps 2-4) without network. Live test: Full Consensus search + extraction (requires network + CONSENSUS_API_KEY). Usage: python3 simple_test.py # Mock test only python3 simple_test.py --live # Mock + live test (needs CONSENSUS_API_KEY) """ import os import sys import json import shutil # Add scripts/ to path SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) SKILL_DIR = os.path.join(SCRIPT_DIR, "..", "..") sys.path.insert(0, os.path.join(SKILL_DIR, "scripts")) TEST_OUTPUT_DIR = os.path.join(SCRIPT_DIR, "test_results") # --------------------------------------------------------------------------- # Mock abstracts with known in vitro / in vivo content # --------------------------------------------------------------------------- MOCK_PAPERS = [ { "pmid": "MOCK001", "doi": "10.1234/mock001", "title": "CDK4/6 inhibition suppresses triple-negative breast cancer cell proliferation in vitro and in vivo", "authors": "Smith J, Jones A, Wang L et al.", "journal": "Cancer Research (2024)", "publication_date": "2024-03-15", "abstract": ( "CDK4/6 inhibitors have shown promise in hormone receptor-positive breast cancer, " "but their role in triple-negative breast cancer (TNBC) remains unclear. " "We investigated the effects of palbociclib on TNBC cell lines in vitro and in vivo. " "MDA-MB-231 and BT-549 cells were treated with palbociclib. Cell viability was assessed " "by MTT assay, and apoptosis was measured by annexin V/PI staining and flow cytometry. " "Western blot analysis showed reduced phosphorylation of Rb protein. " "Colony formation assays demonstrated significantly reduced proliferation. " "In a subcutaneous xenograft model using nude mice, palbociclib significantly inhibited " "tumor growth compared to vehicle control. Tumor volume was reduced by 65% at day 28. " "Immunohistochemistry of tumor sections showed decreased Ki-67 staining. " "Body weight monitoring showed no significant toxicity." ), "keywords": "CDK4/6; breast cancer; palbociclib; xenograft", "url": "https://pubmed.ncbi.nlm.nih.gov/MOCK001/", "source": "PubMed", }, { "pmid": "MOCK002", "doi": "10.1234/mock002", "title": "KRAS G12C inhibitor demonstrates anti-tumor activity in pancreatic cancer cell lines", "authors": "Chen X, Kim Y, Park S", "journal": "Molecular Cancer Therapeutics (2023)", "publication_date": "2023-11-01", "abstract": ( "KRAS G12C mutations are found in a subset of pancreatic cancers. " "We evaluated a novel KRAS G12C inhibitor in pancreatic cancer cell lines. " "PANC-1 and MiaPaCa-2 cells were treated with increasing doses of the inhibitor. " "CCK-8 assay showed dose-dependent reduction in cell viability with IC50 values " "of 2.3 uM and 4.1 uM respectively. Transwell migration assay revealed significantly " "reduced invasion capacity. qPCR analysis demonstrated downregulation of MYC and " "ERK pathway genes. Caspase-3/7 activity was significantly increased, indicating " "apoptosis induction." ), "keywords": "KRAS; pancreatic cancer; targeted therapy", "url": "https://pubmed.ncbi.nlm.nih.gov/MOCK002/", "source": "PubMed", }, { "pmid": "MOCK003", "doi": "10.1234/mock003", "title": "PD-L1 blockade enhances anti-tumor immunity in syngeneic mouse models of NSCLC", "authors": "Rodriguez M, Taylor B, Wilson K", "journal": "Journal of Immunotherapy (2024)", "publication_date": "2024-01-20", "abstract": ( "Immune checkpoint blockade targeting PD-L1 has transformed treatment of non-small " "cell lung cancer. We evaluated anti-PD-L1 antibody in immunocompetent mouse models. " "In a syngeneic allograft model using LLC cells in C57BL/6 mice, anti-PD-L1 treatment " "significantly reduced tumor volume by 58% compared to isotype control. " "Kaplan-Meier analysis showed improved overall survival (median 45 vs 28 days, p<0.01). " "Bioluminescence imaging confirmed reduced tumor burden. Flow cytometry of " "tumor-infiltrating lymphocytes showed increased CD8+ T cells. " "No significant body weight loss or adverse effects were observed." ), "keywords": "PD-L1; NSCLC; immunotherapy; syngeneic", "url": "https://pubmed.ncbi.nlm.nih.gov/MOCK003/", "source": "PubMed", }, { "pmid": "MOCK004", "doi": "10.1234/mock004", "title": "Computational analysis of drug resistance mechanisms in breast cancer", "authors": "Lee H, Brown D", "journal": "Bioinformatics (2024)", "publication_date": "2024-06-10", "abstract": ( "Drug resistance remains a major challenge in breast cancer treatment. " "We developed a computational framework to predict resistance mechanisms " "using transcriptomic data from public databases. Network analysis identified " "key hub genes associated with resistance. Machine learning models achieved " "85% accuracy in predicting resistant vs sensitive samples. " "Pathway enrichment analysis revealed activation of PI3K/AKT signaling." ), "keywords": "drug resistance; computational; breast cancer", "url": "https://pubmed.ncbi.nlm.nih.gov/MOCK004/", "source": "PubMed", }, ] # --------------------------------------------------------------------------- # Mock test # --------------------------------------------------------------------------- def run_mock_test(): """Test extraction logic with mock abstracts (no network needed).""" print("=" * 70) print("MOCK TEST: Preclinical Experiment Extraction") print("=" * 70) # Clean output if os.path.exists(TEST_OUTPUT_DIR): shutil.rmtree(TEST_OUTPUT_DIR) os.makedirs(TEST_OUTPUT_DIR, exist_ok=True) # --- Step 2: Extract experiments --- from extract_experiments import extract_all_experiments experiments = extract_all_experiments(MOCK_PAPERS, output_dir=TEST_OUTPUT_DIR) # --- Validate extraction results --- errors = [] # Paper 1: Should be "both" (in vitro + in vivo) e1 = experiments[0] if e1["experiment_type"] != "both": errors.append(f"MOCK001: Expected 'both', got '{e1['experiment_type']}'") if "MDA-MB-231" not in e1["cell_lines"]: errors.append(f"MOCK001: Missing cell line MDA-MB-231") if "viability" not in e1["assays"]: errors.append(f"MOCK001: Missing assay 'viability'") if "apoptosis" not in e1["assays"]: errors.append(f"MOCK001: Missing assay 'apoptosis'") if "xenograft" not in e1["animal_models"]: errors.append(f"MOCK001: Missing animal model 'xenograft'") if "tumor_growth" not in e1["endpoints"]: errors.append(f"MOCK001: Missing endpoint 'tumor_growth'") if "histology" not in e1["endpoints"]: errors.append(f"MOCK001: Missing endpoint 'histology'") # Paper 2: Should be "in_vitro" only e2 = experiments[1] if e2["experiment_type"] != "in_vitro": errors.append(f"MOCK002: Expected 'in_vitro', got '{e2['experiment_type']}'") if "PANC-1" not in e2["cell_lines"]: errors.append(f"MOCK002: Missing cell line PANC-1") if "MiaPaCa-2" not in e2["cell_lines"]: errors.append(f"MOCK002: Missing cell line MiaPaCa-2") if "migration_invasion" not in e2["assays"]: errors.append(f"MOCK002: Missing assay 'migration_invasion'") # Paper 3: Should be "in_vivo" only (no cell line culture mentioned) e3 = experiments[2] if e3["experiment_type"] != "in_vivo": # LLC is in the syngeneic model keywords, not cell line list, so might be "in_vivo" # But "flow cytometry" is an in_vitro assay keyword... classification may be "both" # Accept both or in_vivo if e3["experiment_type"] != "both": errors.append(f"MOCK003: Expected 'in_vivo' or 'both', got '{e3['experiment_type']}'") if "syngeneic" not in e3["animal_models"]: errors.append(f"MOCK003: Missing animal model 'syngeneic'") if "survival" not in e3["endpoints"]: errors.append(f"MOCK003: Missing endpoint 'survival'") # Paper 4: Should be "unclassified" (computational only) e4 = experiments[3] if e4["experiment_type"] != "unclassified": errors.append(f"MOCK004: Expected 'unclassified', got '{e4['experiment_type']}'") # --- Step 3-4: Synthesis + Export --- from preclinical_synthesis import synthesize_preclinical, export_all synthesis = synthesize_preclinical(MOCK_PAPERS, experiments, target="CDK4/6") export_all(MOCK_PAPERS, experiments, synthesis, target="CDK4/6", disease="breast cancer", output_dir=TEST_OUTPUT_DIR) # --- Validate narrative synthesis --- narrative = synthesis.get("narrative") if narrative is None: errors.append("Synthesis missing 'narrative' key") else: if not narrative.get("landscape_summary"): errors.append("Narrative: empty landscape_summary") if not narrative.get("agreements"): errors.append("Narrative: empty agreements list") if not isinstance(narrative.get("gaps"), list): errors.append("Narrative: gaps is not a list") if not narrative.get("narrative_markdown"): errors.append("Narrative: empty narrative_markdown") dir_counts = narrative.get("finding_directions", {}).get("direction_counts", {}) if not dir_counts: errors.append("Narrative: no finding directions classified") # Therapeutic direction validation td = narrative.get("therapeutic_direction") if not isinstance(td, list): errors.append("Narrative: therapeutic_direction is not a list") elif len(td) == 0: errors.append("Narrative: therapeutic_direction is empty") else: # Mock papers mention inhibitors (palbociclib, etc.) so should suggest inhibition if not any("inhibit" in s.lower() or "CDK4" in s for s in td): errors.append("Narrative: therapeutic_direction missing expected inhibition or target reference") # Validate outputs exist expected_files = [ "experiment_extraction.csv", "preclinical_search_results.csv", "preclinical_synthesis_report.md", "analysis_object.pkl", ] for fname in expected_files: fpath = os.path.join(TEST_OUTPUT_DIR, fname) if not os.path.exists(fpath): errors.append(f"Missing output file: {fname}") else: size = os.path.getsize(fpath) if size == 0: errors.append(f"Empty output file: {fname}") # Verify narrative appears in markdown report report_path = os.path.join(TEST_OUTPUT_DIR, "preclinical_synthesis_report.md") if os.path.exists(report_path): with open(report_path, "r") as f: report_content = f.read() if "## Narrative Synthesis" not in report_content: errors.append("Markdown report missing '## Narrative Synthesis' section") if "### Therapeutic Direction" not in report_content: errors.append("Markdown report missing '### Therapeutic Direction' section") if "### Evidence Gaps" not in report_content: errors.append("Markdown report missing '### Evidence Gaps' section") if "## Full-Text Insights" not in report_content: errors.append("Markdown report missing '## Full-Text Insights' section") # Verify References section with hyperlinked papers if "## References" not in report_content: errors.append("Markdown report missing '## References' section") # Verify PubMed hyperlinks are present (MOCK papers have known PMIDs) if "pubmed.ncbi.nlm.nih.gov/MOCK001" not in report_content: errors.append("Markdown report missing hyperlinked PMID for MOCK001") # Verify paper titles are hyperlinked in "Both" section if "[CDK4/6 inhibition suppresses" not in report_content: errors.append("Markdown report: 'both' paper title not hyperlinked") # Verify DOI links in References if "doi.org/10.1234" not in report_content: errors.append("Markdown report missing DOI links in References") # Verify PDF generation was attempted (may be skipped if deps missing) pdf_path = os.path.join(TEST_OUTPUT_DIR, "preclinical_synthesis_report.pdf") pdf_generated = os.path.exists(pdf_path) and os.path.getsize(pdf_path) > 0 # PDF is optional - don't fail the test, just report # --- Report results --- print("\n" + "=" * 70) if errors: print(f"MOCK TEST FAILED ({len(errors)} errors):") for err in errors: print(f" ✗ {err}") return False else: print("MOCK TEST PASSED") print(f" ✓ All 4 papers classified correctly") print(f" ✓ Cell lines, assays, models, endpoints extracted") print(f" ✓ Synthesis and export completed") print(f" ✓ Narrative synthesis generated with directions and gaps") print(f" ✓ Therapeutic direction inferred") print(f" ✓ Hyperlinked PMIDs/DOIs in markdown report") print(f" ✓ References section with all papers") if pdf_generated: print(f" ✓ PDF report generated") else: print(f" ⚠ PDF skipped (install: pip install markdown weasyprint)") print(f" ✓ All output files generated in {TEST_OUTPUT_DIR}") return True # --------------------------------------------------------------------------- # Live test (optional, requires network) # --------------------------------------------------------------------------- def run_live_test(): """Test full pipeline with real Consensus search (requires network + API key).""" print("\n" + "=" * 70) print("LIVE TEST: Full Pipeline (CDK4 + breast cancer)") print("=" * 70) if not os.getenv("CONSENSUS_API_KEY"): print(" ⚠ CONSENSUS_API_KEY not set — skipping live test") return None live_output = os.path.join(TEST_OUTPUT_DIR, "live_test") if os.path.exists(live_output): shutil.rmtree(live_output) from preclinical_search import search_preclinical from extract_experiments import extract_all_experiments from preclinical_synthesis import synthesize_preclinical, export_all # Step 1: Search (small, 10 results) results = search_preclinical( target="CDK4", disease="breast cancer", max_results=10, years=3, output_dir=live_output ) if not results: print(" ⚠ No results returned (network issue or API error)") return False # Step 2: Extract experiments = extract_all_experiments(results, output_dir=live_output) # Step 3-4: Synthesize + Export synthesis = synthesize_preclinical(results, experiments, target="CDK4") export_all(results, experiments, synthesis, target="CDK4", disease="breast cancer", output_dir=live_output) # Validate errors = [] if len(results) == 0: errors.append("No search results") if len(experiments) == 0: errors.append("No experiments extracted") for fname in ["preclinical_search_results.csv", "experiment_extraction.csv", "preclinical_synthesis_report.md", "analysis_object.pkl"]: if not os.path.exists(os.path.join(live_output, fname)): errors.append(f"Missing: {fname}") print("\n" + "=" * 70) if errors: print(f"LIVE TEST FAILED ({len(errors)} errors):") for err in errors: print(f" ✗ {err}") return False else: print("LIVE TEST PASSED") print(f" ✓ Retrieved {len(results)} papers") print(f" ✓ Extracted experiments from all papers") print(f" ✓ All output files generated in {live_output}") return True # --------------------------------------------------------------------------- # Main # --------------------------------------------------------------------------- if __name__ == "__main__": mock_ok = run_mock_test() if "--live" in sys.argv: live_ok = run_live_test() else: live_ok = None print("\n" + "=" * 70) print("TEST SUMMARY") print("=" * 70) print(f" Mock test: {'PASSED' if mock_ok else 'FAILED'}") if live_ok is not None: print(f" Live test: {'PASSED' if live_ok else 'FAILED'}") else: print(f" Live test: SKIPPED (use --live flag)") sys.exit(0 if mock_ok and (live_ok is None or live_ok) else 1)