Upregulation of cathepsin L gene under mild cold conditions in young Japanese male adults
Bibliographic record
Abstract
BACKGROUND: Physiological thermoregulatory systems in humans have been a key factor for adaptation to local environments after their exodus from Africa, particularly, to cold environments outside Africa. Recent studies using high-throughput sequencing have identified various genes responsible for cold adaptation. However, the molecular mechanisms underlying initial thermoregulation in response to acute cold exposure remain unclear. Therefore, we investigated transcriptional profiles of six young Japanese male adults exposed to acute cold stress. METHODS: In a climatic chamber, the air temperature was maintained at 28°C for 65 min and was then gradually decreased to 19°C for 70 min. Saliva samples were obtained from the subjects at 28°C before and after 19°C cold exposure and were used for RNA sequencing. RESULTS: fold change value > 1; thus, this gene was identified as a differentially expressed gene. Given that the cathepsin L protein is related to invasion of the novel coronavirus (SARS-CoV-2), mild cold stress might alter the susceptibility to coronavirus disease-19 in humans. The gene ontology enrichment analysis for 14 genes with FDR < 0.05 suggested that immune-related molecules could be activated by mild cold stress. CONCLUSIONS: The results obtained from this study indicate that CTSL expression levels can be altered by acute mild cold stress.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".