Objectives and achievements of the HUMN project on its 26th anniversary
Bibliographic record
Abstract
Micronuclei (MN) are a nuclear abnormality that occurs when chromosome fragments or whole chromosomes are not properly segregated during mitosis and consequently are excluded from the main nuclei and wrapped within nuclear membrane to form small nuclei. This maldistribution of genetic material leads to abnormal cellular genomes which may increase risk of developmental defects, cancers, and accelerated aging. Despite the potential importance of MN as biomarkers of genotoxicity, very little was known about the optimal way to measure MN in humans, the normal ranges of values of MN in healthy humans and the prospective association of MN with developmental and degenerative diseases prior to the 1980's. In the early 1980's two important methods to measure MN in humans were developed namely, the cytokinesis-block MN (CBMN) assay using peripheral blood lymphocytes and the Buccal MN assay that measures MN in epithelial cells from the oral mucosa. These discoveries greatly increased interest to use MN assays in human studies. In 1997 the Human Micronucleus (HUMN) project was founded to initiate an international collaboration to (i) harmonise and standardise the techniques used to perform the lymphocyte CBMN assay and the Buccal MN assay; (ii) establish and collate databases of MN frequency in human populations world-wide which also captured demographic, lifestyle and environmental genotoxin exposure data and (iii) use these data to identify the most important variables affecting MN frequency and to also determine whether MN predict disease risk. In this paper we briefly describe the achievements of the HUMN project during the period from the date of its foundation on 9th September 1997 until its 26th Anniversary in 2023, which included more than 200 publications and 23 workshops world-wide.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".