Modern approaches to understanding stress and disease susceptibility: A review with special emphasis on respiratory disease
Bibliographic record
Abstract
Studies in animals and humans link both physical and psychological stress with an increased incidence and severity of respiratory infections. For this manuscript we define stress as the physiological responses an individual undergoes while adjusting to a continually changing environment. It is known that stressors of various types (psychological/physical) can alter the physiological levels of certain hormones, chemokines and cytokines. These alterations send information to the central nervous system to take necessary action which then sends messages to appropriate organs/tissues/cells to respond. These messages can either activate or suppress the immune system as needed and failure to compensate for this by the body can lead to serious health-related problems. Little is known how stress affects disease susceptibility, yet understanding this mechanism is important for developing effective treatments, and for improving health and food quality. The current review focuses on (a) the effects of psychological stressors in humans and animals, (b) various methodologies employed to understand stress responses and their outcomes, and (c) the current status of the attempts to correlate stress and disease with respiratory disease as model system. The methodologies included in this review span traditional epidemiological, behavioral and immunological studies to current high throughput genomic, proteomic, metabolomic/metabonomic approaches. With the advent of various newer omics and bioinformatics methodologies we postulate that it will become feasible to understand the mechanisms through which stress can influence disease onset. Although the literature in this area is limited because of the infancy of this research area, the objective of this review is to illustrate the power of new approaches to address complex biological questions. These new approaches will also aid in our understanding how these processes are related to the dynamics and kinetics of changes in expression of multiple genes at various levels.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".