Longitudinal Analysis of Diabetes Mellitus Risk: Smoking Status and Smoking Cessation.

Background/Objectives: Smoking cessation is acknowledged for its health benefits. However, it paradoxically increases diabetes mellitus (DM) risk shortly after quitting due to weight gain. This research aimed to investigate how smoking status could affect the development of DM, focusing on how the risk of acquiring diabetes changed over time after quitting smoking, independent of variables such as weight gain. Methods: The data of 386,558 participants of the Kangbuk Samsung Health Study, excluding those with pre-existing DM, were examined. Smoking status and its long-term effects on DM risk were assessed using multivariate Cox proportional hazards models. Lifestyle factors, including weight change, physical activity levels, and alcohol intake, were adjusted as time-varying covariates throughout the follow-up period. Results: Modified hazard ratios (HRs) indicated no notable disparity in DM risk between individuals who previously smoked and those who had never smoked (HR: 1.04, 95% CI: 0.999-1.08, p-value < 0.001). In contrast, current smokers exhibited a significantly increased DM risk (HR: 1.29, 95% CI: 1.24-1.35, p-value < 0.001). Within the first six years post-cessation, former smokers initially faced a higher DM risk than never smokers (0-2 years, HR: 1.22, 95% CI: 1.15-1.31, p-value < 0.001; 3-5 years, HR: 1.11, 95% CI: 1.04-1.20, p-value < 0.001). After 12 years, they realigned with never smokers (12-46 years, HR: 0.92, 95% CI: 0.86-0.98, p-value = 0.002). Current smokers consistently showed a higher DM risk (0-9 years, HR: 1.29, 95% CI: 1.14-1.46, p-value < 0.001). Adjusting for covariates such as weight change and physical activity did not alter these findings. Conclusions: Our results indicated that former smokers initially experienced an elevated risk of DM relative to never smokers. This increased risk aligned with the risk of never smokers after six years, and the risk continued to improve after 12 years compared to never smokers. This contrasted with current smokers, who maintained a heightened risk of DM, even when adjustments were made for weight change, physical activity, and alcohol intake as time-varying covariates.

The Ability of the STAR-CAP Staging System to Prognosticate the Risk of Subsequent Therapies and Metastases After Initial Treatment of M0 Prostate Cancer.

INTRODUCTION: The International Staging Collaboration for Prostate Cancer (STAR-CAP) has been proposed as a risk model for prostate cancer with superior prognostic power compared to the current staging system. This study aimed to evaluate the performance of STAR-CAP in predicting the risk of subsequent therapy after initial treatment and the risk of developing metastases. PATIENTS AND METHODS: The study included 3425 men from an institutional observational registry with a median age of 64.9 years and a median follow-up time of 5.4 years. The primary endpoints were metastases and progression to additional therapy after initial therapy (radiation ± surgery). The risk of progression in the STAR-CAP group was estimated using a competing risk model (death). RESULTS: The results showed that patients with STAR-CAP stages 1A-1C had a similar risk of requiring additional therapies and developing metastasis. Compared to stage IC, each stage from 2A to 3B incrementally increased the risk of subsequent therapy (hazard ratio (HR) 1.4-5.8, respectively) and metastases (HR 1.5-10.8, respectively). The 5-year probability of receiving subsequent therapy for a patient with stage IC was 8.6%, which increased from 11.4% to 37.4% for those with stages 2A to 3B. The 5-year probability of developing metastases for patients with stage IC was 1.5%, which increased from 2.2% to 8.2% for patients with stages 2A to 3B. CONCLUSIONS: The probability of receiving subsequent therapy was higher for patients undergoing surgery, while radiation therapy patients were more likely to receive treatment with intensified multimodality therapies upfront.

Applications of Xylosides in the Manipulation of Stem Cell Niche to Regulate Human Neural Stem Cell Differentiation and Neurite Outgrowth.

The extracellular matrix (ECM) plays a pivotal role in the regulation of neural stem cell differentiation, axon guidance and growth, and neural plasticity. Glycosaminoglycans, such as heparan sulfate and chondroitin sulfate, are significant components of brain ECM that dictates neurogenesis and neural repair. Herein, we describe a simple method to assess the effect of xylsoides, which serve as primers and inhibitors of GAG biosynthesis, on human neural stem cell differentiation and neurite outgrowth in in vitro culture conditions.

A review of wearable biosensors for sweat analysis.

Recent advances in the skin-interfaced wearable sweat sensors allow a personalized daily diagnosis and prognosis of the diseases in a form of a non-invasive, portable, and continuous monitoring system. Especially, the soft microfluidic system provides robust quantitative analysis platforms that integrate sweat sampling, storing, and various sensing capabilities. This review systematically introduces the sweat collecting mechanism using soft microfluidic valves, including calculation of sweat storage and loss. In terms of sweat analysis, colorimetric (e.g. enzymatic, chemical, or their mixed reactions), electrochemical (e.g. voltammetric, potentiometric, amperometric, or conductometric), and multiplex measurements of sweat contents facilitate diagnosis of diseases via analysis of combined multiple data, such as vital signals (e.g. ECG, EMG, EEG, etc.) and information from the skin (e.g. temperature, GSR, etc.). The integration of wireless communication with the microfluidic systems enables point-of-care health monitoring for disease and specific physiological status.

A review of BioFET's basic principles and materials for biomedical applications.

Interest in biomolecular sensors for diagnosis of early diseases and prognosis of the diseases is increasing day by day. Among them, FET-based sensors are very useful in that of their versatile operating characteristics using various materials. Herein, after addressing the basic principles of BioFET, we conduct an overall review of BioFET on two of the main structural elements: transducing materials and probes. Transducing materials were classified into graphene, carbon nanotube, silicon, MOF, etc., and probes were classified into antibodies, enzymes, aptamers, etc.. The important elements in designing BioFETs, such as electrical properties of each material, Debye length, and fabrication process are introduced along with their respective structures and materials. After the review of each of these structures and characteristics, examples are discussed along with sensitivity, selectivity, and limit of detection. In addition to the operating aspects of the senser, novel processes, treatments, and materials that can be considered for various purposes are also introduced. Based on the understanding, an overview of diverse examples is given by dividing the applications of BioFET into three main types: antigen sensing, biomarker sensing, and drug effect monitoring. Focusing on these general reviews, we conclude how the future direction of development will move forward and what the main challenge is.