Expanding the search for genetic biomarkers of Parkinson's disease into the living brain
Bibliographic record
Abstract
Altered gene expression related to Parkinson's Disease (PD) has not been described in the living brain, yet this information may support novel discovery pertinent to disease pathophysiology and treatment. This study compared the transcriptome in brain biopsies obtained from living PD and Control patients. To evaluate the novelty of this data, a comprehensive literature review also compared differentially expressed gene (DEGs) identified in the current study with those reported in PD cadaveric brain and peripheral tissues. RNA was extracted from rapidly cryopreserved frontal lobe specimens collected from PD and Control patients undergoing neurosurgical procedures. RNA sequencing (RNA-Seq) was performed and validated using quantitative polymerase chain reaction. DEG data was assessed using bioinformatics and subsequently included within a comparative analysis of PD RNA-Seq studies. 370 DEGs identified in living brain specimens reflected diverse gene groups and included key members of trophic signaling, apoptosis, inflammation and cell metabolism pathways. The comprehensive literature review yielded 7 RNA-Seq datasets generated from blood, skin and cadaveric brain but none from a living brain source. From the current dataset, 123 DEGs were identified only within the living brain and 267 DEGs were either newly found or had distinct directional change in living brain relative to other tissues. This is the first known study to analyze the transcriptome in brain tissue from living PD and Control patients. The data produced using these methods offer a unique, unexplored resource with potential to advance insight into the genetic associations of PD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".