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Mesenchymal stem cells (MSCs), coming from a wide range of sources, have multi-directional differentiation ability. MSCs play vital roles in immunomodulation, hematopoiesis and tissue repair. The microenvironment of cells often refers to the intercellular matrix, other cells, cytokines and humoral components. It is also the place for cells’ interaction. The stability of the microenvironment is pivotal for maintaining cell proliferation, differentiation, metabolism and functional activities. Abnormal changes in microenvironment components can interfere cell functions. In some diseases, MSCs can interact with the microenvironment and accelerate disease progression. This review will discuss the characteristics of MSCs and their microenvironment, as well as the interaction between MSCs and microenvironment in disease.

Against the backdrop of China’s urban digital transformation, the construction of smart communities can contribute significant social value. This paper examines the needs of community residents, managers, and workers and proposes a user-oriented model to enhance the suitability of requirements and improve management efficiency. A case study was conducted in Ningbo, China, to analyze user needs and the strategies implemented during the community’s digital construction process. This paper presents the study’s findings and discusses the potential of adopting a Demand-Response Model as a universal framework for smart community construction.

Classifier has been widely applied in machine learning, such as pattern recognition, medical diagnosis, credit scoring, banking and weather prediction. Because of the limited local storage at user side, data and classifier has to be outsourced to cloud for storing and computing. However, due to privacy concerns, it is important to preserve the confidentiality of data and classifier in cloud computing because the cloud servers are usually untrusted. In this work, we propose a framework for privacy-preserving outsourced classification in cloud computing (POCC). Using POCC, an evaluator can securely train a classification model over the data encrypted with different public keys , which are outsourced from the multiple data providers. We prove that our scheme is secure in the semi-honest model

The abnormal number and functional deficiency of immune cells are the pathological basis of various diseases. Recent years, the imbalance of Th17/regulatory T (Treg) cell underlies the occurrence and development of inflammation in autoimmune diseases (AID). Currently, studies have shown that material and energy metabolism is essential for maintaining cell survival and normal functions and the altered metabolic state of immune cells exists in a variety of AID. This review summarizes the biology and functions of Th17 and Treg cells in AID, with emphasis on the advances of the roles and regulatory mechanisms of energy metabolism in activation, differentiation and physiological function of Th17 and Treg cells, which will facilitate to provide targets for the treatment of immune-mediated diseases.

Diabetic foot ulcer (DFU) is a leading cause of high-level amputation in DM patients, with a low wound healing rate and a high incidence of infection. Vascular endothelial growth factor (VEGF) plays an important role in diabetes mellitus (DM) related complications. This study aims to explore the VEGF expression and its predictive value for prognosis in DFU, in order to provide basis for the prevention of DFU related adverse events. We analyzed 502 patients, with 328 in healing group and 174 in non-healing/recurrent group. The general clinical data and laboratory indicators of patients were compared through Spearman correlation analysis, ROC analysis and logistic regression analysis. Finally, the independent risk factors for adverse prognosis in DFU patients were confirmed. Spearman analysis reveals a positive correlation between the DFU healing rate and ABI, VEGF in wound tissue, and positive rate of VEGF expression, and a negative correlation with DM duration, FPG, HbA1c, TC, Scr, BUN, and serum VEGF. Further logistic regression analysis finds that the DM duration, FPG, HbA1c, ABI, serum VEGF, VEGF in wound tissue, and positive rate of VEGF expression are the independent risk factors for adverse prognosis in DFU ( p  < 0.05). DM duration, FPG, HbA1c, ABI, serum VEGF, VEGF in wound tissue, and positive rate of VEGF expression are the independent risk factors for prognosis in DFU patients. Patients with these risk factors should be screened in time, which is of great significance to prevent DFU related adverse events and improve outcomes.

The coordinated operation of hybrid photovoltaic (PV) and Small Modular Reactor (SMR) microgrids represents a promising pathway to achieve resilient, low-carbon energy supply in modern power systems. However, effective management of such systems requires advanced optimization frameworks that simultaneously address cost minimization, carbon emission reduction, and operational resilience under multi-source uncertainties. This paper proposes a comprehensive scheduling framework for hybrid PV-SMR microgrids, integrating multi-scale energy storage–lithium-ion batteries for short-term balancing and hydrogen storage for long-term seasonal regulation–while explicitly incorporating demand response flexibility. The proposed framework adopts a multi-objective distributionally robust optimization (DRO) approach to capture uncertainties in solar generation and load fluctuations, ensuring robust yet cost-effective dispatch decisions. The mathematical model addresses the multi-timescale coordination between variable PV generation, slow-ramping nuclear power, and dynamic battery and hydrogen storage operations. Key constraints include power balance, SMR ramping limits, battery state-of-charge evolution, hydrogen production and consumption cycles, and resilience-driven critical load prioritization. Furthermore, a real-time reinforcement learning (RL)-assisted mechanism enhances the system’s adaptability to evolving operational states, enabling dynamic adjustment of storage and demand response strategies based on live system feedback. A comprehensive case study is conducted on a 100 MW hybrid microgrid, integrating 40 MW of PV, a 50 MW SMR, a 20 MWh battery storage system, and a 15-ton hydrogen storage facility, supplying industrial and residential loads under realistic uncertainty scenarios. Results demonstrate that the proposed optimization achieves a 17.5% reduction in operational cost and a 32.8% reduction in carbon emissions compared to conventional microgrid scheduling, while enhancing resilience by maintaining continuous supply for critical loads even under extreme weather stress. The integration of DRO and reinforcement learning provides a 28% improvement in flexibility under solar variability, confirming the importance of adaptive, uncertainty-aware optimization for future hybrid microgrids. This work contributes an advanced, scalable framework for multi-energy hybrid microgrid management, providing valuable insights for resilient and low-carbon community microgrid development in the renewable-dominated era.

The increasing proliferation of residential photovoltaic (PV) systems in microgrids offers significant potential for enhancing renewable energy self-consumption and reducing dependency on external grid power. However, the inherent intermittency of solar generation and the mismatch between peak generation and household demand patterns require effective demand-side flexibility. Traditional demand response programs, often based solely on financial incentives or dynamic pricing, have demonstrated limited success in sustaining user engagement. To address these challenges, this paper proposes a novel gamification-driven demand response framework for PV-integrated microgrids, designed to simultaneously optimize operational cost, renewable energy utilization, user participation, and load-shifting comfort. By integrating behavioral adaptation modeling directly into the optimization process, the proposed framework captures the nonlinear and dynamic responses of households to gamification incentives, allowing for a more realistic and behaviorally-grounded approach to microgrid scheduling. The optimization problem is formulated as a multi-objective model and solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which efficiently explores the trade-offs between cost minimization, PV self-consumption maximization, gamification-driven participation enhancement, and household comfort preservation. Compared to conventional demand response mechanisms, the proposed method explicitly incorporates evolving user behavior, dynamic incentive distribution, and social influence propagation, ensuring that demand-side flexibility is unlocked through both financial and psychological mechanisms.

Environmental endocrine disruptors (EEDs) in beverages may enter the human body by ingestion and thus may represent a potential health risk. In this study, phthalates, bisphenol A, and its analogues, parabens, benzophenone-type UV filters, and triclosan (TCS) were analyzed in beverage samples ( n  = 116) collected from local markets in Guangzhou, South China. Twelve of 30 target compounds were found in > 50% samples, and for the first time, TCS was found in a majority of beverages from China (~ 80%). Among all analytes, concentrations of total phthalates (median = 14.4 ng/mL) were generally two orders of magnitude higher than other target EEDs, and concentrations of total benzophenone-type UV filters (0.02 ng/mL) and TCS (0.01 ng/mL) were the lowest. Among all targets, phthalates were predominant, accounting for > 99% of the total EEDs, and dimethyl phthalate was frequently detected in beverages (> 60%). In addition, we estimated the daily intake (EDI) of EEDs for Chinese populations of different age groups based on the daily consumption of beverages. The EDIs of total EEDs were the highest for toddlers (mean = 14,200 ng/kg-bw/day) followed by children and teenagers (3420 ng/kg-bw/day), adults (1950 ng/kg-bw/day), the elderly (1740 ng/kg-bw/day), and infants (70 ng/kg-bw/day). Compared to all food categories, EEDs from beverage consumption accounted for ~ 0.1% (parabens) to 20% (phthalates) of total exposure from diet. However, intakes of phthalates, bisphenols, and TCS from beverages were comparable to those from other potential sources (food, dust, personal care products, cloth, and medicines). Furthermore, the cumulative risks of EEDs by beverage consumption were not high, which indicated that EEDs in beverages might not represent a potential human health risk for Chinese populations.

While vibration harvesting shows promise for powering sensors, effectively harvesting low-frequency, multidirectional ambient vibrations remains challenging. This article presents a novel electromagnetic vibration energy harvesting device (EVEHD) with three key innovations: a dual-mode mass-pendulum configuration—dual-mass coupling (series mode) amplifies induced voltage, and dual-mass uncoupled (parallel) mode enables multifrequency harvesting—spring-position-based frequency tuning (4.5–16.7 Hz in series mode; dual-band 3.7–9.3/5–13.3 Hz in parallel mode), and an optimized energy conversion structure, boosting output by 85.2%. The findings were validated through theoretical modeling, FEM simulations, and shaker tests, the EVEHD generating a maximum voltage of 2 V and a power of 769.2 mW under a base excitation amplitude of 0.5 g at 16.7 Hz. This work reveals the potential of this multidirectional EVEHD for power generation and application in self-powered systems.

Background This study aimed to search for blood biomarkers among the profiles of patients with RA-ILD by using machine learning classifiers and probe correlations between the markers and the characteristics of RA-ILD. Methods A total of 153 RA patients were enrolled, including 75 RA-ILD and 78 RA-non-ILD. Routine laboratory data, the levels of tumor markers and autoantibodies, and clinical manifestations were recorded. Univariate analysis, least absolute shrinkage and selection operator (LASSO), random forest (RF), and partial least square (PLS) were performed, and the receiver operating characteristic (ROC) curves were plotted. Results Univariate analysis showed that, compared to RA-non-ILD, patients with RA-ILD were older ( p  < 0.001), had higher white blood cell ( p  = 0.003) and neutrophil counts ( p  = 0.017), had higher erythrocyte sedimentation rate ( p  = 0.003) and C-reactive protein ( p  = 0.003), had higher levels of KL-6 ( p  < 0.001), D-dimer ( p  < 0.001), fibrinogen ( p  < 0.001), fibrinogen degradation products ( p  < 0.001), lactate dehydrogenase ( p  < 0.001), hydroxybutyrate dehydrogenase ( p  < 0.001), carbohydrate antigen (CA) 19–9 ( p  < 0.001), carcinoembryonic antigen ( p  = 0.001), and CA242 ( p  < 0.001), but a significantly lower albumin level ( p  = 0.003). The areas under the curves (AUCs) of the LASSO, RF, and PLS models attained 0.95 in terms of differentiating patients with RA-ILD from those without. When data from the univariate analysis and the top 10 indicators of the three machine learning models were combined, the most discriminatory markers were age and the KL-6, D-dimer, and CA19-9, with AUCs of 0.814 [95% confidence interval (CI) 0.731–0.880], 0.749 (95% CI 0.660–0.824), 0.749 (95% CI 0.660–0.824), and 0.727 (95% CI 0.637–0.805), respectively. When all four markers were combined, the AUC reached 0.928 (95% CI 0.865–0.968). Notably, neither the KL-6 nor the CA19-9 level correlated with disease activity in RA-ILD group. Conclusions The levels of KL-6, D-dimer, and tumor markers greatly aided RA-ILD identification. Machine learning algorithms combined with traditional biostatistical analysis can diagnose patients with RA-ILD and identify biomarkers potentially associated with the disease.