Feasibility of photoacoustic imaging for the non‐invasive quality management of stored blood bags
Bibliographic record
Abstract
Background and Objectives During the in vitro storage of red blood cells (RBCs), unfavourable changes (storage lesions) cause a rapid consumption of intracellular diphosphoglycerate. The latter deregulates the oxygen‐haemoglobin binding potential, subsequently increasing oxygen saturation (SO2) and membrane degradation, transforming RBCs from biconcave discs to rigid spherical bodies (spheroechinocytes). Current laboratory techniques invasively extract RBC samples to assess the quality of red cell concentrate (RCC) units. Optical technologies could provide a means of assessing quality non‐invasively. Materials and Methods A photoacoustic (PA) imaging technique was developed for acquiring the SO2 of blood bags non‐invasively. Seven RCC units were monitored every 3–5 days until expiry (6 weeks). Measurements were validated against a conventional blood gas analyzer (BGA). Using an image flow cytometry assay, morphological profile trends were compared against the SO2 trends during blood bag storage. Results A strong correlation (r2 ≥ 0·95) was found when comparing temporal data between PA and BGA SO2 measurements. Inter‐sample PA variability was found to be similar to that produced by BGA (±0·8%). A strong correlation was found to exist between the temporal changes in SO2 and relative spheroechinocyte population (0·79 ≤ r2 ≤ 0·97). Conclusion This study suggests that PA imaging can non‐invasively track the SO2 of stored RBCs non‐invasively. By longitudinally monitoring the change in SO2, it is possible to infer the effects of the storage lesion on RBC morphology. This non‐invasive monitoring technique allows for the assessment of blood bags, without compromising sterility pre‐transfusion.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".