Neuroendocrine‐immune crosstalk in vertebrates and invertebrates: implications for host defence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary 1. Communication among cells, tissues and organ systems is essential for survival. Vertebrate and invertebrate animals rely primarily on three physiological systems for intra‐organismal communication: the nervous, endocrine and immune systems. Rather than acting independently of one another, these systems communicate in an integrated fashion to coordinate suites of species‐appropriate physiological and behavioural responses. 2. Our understanding of how these three systems are coordinated remains incomplete, in part because the importance of the immune system as part of this regulatory network has only recently been recognized. In contrast to the well‐established integrative approach to the study of the endocrine and nervous systems, the study of immunity has traditionally occurred in relative isolation from other physiological systems. Immunity was typically considered to be largely buffered from environmental perturbations. 3. In the last several decades, however, this simplistic view has changed dramatically; we now know that a wide variety of extrinsic and intrinsic factors can affect immune responses (reviewed in: Ader, Felten & Cohen 2001 ). This altered perspective has led to the development of new scientific disciplines including psychoneuroimmunology ( Ader & Cohen 1981 ) and ecological immunology ( Sheldon & Verhulst 1996 ). 4. These new research fields focus on the connections among the endocrine, nervous and immune systems. These fields also examine how environmental factors influence interactions among the three systems, and the implications of these interactions for behaviour and host defence. A comparative approach will benefit the search for the adaptive functions of these interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it