Diagnosis of Human Immunodeficiency Virus Infection Using an Immunoglobulin E-Based Assay
Bibliographic record
Abstract
Immunoglobulin assays that are sensitive and specific for detecting human immunodeficiency virus type 1 (HIV-1) infection are especially important in developing countries where PCR and viral culture may not be readily available. Immunoglobulin E (IgE), which is elevated in HIV-1 infection, is the only antibody that does not cross the placenta, making it potentially valuable for viral detection in both children and adults. This study developed an assay for detection of HIV specific IgE antibodies in adults. A total of 170 serum samples from 170 adults (116 HIV positive and 54 HIV negative) were analyzed. Serum or plasma samples were treated by using the protein G affinity method. The HIV status was determined by using two IgG enzyme-linked immunosorbent assays (ELISAs) and one Western blot evaluation. The IgE enzyme immunoassay test for HIV-1 correctly identified the HIV status in 98.8% of the samples (168 of 170). One false-positive and one false-negative test occurred with the IgE ELISA, as well as with the IgG ELISA test but were correctly identified by the IgE test. Analysis of the data demonstrated a high specificity (99%) and sensitivity (99%) of the IgE test, with 95% confidence intervals. The IgE assay appears to be sensitive and specific, suggesting that IgE-specific antibodies offer an effective method to detect HIV-1 infection in adults.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".