Editorial: Interplay between gut microbiota and the immune system in liver surgery and liver diseases
Bibliographic record
Abstract
Interplay between gut microbiota and the immune system in liver surgery and liver diseases Gut microbiota and liver diseases Microbiota refers to the assemblage of microbes present in the environment (as first defined by Lederberg and McCray (Lederberg and Mccray, 2001).There is no conclusive evidence that individuals, or even different body locations, harbor a "core" set of these microorganisms.In this regard, every person harbors a symbiotic community of microbes in their digestive tract, the gut microbiota.A wide range of factors influence microbiota regulation, including host characteristics, dietary patterns, and environmental and microbiological factors.A dynamic equilibrium exists between the microbiota and the host, in which the former plays a role both locally and remotely in fundamental physiological processes such as inflammation and immunity.The microbes that live in our gut are capable of producing metabolites that protect the host from pathogens, but they can also produce molecules that are detrimental to the host when the relationship between the host and gut microbes is disturbed (a state known as dysbiosis) (Alvarez-Mercado et al., 2023).A wide range of evidence has shown that the gut microbiota plays a dual role in maintaining the health of the host and in the development of diseases such as liver disease (Zheng et al., 2020)."Liver disease" refers to a variety of conditions that impair the ability of the liver to function normally.The progression of liver disease may result in scarring and more serious complications in the future.Nowadays, liver disease is a global burden that causes a tremendous socioeconomic cost (Giuffre et al., 2020).Advanced liver damage can manifest itself in the form of steatosis, fibrosis, and cirrhosis.These lesions can have alcoholic, viral, genetic, and metabolic origins.In fact, the increasing incidence of obesity and diabetes is causing non-alcoholic steatohepatitis to keep rising (Levicǎr et al., 2007).
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.037 | 0.026 |
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".