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Lipid peroxidation and mitochondrial damage impair insulin sensitivity in skeletal muscle. Sirtuin-1 (SIRT1) protects mitochondria and activates under energy restriction. Dapagliflozin (Dapa) is an antihyperglycaemic agent that belongs to the sodium-glucose cotransporter-2 (SGLT2) inhibitors. Evidence shows that Dapa can induce nutrient deprivation effects, providing additional metabolic benefits. This study investigates whether Dapa can trigger nutrient deprivation to activate SIRT1 and enhance insulin sensitivity in skeletal muscle. We treated diet-induced obese (DIO) mice with Dapa and measured metabolic parameters, lipid accumulation, oxidative stress, mitochondrial function, and glucose utilization in skeletal muscle. β-hydroxybutyric acid (β-HB) was intervened in C2C12 myotubes. The role of SIRT1 was verified by RNA interference. We found that Dapa treatment induced nutrient deprivation state and reduced lipid deposition and oxidative stress, improved mitochondrial function and glucose tolerance in skeletal muscle. The same positive effects were observed after β-HB intervening for C2C12 myotubes, and the promoting effects on glucose utilization were diminished by SIRT1 RNA interference. Thus, Dapa promotes a nutrient deprivation state and enhances skeletal muscle insulin sensitivity via SIRT1 activation. In this study, we identified a novel hypoglycemic mechanism of Dapa and the potential mechanistic targets.

Aims/Introduction We investigated the association between impaired fasting glucose (IFG) and cardiometabolic multimorbidity (CMM) in the Chinese population. Materials and Methods We included 119,368 participants, free of diabetes mellitus and cardiovascular disease, who participated in the health examination (2006, 2008, 2010) of the Kailuan Study. According to World Health Organization diagnostic criteria, participants were divided into normal fasting blood glucose (FBG) (<6.1 mmol/L) and IFG (FBG 6.1–6.9 mmol/L) groups. CMM was defined as having two or more cardiometabolic diseases, including myocardial infarction, stroke and diabetes mellitus. We used Cox proportional hazards models to evaluate associations between IFG and CMM. Results During a median follow‐up period of 13.94 years, 2,432 CMM incident events occurred. After adjusting potential confounders, the hazard ratio (HR) and 95% confidence interval (CI) for CMM in the IFG group was 2.83 (95% CI 2.58–3.10) versus the normal FBG group. The HR of IFG for diabetes mellitus was 3.43 (95% CI 3.30–3.55), which was >1.25 (95% CI 1.13–1.37) for myocardial infarction, 1.16 (95% CI 1.07–1.25) for ischemic stroke and 1.06 (95% CI 0.88–1.27) for hemorrhagic stroke. Compared with normal FBG, HRs for risk of IFG for CMM were 2.73 (95% CI 2.48–3.02) in men and 3.86 (95% CI 2.92–5.09) in women. Conclusion IFG was a risk factor for CMM. The effect of IFG on diabetes mellitus was stronger than that on other cardiometabolic diseases. The effects of IFG for CMM differed by sex. This study focuses on analyzing the association between impaired fasting glucose and cardiometabolic multimorbidity in the Chinese population. Our study confirmed that impaired fasting glucose is a risk factor for cardiometabolic multimorbidity, compared with normal fasting blood glucose, impaired fasting glucose increased the risk of cardiometabolic multimorbidity by 183%, and the risk was higher in women than that in men.

Reverse iontophoresis (RI) is a promising non-invasive, wearable technology for the transdermal extraction of interstitial fluid (ISF), which contains rich biomarkers relevant to health status. Despite the advancement of wearable sensors, this technology is still restricted for accurate non-invasive biomarkers detection. The main challenge lies in the instability of ISF extraction during RI. We found that this instability is primarily caused by the skin surface pH variations because of the interaction between the RI-induced H + movement and the skin recovery ability. Here, we investigated how the skin surface pH affected RI, theoretically and experimentally; and developed a wearable device and a calibration method to enable accurate non-invasive ISF glucose detection, accordingly. The result showed that glucose prediction accuracy was markedly improved, with mean absolute relative difference (MARD) decreased from 34.44% to 14.78% across both healthy and diabetic volunteers. Reverse iontophoresis offers non-invasive extraction of interstitial fluid but it has proven hard to be used for glucose monitoring. Here, the authors establish the mechanism by which skin surface pH modulates RI through zeta potential changes of keratin, supported by theoretical analysis and numerical simulations

Real‐time foot pressure monitoring using wearable smart systems, with comprehensive foot health monitoring and analysis, can enhance quality of life and prevent foot‐related diseases. However, traditional smart insole solutions that rely on basic data analysis methods of manual feature extraction are limited to real‐time plantar pressure mapping and gait analysis, failing to meet the diverse needs of users for comprehensive foot healthcare. To address this, we propose a deep learning‐enabled smart insole system comprising a plantar pressure sensing insole, portable circuit board, deep learning and data analysis blocks, and software interface. The capacitive sensing insole can map both static and dynamic plantar pressure with a wide range over 500 kPa and excellent sensitivity. Statistical tools are used to analyze long‐term foot pressure usage data, providing indicators for early prevention of foot diseases and key data labels for deep learning algorithms to uncover insights into the relationship between plantar pressure patterns and foot issues. Additionally, a segmentation method assisted deep learning model is implemented for exercise‐fatigue recognition as a proof of concept, achieving a high classification accuracy of 95%. The system also demonstrates various foot healthcare applications, including daily activity statistics, exercise injury avoidance, and diabetic foot ulcer prevention. This paper proposes a deep‐learning enabled wearable smart insole system. With a highly sensitive capacitive pressure sensing insole and deep‐learning enabled data analysis process, it provides long‐term data analysis, early prevention indicators, and deep insights into the relationship between plantar pressure and foot issues. Various foot‐healthcare applications are proven, including daily statistics, exercise injury avoidance, and diabetic foot ulcer prevention.

Our previous study demonstrated that digital diabetes care model (DDCM) created by multidisciplinary care team (MDCT) can improve glycemic control for patients with diabetes than usual care. Therefore, we aimed to explore long-term glycemic control with DDCM and influencing factors in type 2 diabetic cohort, in order to make a portrait for diabetes with goal-achieved HbA1c in clinics.BackgroundOur previous study demonstrated that digital diabetes care model (DDCM) created by multidisciplinary care team (MDCT) can improve glycemic control for patients with diabetes than usual care. Therefore, we aimed to explore long-term glycemic control with DDCM and influencing factors in type 2 diabetic cohort, in order to make a portrait for diabetes with goal-achieved HbA1c in clinics.A total of 1198 outpatients with type 2 diabetes using DDCM for at least 12 months were recruited as a cohort. Medical records and specific DDCM indexes were collected. The influencing factors for glycemic control were explored by multivariate logistic regression analysis, followed by an internal and external validation.MethodsA total of 1198 outpatients with type 2 diabetes using DDCM for at least 12 months were recruited as a cohort. Medical records and specific DDCM indexes were collected. The influencing factors for glycemic control were explored by multivariate logistic regression analysis, followed by an internal and external validation.A total of 887 patients were finally included. HbA1c target-achieving rate was increased from 39.83% at baseline to 71.79% after 3-month follow-up. A shorter duration of diabetes, more frequent self-monitoring of blood glucose, lower HbA1c level at baseline, and less frequent emergency out-of-hospital follow-ups were influencing factors for HbA1c <7% at 12-month follow-up. AUC of the prediction model was 0.790, with a sensitivity of 69.7% and specificity of 76.1%. Internal and external validation in patients using the DDCM monitored by MDCT indicated that the DDCM was robust (AUC =0.783 and 0.723, respectively).ResultsA total of 887 patients were finally included. HbA1c target-achieving rate was increased from 39.83% at baseline to 71.79% after 3-month follow-up. A shorter duration of diabetes, more frequent self-monitoring of blood glucose, lower HbA1c level at baseline, and less frequent emergency out-of-hospital follow-ups were influencing factors for HbA1c <7% at 12-month follow-up. AUC of the prediction model was 0.790, with a sensitivity of 69.7% and specificity of 76.1%. Internal and external validation in patients using the DDCM monitored by MDCT indicated that the DDCM was robust (AUC =0.783 and 0.723, respectively).Our findings made a portrait for T2DM with goal-achieved HbA1c in our DDCM. It is important to recognize associated factors for health providers to make personalized intervention in clinical practice.ConclusionOur findings made a portrait for T2DM with goal-achieved HbA1c in our DDCM. It is important to recognize associated factors for health providers to make personalized intervention in clinical practice.

In the study, type 2 diabetic rat model was established using streptozotocin (STZ) combined with a high-fat diet, and the rats were divided into control and diabetic groups. Diabetic groups were further divided into nonintervening, simvastatin, Didang Decoction (DDD) early-phase intervening, DDD mid-phase intervening, and DDD late-phase intervening groups. The expression level of MLCK was detected using Western Blot analysis, and the levels of cyclic adenosine monophosphate (cAMP), protein kinase C (PKC), and protein kinase A (PKA) were examined using Real Time PCR. Under the electron microscope, the cells in the early-DDD-intervention group and the simvastatin group were significantly more continuous and compact than those in the diabetic group. Compared with the control group, the expression of cAMP-1 and PKA was decreased in all diabetic groups, whereas the expression of MLCK and PKC was increased in early- and mid-phase DDD-intervening groups ( P < 0.05 ); compared with the late-phase DDD-intervening group, the expression of cAMP-1 and PKA was higher, but the level of MLCK and PKC was lower in early-phase DDD-intervening group ( P < 0.05 ). In conclusion, the early use of DDD improves the permeability of vascular endothelial cells by regulating the MLCK signaling pathway.

The anchor-free object detection CenterNet has the problems that the utilization rate of detected object features is low, which is difficult to detect morphological changes and blurred edge objects, susceptible to interference from irrelevant information in complex backgrounds. To solve the problems above, we propose a novel anchor-free method called SDA-DLA34 in this paper. First, to solve the problem that morphological changes and blurred edge objects are difficult to detect, it is proposed that to introduce a series of deformable convolution to replace the ordinary convolution in DLA34, which effectively improve the network perception ability of morphological changes and blurred edge objects. Second, to solve the problem of low utilization of object features, it is proposed that to introduce the soft pooling layers to replace max pooling layers in the down-sampling process of DLA34, which could reduce the loss of object feature information, especially small objects. Finally, in order to pay more attention to the key information, reducing the influence of background and other irrelevant information, it is proposed to introduce attention mechanism in DLA34 to enhance the ability of the network to extract key features of the object. Experiments on MS COCO and Pascal VOC datasets have been conducted, the results show that the SDA-DLA34 is superior to to the current mainstream methods. Compared with the DLA34, the mAP, AP 0.5 and AP 0.75 of SDA-DLA34 increase by 8.1%, 8.0% and 6.7% respectively.

This paper proposes a novel fiber attenuated total reflection (ATR) sensor with silver nanoparticles (AgNPs) on the flattened structure based on mid-infrared spectroscopy for detecting low concentration of glucose with high precision. The flattened structure was designed to add the effective optical path length to improve the sensitivity. AgNPs were then deposited on the surface of the flattened area of the fiber via chemical silver mirror reaction for further improving the sensitivity by enhancing the infrared absorption. Combining the AgNPs modified flattened fiber ATR sensor with a CO2 laser showed a strong mid-infrared glucose absorption, with an enhancement factor of 4.30. The glucose concentration could be obtained by a five-variable partial least-squares model with a root-mean-square error of 4.42 mg/dL, which satisfies clinical requirements. Moreover, the fiber-based technique provides a pretty good method to fabricate miniaturized ATR sensors that are suitable to be integrated into a microfluidic chip for continuous glucose monitoring with high sensitivity.

Tian et al. introduced a wearable AI enabled smart insole system that serves as a promising prototype for future foot healthcare applications. The foot healthcare system validated a range of foot healthcare applications, including daily statistics, prevention of sports injuries, and effective management of diabetic foot ulcers.

The quest to understand the fundamentals of optimal glass-forming compositions in multi-component alloys has long been challenging. To gain insights into the mechanism, a systematic study of glass compositions is conducted using a new strategy of entropy engineering, imposed by the integration of eutectic or intermetallic phases featured by their low melting entropies, which can be determined experimentally and precisely. The optimal composition designed using entropy engineering is further compared with that derived from the conventional \"deep eutectic\" principle and empirical trial-and-error. For the ternary Cu-Zr-Ti alloys, a series of compositions are achieved, and the ranking of their glass-forming ability indicates a correlation with the melting entropies of initial phases. In particular, the optimal glass-forming composition of Cu Zr Ti is designed using two initial phases of intermetallic Cu Zr and eutectic Cu Ti with the lowest melting entropies. This designed composition is remarkably equivalent to the reported one, Cu Zr Ti . It is more meaningful that this study uncovers the phase competition mechanism involved in glass formation in a quantitative way for the first time, emphasizing the importance of melting entropies in screening and balancing the competing phases upon glass formation.