[Dysbiosis of Gut Microbiota in Patients with Post-Stroke Cognitive Impairment].
Bibliographic record
Abstract
OBJECTIVE: To identify the differences in the composition of gut microbiota of patients with post-stroke cognitive impairment (PSCI) in comparison with the normal cognition healthy controls (HC), and to study the potential association between gut microbiota and cognition function. METHODS: A total of 24 patients were recruited for the PSCI group, which was matched with 23 healthy subjects with no history of cardiovascular disease recruited over the same period for the control group. Fecal samples were collected for both groups, and Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to evaluate cognitive functions. The abundance, diversity and group difference of gut bacterial communities were determined with 16S rRNA gene sequencing, and the correlations between differences in bacterial species of the gut microbiota and cognitive function scores were examined with redundancy analysis (RDA)/canonical correspondence analysis (CCA). RESULTS: of phylum Bacteroidetes were significantly enriched in comparison with those of the HC (LDA score>2), and these bacteria were negatively correlated with MMSE and MoCA scores. There were also correlations among these bacteria. CONCLUSION: In this study, we observed compositional differences between the gut microbiota of PSCI patients and those of HC, and revealed that the differences were correlated, to some degree, to the cognitive functions, which will provide new perspectives for the clinical diagnosis and treatment of PSCI.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".