Evaluation of tolerability and safety of transcranial electrical stimulation with gel particle electrodes in healthy subjects.

Background: With the advancement of transcranial electrical stimulation (tES) technology, an increasing number of stimulation devices and treatment protocols have emerged. However, safety and tolerability remain critical concerns before new strategies can be implemented. Particularly, the use of gel particle electrodes brings new challenges to the safety and tolerability of tES, which hinders its widespread adoption and further research.

Achieving Dendrite-Free Lithium Metal Batteries by Constructing a Dense Lithiophilic CuSe/CuO Heterojunction Tip.

Lithium (Li) metal batteries (LMBs) have garnered widespread attention due to their high specific capacity. However, the growth of lithium dendrite severely limits their practical applications. Herein, a novel strategy is proposed to regulate the overall potential strength and lithium ions (Li) concentration on the surface of the current collector by utilizing densely distributed tip effects.

Association of MRI-based knee osteoarthritis structural phenotypes with short-term structural progression and subsequent total knee replacement.

Background: The failure of disease-modifying osteoarthritis drugs (DMOADs) trials lies mainly in the heterogeneity of the disease, which calls for a more precise population with specific progression and outcomes. This study aimed to determine whether and which MRI-based structural phenotype of knee osteoarthritis (KOA) is associated with short-term structural progression and subsequent total knee replacement (TKR).

Methods: A longitudinal study was conducted among participants with baseline Kellgren-Lawrence grade (KLG) ≥ 2 from the Osteoarthritis Initiative (OAI).

Boosting the performance of molecular property prediction via graph-text alignment and multi-granularity representation enhancement.

Deep learning is playing an increasingly important role in accurate prediction of molecular properties. Prior to being processed by a deep learning model, a molecule is typically represented in the form of a text or a graph. While some methods attempt to integrate these two forms of molecular representations, the misalignment of graph and text embeddings presents a significant challenge to fuse two modalities.

Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA).

Rational design of soluble expressed human aldehyde dehydrogenase 2 with high stability and activity in pepsin and trypsin.

Acetaldehyde dehydrogenase 2 (ALDH2) is a crucial enzyme in alcohol metabolism, and oral administration of ALDH2 is a promising method for alcohol detoxification. However, recombinant ALDH2 is susceptible to hydrolysis by digestive enzymes in the gastrointestinal tract and is expressed as inactive inclusion bodies in E. coli.

A Highly-Lithiophilic MnO/ZnO-Modified Carbon Nanotube Film for Dendrite-Free Lithium Metal Anodes.

Lithium metal anode is deemed as a potential candidate for high energy density batteries, which has attracted increasing attention. Unfortunately, Li metal anode suffers from issues such as dendrite grown and volume expansion during cycling, which hinders its commercialization. Herein, we designed a porous and flexible self-supporting film comprising of single-walled carbon nanotube (SWCNT) modified with a highly-lithiophilic heterostructure (MnO/ZnO@SWCNT) as the host material for Li metal anodes.

A method for the early prediction of chronic diseases based on short sequential medical data.

Noncommunicable diseases (NCDs) have become the leading cause of death worldwide. NCDs' chronicity, hiddenness, and irreversibility make patients' disease self-awareness extremely important in disease control but hard to achieve. With an accumulation of electronic health record (EHR) data, it has become possible to predict NCDs early through machine learning approaches.

Positive association of nap duration with risk of non-alcoholic fatty liver disease in an occupational population in Guangdong Province, China: a cross-sectional study.

Background: A lack of sleep or disorder in sleep-wake cycles has been associated with metabolic impairments. However, few studies have investigated the association between daytime napping duration and the risk of non-alcoholic fatty liver disease. This study aimed to investigate the association of daytime napping duration with the risk of non-alcoholic fatty liver disease in a Chinese population.

Breakfast consumption frequency is associated with dyslipidemia: a retrospective cohort study of a working population.

Background: Dyslipidemia is a significant contributor to cardiovascular and cerebrovascular diseases. Research on the relationship between breakfast consumption frequency and dyslipidemia in the working population is lacking. Therefore, we aimed to investigate this relationship based on a retrospective cohort study of a large working population in China.

Tea Consumption is Associated with an Increased Risk of Hyperuricemia in an Occupational Population in Guangdong, China.

Purpose: Chronic hyperuricemia leads to long-term deposition of monosodium urate crystals that may damage the joint structure and affect quality of life. Although hyperuricemia prevalence varies, most studies indicate increased cases of hyperuricemia worldwide. The relationship between hyperuricemia and tea consumption is uncertain.

An Augmented Model with Inferred Blood Features for the Self-diagnosis of Metabolic Syndrome.

Background And Objectives: The penetration rate of physical examinations in China is substantially lower than that in developed countries. Therefore, an auxiliary approach that does not depend on hospital health checks for the diagnosis of metabolic syndrome (MetS) is needed.

Methods: In this study, we proposed an augmented method with inferred blood features that uses self-care inputs available at home for the auxiliary diagnosis of MetS.

A 135-190 GHz Broadband Self-Biased Frequency Doubler using Four-Anode Schottky Diodes.

This paper describes the design and demonstration of a 135-190 GHz self-biased broadband frequency doubler based on planar Schottky diodes. Unlike traditional bias schemes, the diodes are biased in resistive mode by a self-bias resistor; thus, no additional bias voltage is needed for the doubler. The Schottky diodes in this verification are micron-scaled devices with an anode area of 6.

CD-KES: An Ontology Based Knowledge Education System for Patients with Chronic Diseases and Its Constructing Approach.

Patients' participation plays a crucial role in the management of chronic diseases. Educating patients about their diseases allows patients to self-regulate their daily health conditions more reasonably and effectively. This study focuses on an informative way to develop daily education and guidance among patients.

Development of a Service-Oriented Sharable Clinical Decision Support System Based on Ontology for Chronic Disease.

Clinical decision support systems (CDSSs) have been proved as an efficient way to improve health care quality. However, the inflexibility in integrating multiple clinical practice guidelines (multi-CPGs), the mass input workload of patient data, and the difficulty in system sharing become barriers of CDSSs implementation. In this paper, we proposed a framework of CDSS for chronic disease based on ontology and service-oriented architecture (SOA) to improve these defects.