Differential abundance of mDC subsets predict response to Hepatitis B vaccination
Bibliographic record
Abstract
Abstract Vaccination as a strategy for the prevention of infectious disease has been one of the greatest success stories in modern medicine, resulting in the complete eradication of smallpox, the near eradication of polio, and the drastic lowering in the incidences of measles, mumps, influenza and other common diseases. In contrast to these remarkable successes, recent failures in the development of effective vaccines against other major public health threats, including HIV, TB and malaria have highlighted the limitations to the current approach of empirical vaccine design and the need to better understand the basic principles of how to recognize and elicit immune responses that generate effective and durable protective immunity. Single cell transcriptional profiling by RNA sequencing (scRNAseq) is a powerful tool for exploring the phenotypic diversity of cell populations present in peripheral blood and other tissue specimens in an unbiased fashion. We applied scRNAseq to FACS sorted blood samples collected from participants before and after challenge with the licensed Hepatitis B vaccine, and identified several innate cell subtypes using unbiased clustering analysis. Marker genes specific for each cell subtype were identified using random forest machine learning for use in qPCR. Our results suggest that the relative levels of myeloid dendritic cells (mDCs) specifically expressing the N-myc target gene, NDRG2, prior to vaccination predict serum antibody responses to the HepB vaccine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".