Recent Progress in PDMS-Based Microfluidics Toward Integrated Organ-on-a-Chip Biosensors and Personalized Medicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The organ-on-a-chip (OoC) technology holds significant promise for biosensors and personalized medicine by enabling the creation of miniature, patient-specific models of human organs. This review studies the recent advancements in the application of polydimethylsiloxane (PDMS) microfluidics for OoC purposes. It underscores the main fabrication technologies of PDMS microfluidic systems, such as photolithography, injection molding, hot embossing, and 3D printing. The review also highlights the crucial role of integrated biosensors within OoC platforms. These electrochemical, electrical, and optical sensors, integrated within the microfluidic environment, provide valuable insights into cellular behavior and drug response. Furthermore, the review explores the exciting potential of PDMS-based OoC technology for personalized medicine. OoC devices can forecast drug effectiveness and tailor therapeutic strategies for patients by incorporating patient-derived cells and replicating individual physiological variations, helping the healing process and accelerating recovery. This personalized approach can revolutionize healthcare by offering more precise and efficient treatment options. Understanding OoC fabrication and its applications in biosensors and personalized medicine can play a pivotal role in future implementations of multifunctional OoC biosensors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it