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Abstract The incidence of stroke and neurodegenerative diseases is gradually increasing in modern society, but there is still no treatment that is effective enough. Stem cells are cells that can reproduce (self‐renew) and differentiate into the body, which have shown significance in basic research, while doctors have also taken them into clinical trials to determine their efficacy and safety. Existing clinical trials mainly include middle‐aged and elderly patients with stroke or Parkinson's disease (mostly 40–80 years old), mainly involving injection of mesenchymal stem cells and bone marrow mesenchymal stem cells through the veins and the putamen, with a dosage of mostly 106–108 cells. The neural and motor functions of the patients were restored after stem cell therapy, and the safety was found to be good during the follow‐up period of 3 months to 5 years. Here, we review all clinical trials and the latest advances in stroke, Alzheimer's disease, and Parkinson's disease, with the hope that stem cell therapy will be used in the clinic in the future to achieve effective treatment rates and benefit patients.

Abstract Neonatal hypoxic‐ischemic encephalopathy (NHIE) is one of the major diseases in newborns during the perinatal stage, which globally is the main reason for children's morbidity and mortality. However, the mechanism of NHIE still remains poorly clear. In this study, the 7‐day‐old rats were subjected to hypoxic‐ischemia (HI), then brain damage was detected. Afterward, the expression of eva‐1 homolog C (EVA1C) was measured in vitro by establishing the oxygen‐glucose deprivation (OGD) model in SHSY5Y cells and human fetal neurons. Subsequently, the potential function and mechanism of EVA1C were explored by silencing EVA1C and alternative splicing prediction. As a result, obvious neurobehavioral impairment and brain infarction were detected through Zea‐Longa score and TTC staining; meanwhile, neuron injury was tested by HE and Nissl staining post HI. Moreover, it was found that the expression of EVA1C was notably upregulated in SHSY5Y cells and human fetal neurons after OGD. In addition, cell survival and growth were increased after silencing EVA1C, which might be associated with alternative splicing. In conclusion, EVA1C interference exhibited potential in promoting neuron survival and growth, associated with exon skipping with the alternative splicing site in 34613318:34687258, which may provide the basis for the therapeutic target and mechanism research of NHIE.

Abstract This study aimed to decipher the effect of glycoprotein nonmetastatic melanoma protein B (GPNMB) on neonatal hypoxic–ischemic encephalopathy (NHIE) and its potential molecular mechanism. The hypoxic–ischemic (HI) model was established in 7‐day‐old rats, and then, Zea‐Longa scores and Nissl staining were performed to measure brain damage post‐HI. In addition, gene sequencing was used to detect the differential expression genes (DEGs), and then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used to determine the function of DEGs. Furthermore, an oxygen–glucose deprivation (OGD) model was developed in SY5Y cells and human fetal neurons, and then, the level of GPNMB was verified by quantitative real‐time polymerase chain reaction. In addition, methyl thiazolyl tetrazolium and cell counting kit‐8 assays were applied after GPNMB interference. Finally, the alternative splicing of GPNMB expression was analyzed using Splice Grapher software. The results indicated that HI induced marked neurological impairment and neuron injury in rats. Also, GPNMB was the most obviously upregulated gene in DEGs. Additionally, GPNMB was upregulated significantly in SY5Y and fetal neurons after OGD, and GPNMB‐si promoted an increase in cell viability and number. Moreover, we found that the GPNMB alternative splicing type was the Alternative 3′ splice site, with the alternative splicing site in 143382985:143404102. Herein, GPNMB promotes a crucial regulatory mechanism with alternative splicing for neuronal survival after NHIE.

Introduction It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis.Research design and methods The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists.Results MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, pFDR=1.15×10−3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, pFDR=1.15×10−3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, pFDR=1.59×10−4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, pFDR=3.50×10−4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality.Conclusions Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Abstract To screen out the prospective biomarkers of viral encephalitis (VE), analyze the biological process and signaling pathways involved by differentially expressed proteins (DEPs). A total of 11 cerebrospinal fluid (CSF) samples with VE and 5 with non‐nervous system infection were used to perform label‐free proteomic techniques. Then, the bioinformatic analysis of DEPs was applied by Interproscan software. Moreover, 73 CSF samples in the VE group and 53 in the control group were used to verify the changes of some DEPs by enzyme‐linked immunosorbent assay (ELISA). Thirty‐nine DEPs were identified, including 18 upregulated DEPs and 21 downregulated DEPs. DEPs were mainly enriched in cell adhesion molecules by Kyoto Encyclopedia of Genes and Genomes analysis pathway analysis. The DEPs related to axon tissue were obviously downregulated and the most significant downregulated proteins were neurexin 3, neurofascin, and neuroligin 2 (NLGN2). Moreover, the protein expression of NLGN2 in the VE group was significantly higher than that in the control group by ELISA. The correlation analysis of NLGN2 in the VE group revealed that there was a weak positive correlation with CSF protein and a weak negative correlation with CSF chloride. The clinical VE may be closely related to NLGN2 and the cell adhesion molecule pathway.

Aims: To explore the population pharmacokinetics of colistin sulfate and to optimize the dosing strategy for critically ill patients.Methods: The study enrolled critically ill adult patients who received colistin sulfate intravenously for more than 72 h with at least one measurement of plasma concentration. Colistin concentrations in plasma or urine samples were measured by ultraperformance liquid chromatography tandem mass spectrometry (LC-MS/MS). The population pharmacokinetics (PPK) model for colistin sulfate was developed using the Phoenix NLME program. Monte Carlo simulation was conducted to evaluate the probability of target attainment (PTA) for optimizing dosing regimens.Results: A total of 98 plasma concentrations from 20 patients were recorded for PPK modeling. The data were adequately described by a two-compartment model with linear elimination. During modeling, creatinine clearance (CrCL) and alanine aminotransferase (ALT) were identified as covariates of the clearance (CL) and volume of peripheral compartment distribution (V2), respectively. In addition, colistin sulfate was predominantly cleared by the nonrenal pathway with a median urinary recovery of 10.05% with large inter-individual variability. Monte Carlo simulations revealed a greater creatinine clearance associated with a higher risk of sub-therapeutic exposure to colistin sulfate. The target PTA (≥90%) of dosage regimens recommended by the label sheet was achievable only in patients infected by pathogens with MIC ≤0.5 mg/L or with renal impairments.Conclusion: Our study showed that the dose of intravenous colistin sulfate was best adjusted by CrCL and ALT. Importantly, the recommended dosing regimen of 1.0–1.5 million units daily was insufficient for patients with normal renal functions (CrCL ≥80 ml/min) or those infected by pathogens with MIC ≥1.0 mg/L. The dosage of colistin sulfate should be adjusted according to renal function and drug exposure.

Evidence supports the observational associations of gut microbiota with a variety of psychiatric disorders, but the causal nature of such associations remains obscure. Aiming to comprehensively investigate their causal relationship and to identify specific causal microbe taxa for psychiatric diseases, we conducted a two-sample Mendelian randomization (MR) analysis of gut microbiome with 15 psychiatric diseases. Specifically, the microbiome genome-wide association study (GWAS) in 18,473 individuals from the MiBioGen study was used as exposure sample, and the GWAS for 15 psychiatric diseases was used as outcome samples. One-hundred ninety bacterial taxa from six levels were available for analysis. At a multiple-testing corrected significance level (phylum P < 5.56 × 10–3, class P < 3.33 × 10–3, order P < 2.63 × 10–3, family P < 1.67 × 10–3, genus P < 4.90 × 10–4, and species P < 3.33 × 10–3), the following eight causal associations from seven bacterial features (one phylum + three classes + one order + one family + one species) were identified: family Prevotellaceae with autism spectrum disorder (P = 5.31 × 10–4), class Betaproteobacteria with bipolar disorder (P = 1.53 × 10–3), class Actinobacteria with schizophrenia (P = 1.33 × 10–3), class Bacteroidia and order Bacteroidales with Tourette syndrome (P = 2.51 × 10–3 and 2.51 × 10–3), phylum Actinobacteria and class Actinobacteria with extroversion (P = 8.22 × 10–4 and 1.09 × 10–3), and species Clostridium innocuum with neuroticism (P = 8.92 × 10–4). Sensitivity analysis showed no evidence of reverse causality, pleiotropy, and heterogeneity. Our findings offered novel insights into the gut microbiota–mediated development mechanism of psychiatric disorders.

BackgroundGrowing evidence has shown that alterations in gut microbiota composition are associated with multiple autoimmune diseases (ADs). However, it is unclear whether these associations reflect a causal relationship.ObjectiveTo reveal the causal association between gut microbiota and AD, we conducted a two-sample Mendelian randomization (MR) analysis.Materials and MethodsWe assessed genome-wide association study (GWAS) summary statistics for gut microbiota and six common ADs, namely, systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, type 1 diabetes (T1D), and celiac disease (CeD), from published GWASs. Two-sample MR analyses were first performed to identify causal bacterial taxa for ADs in discovery samples. Significant bacterial taxa were further replicated in independent replication outcome samples. A series of sensitivity analyses was performed to validate the robustness of the results. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.ResultsCombining the results from the discovery and replication stages, we identified one causal bacterial genus, Bifidobacterium. A higher relative abundance of the Bifidobacterium genus was associated with a higher risk of T1D [odds ratio (OR): 1.605; 95% CI, 1.339–1.922; PFDR = 4.19 × 10−7] and CeD (OR: 1.401; 95% CI, 1.139–1.722; PFDR = 2.03 × 10−3), respectively. Further sensitivity analyses validated the robustness of the above associations. The results of reverse MR analysis showed no evidence of reverse causality from T1D and CeD to the Bifidobacterium genus.ConclusionThis study implied a causal relationship between the Bifidobacterium genus and T1D and CeD, thus providing novel insights into the gut microbiota-mediated development mechanism of ADs.

Abstract Yaks live in the Qinghai-Tibet Plateau for a long time where oxygen is scarce, but can ensure the smooth development of testis and spermatogenesis. The key lies in the functional regulation of the Sertoli cells under hypoxia. In this study, we sequenced yak Sertoli cells cultured in normal oxygen concentration (Normoxia) and treated with low oxygen concentration (Hypoxia) by whole transcriptomics, and screened out 194 differentially expressed mRNAs (DEmRNAs), 934 differentially expressed LncRNAs (DELncRNAs) and 129 differentially expressed miRNAs (DEmiRNAs). GO and KEGG analysis showed that these differential genes were mainly concentrated in PI3K-AKT, MAPK, RAS, and other signaling pathways, and were associated with glucose metabolism, tight junction, steroid hormone synthesis, cell fusion, and immunity of yak Sertoli cells. We constructed the gene interaction network of yak Sertoli cells in hypoxia and screened out the relationship pairs related to glucose metabolism and tight junction. The results suggested that the changes in energy metabolism, tight junction, and immune regulation of yak Sertoli cells under hypoxia might provide favorable conditions for spermatogenesis. This study provides data for further study on the role of non-coding RNA in testis development and spermatogenesis of yak.

Wastewater treatment plants (WWTPs) are important and energy-intensive municipal infrastructures. High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality. However, water–energy nexus analysis and models for WWTPs have rarely been reported to date. In this study, a cloud-model-based energy consumption analysis (CMECA) of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters. The principal component analysis (PCA) and K-means clustering were applied to classify the influent condition using water quality and volume data. The energy consumption of the WWTP is divided into five standard evaluation levels, and its cloud digital characteristics (CDCs) were extracted according to bilateral constraints and golden ratio methods. Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity. The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption, represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption. The local WWTP has high energy consumption with 0.3613 kW·h·m−3 despite low influent concentration and volumes, across four consumption levels from low (I) to relatively high (IV), showing an unsatisfactory operation and management level. The average oxygenation capacity, internal reflux ratio, and external reflux ratio during high energy efficiency days recognized by further clustering were obtained (0.2924–0.3703 kg O2·m−3, 1.9576–2.4787, and 0.6603–0.8361, respectively), which could be used as a guide for the days with low energy efficiency. Consequently, this study offers a water–energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

Abstract Background Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. Methods Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). Results The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians. Conclusions This study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

In recent years, there has been significant interest in photocatalytic technologies utilizing semiconductors and photosensitizers responsive to solar light, owing to their potential for energy and environmental applications. Current efforts are focused on enhancing existing photocatalysts and developing new ones tailored for environmental uses. Anthraquinones (AQs) serve as redox-active electron transfer mediators and photochemically active organic photosensitizers, effectively addressing common issues such as low light utilization and carrier separation efficiency found in conventional semiconductors. AQs offer advantages such as abundant raw materials, controlled preparation, excellent electron transfer capabilities, and photosensitivity, with applications spanning the energy, medical, and environmental sectors. Despite their utility, comprehensive reviews on AQs-based photocatalytic systems in environmental contexts are lacking. In this review, we thoroughly describe the photochemical properties of AQs and their potential applications in photocatalysis, particularly in addressing key environmental challenges like clean energy production, antibacterial action, and pollutant degradation. However, AQs face limitations in practical photocatalytic applications due to their low electrical conductivity and solubility-related secondary contamination. To mitigate these issues, the design and synthesis of graphene-immobilized AQs are highlighted as a solution to enhance practical photocatalytic applications. Additionally, future research directions are proposed to deepen the understanding of AQs' theoretical mechanisms and to provide practical applications for wastewater treatment. This review aims to facilitate mechanistic studies and practical applications of AQs-based photocatalytic technologies and to improve understanding of these technologies.

Effective management of large basins necessitates pinpointing the spatial and temporal drivers of primary index exceedances and urban risk factors, offering crucial insights for basin administrators. Yet, comprehensive examinations of multiple pollutants within the Yangtze River Basin remain scarce. Here we introduce a pollution inventory for urban clusters surrounding the Yangtze River Basin, analyzing water quality data from 102 cities during 2018–2019. We assessed the exceedance rates for six pivotal indicators: dissolved oxygen (DO), ammonia nitrogen (NH3–N), chemical oxygen demand (COD), biochemical oxygen demand (BOD), total phosphorus (TP), and the permanganate index (CODMn) for each city. Employing random forest regression and SHapley Additive exPlanations (SHAP) analyses, we identified the spatiotemporal factors influencing these key indicators. Our results highlight agricultural activities as the primary contributors to the exceedance of all six indicators, thus pinpointing them as the leading pollution source in the basin. Additionally, forest coverage, livestock farming, chemical and pharmaceutical sectors, along with meteorological elements like precipitation and temperature, significantly impacted various indicators' exceedances. Furthermore, we delineate five core urban risk components through principal component analysis, which are (1) anthropogenic and industrial activities, (2) agricultural practices and forest extent, (3) climatic variables, (4) livestock rearing, and (5) principal polluting sectors. The cities were subsequently evaluated and categorized based on these risk components, incorporating policy interventions and administrative performance within each region. The comprehensive analysis advocates for a customized strategy in addressing the discerned risk factors, especially for cities presenting elevated risk levels.

Background: Accurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability. Materials and methods: Patients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival. Results: A total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71–0.84; test: 0.77, 95%CI 0.68–0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates. Conclusions: Our study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Context: Observational studies have demonstrated associations between plasma proteins and obesity, but evidence of causal relationship remains to be studied. Objective: We aimed to evaluate the causal relationship between plasma proteins and body composition. Methods: We conducted a 2-sample Mendelian randomization (MR) analysis based on the genome-wide association study (GWAS) summary statistics of 23 body composition traits and 2656 plasma proteins. We then performed hierarchical cluster analysis to evaluate the structure and pattern of the identified causal associations, and we performed gene ontology enrichment analysis to explore the functional relevance of the identified proteins. Results: We identified 430 putatively causal effects of 96 plasma proteins on 22 body composition traits (except obesity status) with strong MR evidence (P < 2.53 × 10 − 6, at a Bonferroni-corrected threshold). The top 3 causal associations are follistatin (FST) on trunk fat-free mass (Beta = −0.63, SE = 0.04, P = 2.00 × 10−63), insulin-like growth factor-binding protein 1 (IGFBP1) on trunk fat-free mass (Beta = −0.54, SE = 0.03, P = 1.79 × 10−57) and r-spondin-3 (RSPO3) on WHR (waist circumference/hip circumference) (Beta = 0.01, SE = 4.47 × 10−4, P = 5.45 × 10−60), respectively. Further clustering analysis and pathway analysis demonstrated that the pattern of causal effect to fat mass and fat-free mass may be different. Conclusion: Our findings may provide evidence for causal relationships from plasma proteins to various body composition traits and provide basis for further targeted functional studies. [ABSTRACT FROM AUTHOR]

Intertemporal decision making is the process by which individuals make judgments or choices regarding outcomes that occur at different times. Although intertemporal decision making is widely investigated, most studies explore it in terms of individual decision making, while neglecting group decision making, which holds more practical significance and adaptive value. This study recommends adopting a “two-process” approach that uses self-assessment questionnaires, audiovisual recordings, and visual decision-making tools to quantify interpersonal interaction processes and information processing in group intertemporal decision-making settings. In this way, studies can reveal the psychological and theoretical mechanisms of the group intertemporal decision-making process. At the level of interpersonal interaction processes, such an approach can identify the differential mechanisms between group and individual intertemporal decision making. In terms of information processing, it can reveal the mechanisms of the decision-making process in group intertemporal decision making. The findings of such work can provide a basis for interventions and nudges that encourage more visionary group intertemporal decision making.

Understanding the spatial distribution of the crop yield gap (YG) is essential for improving crop yields. Recent studies have typically focused on the site scale, which may lead to considerable uncertainties when scaled to the regional scale. To mitigate this issue, this study used a process-based and remote sensing driven crop yield model for winter wheat (PRYM-Wheat), which was derived from the boreal ecosystem productivity simulator (BEPS), to simulate the YG of winter wheat in the North China Plain from 2015 to 2019. Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield (Ya). The distribution of Ya across the North China Plain showed great heterogeneity, decreasing from southeast to northwest. The remote sensing-estimated results show that the average YG of the study area was 6 400.6 kg ha−1. The YG of Jiangsu Province was the largest, at 7 307.4 kg ha−1, while the YG of Anhui Province was the smallest, at 5 842.1 kg ha−1. An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation, but there was a weak negative correlation between YG and accumulated temperature. In addition, the YG was positively correlated with elevation. In general, studying the specific features of the YG can provide directions for increasing crop yields in the future.

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security. However, using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions. Thus, we proposed a new approach to approximating irrigations of winter wheat over the North China Plain (NCP), where irrigation occurs extensively during the winter wheat growing season. This approach used irrigation pattern parameters (IPPs) to define the irrigation frequency and timing. Then, they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat (PRYM–Wheat), to improve the regional estimates of winter wheat over the NCP. The IPPs were determined using statistical yield data of reference years (2010–2015) over the NCP. Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield, with an increase and decrease in the correlation coefficient (R) and root mean square error (RMSE) of 0.15 (about 37%) and 0.90 t ha–1 (about 41%), respectively. The data in validation years (2001–2009 and 2016–2019) were used to validate PRYM–Wheat. In addition, our findings also showed R (RMSE) of 0.80 (0.62 t ha–1) on a site level, 0.61 (0.91 t ha–1) for Hebei Province on a county level, 0.73 (0.97 t ha–1) for Henan Province on a county level, and 0.55 (0.75 t ha–1) for Shandong Province on a city level. Overall, PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years, providing a scientific basis for ensuring regional food security.

Background: Age at natural menopause (ANM) is an important index for women's health. Either early or late ANM is associated with a series of adverse outcomes later in life. Despite being an inheritable trait, its genetic determinant has not yet been fully understood.Methods: Aiming to better characterize the genetic architecture of ANM, we conducted genome-wide association study (GWAS) meta-analyses in European-specific as well as trans-ancestry samples by using GWAS summary statistics from the following 3 large studies: the Reproductive Genetics Consortium (ReproGen; N = 69 626), the UK Biobank cohort (UKBB; N = 111 593) and the BioBank Japan Project (BBJ; N = 43 861), followed by a series of bioinformatical assessments and functional annotations.Results: By integrating the summary statistics from the 3 GWAS of up to 225 200 participants, this largest meta-analysis identified 49 novel loci and 3 secondary signals that were associated with ANM at the genome-wide significance level (P < 5 × 10-8). No population specificity or heterogeneity was observed at most of the associated loci. Functional annotations prioritized 90 candidate genes at the newly identified loci. Among the 26 traits that were genetically correlated with ANM, hormone replacement therapy (HRT) exerted a causal relationship, implying a causal pattern by which HRT was determined by ANM.Conclusion: Our findings improved our understanding of the etiology of female menopause, as well as shed light on potential new therapies for abnormal menopause. [ABSTRACT FROM AUTHOR]

Sulfamethoxazole (SMX) is a significant environmental concern due to its adverse effects and ecological risks. SMX elimination in aquatic environments via photocatalysis presents a viable solution, given its high oxidation potential. However, such a solution remains controversial, primarily due to a lack of selectivity. Here we introduce a molecularly imprinted TiO2@Fe2O3@g-C3N4 (MFTC) photocatalyst designed for the selective degradation of SMX. To assess MFTC's selectivity, we applied it to degrade synthetic wastewater containing SMX alongside interfering species sulfadiazine (SDZ), ibuprofen (IBU), and bisphenol A (BPA). The results demonstrated a selective degradation efficiency rate of 96.8%, nearly twice that of competing pollutants. The molecularly imprinted sites within the catalyst played a crucial role by selectively capturing SMX and enhancing its adsorption, thereby improving catalytic efficiency. The degradation process involved •OH and •O2− free radicals, with a newly proposed double Z-scheme mechanism and potential pathway for SMX degradation by the MFTC photocatalytic system. This study enriches the application of photocatalysis using molecularly imprinted nanocomposite materials for treating complex pollutant mixtures in water.

Summary: To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT.

Summary: Background: MRI is the routine examination to surveil the recurrence of nasopharyngeal carcinoma, but it has relatively lower sensitivity than PET/CT. We aimed to find if artificial intelligence (AI) could be competent pre-inspector for MRI radiologists and whether AI-aided MRI could perform better or even equal to PET/CT. Methods: This multicenter study enrolled 6916 patients from five hospitals between September 2009 and October 2020. A 2.5D convolutional neural network diagnostic model and a nnU-Net contouring model were developed in the training and test cohorts and used to independently predict and visualize the recurrence of patients in the internal and external validation cohorts. We evaluated the area under the ROC curve (AUC) of AI and compared AI with MRI and PET/CT in sensitivity and specificity using the McNemar test. The prospective cohort was randomized into the AI and non-AI groups, and their sensitivity and specificity were compared using the Chi-square test. Findings: The AI model achieved AUCs of 0.92 and 0.88 in the internal and external validation cohorts, corresponding to the sensitivity of 79.5% and 74.3% and specificity of 91.0% and 92.8%. It had comparable sensitivity to MRI (e.g., 74.3% vs. 74.7%, P = 0.89) but lower sensitivity than PET/CT (77.9% vs. 92.0%, P

Here, the authors report the discovery of a redox switch for AcrIIC1, which regulates AcrIIC1’s monomer-dimer interconversion and inhibitory activity on Cas9. By reporting the structures of different redox states of NmeAcrIIC1, they identify a pair of conserved cysteines which mediate the redox switch. The replacement of the redox-sensitive cysteines allows the generation of a more stable and robust variant under various cellular environments.