Lower SARS-CoV-2–specific humoral immunity in people living with HIV-1 recovered from nonhospitalized COVID-19
Bibliographic record
Abstract
People living with HIV-1 (PLWH) exhibit more rapid antibody decline following routine immunization and elevated baseline chronic inflammation than people without HIV-1 (PWOH), indicating potential for diminished humoral immunity during SARS-CoV-2 infection. Conflicting reports have emerged on the ability of PLWH to maintain humoral protection against SARS-CoV-2 coinfection during convalescence. It is unknown whether peak COVID-19 severity, along with HIV-1 infection status, associates with the quality and quantity of humoral immunity following recovery. Using a cross-sectional observational cohort from the United States and Peru, adults were enrolled 1-10 weeks after SARS-CoV-2 infection diagnosis or symptom resolution. Serum antibodies were analyzed for SARS-CoV-2-specific response rates, binding magnitudes, ACE2 receptor blocking, and antibody-dependent cellular phagocytosis. Overall, (a) PLWH exhibited a trend toward decreased magnitude of SARS-CoV-2-specific antibodies, despite modestly increased overall response rates when compared with PWOH; (b) PLWH recovered from symptomatic outpatient COVID-19 had comparatively diminished immune responses; and (c) PLWH lacked a corresponding increase in SARS-CoV-2 antibodies with increased COVID-19 severity when asymptomatic versus symptomatic outpatient disease was compared.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".