Commensal microbes modulate gut-systemic impacts of malnutrition : from neurocognitive function to NAFLD
Bibliographic record
Abstract
Fecal-oral contamination promotes the persistence of early-life malnutrition. Systemic consequences of malnutrition include stunting, poor immune function, metabolic shifts, and neurocognitive impairment, but the underlying pathology and precise role of fecal microbes remain largely unknown. To address these knowledge gaps, I have utilized an established murine model (MAL-BG) that combines malnutrition and iterative exposure to fecal commensals. MAL-BG mice exhibit altered behavioural and cognitive deficits—poor spatial memory and learning plasticity—putatively linked to aberrant microglia phagocytosis. Microglial alterations occurred independently from neuroinflammation and blood-brain barrier (BBB) disruption, but were linked to systemic lipoxidative stress. Fecal-oral contamination exacerbated systemic, malnutrition-induced oxidative stress within the gut, brain, and liver. Beyond oxidative damage, malnourished livers exhibit fatty liver features. Largely studied in the context of obesity, undernutrition can also trigger NAFLD (non-alcoholic fatty liver disease). A combination of histology, liver metabolomics, and microbiome analyses were performed to assess the impact of diet and gut microbes in the pathology and reversal of undernutrition-induced fatty liver. Intriguingly, fatty liver histology was only observed in the early-life, but not adult, MAL-BG model despite similar liver metabolomic profiles. These findings indicate a crucial window in early-life development that, when disrupted by nutritional deficits, likely shapes liver health trajectories. Importantly, dietary intervention largely mitigated aberrant metabolomic and microbiome features in MBG mice. Collectively, my doctoral work explores (1) gut-brain and (2) gut-liver interactions in the context of undernutrition and intervention. I anticipate my findings will not only provide valued insight into gut microbiota-systemic interactions, but also identify putative therapeutic targets to halt or reverse consequences of childhood malnutrition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".