Reciprocity in Microbiome and Immune System Interactions and its Implications in Disease and Health
Bibliographic record
Abstract
Adaptation of the whole microbial normal flora residing in a host to its natural habitat over an evolutionary peroid has resulted in peaceful coexistence with mutual benefits for both microbiota and host in steady state. This symbiotic relationship between host and microbiota has a significant impact on shaping the immune response in the host to achieve an immune tolerance to microbiota but retaining the ability to respond to invading pathogens. Perturbation of this balance by manipulation of microbial communities in the host can lead to immune dysregulation and susceptibility to diseases. By studying the host in the absence of microbiota or with alteration of microbiota the complexity of microbial impact on the immune system can be resolved. Conversely, the study of microbiota in the absence of immune system factors can show how the immune system contributes to preservation of the host-microbiota balance. The absence of molecules involved in innate or adaptive immunity in knockout models can perturb the balance between host and microbiota further adding to more immune dysregulation. A better understanding of Microbiome-immune system interaction provides a new opportunity to identify biomarkers and drug targets. This will allow the development of new therapeutic agents for modulating the immune system to improve health with little or no toxicity. The study of interplay between host and microbiota has a promising role in the design of therapeutic interventions for immunopathological diseases arising from imbalanced host and microbiota 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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".