Inflammation unites diverse acute and chronic diseases
Bibliographic record
Abstract
BACKGROUND: Inflammation and immunity contribute pivotally to diverse acute and chronic diseases. Inflammatory pathways have become increasingly targets for therapy. Yet, despite substantial similarity in mechanisms and pathways, the scientific, medical, pharma and biotechnology sectors have generally focused programs finely on a single disease entity or organ system. This insularity may impede progress in innovation and the harnessing of powerful new insights into inflammation biology ripe for clinical translation. METHODS: A multidisciplinary thinktank reviewed highlights how inflammation contributes to diverse diseases, disturbed homeostasis, ageing and impaired healthspan. We explored how common inflammatory and immune mechanisms that operate in key conditions in their respective domains. This consensus review highlights the high degree of commonality of inflammatory mechanisms in a diverse array of conditions that together contribute a major part of the global burden of morbidity and mortality and present an enormous challenge to public health and drain on resources. RESULTS: We demonstrate how that shared inflammatory mechanisms unite many seemingly disparate diseases, both acute and chronic. The examples of infection, cardiovascular conditions, pulmonary diseases, rheumatological disorders, dementia, cancer and ageing illustrate the overlapping pathogenesis. We outline opportunities to synergize, reduce duplication and consolidate efforts of the clinical, research and pharmaceutical communities. Enhanced recognition of these commonalties should promote cross-fertilization and hasten progress in this rapidly moving domain. CONCLUSIONS: Greater appreciation of the shared mechanisms should simplify understanding seemingly disparate diseases for clinicians and help them to recognize inflammation as a therapeutic target which the development of novel therapies is rendering actionable.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".