Advances in pathology of diabetes from pancreatic islets to neuropathy—a tribute to <scp>P</scp>aul <scp>L</scp>angerhans
Bibliographic record
Abstract
There emerges a world epidemic of diabetes, afflicting over 3.8 billion people globally. The socio-economic burden of this disorder is tremendous and there is an urgent need to solve the problems incurred from this disorder and to establish an efficient way of prevention and treatment. Fundamental pathology of diabetes has been too diverse to reach a simple etiology and the mechanisms of how the lesions specific to diabetes develop are yet to be clear. Nevertheless, there has been slow but significant advancement in the understanding of the disease based on characterization of the salient features of pathological lesions in human diabetic subjects. Progressive decline of islet β cells associated with increased α cell volume density was found to account for clinical manifestation of hypoinsulinemia and hyperglucagonemia in type 2 diabetes. Concurrently, signs of complications represented by distal nerve fiber loss in the skin commences from the beginning of this disease. Thus the pathological studies disclosed the major attributes in this disorder targeting the islet of pancreas and epidermal nerve, both of which were discovered by Paul Langerhans more than 140 years ago. In this review, I attempt to summarize the progress in pathology of diabetes which Langerhans opened this field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".