Current and future uses of real-time polymerase chain reaction and microarrays in the study of intestinal microbiota, and probiotic use and effectiveness
Bibliographic record
Abstract
Probiotics are defined as live microorganisms that confer a health benefit to the host when administered in adequate amounts. In addition to human health benefits, probiotics can improve various aspects of growth and performance in livestock and poultry, as well as control undesirable microorganisms in food animals. Studies indicate that probiotics can prevent or treat certain conditions, including atopic disease in infants, food allergy, infection after surgery, acute diarrhea, and symptoms associated with irritable bowel syndrome. Understanding the complete mechanism, effectiveness, and potential use of probiotics is limited by the availability and sensitivity of current methods (i.e., culturing techniques). In recent years, real-time polymerase chain reaction (PCR) and microarrays have become prominent and promising methods to examine quantitative changes of specific members of the microbial community and the influence of probiotics on the structure and function of human and animal intestinal ecosystems. Culture-independent studies have established that only a fraction of organisms present in feces are cultivable, therefore, results obtained by cultivation are limited. Conversely, in-depth knowledge of microbial genomes has enabled real-time PCR and microarrays to be more sensitive and has resulted in precise methods for comprehensive analysis of the complex gut microbiota. Additionally, these technologies can assess the influence of intestinal microorganisms on host metabolism, nutrient status, and disease. This paper reviews method technologies and applications of real-time PCR and microarray assays as they relate to the effect and use of probiotics on the intestinal microbiota and gastrointestinal disease.
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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 | 0.022 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".