Designing and Validating a New Method the TUNEL Microwave (TUNEL-MW) for Rapid Quantification of Apoptosis in Islets Following Isolation and Post-Thaw
Bibliographic record
Abstract
Background: Among the current quality control assays used in islet transplantation, there is an urgent need for more appropriate assays that measure cell damage via apoptosis that are accurate and rapid. Although the Terminal Uridine Nucleotide End Labeling (TUNEL) is a popular marker for apoptosis, the protocol takes 4 hours to complete. In this regard, microwave assisted histoprocessing, which shortens the time taken for processing, holds promise. Keeping this in mind, a new TUNEL Microwave (TUNEL-MW) method, for rapid quantification of apoptosis, was designed, developed and validated. Method: Two lots of post-thaw isolated human islets cultured for 24 hours, 3 days, 5 days and 7 days i.e. 8 samples, were used for the study. Dewaxed and rehydrated tissues were processed for routine histology, stained with haematoxylin and eosin (H&E) and the conventional TUNEL was carried out as per manufacturer’s instructions. For the TUNEL-MW, kit instructions were modified and microwave-assisted histoprocessing was done. The assessment of apoptotic index (AI%) by light microscopy (LM) was carried out by a pathologist who was completely blinded to the study. Results: The new TUNEL-Microwave (TUNEL-MW) developed by us reduced processing time from 4 hours to 30 minutes (saving 3½ hours). Results were validated by univariate linear regression (r2>0.990), coefficient of variation (<5% between all three methods) and the Bland Altman plot comparing AI% determined by the new TUNEL-MW with the conventional TUNEL and with LM (gold standard). Conclusion: TUNEL Microwave appears to be an ideal method. It is simple and takes just 30 minutes to perform and can therefore be used along with existing quality control measures to rule out or measure apoptosis prior to islet release for islet transplantation.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".