The epithelial barrier theory and its associated diseases
Bibliographic record
Abstract
The prevalence of many chronic noncommunicable diseases has been steadily rising over the past six decades. During this time, over 350,000 new chemical substances have been introduced to the lives of humans. In recent years, the epithelial barrier theory came to light explaining the growing prevalence and exacerbations of these diseases worldwide. It attributes their onset to a functionally impaired epithelial barrier triggered by the toxicity of the exposed substances, associated with microbial dysbiosis, immune system activation, and inflammation. Diseases encompassed by the epithelial barrier theory share common features such as an increased prevalence after the 1960s or 2000s that cannot (solely) be accounted for by the emergence of improved diagnostic methods. Other common traits include epithelial barrier defects, microbial dysbiosis with loss of commensals and colonization of opportunistic pathogens, and circulating inflammatory cells and cytokines. In addition, practically unrelated diseases that fulfill these criteria have started to emerge as multimorbidities during the last decades. Here, we provide a comprehensive overview of diseases encompassed by the epithelial barrier theory and discuss evidence and similarities for their epidemiology, genetic susceptibility, epithelial barrier dysfunction, microbial dysbiosis, and tissue inflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 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".