High-Linearity Hydrogel-Based Capacitive Sensor Based on Con A-Sugar Affinity and Low-Melting-Point Metal.

Continuous glucose monitoring (CGM) plays an important role in the treatment of diabetes. Affinity sensing based on the principle of reversible binding to glucose does not produce intermediates, and the specificity of concanavalin A (Con A) to glucose molecules helps to improve the anti-interference performance and long-term stability of CGM sensors. However, these affinity glucose sensors have some limitations in their linearity with a large detection range, and stable attachment of hydrogels to sensor electrodes is also challenging. In this study, a capacitive glucose sensor with high linearity and a wide detection range was proposed based on a glucose-responsive DexG-Con A hydrogel and a serpentine coplanar electrode made from a low-melting-point metal. The results show that within the glucose concentration range of 0-20 mM, the sensor can achieve high linearity (R2 = 0.94), with a sensitivity of 33.3 pF mM-1, and even with the larger glucose concentration range of 0-30 mM the sensor can achieve good linearity (R2 = 0.84). The sensor also shows resistance to disturbances of small molecules, good reversibility, and long-term stability. Due to its low cost, wide detection range, high linearity, good sensitivity, and biocompatibility, the sensor is expected to be used in the field of continuous monitoring of blood glucose.

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .

Radiofrequency ablation for liver tumors abutting complex blood vessel structures: treatment protocol optimization using response surface method and computer modeling.

OBJECTIVE: To achieve a result of a large tumor ablation volume with minimal thermal damage to the surrounding blood vessels by designing a few clinically-adjustable operating parameters in radiofrequency ablation (RFA) for liver tumors abutting complex vascular structures. METHODS: Response surface method (RSM) was employed to correlate the ablated tumor volume (Ra) and thermal damage to blood vessels (Dt) based on RFA operating parameters: ablation time, electrode position, and insertion angle. A coupled electric-thermal-fluid RFA computer model was created as the testbed for RSM to simulate RFA process. Then, an optimal RFA protocol for the two conflicting goals, namely (1) large tumor ablation and (2) small thermal damage to the surrounding blood vessels, has been achieved under a specific ablation environment. RESULTS: Linear regression analysis confirmed that the RFA protocol significantly affected Ra and Dt (the adjusted coefficient of determination Radj2 = 93.61% and 95.03%, respectively). For a proposed liver tumor scenario (liver tumor with a dimension of 4×3×2.9 cm3 abutting a complex vascular structure), an optimized RFA protocol was found based on the regression results in RSM. Compared with a reference RFA protocol, in which the electrode was centered in the tumor with a 12-min ablation time, the optimized RFA protocol has increased Ra  from 98.1% to 99.6% and decreased Dt from 4.1% to 0.4%, achieving nearly the complete ablation of proposed liver tumor and ignorable thermal damages to vessels. CONCLUSION: This work showed that it is possible to design a few clinically-adjustable operating parameters of RFA for achieving a large tumor ablation volume while minimizing thermal damage to the surrounding blood vessels.

Modeling and ex vivo experimental validation of liver tissue carbonization with laser ablation.

OBJECTIVE: The purpose of this study was to model the process of liver tissue carbonization with laser ablation (LA). METHODS: A dynamic heat source model was proposed and combined with the light distribution model as well as bioheat transfer model to predict the development of tissue carbonization with laser ablation (LA) using an ex vivo porcine liver tissue model. An ex vivo laser ablation experiment with porcine liver tissues using a custom-made 1064 nm bare fiber was then used to verify the simulation results at 3, 5, and 7 W laser administrations for 5 min. The spatiotemporal temperature distribution was monitored by measuring the temperature changes at three points close the fiber during LA. Both the experiment and simulation of the temperature, tissue carbonization zone, and ablation zone were then compared. RESULTS: Four stages were recognized in the development of liver tissue carbonization during LA. The growth of the carbonization zone along the fiber axial and radial directions were different in the four stages. The carbonization zone along the fiber axial direction (L2) grew in the four stages with a sharp increase in the initial period and a minor increase in Stage 4. However, the change in the carbonization zone along the fiber radial direction (D2) increased dramatically (Stage 1) to a long-time plateau (Stages 2 and 3) followed by a slow growth in Stage 4. An acceptable agreement between the computer simulation and ex vivo experiment in the temperature changes at the three points was found at all three testing laser administrations. A similar result was also obtained for the dimensions of coagulation zone and ablation zone between the computer simulation and ex vivo experiment (carbonization zone: 2.99± 0.10 vs. 2.78 mm2, 67.39± 0.09 vs. 63.53 mm2, and 90.53± 0.11 vs. 85.15 mm2; ablation zone: 68.95± 0.28 vs. 65.29 mm2, 182.11± 0.24 vs. 213.81 mm2, and 244.80± 0.06 vs. 251.79 mm2 at 3, 5, and 7 W, respectively). CONCLUSION: This study demonstrates that the proposed dynamic heat source model combined with the light distribution model as well as bioheat transfer model can predict the development of liver tissue carbonization with an acceptable accuracy. This study contributes to an improved understanding of the LA process in the treatment of liver tumors.

Irreversible Electroporation Enhanced by Radiofrequency Ablation: An In Vitro and Computational Study in a 3D Liver Tumor Model.

In the present study, we used a computational and experimental study in a 3D liver tumor model (LTM) to explore the tumor ablation enhancement of irreversible electroporation (IRE) by pre-heating with radiofrequency ablation (RFA) and elucidate the mechanism whereby this enhancement occurs. Three ablation protocols, including IRE alone, RFA45 → IRE (with the pre-heating temperature of 45 °C), and RFA60 → IRE (with the pre-heating temperature of 60 °C) were investigated. Both the thermal conductivity and electrical conductivity of the 3D LTM were characterized with the change in the pre-heating temperature. The results showed, compared to IRE alone, a significant increase in the tumor ablation volume (19.59 [Formula: see text] 0.61 vs. 15.29 ± 0.61 mm3, p = 0.002 and 22.87 [Formula: see text] 0.35 vs. 15.29 ± 0.61 mm3, p < 0.001) was observed with both RFA45 → IRE and RFA60 → IRE, leading to a decrease in lethal electric filed strength (8 and 17%, correspondingly). The mechanism can be attributed to the change of cell microenvironment by pre-heating and/or a synergistic effect of RFA and IRE. The proposed enhancing method might contribute to the improvement of interventional oncology in the treatment of large tumors close to critical organs (e.g., large blood vessels and bile ducts).

Modeling and Quantification of Impact of Psychological Factors on Rehabilitation of Stroke Patients.

The brain damage could lead to the loss of the central nervous system, so a stroke patient may lose the function of dominating his/her body. The rehabilitation aims to maximize the potential to restore a patient who has an impairment. Traditional rehabilitation is to train a patient's muscles and joints under the guidance of doctors to improve the strength of muscles and restore the motor function of joints. However, stroke patients are usually depressed, lonely, and irritable, and they might easily generate negative emotions during a rehabilitation process. With a sole goal of helping patients restore their body functions from the physiology perspective, the traditional rehabilitation took little consideration on the impact of rehabilitation, which is reflected and measured from the perspective of emotions. Therefore, we suggest adding affective regulation to the stroke rehabilitation; in such a way, the patients' exercise could be completed with high intrinsic motivation, and the performance of the rehabilitation process can be enhanced. Two main contributions in the presented works are: 1) the expanded emotional model to represent the status of stroke patients where the impact of psychological factors can be taken into consideration and 2) the quantifiable measurement of rehabilitation performance as well as the corresponding design of experiments to verify the positive impact of psychological adjustment on human subjects. Note that due to the limited conditions, the experimental verification was performed on healthy college students. Since our work focused on modeling and quantification of psychological factors, it is reasonable to expend our work to other human subjects including stoke patients.

Driver fatigue detection based on eye state.

BACKGROUND: Nowadays, more and more traffic accidents occur because of driver fatigue. OBJECTIVE: In order to reduce and prevent it, in this study, a calculation method using PERCLOS (percentage of eye closure time) parameter characteristics based on machine vision was developed. It determined whether a driver's eyes were in a fatigue state according to the PERCLOS value. METHODS: The overall workflow solutions included face detection and tracking, detection and location of the human eye, human eye tracking, eye state recognition, and driver fatigue testing. The key aspects of the detection system incorporated the detection and location of human eyes and driver fatigue testing. The simplified method of measuring the PERCLOS value of the driver was to calculate the ratio of the eyes being open and closed with the total number of frames for a given period. RESULTS: If the eyes were closed more than the set threshold in the total number of frames, the system would alert the driver. CONCLUSION: Through many experiments, it was shown that besides the simple detection algorithm, the rapid computing speed, and the high detection and recognition accuracies of the system, the system was demonstrated to be in accord with the real-time requirements of a driver fatigue detection system.