Translational Research in Pediatrics IV: Solid Tissue Collection and Processing
Bibliographic record
Abstract
Solid tissues are critical for child-health research. Specimens are commonly obtained at the time of biopsy/surgery or postmortem. Research tissues can also be obtained at the time of organ retrieval for donation or from tissue that would otherwise have been discarded. Navigating the ethics of solid tissue collection from children is challenging, and optimal handling practices are imperative to maximize tissue quality. Fresh biopsy/surgical specimens can be affected by a variety of factors, including age, gender, BMI, relative humidity, freeze/thaw steps, and tissue fixation solutions. Postmortem tissues are also vulnerable to agonal factors, body storage temperature, and postmortem intervals. Nonoptimal tissue handling practices result in nucleotide degradation, decreased protein stability, artificial posttranslational protein modifications, and altered lipid concentrations. Tissue pH and tryptophan levels are 2 methods to judge the quality of solid tissue collected for research purposes; however, the RNA integrity number, together with analyses of housekeeping genes, is the new standard. A comprehensive clinical data set accompanying all tissue samples is imperative. In this review, we examined: the ethical standards relating to solid tissue procurement from children; potential sources of solid tissues; optimal practices for solid tissue processing, handling, and storage; and reliable markers of solid tissue quality.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".