Decreased Cytotoxic Lymphocyte Counts in Alexithymia
Bibliographic record
Abstract
BACKGROUND: Alexithymia is a psychological trait characterized by a difficulty in verbalizing feelings, which has been associated with a number of illnesses, including bronchial asthma and cancer. METHODS: In order to understand how psychological variables such as alexithymia affect physical health, we compared the lymphocyte subsets of men (n = 97, mean 30.6) rated as high and low alexithymic when measured by the Toronto Alexithymia Scale (TAS). We analyzed our data by considering alexithymia a categorical variable, using TAS scores of 62 and below, and 74 and above, and by considering alexithymia a continuous variable, using the mean TAS score (64.01) separating high from low alexithymia. RESULTS: When alexithymia was considered a categorical variable, highly alexithymic men had significantly lower numbers of the most cytotoxic natural killer (NK) subset, (CD57-CD16+ cells). When alexithymia was considered a continuous variable, in addition to the NK subset, killer effector T cell (CD8+CD11a+ cells) count was also significantly lower. These results were obtained after controlling for possible effects of smoking and alcohol intake. CONCLUSIONS: These results suggest that the negative modulation of cellular immunity, especially the cytotoxic lymphocytes, may be one mechanism which, combined with other factors that have a negative effect on the immune system such as stress, results in the association between alexithymia and ill health. It is suggested that future studies should, in addition to cell counts, attempt to identify the effects of psychological variables on the cytolytic activity of cytotoxic lymphocytes. Furthermore, follow-up studies should monitor the subjects over the years to demonstrate that alexithymia-mediated negative modulation of the immune system results in clinical pathologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".