Exploring the Interplay between Calcium Oxalate Crystals and Renal Tubular Epithelial Cell Injury: Implications for the Formation and Prevention of Kidney Stones
Bibliographic record
Abstract
Kidney stones are a prevalent and clinically significant disease that affects millions of individuals worldwide, which have emerged as a significant global public health concern. The majority of kidney stones are composed of calcium oxalate (CaOx). The mechanisms of stone formation and development are unclear, involving a complex interplay of physical and biochemical processes. The injury of tubular epithelial cells (TECs) represents a pivotal event in the pathogenesis of this condition, as it initiates oxidative stress and immune-inflammatory reactions. Macrophages play a pivotal role in the inflammatory process, interacting with a multitude of molecules and pathways, thereby influencing the stone formation. Furthermore, apoptosis and autophagy induce TECs injury and contribute to the pathogenesis of CaOx stones. The current treatment strategies mainly focus on the management of crystal-cell interactions and the protection of TECs, in conjunction with the application of antioxidants, anti-inflammatory agents, and inhibitors of apoptosis and autophagy. These strategies have demonstrated promising results. Future research will aim to modulate the immune-inflammatory response, offering hope for the effective prevention of stone recurrence.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".