Renal Considerations in COVID-19: Biology, Pathology, and Pathophysiology
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has emerged into a worldwide pandemic of epic proportion. Beyond pulmonary involvement in coronavirus disease 2019 (COVID-19), a significant subset of patients experiences acute kidney injury. Patients who die from severe disease most notably show diffuse acute tubular injury on postmortem examination with a possible contribution of focal macro- and microvascular thrombi. Renal biopsies in patients with proteinuria and hematuria have demonstrated a glomerular dominant pattern of injury, most notably a collapsing glomerulopathy reminiscent of findings seen in human immunodeficiency virus (HIV) in individuals with apolipoprotein L-1 (APOL1) risk allele variants. Although various mechanisms have been proposed for the pathogenesis of acute kidney injury in SARS-CoV-2 infection, direct renal cell infection has not been definitively demonstrated and our understanding of the spectrum of renal involvement remains incomplete. Herein we discuss the biology, pathology, and pathogenesis of SARS-CoV-2 infection and associated renal involvement. We discuss the molecular biology, risk factors, and pathophysiology of renal injury associated with SARS-CoV-2 infection. We highlight the characteristics of specific renal pathologies based on native kidney biopsy and autopsy. Additionally, a brief discussion on ancillary studies and challenges in the diagnosis of SARS-CoV-2 is presented.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".