Understanding the Link Between Allergy and Neurodevelopmental Disorders: A Current Review of Factors and Mechanisms
Bibliographic record
Abstract
Both allergic diseases and neurodevelopmental disorders are non-communicable diseases (NCDs) that not only impact on the quality of life and but also result in substantial economic burden. Immune dysregulation and inflammation are typical hallmarks in both allergic and neurodevelopmental disorders, suggesting converging pathophysiology. Epidemiological studies provided convincing evidence for the link between allergy and neurodevelopmental diseases such as attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Possible factors influencing the development of these disorders include maternal depression and anxiety, gestational diabetes mellitus, maternal allergic status, diet, exposure to environmental pollutants, microbiome dysbiosis, and sleep disturbances that occur early in life. Moreover, apart from inflammation, epigenetics, gene expression, and mitochondrial dysfunction have emerged as possible underlying mechanisms in the pathogenesis of these conditions. The exploration and understanding of these shared factors and possible mechanisms may enable us to elucidate the link in the comorbidity.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".