Kaposi's sarcoma herpesvirus seroprevalence among blood donors in Uganda
Bibliographic record
Abstract
BACKGROUND: Kaposi's sarcoma herpesvirus (KSHV) causes a life-long infection that can progress to several types of KSHV-associated diseases. There is evidence for transfusion transmission of KSHV. In endemic regions, such as sub-Saharan African, KSHV seroprevalence is >40%. However, previous studies of blood donors utilized immunoassays that detect KSHV-associated disease-specific antigens, which may underestimate the true burden of KSHV in a healthy population. STUDY DESIGN AND METHODS: We utilized samples from an on-going transfusion transmitted infection clinical trial to estimate the seroprevalence of KSHV among 4921 blood donations from healthy donors in Uganda collected between October 2019 and December 2022. A multiplexed bead-based assay was used to measure plasma IgG against five antigens encoded by the K8.1, K10.5, ORF73, ORF38, and ORF25 genes of KSHV. Significant associations between donor characteristics and seroprevalence were assessed by chi-square tests. RESULTS: Overall, KSHV seroprevalence was 69.1%. Seroprevalence was higher in units collected from older donors compared with younger donors and male donors (71.9% [95% confidence interval (CI) = 70.4%-73.3%]) compared with female donors (61.3% [95% CI = 58.6%-64.0%]; p < .001). KSHV seroprevalnce was higher among units collected from donors positive for T. pallidum (82.5% [95% CI = 73.8%-89.3%]) compared with units collected from donors who were negative (68.8% [95% CI = 67.5%-70.1%]; p < .001). KSHV seroprevalence was higher in units that tested positive for HIV, HBV, or HCV, though these results were not statistically significant. CONCLUSION: Given the high seroprevalence and limited availability of lab assays that detect active KSHV infections, methods such as leukoreduction or pathogen reduction should be considered to potentially reduce the risk of transfusion transmission of KSHV.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".