121 Viral & bacterial RNA transcripts of substantia nigra and olfactory bulb in parkinson disease
Bibliographic record
Abstract
Introduction It is known that several viral species can induce parkinsonism in animals and humans, but no specific virus has been discovered in the classical human form of PD. Methods To investigate this further, we used next generation sequencing (NGS) to analyse post-mortem transcriptomes of substantia nigra (SN) and olfactory bulb (OB) in four PD cases and four multiple sclerosis patients who acted as positive controls. Bioinformatic analysis of the data set removed the majority of human transcripts and the remaining sequence data were compared to existing viral sequence data sets to search for signature viral sequences. Results Despite high read numbers and good quality NGS data, no viral or bacterial transcripts could be identified from either of the tissues examined. The few matches to existing viral databases were to viruses which rarely, if ever, infect humans and no multiple hits (matches to more than one gene of the same virus) were observed. Similarly, no bacterial sequences were found although it should be noted the analysis was carried out at the mRNA level. Conclusion This small but detailed analysis provided no evidence of RNA viral signatures in the OB or SN in the four samples taken from Parkinson’s disease brains. It is still possible that other brain areas known to display Lewy body pathology may contain viral sequences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".