Biological Sex As a Critical Variable in CD4 <sup>+</sup> Effector T Cell Function in Preclinical Models of Multiple Sclerosis
Bibliographic record
Abstract
Significance: T cells play a pivotal role in maintaining adaptive immune responses against pathogens. However, misdirected T cell responses against self-tissues may lead to autoimmune disease. Biological sex has profound effects on T cell function and is an important determinant of disease incidence and severity in autoimmune diseases such as multiple sclerosis (MS). Recent Advances: Many autoimmune diseases skew toward higher female incidence, including MS; however, it is has become increasingly more accepted that men living with MS are more prone to developing a progressive disease course and to having worsened disease outcomes. Critical Issues: In this review, we discuss what is known about the role of biological sex on T cell development and differentiation, examining evidence that male sex can augment T helper 17 (Th17) responses. Next, we outline what is known about sex differences in animal models of MS, and about the distinct roles played by sex hormones versus sex chromosomes in pathogenesis in these models. Finally, we discuss recent advances that examine the molecular basis for worsened disease outcomes in males, with a particular focus on the role played by Th17 cells in these models. Future Directions: Better understanding the role of biological sex in T cell function may pave the way to effective personalized treatment strategies in MS and other autoimmune diseases. Antioxid. Redox Signal. 37, 135–149.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".