Determining the microbial species content in tissue from Alzheimer’s and Parkinson’s Disease patients
Bibliographic record
Abstract
Abstract The aetiology of Alzheimer’s disease (AD) and Parkinson’s Disease (PD) are unknown and tend to manifest at a later stage in life; even though these diseases have different pathogenic mechanisms, they are both characterized by neuroinflammation in the brain. Links between bacterial and viral infection and AD/PD has been suggested in several studies, however, few have attempted to establish a link between fungal infection and AD/PD. In this study we develop and describe a nanopore-based sequencing approach to characterise the presence or absence of fungi in both human brain tissue and cerebrospinal fluid (CSF). This approach detects fungal DNA in human brain and CSF samples even at low levels, whereas our quantitative polymerase chain reaction (qPCR) assay (FungiQuant) was unable to detect fungal DNA in the same samples. Comparison against kit-controls showed ubiquitous low-level fungal contamination that we observed in healthy human brains and CSF as well AD/PD brains and CSF. We use this technique to demonstrate the presence of fungal DNA in healthy human brains as well as AD/PD brains, with Alternaria spp ., Colletotrichum graminicola , and Filobasidium floriforme as the most prominent species. In addition, antibiotic resistant Pseudomonas spp . was identified within the brain of an AD patient. Our method will be broadly applicable to investigating potential links between microbial infection and AD/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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".