Identification and specificity of broadly neutralizing antibodies against <scp>HIV</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Beginning in 2009, studies of the humoral responses of HIV-positive individuals have led to the identification of scores, if not hundreds, of antibodies that are both broadly reactive and potently neutralizing. This development has provided renewed impetus toward an HIV vaccine and led directly to the development of novel immunogens. Advances in identification of donors with the most potent and broad anti-HIV serum neutralizing responses were crucial in this effort. Equally, development of methods for the rapid generation of human antibodies from these donors was pivotal. Primarily these methods comprise single B-cell culture coupled to high-throughput neutralization screening and flow cytometry-based sorting of single B cells using HIV envelope protein baits. In this review, the advantages and disadvantages of these methodologies are discussed in the context of the specificities targeted by individual antibodies and the need for further improvements to evaluate HIV vaccine candidates.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it