Clinical and pathological findings of SARS-CoV-2 infection and concurrent IgA nephropathy: a case report
Bibliographic record
Abstract
BACKGROUND: Since the Coronavirus Disease 2019 (COVID-19) outbreak, there is accumulating data on the clinical characteristics, treatment strategies and prognosis of COVID-19 in patients with concurrent renal disease. Postmortem investigations reveal renal involvement in COVID-19, and most recently, several biopsy researches reveal that acute tubular injury, as well as glomerular nephropathy such as collapsing glomerulopathy were common histological findings. However, to our best knowledge, there is limited data regarding IgA nephropathy in the setting of COVID-19. CASE PRESENTATION: In the present case, we report a 65-year old Chinese woman who presented with dark-colored urine, worsening proteinuria and decreased renal function after COVID-19 infection. She received a renal biopsy during COVID-19 infection. The renal biopsy revealed IgA nephropathy without any evidence for SARS-Cov-2. The findings suggest that the renal abnormalities were a consequence of exacerbation of this patient's underlying glomerular disease after COVID-19 infection. After a regimen of 3-day course of glucocorticoid and angiotensin II receptor blocker therapy, the patient recovered and remained stable upon follow-up. CONCLUSIONS: It is important to consider the underlying glomerular disease exacerbation as well as virus induced injury when dealing with renal abnormalities in patients with COVID-19. A kidney biopsy may be indicated to exclude a rapidly progressive glomerular disease.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".