(Invited) Translational Applications of Nanostructured Biosensors: Diagnostics at the Point of Care
Bibliographic record
Abstract
Development of diagnostic devices with clinically relevant sensitivity and rapidity is highly desirable for decreasing the delay between diagnosis and treatment. Diagnostic inefficiency permeates multiple medical fields including infectious diseases and antimicrobial resistance (both recognized by WHO among paramount threats and research priorities). Molecular detection is also central to cancer, where therapies are often out of step with disease complexity and progression. The respective challenges may be addressed through the application of nanomaterial and high-throughput devices that offer unique advantages. In Mahshid Lab, we develop novel paradigms in point of care diagnosis via synergistically combining innovative nanostructured sensors with fluidic sample delivery systems and biomolecular assay capabilities (Nano/Bio diagnostic devices).From an engineering perspective, the lab seeks to use the remarkable intrinsic properties of novel nanomaterials, to render them capable of sensing the specific biomolecules. Such miniaturized sensors could be integrated with automated lab-chip devices and deployed to diagnose molecular changes in biological systems and in disease such as cancer (by targeting new cancer biomarkers) or to detect infectious agents in biological samples, e.g. in blood, saliva and urine. From a health industry perspective, we target the advancement of the automated and portable tools for in-field testing, remote locations and hospitals in close collaboration with clinicians to validate the devices with clinical samples. In particular and in the context of infectious disease, Mahshid lab has developed SALIVERA analogous to a qPCR, and NFluidEX analogous to a glucometer, that enabled rapid portable automated monitoring of SARS-CoV-2 infection in patient saliva and antibodies in patient blood, respectively. In the context of cancer, Mahshid lab has developed MoSERS, anon-chip approach for molecular profiling of extracellular vesicles (a new cancer biomarker)on-chip approach for molecular profiling of extracellular vesicles (a new cancer biomarker)in plasma and cerebrospinal fluid of glioblastoma patients. The proposed hybrid devices are capable of working with small sample volumes and precise dosing of reagents, enabling the transition to a portable diagnostic tool.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.019 |
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".