Bibliographic record
Abstract
The rise of nanoscience has revolutionized biomedical research, resulting in bio-nanotechnologies that are radically improving our health and wellness. These unprecedented advancements can be attributed to the new tools and techniques that are used to effectively and reliably investigate and interact with biological systems. Enabling conversion between biological and electronic signals would provide a way to construct powerful and affordable bioelectronic technologies. However, currently, there is no general way to do that primarily because the solid-state digital and the soft and wet biological worlds are disconnected in the modern electronics industry. By converging the principles and toolkits of molecular biology with digital electronics, herein, I present the development and application of a new type of bioelectric signal transduction method, modeled as a ‘molecular pendulum’ (MP). The resulting devices utilizing the MP mechanism are reagent-free, i.e., they are self-contained and function autonomously without needing any external reagents, and self-regenerating, i.e., they adaptively change their response to report on the dynamic changes in their host environment. These properties make the MP strategy ideal for developing new biosensing devices including innovative wearable and implantable medical technologies that have not yet been possible. First, I describe the device architecture and working principle of the MP sensing strategy and highlight their unique key properties. Then, I present several applications of the MP method including the development of a direct, reagent-free detection system for emerging pathogens, a synthetic bioreceptor-based MP system for monitoring cardiac abnormalities, and easily programmable, field-deployable MP devices for body-interfaced biosensing systems. The clinical significance of these new bioelectronic technologies has been demonstrated by validating them in analyzing different types of human samples including saliva, sweat, and whole blood, and comparing them with existing gold-standard methods such as PCR and ELISA tests. The pursuit of interfacing electronics with the wondrous biological world presented in this thesis will hopefully inspire and guide excellence and innovation in future bioelectronic technologies for human health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".