Flow cytometry optimizing the diagnostic approach in inborn errors of immunity: experience from Egypt
Bibliographic record
Abstract
BACKGROUND: Human inborn errors of immunity (IEI) are a group of inherited genetic disorders of the immune system. IEI Patients suffer from severe repeated infections, autoimmunity, lymphadenopathy and/or increased susceptibility to malignancies. IEI are due to absence, disproportion, or loss of function of immune cells; mostly inherited in autosomal recessive manner, hence are more common in countries with high rate of consanguinity. Definite diagnosis of IEI is achieved by genetic analysis, however it is not always available. AIM: to report on different IEI categories and impact of expanding the use of flow cytometry (FCM) in diagnosis, categorization and follow up of IEI patients in a highly consanguineous population. METHODS: Retrospective chart review on different IEI categories diagnosed at the primary immunodeficiency center in Cairo University Specialized Pediatric hospital from 2011 to 2021 based on expanding the use of FCM. RESULTS: 1510 IEI patients were diagnosed; 480 were diagnosed genetically with FMF, 11 with cystic fibrosis and 1019 patients were diagnosed with other IEI disorders. Phagocytic defects were the commonest (30%) followed by severe combined immunodeficiency (22%) and combined immunodeficiency (18.3%). FCM testing properly diagnosed and categorized 73% of the cases. CONCLUSION: Using multi-color FCM to evaluate immune cells populations, subpopulations, functions, and intracellular proteins expression is proved a useful cost-effective method for screening, categorization and follow up of IEI patients. FCM can improve the diagnosis of IEI significantly when tests are properly targeted and well designed. This study presents a 10-year experience in diagnosis of IEI using FCM at a tertiary referral center in a setting of limited resources and yet high prevalence of IEI.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".