Toward Reagentless and Universal Biomolecular Sensing: Molecular Pendulum-Based Bioanalysis
Bibliographic record
Abstract
Continuous monitoring of physiologically relevant analytes remains an unmet need of high interest to the medical community. Complex biological environments, slow-release affinity receptors, and short sensor lifetimes are just some of the many challenges that stand in the way of delivering real-time analysis for disease diagnosis, prevention, and treatment. Electrochemical biomolecular sensors are poised to address many of these challenges, given their demonstrated ability to detect a wide range of analytes, from proteins to small molecules, in various in vivo applications. Our laboratory has a strong interest in developing electrochemical biomolecular sensors for long-term continuous health monitoring with the ultimate goal of achieving a universal sensing platform.In this Account, we summarize our group's efforts to develop a universal, reagentless continuous monitoring platform for a multitude of biologically relevant targets. We first introduced the molecular pendulum (MP) sensing approach in 2021, which enabled the detection of a variety of essential protein analytes in their physiologically relevant ranges. In subsequent work, we have addressed some limitations to MP universality, first by expanding the analyte scope to include viral particles and electroactive small molecules. We further demonstrated that the MP platform could be integrated with a variety of target receptors, including antibodies, nanobodies, and aptamers, further expanding the receptor space and analyte range of this platform. To address one of the most significant challenges facing the biomolecular sensing community─the inability to overcome strong receptor binding and continuously monitor analytes─we developed an active-reset method for the MP, enabling the continuous detection of proteins through oscillatory receptor regeneration. To integrate sensors into bioelectronic interfaces, we have demonstrated MP function in various microneedle platforms capable of interstitial fluid sampling and monitoring. This platform enabled our laboratory to begin performing a wide range of in vivo tests, as we look forward to new implantable and wearable form factors. Combining all the above factors, we have started to utilize our MP sensing systems to gain critical insights into physiological mechanisms such as inflammation and circadian rhythm disruption by monitoring molecular fluctuations. Given the success of the MP system in targeting a large variety of analytes with high sensitivity, receptor modularity, and in vivo compatibility, we believe that MP sensing can be expanded further and has high potential to serve as a model for universal biomolecular sensing.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".