Bibliographic record
Abstract
Diabetes, as a leading cause of chronic kidney disease (CKD) and diabetic kidney disease (DKD), underscores a significant concern, especially due to its association with health decline and mortality. In this context, the roles of ketone bodies, especially beta-hydroxybutyrate are increasingly recognized for their impact in renal physiology and the pathology of DKD. Moving beyond their conventional perception as metabolic by products, ketone bodies have been found to play a crucial role in renal health, particularly under the stresses of diabetic conditions. Serving as alternative energy sources during periods of glucose scarcity, they also function as important signaling molecules. These ketones significantly influence oxidative stress, nutrient-sensing pathways, and mitochondrial function within the kidneys. The adaptability of renal cells to utilize ketone bodies in diabetes highlights a dynamic metabolic interplay, essential for understanding renal health. The exploration of ketone body metabolism modulation, particularly through interventions like SGLT2 inhibitors and ketogenic diets, opens new avenues in managing DKD. Such insights pave the way for rethinking the role of ketone bodies in renal pathology and diabetes, pointing to novel research directions and therapeutic potentials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".