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This paper presents an improved osprey optimization algorithm (IOOA) to solve the problems of slow convergence and local optimality. First, the osprey population is initialized based on the Sobol sequence to increase the initial population’s diversity. Second, the step factor, based on Weibull distribution, is introduced in the osprey position updating process to balance the explorative and developmental ability of the algorithm. Lastly, a disturbance based on the Firefly Algorithm is introduced to adjust the position of the osprey to enhance its ability to jump out of the local optimal. By mixing three improvement strategies, the performance of the original algorithm has been comprehensively improved. We compared multiple algorithms on a suite of CEC2017 test functions and performed Wilcoxon statistical tests to verify the validity of the proposed IOOA method. The experimental results show that the proposed IOOA has a faster convergence speed, a more robust ability to jump out of the local optimal, and higher robustness. In addition, we also applied IOOA to the reactive power optimization problem of IEEE33 and IEEE69 node, and the active power network loss was reduced by 48.7% and 42.1%, after IOOA optimization, respectively, which verifies the feasibility and effectiveness of IOOA in solving practical problems.

Despite recent advances in tumor diagnosis and treatment technologies, the number of cancer cases and deaths worldwide continues to increase yearly, creating an urgent need to find new methods to prevent or treat cancer. Sulforaphane (SFN), as a member of the isothiocyanates (ITCs) family, which is the hydrolysis product of glucosinolates (GLs), has been shown to have significant preventive and therapeutic cancer effects in different human cancers. Early studies have shown that SFN scavenges oxygen radicals by increasing cellular defenses against oxidative damage, mainly through the induction of phase II detoxification enzymes by nuclear factor erythroid 2-related factor 2 (Nrf2). More and more studies have shown that the anticancer mechanism of SFN also includes induction of apoptotic pathway in tumor cells, inhibition of cell cycle progression, and suppression of tumor stem cells. Therefore, the application of SFN is expected to be a necessary new approach to treating cancer. In this paper, we review the multiple molecular mechanisms of SFN in cancer prevention and treatment in recent years, which can provide a new vision for cancer treatment.

This paper presents a hybrid prediction model, ECOA-BiTCN-BiLSTM, for predicting dew in cold areas. The model integrates BiTCN and BiLSTM neural networks to enhance performance. An enhanced Crayfish optimization algorithm (ECOA) with four mixed strategies was employed to optimize the model’s hyperparameters and reduce the impact of arbitrary selection. The proposed ECOA-BiTCN-BiLSTM model was validated using dew data from farmland in a northeastern Chinese city. Comparative experiments were conducted against the BiTCN model, the BiLSTM model, the original BiTCN-BiLSTM model, and other models optimized with advanced swarm intelligence algorithms. The experimental results demonstrate that the proposed model achieved a mean absolute error (MAE) of 0.002424, a root mean square error (RMSE) of 0.003984, and a mean absolute percentage error (MAPE) of 0.123050, with a coefficient of determination R 2 of 0.999840. These results indicate that the ECOA-BiTCN-BiLSTM model outperforms the other prediction models across all evaluated metrics, offering higher prediction accuracy and highly effective prediction models.

There is little known about the contribution of exosomal microRNAs (exomiRs) in the children’s cardiac surgery-associated acute kidney injury (CSA-AKI). This study aimed to find diagnostic biomarkers for predicting CSA-AKI in children. A prospective observational study was conducted from April 2020 to March 2021.According to the changes of serum creatinine (SCr) value and urine volume within 48 h, the children were divided into acute kidney injury (AKI) group and non-AKI group. Serum samples were collected 4 h after cardiac surgery. Isolation of extracellular vesicles (EVs) and extraction of exomiRs from serum samples. Illumina high-throughput sequencing was used to quantify exomiRs and screen candidate microRNAs (miRNAs). Expression levels of candidate miRNAs were validated using droplet digital polymerase chain reaction (ddPCR). Normal and injuried rats’ kidney tissue were collected for tissue validation. In the pre-experimental stage (4 AKI vs. 4 non-AKI), hsa-miR-184, hsa-miR-4800-3p, hsa-miR-203a-3p and hsa-miR-6766-3p were selected as candidate genes. In the verification stage (8 AKI vs. 12 non-AKI), the expression of hsa-miR-184 in AKI group was significantly lower than that in non-AKI group ( P  = 0.031), and the expression of hsa-miR-4800-3p and hsa-miR-6766-3p in AKI group was significantly higher than that in non-AKI group ( P  = 0.01 and P  = 0.047). There was no significant difference in the expression of hsa-miR-203a-3p between the two groups ( P  > 0.05). The expression of rats’ kidney tissue rno-miR-184 in AKI group was significantly lower than that in the normal group ( P  = 0.044). The area under the curve (AUC) of AKI predicted by hsa-miR-184 is 0.7865 and the AUC of hsa-miR-6766-3p is 0.7708. Combined with two kinds of miRNAs, the area under the curve of AKI is predicted to be 0.8646. The change of exomiRs level in circulatory system occurred in the early stage after cardiac operation, and the changes of hsa-miR-184 and hsa-miR-6766-3p content in circulatory system could predict CSA-AKI well.

Background : In recent years, the incidence of Crohn’s disease (CD) has shown a significant global increase, with numerous studies demonstrating its correlation with various cancers. This study aims to identify novel biomarkers for diagnosing CD and explore their potential applications in pan‐cancer analysis. Methods : Gene expression profiles were retrieved from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified using the “limma” R package. Key biomarkers were selected through an integrative machine learning pipeline combining LASSO regression, neural network modeling, and Support Vector Machine‐Recursive Feature Elimination (SVM‐RFE). Six hub genes were identified and further validated using the independent dataset GSE169568. To assess the broader relevance of these biomarkers, a standardized pan‐cancer dataset from the UCSC database was analyzed to evaluate their associations with 33 cancer types. Results : Among the identified biomarkers, S100 calcium binding protein P (S100P) and S100 calcium binding protein A8 (S100A8) emerged as key candidates for CD diagnosis, with strong validation in the independent dataset. Notably, S100P displayed significant associations with immune cell infiltration and patient survival outcomes in both liver and lung cancers. These findings suggest that chronic inflammation and immune imbalances in CD may not only contribute to disease progression but also elevate cancer risk. As an inflammation‐associated biomarker, S100P holds particular promise for both CD diagnosis and potential cancer risk stratification, especially in liver and lung cancers. Conclusion : Our study highlights S100P and S100A8 as potential diagnostic biomarkers for CD. Moreover, the pan‐cancer analysis underscores the broader clinical relevance of S100P, offering new insights into its role in immune modulation and cancer prognosis. These findings provide a valuable foundation for future research into the shared molecular pathways linking chronic inflammatory diseases and cancer development.

Metagenomic next-generation sequencing (mNGS) is a powerful method for pathogen detection. In this study, we assessed the value of mNGS for bronchoalveolar lavage (BAL) samples in the diagnosis of pulmonary infections. From February 2018 to April 2019, BAL samples were collected from 235 patients with suspected pulmonary infections. mNGS and microbial culture were performed to evaluate the effectiveness of mNGS in pulmonary infection diagnosis. We employed mNGS to evaluate the alpha diversity, results suggesting that patients with confirmed pathogens had a lower microbial diversity index compared to that of patients with uncertain pathogens. For the patients admitted to the respiratory intensive care unit (RICU) or on a ventilator, they experienced a lower diversity index than that of the patients in the general ward or not on a ventilator. In addition, mNGS of BAL had a diagnostic sensitivity of 88.89% and a specificity of 14.86% in pulmonary infection, with 21.16% positive predictive value (PPV) and 83.87% negative predictive value (NPV). When rare pathogens were excluded, the sensitivity of mNGS decreased to 73.33%, and the specificity increased to 41.71%. For patients in the simple pulmonary infection group and the immunocompromised group, the main infection types were bacterial infection (58.33%) and mixed-infection (43.18%). Furthermore, mNGS had an advantage over culture in describing polymicrobial ecosystem, demonstrating the microbial distribution and the dominant strains of the respiratory tract in patients with different underlying diseases. The study indicated that mNGS of BAL samples could provide more accurate diagnostic information in pulmonary infections and demonstrate the changes of respiratory microbiome in different underlying diseases. This method might play an important role in the clinical use of antimicrobial agents in the future.

Background China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model. Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model. Results We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model ( P  = 0.0001) with an annual average percent change (AAPC) of − 3.3 (95% CI: − 4.3 to − 2.2, P  < 0.001). A seasonality was observed across the 14 years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1) 12 which can be written as (1-B) (1-B 12 ) X t  = (1–0.42349B) (1–0.43338B 12 ) ε t , with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5–84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (> 70 cases per 100,000) were influenced by the autoregressive component for the past 14 years. Conclusion In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past 14 years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

Plant immunity frequently incurs growth penalties, which known as the trade-off between immunity and growth. Heterosis, the phenotypic superiority of a hybrid over its parents, has been demonstrated for many traits but rarely for disease resistance. Here, we report that the central circadian oscillator, CCA1 , confers heterosis for bacterial defense in hybrids without growth vigor costs, and it even significantly enhances the growth heterosis of hybrids under pathogen infection. The genetic perturbation of CCA1 abrogated heterosis for both defense and growth in hybrids. Upon pathogen attack, the expression of CCA1 in F 1 hybrids is precisely modulated at different time points during the day by its rhythmic histone modifications. Before dawn of the first infection day, epigenetic activation of CCA1 promotes an elevation of salicylic acid accumulation in hybrids, enabling heterosis for defense. During the middle of every infection day, diurnal epigenetic repression of CCA1 leads to rhythmically increased chlorophyll synthesis and starch metabolism in hybrids, effectively eliminating the immunity-growth heterosis trade-offs in hybrids. There is frequently a trade-off between plant immunity and growth. Here the authors show that the epigenetic control of CCA1, encoding a core component of the circadian oscillator, simultaneously promotes heterosis for both defense and growth in hybrids under pathogen invasion.

This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm’s searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.

Background Extracellular vesicles (EVs) secreted by mesenchymal stromal cells (MSCs) have been shown to provide significant protection against renal ischemia–reperfusion injury (IRI). Hypoxia has emerged as a promising strategy to enhance the tissue repair capabilities of MSCs. However, the specific effects of hypoxia on MSCs and MSC-EVs, as well as their therapeutic potential in renal IRI, remain unclear. In this study, we investigated the alterations occurring in MSCs and the production of MSC-EVs following hypoxia pre-treatment, and further explored the key intrinsic mechanisms underlying the therapeutic effects of hypoxic MSC-EVs in the treatment of renal IRI. Methods Human umbilical cord MSCs were cultured under normoxic and hypoxic conditions. Proliferation and related pathways were measured, and RNA sequencing was used to detect changes in the transcriptional profile. MSC-EVs from both normoxic and hypoxic conditions were isolated and characterized. In vivo, the localization and therapeutic effects of MSC-EVs were assessed in a rat renal IRI model. Histological examinations were conducted to evaluate the structure, proliferation, and apoptosis of IRI kidney tissue respectively. Renal function was assessed by measuring serum creatinine and blood urea nitrogen levels. In vitro, the therapeutic potential of MSC-EVs were measured in renal tubular epithelial cells injured by antimycin A. Protein sequencing analysis of hypoxic MSC-EVs was performed, and the depletion of Glutathione S-Transferase Omega 1 (GSTO1) in hypoxic MSC-EVs was carried out to verify its key role in alleviating renal injury. Results Hypoxia alters MSCs transcriptional profile, promotes their proliferation, and increases the production of EVs. Hypoxia-pretreated MSC-EVs demonstrated a superior ability to mitigate renal IRI, enhancing proliferation and reducing apoptosis of renal tubular epithelial cells both in vivo and in vitro. Protein profiling of the EVs revealed an accumulation of numerous anti-oxidative stress proteins, with GSTO1 being particularly prominent. Knockdown of GSTO1 significantly reduced the antioxidant and therapeutic effects on renal IRI of hypoxic MSC-EVs. Conclusions Hypoxia significantly promotes the generation of MSC-EVs and enhances their therapeutic effects on renal IRI. The antioxidant stress effect induced by GSTO1 is identified as one of the most critical underlying mechanisms. Our findings highlight that hypoxia-pretreated MSC-EVs represent a novel and promising therapeutic strategy for renal IRI. Graphical Abstract