Parkinson’s Evidence Audit: LRRK2–SNCA–PARK7
Research use only. Not medical advice. No drug recommendations.

GaiaLab note:
GaiaLab is evolving. I’m publishing public evidence audits first—then rolling improvements into the product once they’re robust and repeatable.

Watch (YouTube): https://youtu.be/sgheeTQR94I
Key PMIDs: 39153957, 35675433, 39487783, 39983584, 38368937, 33318422, 38391909, 34360787, 40140103, 39711195

Colorectal cancer isn’t a single bad gene you can “switch off.” It’s a living system—learning, rerouting, and evolving under pressure. In this episode, we follow the science where it gets uncomfortable: what if parts of our immune defense are quietly helping tumors escape?

Inside the “precision pivot,” we unpack five surprising shifts redefining CRC treatment strategy:

• The Neutrophil Betrayal: how ROS, proteases, and cytokines may “edit” oncogenic signaling via post-translational changes (still exploratory—but provocative).

• The Precision Sweet Spot: why BRAF residues like F582/F655 could offer high impact with lower systemic toxicity potential.

• Sorafenib’s Second Act: the repurposing gamble—targeting the BRAF/KRAS/NRAS axis and what the evidence does and doesn’t say.

• The BRAF Paradox: why BRAF monotherapy can backfire in CRC—and why combo suppression is becoming the rule.

• The “Moderate” Mutant: quieter BRAF variants that don’t roar… they hum—and still drive disease.

This is not medical advice—just a deep, story-driven tour through the biology shaping the next era of CRC care.
🎧 Follow GaiaLab: From Question to Insights for more science that thinks in systems, not slogans.

🧪 Try GaiaLab: https://gaialab-production.up.railway.app/

▶️ YouTube: https://youtu.be/AD0GfWRKJOs

In this episode, we demo GaiaLab on three Alzheimer’s-associated genes — APP, PSEN1, and APOE — to produce an evidence-first synthesis with confidence scoring, contradiction flags, and reproducible snapshots you can replay and audit.

Run summary
• 3 genes analyzed • 2 pathways found • 9 papers reviewed • 91,782 ms
• Evidence quality: Strong consensus (84%) | Low contention (16%)
• Quality score: 67/100 | Evidence Density: 28% | Contradiction Index: 16%
• Citation coverage: 100% | Sources available: 15/16

Top outputs
• Pathway 1: Alzheimer disease — Homo sapiens (p=1.11e-3, medium confidence)
• Pathway 2: Notch signaling — Homo sapiens (p=7.63e-3, medium confidence)
• Strategy flagged: BACE1 inhibition to reduce Aβ production (low confidence, medium risk; evidence sparse)
• Repurposing snapshot: 10 candidates flagged (avg score 45%); top candidate: Lecanemab (45% match; partial target match 33%; target APOE)

Limitations noted by the run
• Low evidence density → findings may be preliminary
• DrugBank unavailable → drug evidence incomplete
• DisGeNET disabled → disease association evidence incomplete

Try GaiaLab: https://gaialab-production.up.railway.app/
Replayable snapshots + evidence reports included.
Research Use Only — Not medical advice.

In this episode, I use GaiaLab (evidence-first biological intelligence) to analyze TP53, BRCA1, and EGFR in triple-negative breast cancer (TNBC) — surfacing prioritized pathways, therapeutic strategies, and testable hypotheses.

Highlights

• Top pathway signal: EGFR tyrosine kinase inhibitor (TKI) resistance (p = 1.68e-4)

• Therapy lanes: EGFR-targeted approaches + PARP inhibitors in BRCA-mutated TNBC

• Hypotheses to validate: BRCA1–EGFR resistance axis, TP53–metabolism targeting, and a BRCA1+EGFR biomarker panel

Trust & limits (important)

• Consensus 52% vs Contention 48%

• Evidence Density 34% (sparse)

• Contradiction Index 100% (high) — treat this as hypothesis-generating

• Research synthesis only — not medical advice

Slides (PDF)

https://drive.google.com/file/d/1095BEy5QBoy7B0qLEYZaSe5Qwb_mO7tY/view?usp=sharing

Next episode:

I’ll publish the YouTube video version and open submissions where you can send 2–5 genes + a disease context for analysis.

GaiaLab is an evidence-first biological intelligence engine that turns a research question (or gene list) into traceable, cross-validated insights—fast.

In this episode: From Question → Insights

• How GaiaLab frames a biological question

• Evidence pulled from curated sources

• Cross-validation across independent datasets

• Traceable claims + contradictions flagged

• Outputs you can replay and reuse

Watch the video on YouTube: https://www.youtube.com/watch?v=7A0XY-Gf7So
Try GaiaLab: https://gaialab-production.up.railway.app/

Notes:

This content is for research/educational purposes and is not medical advice.