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In contrast to other solid tumors within the abdominal cavity, epithelial ovarian cancers (EOCs) tend to undergo peritoneal metastasis. Thus, the peritoneal immune microenvironment is crucial for EOC progression. Previous reports indicate that the main immune cells within the peritoneum are M2 macrophages, specifically tumor‐associated macrophages (TAMs). The communication between TAMs and tumor cells plays an important role in EOC development, and exosomes, acting as micro–message carriers, occupy an essential position in this process. Microarray analyses of exosomes revealed that miR‐221‐3p was enriched in M2 exosomes. Furthermore, miR‐221‐3p suppressed cyclin‐dependent kinase inhibitor 1B (CDKN1B) directly. Thus, miR‐221‐3p contributed to the proliferation and G1/S transition of EOC cells. Additionally, low levels of CDKN1B were associated with EOC progression and poor prognosis. These observations suggest that TAMs‐derived exosomal miR‐221‐3p acts as a regulator of EOC progression by targeting CDKN1B. The results of this study confirm that certain exosomal microRNAs may provide novel diagnostic biomarkers and therapeutic targets for EOC. These observations propose TAMs‐derived exosomal miR‐221‐3p is one of the regulators in epithelial ovarian cancer (EOC) progression through targeting cyclin‐dependent kinase inhibitor 1B. These findings suggest that these exosomes or associated microRNAs might provide novel diagnostic biomarkers and treatments for EOC.

The study aimed to explore the factors influencing cognitive impairment among middle-aged and elderly individuals in China, utilizing data from the China Health and Retirement Longitudinal Study (CHARLS). By examining the combined effects of socioeconomic factors, health behaviors, chronic diseases, and other multidimensional factors, this research seeks to provide evidence for the development of targeted public health policies and interventions. This study utilized data from the 2018 wave of the CHARLS database, including a total of 19,307 participants aged 45 and older. Cognitive impairment was assessed using the Mini-Mental State Examination (MMSE), with thresholds adjusted for education levels. A combination of LASSO (Least Absolute Shrinkage and Selection Operator) regression and logistic regression models was employed to identify significant risk and protective factors for cognitive impairment. RCS (Restricted cubic splines) were used to explore the dose-response relationship between sleep duration and cognitive function. The analysis identified several significant risk factors for cognitive impairment, including age, urban household registration, self-rated health as fair, chronic disease, impaired instrumental activities of daily living (IADL), alcohol consumption and exercise. Protective factors included sleep duration, being female, divorced or widowed, self-rated health as poor and disability. The study also found that sleep duration followed a U-shaped relationship with cognitive function, with an optimal sleep duration of 5.83 hours per day. This study highlights the multifactorial nature of cognitive impairment and underscores the importance of early interventions targeting modifiable risk factors such as chronic diseases and lifestyle behaviors. The findings contribute to the growing body of knowledge on cognitive impairment in China's aging population and provide a basis for evidence-based public health strategies.

Glutathione plays many important roles in biological processes; however, the dynamic changes of glutathione concentrations in living cells remain largely unknown. Here, we report a reversible reaction-based fluorescent probe—designated as RealThiol (RT)—that can quantitatively monitor the real-time glutathione dynamics in living cells. Using RT, we observe enhanced antioxidant capability of activated neurons and dynamic glutathione changes during ferroptosis. RT is thus a versatile tool that can be used for both confocal microscopy and flow cytometry based high-throughput quantification of glutathione levels in single cells. We envision that this new glutathione probe will enable opportunities to study glutathione dynamics and transportation and expand our understanding of the physiological and pathological roles of glutathione in living cells. Fluorescent sensors for small biomolecules are needed to shed insight into real-time cellular processes. Here the authors develop RealThiol, a sensor that can quantitatively monitor glutathione dynamics in living cells, and measure increased antioxidant capability of activated neurons and glutathione changes during ferroptosis.

In this study, a current model predictive controller (MPC) is designed for a permanent magnet synchronous motor (PMSM) where the speed of the motor can be regulated precisely. First, the mathematical model, the specifications, and the drive topology of the PMSM are introduced, followed by an elaboration of the design of the MPC. The MPC is then used to predict the current in a discrete-time calculation. The phase current at the next sampling step can be estimated to compensate the current errors, thereby modifying the three-phase currents of the motor. Next, Simulink modeling of the MPC algorithm is given, with three-phase current waveforms compared when the motor is operated under the designed MPC and a traditional vector control for PMSM. Finally, the speed responses are measured when the motor is controlled by traditional control methods and the MPC approach under varied speed references and loads. In comparison with traditional controllers, both the simulation and the experimental results suggest that the MPC for the PMSM can improve the speed-tracking performance of the motor and that this motor has a fast speed response and small steady-state errors under the rated load.

Grapes, due to their widespread global distribution and significant commercial value, have become central research subjects in the global fruit industry [...]

This study aims to investigate the association between multimorbidity and the incidence of depression in the middle-aged and elderly populations in China, focusing on the impact of multiple chronic diseases on mental health outcomes. This research comprises a rigorous retrospective cohort study utilizing the esteemed China Health and Retirement Longitudinal Study (CHARLS) data. Follow-up data were meticulously analyzed from 2013 (Wave 2) to 2020 (Wave 5), with data from 2015 serving as the essential baseline for examining multimorbidity and depression. We employed Directed Acyclic Graphs (DAGs) to identify potential confounding factors, applying overlap weighting to effectively mitigate their influence, thus ensuring that intergroup comparisons mirror the integrity of a randomized trial. The association between multimorbidity and depression was systematically evaluated through Cox regression models, supplemented by thorough subgroup analyses and sensitivity analyses to confirm the robustness of our findings. Our analysis included 3,495 participants aged 45 and older. Multimorbidity was found to significantly elevate the risk of depression (Hazard Ratio [HR] = 1.431, 95% Confidence Interval [CI] 1.202-1.703). This risk was particularly pronounced in women (HR = 1.617, 95% CI: 1.290-2.027) and older adults (HR = 1.482, 95% CI: 1.221-1.799). The risk of depression increased with the number of chronic diseases, particularly with two (HR = 1.423, 95% CI: 1.192-1.701) or three (HR = 2.045, 95% CI: 1.503-2.784) chronic conditions. However, this association diminished in significance when four chronic conditions were present (HR = 0.815, 95% CI: 0.444-1.497). The pronounced association between multimorbidity and depression in middle-aged and elderly individuals highlights an urgent public health issue, particularly for women and older adults. This study's use of overlap weighting provides strong evidence of the substantial impact of multimorbidity on mental health. It underscores the immediate need for targeted interventions to improve mental well-being in these vulnerable groups.

Background Improving maternal and child health has been a global priority since the early 2000s, with a focus on reducing perinatal complications and improving overall maternal well-being. Sleep characteristics influence various health outcomes, yet their role in perinatal complications and adverse outcomes remains poorly understood. Methods A Mendelian randomization analysis was conducted, using seven common sleep characteristics (sleeplessness, sleep duration, getting up in the morning, daytime napping, morning/evening person, narcolepsy, snoring) as exposure factors and twelve common perinatal complications and adverse outcomes (preterm birth, polyhydramnios, slow fetal growth and fetal malnutrition, dystocia, umbilical cord-related complications, postpartum hemorrhage, fetal distress, gestational diabetes, pregnancy hypertension, eclampsia, abruptio placentae, placenta previa) as outcomes. A two-sample Mendelian randomization analysis was performed to infer causal effects. Results The inverse variance weighted (IVW) analysis showed that sleeplessness was associated with preterm birth, sleep duration with gestational diabetes, and narcolepsy with pregnancy hypertension and eclampsia. These results were consistently supported by other methods, suggesting that sleep characteristics are causal risk factors for perinatal complications and adverse outcomes. Conclusion This study found that sleeplessness is associated with preterm birth, sleep duration with gestational diabetes, and narcolepsy with pregnancy hypertension and eclampsia. These findings contribute to a better understanding of the impact of sleep characteristics on common perinatal complications and adverse outcomes. Targeting sleep interventions, such as improving sleep duration and addressing sleep disorders like sleeplessness and narcolepsy, may reduce the incidence of preterm birth, gestational diabetes, and pregnancy hypertension, offering effective strategies to improve maternal and infant health outcomes.

Background Dengue fever continues to exert significant global impact, affecting populations worldwide with considerable public health and economic consequences. There is no antiviral drug for dengue. This study focuses on hypericin, a naturally occurring compound from Hypericum perforatum L. whose anti-dengue properties have been underexplored. We systematically examined its antiviral efficacy against dengue virus (DENV), revealing strong inhibitory effects and clarifying its precise antiviral mechanism. Methods The study assessed the efficacy of hypericin against DENV using various scientific methods like plaque assays and Western blotting. We looked into its antiviral mechanism. We used a time-of-addition approach during our research. Moreover, the basic mechanisms involved were studied through molecular docking, surface plasmon resonance (SPR), and co-immunoprecipitation (Co-IP). Results This study demonstrated that hypericin exhibits broad-spectrum antiviral activity against DENV-2 in cell lines derived from multiple species. In time-of-addition experiments, it showed inhibitory effects under co-treatment, direct virucidal, and post-treatment conditions. Crucially, hypericin primarily blocked viral attachment and entry stages, thereby effectively reducing intracellular viral load. Mechanistic investigations revealed a interaction between hypericin and the E protein, evidenced by a computational docking score of -7.0 kcal/mol and an experimental SPR-derived Kd of 7.18 µM. Furthermore, Co-IP assays demonstrated that hypericin competitively blocks the association between the E protein and its cellular receptor, HSP70. Conclusion As per these findings, the E protein was seen to be a target of hypericin with an antiviral activity against DENV-2 at multiple stages by limiting viral adsorption and viral entry projecting a molecular basis for the candidate molecule as a possible anti-dengue agent.

Objective To construct a recurrence prediction model for Elderly Schizophrenia Patients (ESCZP) and validate the model’s spatial external applicability. Methods The modeling cohort consisted of 365 ESCZP cases from the Seventh People’s Hospital of Dalian, admitted between May 2022 and April 2024. Variables were selected using Lasso-Logistic regression to construct the recurrence prediction model, with a nomogram plotted using the “RMS” package in R 4.3.3 software. Model validation was performed using 1,000 bootstrap resamples. Spatial external validation was conducted using 172 cases ESCZP from the Fourth Affiliated Hospital of Qiqihar Medical College during the same period. The model’s discrimination, accuracy, and clinical utility were assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA). The Hosmer-Lemeshow test was used to evaluate model fit. Results A total of 537 cases ESCZP were included based on inclusion and exclusion criteria, with 150 recurrences within two years and 387 non-recurrences. Lasso-Logistic regression analysis identified Medication status, Premorbid personality, Exercise frequency, Drug adverse reactions, Family care, Social support, and Life events as predictors of ESCZP recurrence. The AUC for the modeling cohort was 0.877 ( 95% CI : 0.837–0.917). For the external validation cohort, the AUC was 0.838 ( 95% CI : 0.776–0.899). Calibration curves indicated that the fit was close to the reference line, demonstrating high model stability. DCA results showed good net benefit at a threshold probability of 80%. Conclusion The nomogram prediction model developed based on Lasso-Logistic regression shows potential in identifying the risk of recurrence in ESCZP. However, further validation and refinement are needed before it can be applied in routine clinical practice.

In order to solve the problems of low efficiency, pollution and large damage of existing polishing methods, the electrical enhanced photocatalysis polishing method for single-crystal silicon carbide was proposed in this paper. The recombination of electrons and holes was inhibited by the combined action of applied electric field and chemical trapping agent, so the efficiency of oxidation was improved, and the polishing of silicon carbide with high efficiency and low damage can be achieved. The effects of various parameters such as polishing pressure, polishing speed, polishing pad, abrasive particles and oxygenation of slurry on polishing effect were studied. 0.025 MPa of polishing pressure, 60 r/min of polishing speed, 5 wt% of SiO2 abrasive particles and synthetic fiber polymer polishing pad can provide the higher MRR of 1.18 μm/h and the best surface quality (Ra = 0.348 nm). In addition, it has been proven that UV, optimized pH, types of photocatalysts, electron trapping agent and voltage are necessary factors for the slurry to exhibit strong oxidizing properties. The MRR is mainly determined by both mechanical action (introduced by abrasive particles) and chemical action (introduced by slurry). The above technology can basically meet the requirements of high-efficiency, ultra-smooth and low damage polishing of single-crystal silicon carbide.