Left out but not Forgotten: Should Closer Attention be Paid to Coinfection with Herpes Simplex Virus Type 1 and HIV?
Bibliographic record
Abstract
Herpes simplex virus type 1 (HSV-1) and type 2 (HSV-2) are among the most common coinfections seen in individuals infected with HIV-1. Most research on HSV-HIV coinfection has focused on HSV-2, and in particular, on its impact on HIV transmission. HSV-2 is associated with micro- and macroulcerations in genital mucosal surfaces, increased numbers of HIV target cells in genital mucosal tissue and increases in plasma HIV viral load of up to 0.5 log(10) copies/mL, such that HSV-2 infection increases the risk of both HIV acquisition and transmission. Because plasma HIV RNA levels are a major determinant of rates of CD4 cell decline, HSV-2 coinfection may also adversely affect the progression of HIV disease. Anti-HSV medications have in fact been associated with reciprocal decreases in HIV viral load in short-term studies. These findings have led to the development of several clinical trials of HSV-2 suppression as strategies for preventing HIV transmission and slowing the rate of HIV disease progression. HSV-1 coinfection has largely been ignored from this growing body of research, yet there are several reasons that this coinfection remains an important issue for study. First, the seroprevalence of HSV-1 is consistently higher than that of HSV-2 among both HIV-infected and HIV-uninfected populations, underscoring the relevance of HSV-1 coinfection to the majority of HIV-infected persons. Second, pre-existing HSV-1 antibodies in individuals may modulate the course of subsequently acquired HSV-2 infection; the implications of such changes on HSV-HIV coinfection remain unexplored. Third, HSV-1 and HSV-2 are closely related viruses that share 83% genetic homology. Their virological and pathobiological similarities suggest that their implications on HIV pathogenesis may be similar as well. Finally, HSV-1 is becoming increasingly relevant because the incidence of genital HSV-1 has risen. Although genital herpes is traditionally associated with HSV-2, recent studies have shown that the majority of serologically confirmed primary genital herpes in some settings is attributable to HSV-1. Because the genital tract is an important site of biological interaction between HSV and HIV, this epidemiological change may be clinically important.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".