Neuroendocrine–immune (NEI) circuitry from neuron–glial interactions to function: Focus on gender and HPA–HPG interactions on early programming of the NEI system
Bibliographic record
Abstract
Bidirectional communication between the neuroendocrine and immune systems during ontogeny plays a pivotal role in programming the development of neuroendocrine and immune responses in adult life. Signals generated by the hypothalamic-pituitary-gonadal axis (i.e. luteinizing hormone-releasing hormone, LHRH, and sex steroids), and by the hypothalamic-pituitary-adrenocortical axis (glucocorticoids (GC)), are major players coordinating the development of immune system function. Conversely, products generated by immune system activation exert a powerful and long-lasting regulation on neuroendocrine axes activity. The neuroendocrine-immune system is very sensitive to preperinatal experiences, including hormonal manipulations and immune challenges, which may influence the future predisposition to several disease entities. We review our work on the ongoing mutual regulation of neuroendocrine and immune cell activities, both at a cellular and molecular level. In the central nervous system, one chief compartment is represented by the astroglial cell and its mediators. Hence, neuron-glial signalling cascades dictate major changes in response to hormonal manipulations and pro-inflammatory triggers. The interplay between LHRH, sex steroids, GC and pro-inflammatory mediators in some physiological and pathological states, together with the potential clinical implications of these findings, are summarized. The overall study highlights the plasticity of this intersystem cross-talk for pharmacological targeting with drugs acting at the neuroendocrine-immune interface.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".