Convergence of Molecular, Nanoscale, and Data-driven Technologies for Therapeutics and Diagnostics
Bibliographic record
Abstract
The next revolution in healthcare will require the convergence of many traditional healthcare sectors to create therapeutic drugs and diagnostics tools that can target challenging diseases. Solutions which utilize molecular processes of cells and proteins, devices which use nanoscale physical principles for precision operation, and data-driven solutions which provide insights beyond current knowledge will all significantly impact clinical practices. In this thesis we aim to explore three main convergent fields: microfluidics for therapeutics research, machine learning for drug discovery, and biosensors for wearable disease management. With microfluidics, we develop a label-free cell sorting platform to sort whole populations of cells from the eye to isolate retinal stem cells, a potential therapeutic target that could reverse blindness. Deterministic lateral displacement cell sorting is implemented with notched microstructures, increasing sorting resolution by limiting cell deformation. Retinal stem cells are sorted and profiled based on size, purifying the population for downstream photoreceptor differentiation experiments. Next with machine learning, we develop an in silico pipeline, kCellect, for therapeutic antibody candidate selection from in vitro phage display biopanning against proteins associated with various cancers. We utilize k-means clustering for motif-based selection of antibody candidate sequences and identify cluster correlations with in vitro sequence frequency data. We discover novel antibodies against CD151 and FZD7 with similar or greater affinity to first-in-class antibodies, and doing so more time and computationally efficiently compared to previous methods. Finally, with biosensors, we design molecular pendulum sensors which consist of rigid DNA, an antibody recognition element to target proteins, and a redox reporter. Under an applied electric field, the pendulums fall, and the kinetics of motion between bound and unbound probes are measured through a surrogate redox electron transfer. We demonstrate the detection of multiple biomarkers of chronic disease in numerous biological fluids, and the continuous monitoring of biomarkers in situ in a mouse’s mouth. The three projects described in this thesis showcase biomedical convergence for the development of advanced therapeutics and diagnostics. The strategies utilized are sensitive to small quantities of biologicals, rapid in processing and development time, and versatile to many challenging disease targets.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".