SARS-CoV-2 infection in dialysis and kidney transplant patients: immunological and serological response
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Dialysis and kidney transplant patients with moderate-severe COVID-19 have a high mortality rate, around 30%, that is similar in the two populations, despite differences in their baseline characteristics. In these groups, the immunology of the disease has been poorly explored. METHODS: Thirty-two patients on dialysis or with kidney transplant and SARS-CoV-2 infection requiring hospitalization (COV group) were included in our study. Lymphocyte subsets, dendritic cell (DC) counts and monocyte activation were studied. SARS-CoV-2 anti-spike/anti-nucleocapsid were monitored, and baseline cytokines and chemokines were measured in 10 patients. RESULTS: The COV group, compared to healthy subjects and uninfected dialysis/kidney transplant controls, showed lower numbers of CD4 + and CD8 + T cells, Natural-Killer (NK), B cells, plasmacytoid and myeloid DCs, while the proportion of terminally differentiated B-cells was increased. IL6, IL10, IFN-α and chemokines involved in monocyte and neutrophil recruitment were higher in the COV group, compared to uninfected dialysis/kidney transplant controls. Patients with severe disease had lower CD4 + , CD8 + and B-cell counts and lower monocyte HLA-DR expression. Of note, when comparing dialysis and kidney transplant patients with COVID-19, the latter group presented lower NK and pDC counts and monocyte HLA-DR expression. Up to 60 days after symptom onset, kidney transplant recipients showed lower levels of anti-spike antibodies compared to dialysis patients. CONCLUSIONS: During SARS-CoV-2 infection, dialysis and kidney transplant patients manifest immunophenotype abnormalities; these are similar in the two groups, however kidney transplant recipients show more profound alterations of the innate immune system and lower anti-spike antibody response.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it