Precise CD4 T‐cell counting using red diode laser excitation: For richer, for poorer
Bibliographic record
Abstract
BACKGROUND: Measuring CD4 T-cell counts at low cost is relevant in dealing with the human immunodeficiency virus (HIV) epidemic throughout the developing world. The recently introduced novel concepts in gating strategies and sample stabilization facilitate affordable immunophenotyping by flow cytometry. However, the impact of these developments is still limited by the high cost of currently available flow cytometers. METHODS: Diode lasers emitting 10-15 mW at 635 nm are one-tenth the size and cost and require one thousandth the power of an equivalent 488-nm argon ion laser. We used the available 635-nm diode-based flow cytometers, including PA-II, Luminex 100, SuperMot, and FACSCalibur, to investigate whether these instruments can generate reliable CD4 counts when used with allophycocyanin (APC) and cyanin-5 (Cy5)-labeled CD4 antibodies. RESULTS: We document the feasibility of obtaining leucocyte differential counts using orthogonal side scatter (SSC) without the need for forward scatter (FSC). Accurate CD4% values among lymphocytes and leucocytes can be obtained by primary CD4 gating using a single CD4 monoclonal antibody conjugated to APC or Cy5. Double immunofluorescence (IF) staining with CD4-APC (FL1) and CD45-APC-Cy7 (FL2) introduces pan-leucogating for a convenient assessment of absolute CD4 counts on double platforms. We demonstrate that small flow cytometers with laser diodes are capable of delivering absolute CD4 T-cell counts with a precision similar to the performance of the current state-of-the-art single-platform instruments (e.g., the CytoronAbsolute; R(2) = 0.961). In this respect, they appear to be superior to the nonflow CD4 counting techniques. CONCLUSIONS: Accurate CD4 counts can be generated at minimal cost on red diode laser-operated flow cytometers, retaining the potential for high throughput capacity without compromising precision. With further improvements in volumetric technology and clinical software, these cytometers may develop into a new generation of inexpensive battery-operated laboratory hardware that combines cellular phenotyping with bead-based multiplexing immunoassays for (HIV) serology.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".