Hemocompatible Coatings of Catheters Equipped with Electrochemical Sensors for Real-Time Monitoring of Critical Parameters in the Blood
Bibliographic record
Abstract
Continuous monitoring of blood parameters at the bedside is crucial for critically ill patients. Miniaturized biosensors integrated into medical devices in direct contact with blood can improve the quality of monitoring by reducing response time in emergencies as well as the invasiveness and blood consumption during the measurements. In such settings, coating biosensors with hemocompatible materials is essential for achieving biocompatibility and maximum acceptance of the devices for direct measurements of blood parameters in the human body. Here, we present an instrumented catheter equipped with a hemocompatible electrochemical sensor to continuously monitor one of the key parameters- glucose, lactate, and pH, directly in blood. Selective amperometric and potentiometric sensors were obtained by modifying the electrodes with enzymes and pH-responsive toluidine blue O. The glucose sensors exhibited a linear response from 0.06 to 10 mM with a sensitivity of −116 ± 16 nA/mM Glucose, while the lactate sensor had a linear response between 5 and 20 mM with a sensitivity of −38 ± 6 nA/mM Lactate . A pH sensitivity of −20.17 ± 2.37 mV/pH for sensors with the hydrogel-covered TBO film has been reached. Hemocompatible properties, crucial for in vivo applications, were achieved by coating the functional electrode surfaces with an additional hydrogel layer based on a four-armed poly(ethylene glycol), cross-linked with the anticoagulant polysaccharide heparin. Initially, the functionalization strategy was thoroughly evaluated in terms of sensor response and hemocompatibility in a planar format; followed by a proof-of-principle demonstration of glucose sensing with a catheter imprinted with the respective electrochemical sensor.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".