The Impact of Bacteriophage on the Aging Brain and Inflammatory Response: Relevance to Parkinson’s Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is associated with age and inflammation.New studies have found a link between gut dysbiosis and the prevalence of PD.Phage 936, which is found in dairy products, has been associated with disruptions in the gut microbiome and leaky gut causalities.The present thesis sought to assess the impact of phage 936 in young or old mice and whether the virus augments or diminishes the impact of an inflammatory (LPS) stimulus.To this end, an initial study was conducted to first determine if bacteriophage alone could actually produce some degree of measurable changes (e.g.neutrophil mobilization or change in cytokine or other circulating immune factors) within the brain.The main study of this thesis then involved young (4-5 months) vs old (15-16 months) mice receiving the phage 936 (or vehicle), followed by LPS (or vehicle) treatment.We then assessed peripheral gut and brain inflammatory changes, as well as assessed motor functioning and sickness.We hypothesized that the old mice that received both the LPS and phage 936 would display the greatest degree of inflammatory and neuronal pathology.However, it is possible that the bacteriophage would diminish the impact of LPS given that phages can neutralize endogenous bacteria and hence, might limit the inflammatory profile.Our findings provide evidence that phage alone does cause measurable changes in inflammatory biomarkers, both peripherally and centrally.We also determined that phage 936 caused behavioral changes, as evidenced by sickness scores and weight loss.It could therefore be concluded that our hypothesis was reasonable; both LPS and age did exacerbate the immunological changes produced by phage, thus producing detectable pathology in mice.We present for the first time, that phage 936, in aged mice may have complex effects that vary with the presence of inflammation (e.g.induced by LPS).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".