Collection, Isolation, and Flow Cytometric Analysis of Human Endocervical Samples
Bibliographic record
Abstract
Despite the public health importance of mucosal pathogens (including HIV), relatively little is known about mucosal immunity, particularly at the female genital tract (FGT). Because heterosexual transmission now represents the dominant mechanism of HIV transmission, and given the continual spread of sexually transmitted infections (STIs), it is critical to understand the interplay between host and pathogen at the genital mucosa. The substantial gaps in knowledge around FGT immunity are partially due to the difficulty in successfully collecting and processing mucosal samples. In order to facilitate studies with sufficient sample size, collection techniques must be minimally invasive and efficient. To this end, a protocol for the collection of cervical cytobrush samples and subsequent isolation of cervical mononuclear cells (CMC) has been optimized. Using ex vivo flow cytometry-based immunophenotyping, it is possible to accurately and reliably quantify CMC lymphocyte/monocyte population frequencies and phenotypes. This technique can be coupled with the collection of cervical-vaginal lavage (CVL), which contains soluble immune mediators including cytokines, chemokines and anti-proteases, all of which can be used to determine the anti- or pro-inflammatory environment in the vagina.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".