Therapeutic Role of Probiotics In Managing Various Diseases
Bibliographic record
Abstract
Since last few decades, probiotics gained the attention of clinicians for their use in the prevention and treatment of multiple diseases. Probiotic bacteria play a critical and functional role in clinical and nutritional applications. In the last decades, a wide number of scientific works and studies shed light on the crucial role of probiotic microorganisms and their potential to cure various ailments and disorders based on the promising findings and results of different in vitro and in vivo investigations, suggesting a powerful connection flanked by these so-called probiotics and the human immuno-modulatory responses. The issues that were noticed are included: fibrocystic, diabetes, acne, colon cancer, cardiovascular, urinary tract infections, atopic eczema syndrome, food allergies and obesity. Probiotics main mechanisms of action include enhanced mucosal barrier function, direct antagonism with pathogens inhibition of bacterial adherence and invasion capacity, boosting of the immune system and regulation of the central nervous system. Enhancement in using drug treatment has led to the appearance of drug-resistance concern, thus probiotics can be a suitable choice. There is a mutual communication between the central nervous system and the liver, the so-called “microbiota-gut-liver axis” as well as a reciprocal communication between the intestinal microbiota and the central nervous system through the “microbiota-gut-brain axis”. This review focuses on the effect of probiotic bacteria and their metabolites on immune-boosting, prevention and treatment of these diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".