Microbial translocation in type 2 diabetes: when bacterial invaders overcome host defence in human obesity
Bibliographic record
Abstract
Metabolic endotoxemia, characterised by systemic influx of bacterial components, impairs lipid handling and glucoregulation.1 Early preclinical studies showed that high fat diet feeding facilitates bacterial translocation to metabolic tissues coinciding with dendritic cell (DC)-mediated inflammation.2 This process was aggravated in mice lacking either the canonical adaptor for inflammatory signalling pathways downstream of Toll-like receptor (TLR) families, MyD882 or the intracellular microbial pattern recognition receptor, NOD2.3 The first conceptual evidence for bacterial translocation in human type 2 diabetes (T2D) was reported in 2011 when Amar et al showed that 16S rDNA gene content in blood was associated with future T2D risk.4 These seminal papers pioneered the concept of bacterial translocation in metabolic diseases. Yet, it remains intensely debated whether bacterial translocation to extraintestinal tissues is a true phenomenon or a result of spurious data, as it is notoriously difficult to robustly distinguish biologically relevant bacterial DNA sequences in very low microbial biomass tissues from environmental contaminants.5 6 Indeed, low bacteria:host cell ratio obviously increases the risk of contamination during surgical procedures, biobanking and wet-laboratory processing of tissue samples. This has revivified the tenet that internal organs are sterile and casted doubt on emerging reports of tissue microbial signatures in a number of diseases, arguing that such findings may be confounded by sample contamination. In GUT, Massier et al challenge again the dogma of sterile internal organs …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".