Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.

Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain.

Stigmast-4-en-3-one from Euonymus alatus (Thunb.) Siebold. improves diabetic retinopathy and angiogenesis mediated by glucocorticoids receptor.

Ethnopharmacological Relevance: Euonymus alatus (Thunb.) Siebold. (EA), a traditional Chinese medicine, is widely used in the treatment of diabetes.

Extracellular vesicles-based point-of-care testing for the diagnosis and monitoring of Alzheimer's disease.

Alzheimer's disease (AD) is a debilitating condition that affects millions of people worldwide. One promising strategy for detecting and monitoring AD early on is using extracellular vesicles (EVs)-based point-of-care testing; however, diagnosing AD using EVs poses a challenge due to the low abundance of EV-biomarkers. Here, we present a fully integrated organic electrochemical transistor (OECT) that enables high accuracy, speed, and convenience in the detection of EVs from AD patients.

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG).

Evaluation of skin sympathetic nervous activity for classification of intracerebral hemorrhage and outcome prediction.

Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction based on skin sympathetic nervous activity (SKNA) signals.

Evaluation of autonomic nervous system activity in intradialytic hypotension using entropy and skin sympathetic nerve activity.

Introduction: The function of the autonomic nervous system (ANS) is crucial in the development of intradialytic hypotension (IDH). This study introduced the entropy of heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) to provide a complementary nonlinear and dynamic perspective for evaluating ANS function concerning IDH.

Methods: 93 patients undergoing hemodialysis (HD) were enrolled, and the baseline data, electrocardiogram (ECG), and SKNA were collected.

Function and inhibition of Haspin kinase: targeting multiple cancer therapies by antimitosis.

Objectives: Haploid germ cell-specific nuclear protein kinase (Haspin) is a serine/threonine kinase as an atypical kinase, which is structurally distinct from conventional protein kinases.

Key Findings: Functionally, Haspin is involved in important cell cycle progression, particularly in critical mitosis regulating centromeric sister chromatid cohesion during prophase and prometaphase, and subsequently ensuring proper chromosome alignment during metaphase and the normal chromosome segregation during anaphase. However, increasing evidence has demonstrated that Haspin is significantly upregulated in a variety of cancer cells in addition to normal proliferating somatic cells.

Autonomic nervous activity analysis based on visibility graph complex networks and skin sympathetic nerve activity.

Autonomic nerve system (ANS) plays an important role in regulating cardiovascular function and cerebrovascular function. Traditional heart rate variation (HRV) and emerging skin sympathetic nerve activity (SKNA) analyses from ultra-short-time (UST) data cannot fully reveal neural activity, thereby quantitatively reflect ANS intensity. Electrocardiogram and SKNA from sixteen patients (seven cerebral hemorrhage (CH) patients and nine control group (CO) patients) were recorded using a portable device.

Efficacy, safety and immunogenicity of hexavalent rotavirus vaccine in Chinese infants.

A randomized, double-blind, placebo-controlled multicenter trial was conducted in healthy Chinese infants to assess the efficacy and safety of a hexavalent live human-bovine reassortant rotavirus vaccine (HRV) against rotavirus gastroenteritis (RVGE). A total of 6400 participants aged 6-12 weeks were enrolled and randomly assigned to either HRV (n ​= ​3200) or placebo (n ​= ​3200) group. All the subjects received three oral doses of vaccine four weeks apart.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus's health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution.

Dynamics of Cardiac Autonomic Responses During Hemodialysis Measured by Heart Rate Variability and Skin Sympathetic Nerve Activity: The Impact of Interdialytic Weight Gain.

Autonomic nervous regulation plays a critical role in end-stage kidney disease (ESKD) patients with cardiovascular complications. However, studies on autonomic regulation in ESKD patients are limited to heart rate variability (HRV) analysis. Skin sympathetic nerve activity (SKNA), which noninvasively reflects the sympathetic nerve activity, has not been used in ESKD patients.

An Artifact-Resistant Feature SKNAER for Quantifying the Burst of Skin Sympathetic Nerve Activity Signal.

Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager−Kaiser energy (TKE) operator to preprocess the SKNA signal, and then candidates of burst areas were segmented by an envelope-based method.

Robust PVC Identification by Fusing Expert System and Deep Learning.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity.

Potent Anti-SARS-CoV-2 Efficacy of COVID-19 Hyperimmune Globulin from Vaccine-Immunized Plasma.

Coronavirus disease 2019 (COVID-19) remains a global public health threat. Hence, more effective and specific antivirals are urgently needed. Here, COVID-19 hyperimmune globulin (COVID-HIG), a passive immunotherapy, is prepared from the plasma of healthy donors vaccinated with BBIBP-CorV (Sinopharm COVID-19 vaccine).

An atrial fibrillation detection system based on machine learning algorithm with mix-domain features and hardware acceleration.

This paper presents a real-time electrocardiogram (ECG) analysis system that can detect atrial fibrillation (AF) using machine learning algorithms without a cloud server. The system takes advantage of the heterogeneous structure of the Zynq system-on-chip (SoC) to optimize the tasks of local implementation of AF detection. The features extraction is based on multi-domain features including entropy features and RR interval features, which is conducted using the embedded micro controller to generate significant features for AF detection.

A novel single-lead handheld atrial fibrillation detection system.

. The single-lead handheld atrial fibrillation (AF) detection device is suitable for daily monitoring or early screening of AF in the hospital. However, the signal quality and the reliability of AF detection algorithm still need to be improved.