Microbiota‐immune alterations in adolescents following early life adversity: A proof of concept study
Bibliographic record
Abstract
Early adverse care has long-term impacts on physical and mental health. The influence of rearing conditions on the infant's gut microbiota and its relationship with developmental health has become more evident. The microbiome is essential for normal growth and metabolism, and the signaling from the gut to the brain may underlie individual differences in resilience later in life. Microbial diversity and composition were determined using 16S rRNA gene amplicon sequencing in fecal samples from 17 adolescents adopted internationally from orphanages into the United States and 18 adolescents reared in birth families who had similar educational and income levels. Analyses focused on diversity of the microbial community structure and differences in the abundance of specific bacterial taxa. Blood samples were used to immunophenotype the numbers of several T-cell subsets and cytomegalovirus (CMV) seropositivity. Negative binomial regression analysis revealed several operational taxonomic units that were significantly different based on early rearing conditions and CMV seropositivity. There were significant associations between the relative abundance of certain taxa, the percentages of T-cell subsets in circulation, and CMV seropositivity. These findings demonstrate a possible link between the gut microbiota and associations with immune alterations initiated by early life adversity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".