Development of Organs-on-Chips and Their Impact on Precision Medicine and Advanced System Simulation
Bibliographic record
Abstract
Drugs may undergo costly preclinical studies but still fail to demonstrate their efficacy in clinical trials, which makes it challenging to discover new drugs. Both in vitro and in vivo models are essential for disease research and therapeutic development. However, these models cannot simulate the physiological and pathological environment in the human body, resulting in limited drug detection and inaccurate disease modelling, failing to provide valid guidance for clinical application. Organs-on-chips (OCs) are devices that serve as a micro-physiological system or a tissue-on-a-chip; they provide accurate insights into certain functions and the pathophysiology of organs to precisely predict the safety and efficiency of drugs in the body. OCs are faster, more economical, and more precise. Thus, they are projected to become a crucial addition to, and a long-term replacement for, traditional preclinical cell cultures, animal studies, and even human clinical trials. This paper first outlines the nature of OCs and their significance, and then details their manufacturing-related materials and methodology. It also discusses applications of OCs in drug screening and disease modelling and treatment, and presents the future perspective of OCs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".