Enigma of the high prevalence of anti-SARS-CoV-2 antibodies in HIV-positive people with no symptoms of COVID-19 in Burkina Faso
Bibliographic record
Abstract
The severe acute respiratory syndrome due to the new coronavirus (SARS-CoV-2), responsible for coronavirus disease (COVID-19), has severely tested the global health response capacity, with predictions of a fatality for developing countries. To evaluate the prevalence of anti-SARS-CoV- 2 antibodies in People Living with HIV (PLHIV) with no COVID-19 symptoms in Burkina Faso. Seroprevalence was estimated by performing a qualitative screening test for SARS-CoV-2-specific immunoglobulins. The STANDARDTM Q COVID-19 IgM/IgG Combo Test kit from SD BIOSENSOR was used. Parameters like HIV plasma viral load, CD4 T cell count and C-Reactive Protein (CRP) expression were estimated. This study enrolled a total of 200 PLHIV aged 4-87 years who are asymptomatic for COVID-19. There were 36 (18%) positive for SARS-CoV-2 IgM and/or IgG of which three (1.50%) were positive for SARS-CoV-2 IgM and 33 (16.50%) for IgG. Among participants diagnosed as IgM positive, 66.67% (2/3) had the highest HIV viral loads with the lowest CD4 T cell counts (p0.0001). The expression of CRP was relatively higher in COVID-19 IgG positive individuals (7.95±12.5 mg/L) than negative individuals (6.26±6.92 mg/L; p=0.37). The rate of IgG and IgM SARS-CoV-2 immunoglobulin carriage (18%), accompanied by a relatively high CRP levels, was revealed in this study among PLHIV. This serologic evidence and mild inflammation suggest that Burkina Faso escaped the worst, not necessarily because there were not many SARS-CoV-2 infections in its population, but because factors including genetic and environmental, might have resulted in many asymptomatic carriers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".