Manufacturing stresses do not differentially impact red blood cells from donors with diabetes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: With the rising prevalence of diabetes and expanded blood donor criteria in Canada, individuals with diabetes are increasingly contributing to the blood supply. However, little is known about how routine manufacturing affects red blood cells (RBCs) from this group. This study examined RBC differences in donors with type 1 (T1D) or type 2 diabetes (T2D) following processing to generate red cell concentrates (RCCs). MATERIALS AND METHODS: Whole blood (WB) donations were collected from voluntary T1D (n = 12), T2D (n = 11) and non-diabetic age/sex-matched (n = 23) donors. Donations were processed via red cell filtration to generate RCCs. At donation, 2.7-mL of WB was collected into EDTA tubes, and 70 mL of processed RCCs was aliquoted into satellite bags. WB-EDTA tubes and RCC satellite bags were characterized on Day 2 post collection. RESULTS: Donors with T1D and T2D had similar, but higher glycated haemoglobin (HbA1c) levels than matched controls (p < 0.001). Processing increased RBC count, haemoglobin and haematocrit in all groups (p < 0.0001). Donors with T2D had decreased mean corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin concentration (MCHC) compared to controls, both pre and post processing (p < 0.05), with a similar trend in p50 (pre: p < 0.01; post: p < 0.05). CONCLUSION: Blood component manufacturing did not exacerbate stress on RBCs from donors with diabetes. Donors with T2D had altered MCH, MCHC and p50 compared to matched controls, which persisted after processing. These findings emphasize the importance of donor health on blood product quality.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".