Analysis of Gut Microbiota Characteristics in Zhuang Ethnic Group Patients with Post-Stroke Cognitive Impairment in Baise City, Guangxi
Bibliographic record
Abstract
Objective: To analyze the intestinal microbiota characteristics in patients with post-stroke cognitive impairment (PSCI) in the Zhuang population of Baise, Guangxi, and to provide a theoretical foundation for understanding the pathogenesis of PSCI and the potential application of fecal microbiota transplantation as a therapeutic strategy. Methods: Clinical baseline data were collected from 30 stroke patients admitted to the Affiliated Hospital of Youjiang Medical University for Nationalities from January 2024 to December 2024, who were designated as the stroke group. Additionally, 30 healthy individuals undergoing routine physical examination were selected as the control group, and 30 patients diagnosed with PSCI were included in the PSCI group. Stool samples were collected from all participants. Genomic DNA was extracted using a specialized fecal DNA extraction kit, followed by amplification and sequencing of the 16S rRNA V3-V4 region. Bioinformatics analysis was performed to assess the microbiota composition. Pearson correlation analysis was used to explore the relationship between microbiota indices and PSCI. Results: The Mini-Mental State Examination (MMSE) scores in the stroke (16.33±4.29) and PSCI (20.53±2.24) groups were significantly lower than that of the control group (23.36±2.44) (P < 0.05). Similarly, Montreal Cognitive Assessment (MoCA) scores in the stroke (23.58±1.55) and PSCI (26.59±1.48) groups were lower than in the control group (28.33±1.45) (P < 0.05). Regarding intestinal microbiota α-diversity, indices such as Chao1 estimator, abundance-based coverage estimator (Ace), Shannon-Wiener diversity index, and Simpson index were significantly lower in the PSCI group compared to the stroke and control groups (P < 0.05), whereas no significant difference was observed between the stroke and control groups (P > 0.05). At the phylum level, Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were identified as the predominant phyla in all three groups, although their relative abundances differed significantly (P < 0.05). The relative abundance of Firmicutes and Actinobacteria was significantly lower in the PSCI group compared to the stroke and control groups, whereas the relative abundance of Bacteroidetes and Proteobacteria was higher in the PSCI group than in the control and stroke groups. At the genus level, the relative abundance of Bifidobacterium and Lactobacillus was lower in the PSCI group compared to the stroke and control groups, while the relative abundance of Bacteroides and Clostridium was higher in the PSCI group (P < 0.05). Pearson correlation analysis revealed a positive correlation between PSCI and Bacteroidetes, Proteobacteria, Bacteroides, and Clostridium (r = 0.327, 0.493, 0.425, respectively), while a negative correlation was found with Firmicutes, Actinobacteria, Lactobacillus, and Bifidobacterium (r = -0.261, -0.503, -0.623, -0.456, respectively) (P < 0.05). Conclusion: Significant alterations in the gut microbiota composition were observed in PSCI patients from the Zhuang population in Baise, Guangxi. These changes may be closely associated with the onset and progression of PSCI, providing a basis for future studies on the role of the gut microbiota in PSCI and potential therapeutic strategies such as fecal microbiota transplantation.
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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".