Prospects for Precision Medicine in Glomerulonephritis Treatment
Bibliographic record
Abstract
BACKGROUND: Glomerulonephritis (GN) consists of a group of kidney diseases that are categorized based on shared histopathological features. The current classifications for GN make it difficult to distinguish the individual variability in presentation, disease progression, and response to treatment. GN is a significant cause of end-stage renal disease (ESRD), and improved therapies are desperately needed because current immunosuppressive therapies sometimes lack efficacy and can lead to significant toxicities. In recent years, the combination of high-throughput genetic approaches and technological advances has identified important regulators contributing to GN. OBJECTIVES: In this review, we summarize recent findings in podocyte biology and advances in experimental approaches that have opened the possibility of precision medicine in GN treatment. We provide an integrative basic science and clinical overview of new developments in GN research and the discovery of potential candidates for targeted therapies in GN. FINDINGS: Advances in podocyte biology have identified many candidates for therapeutic targets and potential biomarkers of glomerular disease. The goal of precision medicine in GN is now being pursued with recent technological improvements in genetics, accessibility of biologic and clinical information with tissue biobanks, high-throughput analysis of large-scale data sets, and new human model systems such as kidney organoids. CONCLUSION: With advances in data collection, technologies, and experimental model systems, we now have vast tools available to pursue precision medicine in GN. We anticipate a growing number of studies integrating data from high-throughput analysis with the development of diagnostic tools and targeted therapies for GN in the near future.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".