Admission risk markers for upper gastrointestinal bleeding: Can urgent endoscopy be avoided?
Bibliographic record
Abstract
inactivates several enzymes and hormones including insulin.12 Binding of modified insulin with its receptor may not properly activate the insulin cascade signaling.Finally, AGEs can impair insulin secretion, 7 and in this case, increased IR may represent a compensatory consequence.On the other hand, preferential impairment of non-oxidative glucose metabolism causes, in a vicious cycle, the intracellular formation of AGEs, oxidative stress, and activation of other pathogenic mediators 13 such as interleukins and TNF-a.TNF-a levels and AGEs correlate positively.Patients with NASH show higher levels of TNF-a than subjects with simple steatosis.14 Furthermore, AGEs are able to recruit macrophages and monocytes.15 Through the binding to RAGE on these cells as well as on hepatocytes, 16 they activate the nuclear transcription factor NF-kB, a key enzyme involved in inflammatory processes, activity of innate immune system, and IR.In conclusion, AGEs, as well as ROS, causing insulin resistance and inflammation, are commonly involved in the development of a number of metabolic diseases.Caloric restriction represents the first line for all these diseases.Excess nourishment and sedentary lifestyle result in production of ROS and AGEs; and vice versa, calorie restraint in animal models and humans reduces insulin resistance, low-grade inflammation, 17 and ameliorate liver function and histology in NASH patients, 18 likely through the significant reduction of reactive products.The question raised is whether the determination of AGEs and ROS can represent a diagnostic tool for evaluating whole-body wellness and monitoring therapy in low-grade inflammatory-and IR-related diseases, including, of course, NASH.Although great effort has been put into this area of research, much remains to be done, including the detection and characterization of the possible roles of these molecules in the progression of NAFLD to NASH and cirrhosis.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".