Protective effects of fecal microbiota transplantation against ischemic stroke and other neurological disorders: an update
Bibliographic record
Abstract
The bidirectional communication between the gut and brain or gut-brain axis is regulated by several gut microbes and microbial derived metabolites, such as short-chain fatty acids, trimethylamine N-oxide, and lipopolysaccharides. The Gut microbiota (GM) produce neuroactives, specifically neurotransmitters that modulates local and central neuronal brain functions. An imbalance between intestinal commensals and pathobionts leads to a disruption in the gut microbiota or dysbiosis, which affects intestinal barrier integrity and gut-immune and neuroimmune systems. Currently, fecal microbiota transplantation (FMT) is recommended for the treatment of recurrent Clostridioides difficile infection. FMT elicits its action by ameliorating inflammatory responses through the restoration of microbial composition and functionality. Thus, FMT may be a potential therapeutic option in suppressing neuroinflammation in post-stroke conditions and other neurological disorders involving the neuroimmune axis. Specifically, FMT protects against ischemic injury by decreasing IL-17, IFN-γ, Bax, and increasing Bcl-2 expression. Interestingly, FMT improves cognitive function by lowering amyloid-β accumulation and upregulating synaptic marker (PSD-95, synapsin-1) expression in Alzheimer’s disease. In Parkinson’s disease, FMT was shown to inhibit the expression of TLR4 and NF-κB. In this review article, we have summarized the potential sources and methods of administration of FMT and its impact on neuroimmune and cognitive functions. We also provide a comprehensive update on the beneficial effects of FMT in various neurological disorders by undertaking a detailed interrogation of the preclinical and clinical published literature.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".