Smartphone Enabled Biosensor Systems For Nutrition Diagnostics And Preventive Care At The Point-Of-Need
Bibliographic record
Abstract
Micronutrient deficiencies and infectious diseases represent major global health burdens and their early diagnostics at the point-of-need (PON) has the potential to improve the current predicament by allowing more rapid and effective patient treatment/management. In this work we show how such diagnostics can be enabled by coupling the two powerful technologies: (1) lab-on-a-chip biosensor systems with capabilities to perform complex biochemical measurements at the PON, and (2) smartphone technology with powerful imaging, computing and communication capabilities which can be exploited to obtain and analyze results from these biosensor systems. In the first and main part of the dissertation, we discuss the development of NutriPhone, a smartphoneenabled biosensor system for micronutrient deficiency diagnosis at the PON. As our initial prototype, a system for quantification of vitamin D levels on a smartphone is demonstrated. The system consists of a smartphone accessory, an app, and an immunoreaction-based biosensor that allows the colorimetric detection of 25-hydroxyvitamin D. For personalized diagnosis of vitamin B12 deficiency, we present an advanced version of NutriPhone which improves upon the limitations of the previous prototype by incorporating a custom lateral flow assay for vitamin B12 capable of processing and analyzing whole blood samples on-chip in 15 min. We validate the effectiveness of the NutriPhone system in a domestic human trial in which it was used to correctly diagnose the B12 status of human participants. We envision this as the first step toward the development of a comprehensive system for the analysis of multiple vitamins/micronutrients from a drop of finger prick blood. In the second part of the dissertation, we discuss other applications of lab-on-a-chip biosensor systems in preventive care, namely: infectious disease diagnostics and traumatic injury prevention. First, we discuss iii the need for rapid differential diagnosis of causative agents behind acute febrile illnesses, and present a novel assay scheme which enables the 4-plex detection of IgG/IgM antibodies to dengue and chikungunya viruses on a single biosensing platform. Second, we discuss the development of an autonomous integrated device using an aptamer-based affinity biosensor for the prevention of hemorrhagic shock, the leading cause of death for people with traumatic injuries. iv
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".