Taming renal inflammation: signaling pathways and therapeutic advances in lupus nephritis
Bibliographic record
Abstract
Lupus nephritis (LN), a serious complication of systemic lupus erythematosus (SLE), involves complex immune dysregulation that leads to chronic renal inflammation and progressive tissue damage. Despite decades of use of standard immunosuppressive therapy, treatment responses remain variable, and many patients experience relapses or develop end-stage renal disease. This review synthesizes emerging insights into the immunopathogenesis of LN, drawing on studies from single-cell transcriptomics, signaling pathway analyses and renal tissue immunology. It examines the role of both innate and adaptive immune cells in mediating disease. The therapeutic landscape is rapidly evolving with novel biologics targeting B cell survival and cytokine signaling, small-molecule inhibitors modulating intracellular pathways, and promising developments in cell-based interventions. Notably, recent clinical case series have demonstrated that CD19-directed chimeric antigen receptor (CAR) T-cell therapy can induce durable drug-free remission in LN, representing a transformative approach to immune modulation. These advances are further supported by the application of multi-omics platforms to refine biomarker-driven disease monitoring and personalized treatment. Integrating immunologic and technological innovations holds the potential to redefine therapeutic strategies in LN. Precision medicine approaches that leverage targeted therapies, immune resetting modalities, and biomarker-guided clinical decisions may significantly improve long-term renal outcomes and patient quality of life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".