Human red blood cell stroma: an alternative to traditional allogeneic adsorption methods
Bibliographic record
Abstract
BACKGROUND: Allogeneic adsorption (alloadsorption) involves incubating an aliquot of patient plasma with red blood cells (RBCs) of a known phenotype to facilitate removal of autoantibodies and allow for detection of remaining, potentially clinically significant alloantibodies. Alloadsorptions are routinely performed using fresh or frozen donor RBCs having particular Rh and Kidd phenotypes, and are usually enzyme or ZZAP treated before adsorptions. RBC stroma would provide a method to prepare large amounts of adsorption material that may be stored frozen and used when needed. STUDY DESIGN AND METHODS: , and rr where at least one individual was Jk(a+b-) and one individual Jk(a-b+), stroma was prepared using digitonin digestion of the RBC membrane. Large amounts of stroma could be obtained and were aliquoted and frozen at -18°C or less for up to 2 years. RESULTS: One hundred seventeen of 309 (38%) samples demonstrated alloantibodies following stromal alloadsorptions. Twenty-two different alloantibodies were identified in the stroma-adsorbed plasma, with an average of two stromal adsorptions resolving the autoantibody reactivity. The specificities of alloantibodies underlying the autoantibody included those in the Rh system (112), Kell system (24), Duffy system (14), Kidd system (30), MNS system (19), Lutheran system (2), and Diego system (2). CONCLUSION: Successful removal of autoantibody using RBC stromal adsorption was obtained in 308 samples and allowed for the identification of underlying alloantibodies in 117 samples. Preparation and use of RBC stroma should significantly benefit immunohematology reference laboratories in their antibody investigations.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".