Single-platform, volumetric, CD45-assisted pan-leucogating flow cytometry for CD4 T lymphocytes monitoring of HIV infection according to the WHO recommendations for resource-constrained settings
Bibliographic record
Abstract
BACKGROUND: Validation of new affordable CD4 T cell measurement technologies is crucial specifically in resource-poor countries for antiretroviral treatment eligibility and immunologic CD4 monitoring of HIV-infected patients. METHODS: The absolute and percentage CD4 T cell counts of 258 HIV-1-infected blood samples (182 adults and 76 children), living in N'Djamena, Chad, were performed by single-platform, volumetric, CD45-assisted pan-leucogating Auto40 flow cytometer (Apogee Flow Systems Ltd, Hemel Hempstead, UK) comparing to the FACSCalibur flow cytometer as a reference method. RESULTS: Absolute and percentage CD4 T cell counts obtained by Auto40 and FACSCalibur of 258 HIV-1-infected blood samples were highly correlated (r = 0.99 and r = 0.96, respectively). The mean absolute bias and percent bias between Apogee Auto40 and FACSCalibur absolute CD4 T cell counts, were -9.4 cells/μl with limits of agreement from -15 to 93 cells/μl, and +2.0% with limits of agreement from -0.9 to 4.9%, respectively. The mean of absolute bias and percent bias between Apogee Auto40 and FACSCalibur of CD4 percentage results were +0.4% (95% CI: -0.02 - 0.86) with limits of agreement from -2.4 to 0.3%, and +3.0% with limits of agreement from -6.6 to 0.6%, respectively. The Auto40 counting allowed to identify the majority of adults with CD4 T cells below 200 cells/μl (sensitivity: 89%; specificity: 99%) or below 350 cells/μl (sensitivity: 94%; specificity:98%); and of children below 750 cells/μl (sensitivity: 99%; specificity: 96%) or below 25% CD4+ (sensitivity: 94%; specificity: 98%). CONCLUSION: The Auto40 analyzer is an alternative flow cytometer for CD4 T lymphocyte enumeration to be used in routine for immunological monitoring according to the current WHO recommendations in HIV-infected adults as well as children living in resource-constrained settings like Chad.
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 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 | 0.002 | 0.014 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".