Nanosensors in the detection of antihypertension drugs, a golden step for medication adherence monitoring
Bibliographic record
Abstract
Hypertension is associated with structural and functional changes in blood vessels with increased arteriosclerosis, vascular inflammation, and endothelial dysfunction. Decreased adherence (compliance) to antihypertensive medications contributes significantly to morbidity and mortality in hypertensive patients. Antihypertensive drugs (AHTDs) and lifestyle changes are the main cornerstones for treating hypertension. Several approaches have been described in the literature for determining AHTDs based on different analytical techniques. Amongst biosensors are one of the most attractive tools due to their inherent advantages. Biosensors are used for the detection of wide range of biomarkers as well as different drugs in past two decades. The main focus of the present study is to review the latest biosensors developed for the detection of AHTDs. Readers of the present study will be able to familiarize themselves with biosensors as advanced and modern diagnostic tools while reviewing the most widely used AHTDs. In the present study, the routine methods are first reviewed and while examining their advantages and disadvantages, biosensors have been introduced as ideal alternative tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".