Development and testing of a lab-in-a-tube biosensor
Bibliographic record
Abstract
Early detection is crucial in delivering timely treatment and improving patient outcomes. Point-of-care (POC) biosensors play an essential role in early detection, allowing for rapid and accurate diagnosis of diseases at the patient’s bedside without the need for expensive equipment or specialized personnel. By performing the analysis on-site, POC diagnostics can offer continuous monitoring and real-time data acquisition of a patient's health status. Thus, there is strong incentive in creating POC biosensors to provide healthcare professionals with greater access to diagnostic information, ultimately improving outcomes and reducing healthcare costs. Herein, the development of a POC lab-in-a-tube biosensor that utilizes simple and scalable fabrication techniques is presented. Electrodes are patterned on low-cost plastic substrates, which can be subsequently rolled and heat-shrunk into miniaturized tubing for flow-through analysis of liquid samples. Heat-shrinking of the device results in 3-dimensional, hierarchically wrinkled electrodes with morphological feature that span several orders of magnitude in size. These wrinkled electrodes demonstrate dramatically increased surface area in a given footprint compared to traditional planar electrodes. Incorporation of modified gold and silver wires allows for sensitive and stable electrochemical detection, enabling fast and quantitative results. These devices are capable of millilitre-per-minute flow rates to allow for rapid sample processing and for increased mass-transport to the electrode surface. The ability to capture analytes was characterized with nucleic acid sequences using pump-driven and blood-collection tube induced flow for rapid and accurate detection. Overall, this work demonstrates the successful development of an electrochemical platform integrated into a plastic tubing capable of rapid detection of flowing analytes. With its ease-of-use and compatibility with a wide range of flow rates, the device has the potential to be incorporated with existing medical tubing and procedures to achieve POC diagnostics.
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 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 | 0.001 |
| 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) | 0.001 | 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".