Efficient Drug Screening and Nephrotoxicity Assessment on Co-culture Microfluidic Kidney Chip
Bibliographic record
Abstract
The function and susceptibility of various drugs are tested with renal proximal tubular epithelial cells; yet, replicating the morphology and kidneys function using the currently available in vitro models remains difficult. To overcome this difficulty, in the study presented in this paper, a device and a three-layer microfluidic chip were developed, which provides a simulated environment for kidney organs. This device includes two parts: (1) microfluidic drug concentration gradient generator and (2) a flow-temperature controlled platform for culturing of kidney cells. In chip study, renal proximal tubular epithelial cells (RPTECs) and peritubular capillary endothelial cells (PCECs) were screened with the drugs to assess the drug-induced nephrotoxicity. Unlike cells cultured in petri dishes, cells cultured in the microfluidic device exhibited higher performance in terms of both cell growth and drug nephrotoxicity evaluation. It is worth mentioning that a significant decrease in cisplatin-induced nephrotoxicity was found because of the intervention of cimetidine in the microfluidic device. In conclusion, the different in the cell performance between the microfluidic device and the petri dishes demonstrates the physiological relevance of the nephrotoxicity screening technology along with the microfluidic device developed in this study. Furthermore, this technology can also facilitate the development of reliable kidney drugs and serve as a useful and efficient test-bed for further investigation of the drug nephrotoxicity evaluation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".