Microbiome on a chip: a promising technology for modeling of human organ microbiomes and their interactions
Bibliographic record
Abstract
The increasing knowledge of the makeup and role of organ microbiomes has created new possibilities for understanding and managing human illnesses. The models used for animal studies conducted in laboratory settings and live animals may not always offer the necessary insights. One in vitro cell culture system known as organ-on-a-chip technology has garnered interest as a way to collect data that accurately reflects human responses. Organ-on-a-chip (OoC) technology, while accurately simulating the function of tissues and organs, has largely covered the differences between animal and human systems. Microbiome-on-a-chip (MoC) offers benefits over other in vitro procedures, permitting dimensional observation of ecological dynamics, microbial growth, and host-associated interactions while regulating and assessing relevant environmental parameters such as pH and O2 in real-time. The fabricated MoC platforms can be designed to test microbiome-enabled therapies, to study culture and pharmacology, antibiotic resistance, and to model multi-organ interactions mediated by the microbiome. In the current overview, we provide a translational perspective and discuss different organs, such as: oral, skin, gut and vaginal microbiota on a chip and recently developed MoC-based devices. The commonly used MoC fabrication methods, such as microfluidics and 3D printing, have been explored, and the potential applications of MoC in microbiome engineering have been suggested.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".