Analysis of Gut Microbiota Characteristics in Zhuang Ethnic Group Patients with Post-Stroke Cognitive Impairment in Baise City, Guangxi
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».