Lab-on-a-Chip Platforms for Detection of Cardiovascular Disease and Cancer Biomarkers
Bibliographic record
Abstract
Cardiovascular disease (CVD) and cancer are two leading causes of death worldwide. CVD and cancer share risk factors such as obesity and diabetes mellitus and have common diagnostic biomarkers such as interleukin-6 and C-reactive protein. Thus, timely and accurate diagnosis of these two correlated diseases is of high interest to both the research and healthcare communities. Most conventional methods for CVD and cancer biomarker detection such as microwell plate-based immunoassay and polymerase chain reaction often suffer from high costs, low test speeds, and complicated procedures. Recently, lab-on-a-chip (LoC)-based platforms have been increasingly developed for CVD and cancer biomarker sensing and analysis using various molecular and cell-based diagnostic biomarkers. These new platforms not only enable better sample preparation, chemical manipulation and reaction, high-throughput and portability, but also provide attractive features such as label-free detection and improved sensitivity due to the integration of various novel detection techniques. These features effectively improve the diagnostic test speed and simplify the detection procedure. In addition, microfluidic cell assays and organ-on-chip models offer new potential approaches for CVD and cancer diagnosis. Here we provide a mini-review focusing on recent development of LoC-based methods for CVD and cancer diagnostic biomarker measurements, and our perspectives of the challenges, opportunities and future directions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".