An assessment of human gastric fluid composition as a function of PPI usage
Bibliographic record
Abstract
The standard of care for chronic gastro-esophageal reflux disease (GERD), which affects up to 40% of the population, is the use of drugs such as proton pump inhibitors (PPIs) that block the production of stomach acid. Despite widespread use, the effects of PPIs on gastric fluid remain poorly characterized. In this study, gastric fluid was collected from patients undergoing cardiac surgery who were not (n = 40) or were (n = 25) actively taking PPIs. Various enzymatic and immunoassays as well as mass spectrometry were utilized to analyze the concentrations of bile, gastricsin, trypsin, and pepsin in the gastric fluid. Proteomic analyses by mass spectrometry suggested that degradation of trypsin at low pH might account, at least in part, for the observation that patients taking PPIs have a greater likelihood of having high concentrations of trypsin in their gastric fluid. In general, the concentrations of all analytes evaluated varied over several orders of magnitude, covering a minimum of a 2000-fold range (gastricsin) and a maximum of a 1 × 10(6) -fold range (trypsin). Furthermore, the concentrations of various analytes were poorly correlated with one another in the samples. For example, trypsin and bile concentrations showed a significant (P < 0.0001) but not strong correlation (r = 0.54). Finally, direct assessment of bacterial concentrations by flow cytometry revealed that PPIs did not cause a profound increase in microbial load in the gastric fluid. These results further delineate the profound effects that PPI usage has on the physiology of the stomach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".