(Invited) Next-Generation Enabling Technologies for Disease Diagnosis and Therapeutic Monitoring
Bibliographic record
Abstract
To enable personalized and precision medicine, it is crucial to monitor patient health status and bring information on disease-related agents and therapeutic drug molecules into the clinic. This requires new technologies to interrogate different body fluids that are rich sources of biomarkers, such as whole blood and interstitial fluid (ISF). Such technologies enable rapid, sensitive and - ultimately - real-time and continuous analysis of the clinically important biomarkers. Micro and nanofabrication and nanomaterials as well as materials chemistry and polymer and molecular engineering are crucial tools in this endeavor. At IDEATION Lab, we apply innovative engineering solutions to advance patient health monitoring using two main technologies: Microneedles and Microfluidics . Our new transdermal biosensing technologies powered by engineered hydrogel microneedles (HMNs), aptamer probes, and in-situ metallic nanoparticle synthesis enable minimally invasive, on-needle, and real-time measurement of clinically important biomarkers in ISF. Our HMN assays expect to pave the way for the next-generation of polymeric-based wearable biosensors. We developed a real-time biosensor driven by microfluidic techniques that continuously updates specific biomolecules’ fluctuating concentration levels with picomolar sensitivity directly in whole blood. For the first time, our microfluidic assay enables measuring the dynamic changes in blood insulin which is an important knowledge gap in diabetes management. Another microfluidic technology integrated with electrochemical biosensor has been recently developed in our lab that enables cervical cancer screening at point-of-care setting. These new advances enrich the level of information that can be collected from different body fluids and provide new means and potentials for highly accurate patient health status monitoring, thus transforming the field of personalized and precision medicine. Figure 1
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.031 |
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".