Minimally Invasive Postmortem Intestinal Tissue Sampling in Malnourished and Acutely Ill Children Is Feasible and Informative
Bibliographic record
Abstract
Abstract Background Intestinal disorders such as environmental enteric dysfunction (EED) are prevalent in low- and middle-income countries (LMICs) and important contributors to childhood undernutrition and mortality. Autopsies are rarely performed in LMICs but minimally invasive tissue sampling is increasingly deployed as a more feasible and acceptable procedure, although protocols have been devoid of intestinal sampling to date. We sought to determine (1) the feasibility of postmortem intestinal sampling, (2) whether autolysis precludes enteric biopsies' utility, and (3) histopathologic features among children who died during hospitalization with acute illness or undernutrition. Methods Transabdominal needle and endoscopic forceps upper and lower intestinal sampling were conducted among children aged 1 week to 59 months who died while hospitalized in Blantyre, Malawi. Autolysis ratings were determined for each hematoxylin and eosin slide, and upper and lower intestinal scoring systems were adapted to assess histopathologic features and their severity. Results Endoscopic and transabdominal sampling procedures were attempted in 28 and 14 cases, respectively, with >90% success obtaining targeted tissue. Varying degrees of autolysis were present in all samples and precluded histopathologic scoring of 6% of 122 biopsies. Greater autolysis in duodenal samples was seen with longer postmortem interval (Beta = 0.06, 95% confidence interval, 0.02–0.11). Histopathologic features identified included duodenal Paneth and goblet cell depletion. Acute inflammation was absent but chronic inflammation was prevalent in both upper and lower enteric samples. Severe chronic rectal inflammation was identified in children as young as 5.5 weeks. Conclusions Minimally invasive postmortem intestinal sampling is feasible and identifies histopathology that can inform mortality contributors.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".