Characteristics of Intestinal Microbiota and Mother's Reproductive Tract Flora in Children With Down Syndrome
Bibliographic record
Abstract
Abstract Background: Patients with Down’s syndrome (DS) often have an increased rate of infections, hypertension, objectivity and gastrointestinal disorders, which are the most common abnormalities and have a significant impact on their daily life. The gut microbiota plays an important role in maintaining gut homeostasis and improving immunity and has been linked to the development of obesity, hypertension, and colon cancer. However, there are few studies on the intestinal flora and the mother's reproductive tract flora of children with DS in childhood. Therefore, 16S sequencing technology was used to analyze and explore the intestinal flora of children with DS and CHD patients in DS and the microbial abundance and diversity composition in the mother's reproductive tract. Results: We found that the gut microbiota in children with DS was mainly composed of Escherichia , Bifidobacterium , Clostridium and Bacteroides , which have significant differences in the abundance and diversity of intestinal flora compared with healthy children, and the abundance of Enterococcus and Erysipelatoclostridium in the intestine of children with CHD was significantly higher than that of children without CHD, and the relative abundance of Lactobacillus in the reproductive tract of mothers with DS was significantly higher than that of mothers with healthy children, which may suggest potential ways of using microbiome composition for prognosis and diagnosis. Through functional analysis, it was found that Down’s syndrome patients significantly downregulated immune system and their cell growth and nucleotide metabolism were lower than those of healthy children. Conclusion: We performed 16S rRNA gene sequencing on fecal samples from 60 children and vaginal swab samples from 63 mothers to identify a number of potentially important taxonomic, functional and microbiomes associated with congenital heart disease and Down’s syndrome. Structural changes and its correlation with the mother’s vaginal flora. Our analysis shows that the ecosystem associated with childhood congenital heart disease affects the selection of bacterial communities in the native microbiota, and we focus on specific bacteria and their relevance to disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".