Development of a Tablet-Based Sensor for Point-of-Care Analyses Utilizing Pullulan-Stabilized Gold Nanoparticles
Bibliographic record
Abstract
The quickly expanding fields of nanotechnology and engineered nanomaterials helped solve serious issues that environments and human health suffer from for a long period of time. By accelerating the diagnoses and providing portable sensors that enable the demarcation of processes in biological systems to a previously unattainable degree, this nanotechnology will have an impact on clinical research and the detection of many analytes. Tablet-based sensors have emerged as a powerful tool for point-of-care analyses, revolutionizing the way healthcare professionals diagnose and monitor patients. These sensors, when integrated with tablets, offer numerous advantages and play a crucial role in enhancing healthcare delivery. This advancement in tablet-based sensors for point-of-care analyses should include features such as; enabling healthcare professionals to conduct rapid and accurate diagnostics of various biomarkers, pathogens, and diseases by providing instant results. This real-time information allows for timely interventions and treatment decisions, reducing the need for sending samples to a laboratory and waiting for results, which can lead to delays in treatment. Importantly, the portability and compactness of tablets are highly required especially in remote or resource-limited settings. Furthermore, tablet-based sensors offer a cost-effective alternative to traditional laboratory-based diagnostics where the need for expensive laboratory equipment is eliminated, reducing the overall cost of diagnostics. Additionally, portable tablet sensors do not require training, enabling healthcare professionals to perform tests without extensive specialized expertise. Notably, patients can interact with the sensors and see the results in real-time, empowering them to actively participate in their healthcare. This engagement fosters better patient-provider communication, improves treatment adherence, and increases patient satisfaction. Finally, tablet-based sensors not only aid in diagnostics but also facilitate continuous monitoring of patients by tracking vital signs, glucose levels, and drug concentrations. This routine monitoring helps healthcare professionals make informed decisions, promptly adjust treatments, prevent health complications, and save the lives of millions of people. Following nanotechnology and based on encapsulation of materials, in this work the fabrication of pullulan stabilized gold nanoparticles tablet (AuNPs-pTab) was used as a point-of-care (POC) analytical device utilizing pullulan-AuNPs solution (AuNPs-pSol) without the need for any extra ingredients, which were subsequently used as colorimetric sensors for glucose and cysteamine detection in human saliva and serum samples, respectively. This newly offered AuNPs-pTab sensor has demonstrated excellent peroxidase-like activity and gives an easy substitute for AuNPs solution with enhanced catalytic efficiency. Additionally, the AuNPs-pTab sensor is a promising platform for point-of-care devices due to its fulfillment of RE-ASSURED criteria (Real-time, Ease of specimen collection, Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable to end users) which is considered of great importance in the field of diagnosis and detection. AuNPs-pTab sensor is an attractive tool that has the potential to open a new horizon in disease diagnosis due to its functionality in H2O2 detection which is a possible biomarker for many diseases. Even though a range of nanozymes has been reported to date for their enzyme-mimicking catalytic activity as a solution-based sensor. However, in remote areas, the need for portable, cost-effective, and one-pot preparation is extremely demanding. Therefore, this work is appealing to researchers working in nanotechnology, and the advancement of innovative portable bioassays as well as point-of-care devices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".