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Schistosomiasis, typically characterized by chronic infection in endemic regions, has the potential to affect liver tissue and pose a serious threat to human health. Detecting and screening for this disease early on is crucial for its prevention and control. However, existing methods encounter challenges such as low sensitivity, time-consuming processes, and complex sample handling. To address these challenges, we report a soluble egg antigen (SEA)-based functionalized gridless and meander-type AlGaN/GaN high electron mobility transistors (HEMT) sensor for the highly sensitive detection of antibodies to Schistosoma japonicum. Immobilization of the self-assembled membrane on the gate surface was verified using a semiconductor parameter analyzer, scanning electron microscope (SEM), and atomic force microscopy (AFM). The developed biosensor demonstrates remarkable performance in detecting anti-SEA, exhibiting a linear concentration range of 10 ng/mL to 100 μg/mL and a sensitivity of 0.058 mA/log (ng/mL). It also exhibits similar excellent performance in serum systems. With advantages such as rapid detection, high sensitivity, miniaturization, and label-free operation, this biosensor can fulfill the requirements for blood defense. [ABSTRACT FROM AUTHOR]

Magnetic micro- and nanoparticles (MPs)-based composite materials are widely used in various applications in electronics, biotechnology, and medicine. This group of silicone composites have advantageous magnetic and mechanical properties as well as sufficient flexibility and biocompatibility. These composites can be applied in medicine for biological sensing, drug delivery, tissue engineering, and as remote-controlled microrobots operating in vivo. In this work, the properties of polydimethylsiloxane (PDMS)-based composites with different percentages (30 wt.%, 50 wt.%, 70 wt.%) of NdFeB microparticles as a filler were characterized. The novelty of the work was to determine the influence of the percentage of MP content and physiological conditioning on the properties of the PDMS-MP composites after in vitro incubation. An important essence of the work was a comprehensive study of the properties of materials important from the point of view of medical applications. Materials were tested before and after conditioning in 0.9 wt.% NaCl solution at a temperature of 37 °C. Several studies were carried out, including thermal, physicochemical, and rheological tests. The results show that with an increase of the incubation time, most of the measured thermal and physicochemical parameters decreased. The presence of the magnetic filler, especially at a concentration of 70 wt.%, has a positive effect on thermal stability and physicochemical and rheological properties. The performed tests provided important results, which can lead to further research for a broader application of magnetic composites in the biomedical field. [ABSTRACT FROM AUTHOR]

Pulse wave (PW) measurement is a highly prominent technique, used in biomedical diagnostics. Development of novel PW sensors with increased accuracy and reduced susceptibility to motion artifacts will pave the way to more advanced healthcare technologies. This paper reports on a comparison of performance of fiber optic pulse wave sensors, based on Fabry–Perot interferometer, fiber Bragg grating, optical coherence tomography (OCT) and singlemode-multimode-singlemode intermodal interferometer. Their performance was tested in terms of signal to noise ratio, repeatability of demodulated signals and suitability of demodulated signals for extraction of information about direct and reflected waves. It was revealed that the OCT approach of PW monitoring provided the best demodulated signal quality and was most robust against motion artifacts. Advantages and drawbacks of all compared PW measurement approaches in terms of practical questions, such as multiplexing capabilities and abilities to be interrogated by portable hardware are discussed. [ABSTRACT FROM AUTHOR]

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important. [ABSTRACT FROM AUTHOR]

For certain musculoskeletal complex rupture injuries, the only treatment available is the use of immobilization splints. This type of treatment usually causes discomfort and certain setbacks in patients. In addition, other complications are usually generated at the vascular, muscular, or articular level. Currently, there is a really possible alternative that would solve these problems and even allows a faster and better recovery. This is possible thanks to the application of engineering on additive manufacturing techniques and the use of biocompatible materials available in the market. This study proposes the use of these materials and techniques, including sensor integration inside the splints. The main parameters considered to be studied are pressure, humidity, and temperature. These aspects are combined and analyzed to determine any kind of unexpected evolution of the treatment. This way, it will be possible to monitor some signals that would be studied to detect problems that are associated to the very initial stage of the treatment. The goal of this study is to generate a smart splint by using biomaterials and engineering techniques based on the advanced manufacturing and sensor system, for clinical purposes. The results show that the prototype of the smart splint allows to get data when it is placed over the arm of a patient. Two temperatures are read during the treatment: in contact with the skin and between skin and splint. The humidity variations due to sweat inside the splint are also read by a humidity sensor. A pressure sensor detects slight changes of pressure inside the splint. In addition, an infrared sensor has been included as a presence detector. [ABSTRACT FROM AUTHOR]

One of the major goals of Health 4.0 is to offer personalized care to patients, also through real-time, remote monitoring of their biomedical parameters. In this regard, wearable monitoring systems are crucial to deliver continuous appropriate care. For some biomedical parameters, there are a number of well established systems that offer adequate solutions for real-time, continuous patient monitoring. On the other hand, monitoring skin hydration still remains a challenging task. The continuous monitoring of this physiological parameter is extremely important in several contexts, for example for athletes, sick people, workers in hostile environments or for the elderly. State-of-the-art systems, however, exhibit some limitations, especially related with the possibility of continuous, real-time monitoring. Starting from these considerations, in this work, the feasibility of an innovative time-domain reflectometry (TDR)-based wearable, skin hydration sensing system for real-time, continuous monitoring of skin hydration level was investigated. The applicability of the proposed system was demonstrated, first, through experimental tests on reference substances, then, directly on human skin. The obtained results demonstrate the TDR technique and the proposed system holds unexplored potential for the aforementioned purposes. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a well-accepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively. [ABSTRACT FROM AUTHOR]

This paper describes a fourth-order cascade-of-integrators with feedforward (CIFF) single-bit discrete-time (DT) switched-capacitor (SC) delta-sigma modulator (DSM) for high-resolution applications. This DSM is suitable for high-resolution applications at low frequency using a high-order modulator structure. The proposed operational transconductance amplifier (OTA), used a feedforward amplifier scheme that provided a high-power efficiency, a wider bandwidth, and a higher DC gain compared to recent designs. A chopper-stabilization technique was applied to the first integrator to remove the 1/f noise from the transistor, which is inversely proportional to the frequency. The designed DSM was implemented using 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The oversampling ratio (OSR) was 128, and the sampling frequency was 128 kHz. At a 500 Hz bandwidth, the signal-to-noise ratio (SNR) was 100.3 dB, the signal-to-noise distortion ratio (SNDR) was 98.5 dB, and the dynamic range (DR) was 103 dB. The measured total power dissipation was 99 µW from a 3.3 V supply voltage. [ABSTRACT FROM AUTHOR]