Nanomaterial-Based Electrochemical Sensor for the Detection of Hydroxyproline
Bibliographic record
Abstract
The physicochemical properties of gold has attracted tremendous research in various fields such as sensing, drug delivery and bio-catalysis. Gold nanoparticles exhibit splendid electron transfer properties, high surface area, good conductivity and increased biocompatibility. Gold-based nanomaterials exhibit high-performance for the detection of biomolecules such as amino acids, proteins and microbes [1]. Hydroxyproline (HYP) is a major amino acid present in connective tissues and extracellular matrix in all animals cells. L-hydroxyproline (L-HYP) is one of the major imino acid present in the connective tissue proteins such as collagen. L-HYP acts as a biomarker in various diseases connected to collagen such as wound healing, idiopathic pulmonary fibrosis, and necrosis. Quantitative analysis of L-HYP level in the body fluids helps in disease diagnosis and early treatment. Here, we report on a facile electrochemical sensor based on gold nanoparticles for the detection of L-HYP using cyclic voltammetry and differential pulse voltammetry. The performance of the fabricated electrochemical sensor was further testeduisng Achilles tendon collagen and human urine samples. The optimized electrochemical sensor exhibits high sensitivity and selectivity. [1] M. Govindhan, M. Amiri, A. Chen. Biosens. Bioelectron. 66 (2015) 474–480.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".