Cellular response of mouse splenocytes to heavy metals exposure
Bibliographic record
Abstract
Among the environmental contaminants recognised for their toxicity and their global presence, heavy metals are certainly a major concern. They can elicit a number of immunomodulatory effects leading ultimately to an enhanced susceptibility (sensitivity) of immune cells to microbial agents and the appearance of neoplastic diseases and autoimmune phenomena. Heavy metals also provoke changes in the function(s) of immune cells. A striking biological effect of heavy metals is the induction of intracellular thiols (cysteine, glutathione, metallothioneins). Thiols are involved in many physiological processes, including protection from free radical damage and detoxification of chemicals. The purpose of this study was to assess the differences of susceptibility (sensitivity) in both pre-activated (concanavalin A was used) and non-pre-activated cells in the presence of heavy metals. Five were evaluated on murine splenocytes. The lymphoblastic proliferation test was performed for lymphocytes and a phagocytosis test for macrophages. Data showed that the levels of thiols in the pre-activated cells are greater than non-pre-activated cells following exposure to various heavy metals; macrophages were more resistant than lymphocytes to the toxic effects of heavy metals, and pre-activated cells were more resistant than cells at rest. One possible explanation is that macrophages produce more thiols than lymphocytes and this provides an increased protection from the deleterious effects of heavy metals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".