Immunological responses in SARS-CoV-2 and HIV co-infection versus SARS-CoV-2 mono-infection: case report of the interplay between SARS-CoV-2 and HIV
Bibliographic record
Abstract
Abstract Background There is an urgent need to understand the interplay between SARS-CoV-2 and HIV to inform risk-mitigation approaches for HIV-infected individuals. Objectives We conclude that people living with HIV (PLWH) who are antiretroviral therapy (ART) naïve could be at a greater risk of morbidity or mortality once co-infected with SARS-CoV-2. Methods Here, we performed extensive immune phenotyping using flow cytometry. Moreover, to compare the range of values observed in the co-infected case, we have included a larger number of mono-infected cases with SARS-CoV-2. We also quantified soluble co-inhibitory/co-stimulatory molecules in the plasma of our patients. Results We noted a robust immune activation characterized by the expansion of CD8+ T cells expressing co-inhibitory/stimulatory molecules (e.g. PD-1, TIM-3, 2B4, TIGIT, CD39, and ICOS) and activation markers (CD38, CD71, and HLA-DR) in the co-infected case. We further found that neutrophilia was more pronounced at the expense of lymphopenia in the co-infected case. In particular, naïve and central memory CD8+ T cells were scarce as a result of switching to effector and effector memory in the co-infected case. CD8+ T cell effector functions such as cytokine production (e.g. TNF-α and IFN-γ) and cytolytic molecules expression (granzyme B and perforin) following anti-CD3/CD28 or the Spike peptide pool stimulation were more prominent in the co-infected case versus the mono-infected case. We also observed that SARS-CoV-2 alters T cell exhaustion commonly observed in PLWH. Conclusion These findings imply that inadequate immune reconstitution and/or lack of access to ART could dysregulate immune response against SARS-CoV-2 infection, which can result in poor clinical outcomes in PLWH. Our study has implications for prioritizing PLWH in the vaccination program/access to ART in resource-constrained settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 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".