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For decades micoorganisms have been engineered for the utilization of lignocellulose-based second-generation (2G) feedstocks, but with the concerns of increased levels of atmospheric CO2 causing global warming there is an emergent need to transition from the utilization of 2G feedstocks to third-generation (3G) feedstocks such as CO2 and its derivatives. Here, we established a yeast platform that is capable of simultaneously converting 2G and 3G feedstocks into bulk and value-added chemicals. We demonstrated that by adopting 3G substrates such as CO2 and formate, the conversion of 2G feedstocks could be substantially improved. Specifically, formate could provide reducing power and energy for xylose conversion into valuable chemicals. Simultaneously, it can form a concentrated CO2 pool inside the cell, providing thermodynamically and kinetically favoured amounts of precursors for CO2 fixation pathways, e.g., the Calvin–Benson–Bassham (CBB) cycle. Furthermore, we demonstrated that formate could directly be utilized as a carbon source by yeast to synthesize endogenous amino acids. The engineered strain achieved a one-carbon (C1) assimilation efficiency of 9.2%, which was the highest efficiency observed in the co-utilization of 2G and 3G feedstocks. We applied this strategy for productions of both bulk and value-added chemicals, including ethanol, free fatty acids (FFAs), and longifolene, resulting in yield enhancements of 18.4%, 49.0%, and ∼100%, respectively. The strategy demonstrated here for co-utilization of 2G and 3G feedstocks sheds lights on both basic and applied research for the up-coming establishment of 3G biorefineries.

Abstract Background Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD. Purpose The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients. Methods A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time. Results COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P

Abstract Biallelic pathogenic variants in TARS2 lead to combined oxidative phosphorylation deficiency, subtype 21 (COXPD21, MIM #615918), which is a rare mitochondrial encephalomyopathy (ME) characterized by early-onset severe axial hypotonia, limb hypertonia, psychomotor developmental delay, epilepsy and brain anomalies. To date, approximately 28 individuals with COXPD21 and 28 TARS2 variants have been identified. In this study, we reported additional four individuals from three unrelated Chinese families with mitochondrial encephalomyopathy caused by pathogenic variants in TARS2, and described the novel clinical phenotypes and genotypic information. In addition to two novel variants (c.512G > A, p.Arg171Lys; c.988dup, p.Arg330Lysfs*4), one previously reported variant (c.470 C > G, p.Thr157Arg) recurred in six Chinese individuals with COXPD21 but was not present in populations of other races. Our findings expanded the mutation spectrum of TARS2 and confirmed that c.470 C > G is a Chinese-specific founder mutation. The novel phenotypes, including reduced fetal movement, eye anomalies and sleep irregularities, observed in our patients enriched the clinical characteristics of COXPD21.

Abstract Background The clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model. Methods Patient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model. Results There were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p

Abstract Background The calmodulin (CaM) and calmodulin-like (CML) proteins play regulatory roles in plant growth and development, responses to biotic and abiotic stresses, and other biological processes. As a popular fruit and ornamental crop, it is important to explore the regulatory mechanism of flower and fruit development of passion fruit. Results In this study, 32 PeCaM/PeCML genes were identified from passion fruit genome and were divided into 9 groups based on phylogenetic analysis. The structural analysis, including conserved motifs, gene structure and homologous modeling, illustrates that the PeCaM/PeCML in the same subgroup have relative conserved structural features. Collinearity analysis suggested that the expansion of the CaM/CML gene family likely took place mainly by segmental duplication, and the whole genome replication events were closely related with the rapid expansion of the gene group. PeCaM/PeCMLs were potentially required for different floral tissues development. Significantly, PeCML26 had extremely high expression levels during ovule and fruit development compared with other PeCML genes, suggesting that PeCML26 had potential functions involved in the development of passion fruit flowers and fruits. The co-presence of various cis-elements associated with growth and development, hormone responsiveness, and stress responsiveness in the promoter regions of these PeCaM/PeCMLs might contribute to their diverse regulatory roles. Furthermore, PeCaM/PeCMLs were also induced by various abiotic stresses. This work provides a comprehensive understanding of the CaM/CML gene family and valuable clues for future studies on the function and evolution of CaM/CML genes in passion fruit. Conclusion A total of 32 PeCaM/PeCML genes were divided into 9 groups. The PeCaM/PeCML genes showed differential expression patterns in floral tissues at different development stages. It is worth noting that PeCML26, which is highly homologous to AtCaM2, not only interacts with multiple BBR-BPC TFs, but also has high expression levels during ovule and fruit development, suggesting that PeCML26 had potential functions involved in the development of passion fruit flowers and fruits. This research lays the foundation for future investigations and validation of the potential function of PeCaM/PeCML genes in the growth and development of passion fruit.

Abstract Background Evidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored. Methods We screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives. Results Five genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines. Conclusion The CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

The cardiotoxicity caused by Dox chemotherapy represents a significant limitation to its clinical application and is a major cause of late death in patients undergoing chemotherapy. Currently, there are no effective treatments available. Our analysis of 295 clinical samples from 132 chemotherapy patients and 163 individuals undergoing physical examination revealed a strong positive correlation between intestinal barrier injury and the development of cardiotoxicity in chemotherapy patients. We developed a novel orally available and intestinal targeting protein nanodrug by assembling membrane protein Amuc_1100 (obtained from intestinal bacteria Akkermansia muciniphila), fluorinated polyetherimide, and hyaluronic acid. The protein nanodrug demonstrated favorable stability against hydrolysis compared with free Amuc_1100. The in vivo results demonstrated that the protein nanodrug can alleviate Dox-induced cardiac toxicity by improving gut microbiota, increasing the proportion of short-chain fatty acid-producing bacteria from the Lachnospiraceae family, and further enhancing the levels of butyrate and pentanoic acids, ultimately regulating the homeostasis repair of lymphocytes in the spleen and heart. Therefore, we believe that the integrity of the intestinal barrier plays an important role in the development of chemotherapy-induced cardiotoxicity. Protective interventions targeting the intestinal barrier may hold promise as a general clinical treatment regimen for reducing Dox-induced cardiotoxicity.

Abstract Idiopathic normal pressure hydrocephalus (iNPH) affects mainly aged populations. The gradual shortening of telomere length (TL) is one of the hallmarks of aging. Whereas the genetic contribution of TL to the iNPH is incompletely understood. We aimed to investigate the causal relationship between TL and iNPH through the Mendelian randomization (MR) analysis. We respectively obtained 186 qualified single nucleotide polymorphisms (SNPs) of TL and 20 eligible SNPs of iNPH for MR analysis. The result of MR analysis showed that genetically predicted longer TL was significantly associated with a reduced odd of iNPH (odds ratio [OR] = 0.634 95% Confidence interval [CI] 0.447–0.899, p = 0.011). The causal association remained consistent in multivariable MR (OR = 0.530 95% CI 0.327–0.860, p = 0.010). However, there was no evidence that the iNPH was causally associated with the TL (OR = 1.000 95% CI 0.996–1.004, p = 0.955). Our study reveals a potential genetic contribution of TL to the etiology of iNPH, that is a genetically predicted increased TL might be associated with a reduced risk of iNPH.

Background: The prediction of response to immunotherapy mostly depends on the programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) status, and the 22C3 pharmDx assay has been approved in esophageal squamous cell carcinoma (ESCC). However, the widespread use of the 22C3 pharmDx assay is limited due to its availability. Thus, alternative PD-L1 assays are needed. We aimed to investigate the analytical and clinical diagnostic performances of four PD-L1 assays and to compare their concordances with the 22C3 pharmDx assay. Methods: The PD-L1 22C3 pharmDx assay was performed on the Dako Autostainer Link 48 platform, three testing assays (PD-L1 E1L3N XP antibody [Ab], PD-L1 BP6099 Ab and PD-L1 CST E1L3N Ab) on the Leica BOND-MAX/III platform, and one testing assay (PD-L1 MXR006 Ab) on the Roche VENTANA Benchmark Ultra platform. A total of 218 ESCC cases from four centers were included in this retrospective study. Professionals from each center stained and read the IHC slides independently and determined the combined positive score (CPS) and the tumor proportion score (TPS). Results: Regarding analytical performance, the four testing assays demonstrated good correlations with the 22C3 pharmDx assay when evaluated by the TPS or CPS (ρ > 0.8 for all four assays). Regarding diagnostic performance (CPS ≥ 10 was used as the cutoff), the four testing assays showed moderate concordances with the 22C3 pharmDx assay (kappa > 0.7 for all four assays). The overall percent agreements between each testing assay and the 22C3 pharmDx assay was at least 87.2 %. Conclusion: This study provides insight into the potential interchangeability of the four PD-L1 assays with the 22C3 pharmDx assay.

Abstract (−)‐Ambrox, a highly prized and commercially significant component of ambergris, finds widespread application in perfumery, cigarettes, cosmetics, and the food industry. Despite considerable attention to this research area over the years, an environmentally friendly and practical method for synthesizing (−)‐ambrox has remained elusive. This study presents a succinct and efficient approach to (−)‐ambrox synthesis, involving two consecutive alkylations at C‐6, followed by an acid‐catalyzed cyclization to give bicyclic ketones starting from (R)‐carvone. Subsequent reduction, Barton Vinyl Iodide synthesis, alkylation, and an acid‐catalyzed cyclization collectively achieved the synthesis of (−)‐ambrox with a satisfactory yield of 26.2 %.

BackgroundThis study aimed to identify prognostic factors for survival and develop a prognostic nomogram to predict the survival probability of elderly patients with lower-grade gliomas (LGGs).MethodsElderly patients with histologically confirmed LGG were recruited from the Surveillance, Epidemiology, and End Results (SEER) database. These individuals were randomly allocated to the training and validation cohorts at a 2:1 ratio. First, Kaplan−Meier survival analysis and subgroup analysis were performed. Second, variable screening of all 13 variables and a comparison of predictive models based on full Cox regression and LASSO-Cox regression analyses were performed, and the key variables in the optimal model were selected to construct prognostic nomograms for OS and CSS. Finally, a risk stratification system and a web-based dynamic nomogram were constructed.ResultsA total of 2307 elderly patients included 1220 males and 1087 females, with a median age of 72 years and a mean age of 73.30 ± 6.22 years. Among them, 520 patients (22.5%) had Grade 2 gliomas, and 1787 (77.5%) had Grade 3 gliomas. Multivariate Cox regression analysis revealed four independent prognostic factors (age, WHO grade, surgery, and chemotherapy) that were used to construct the full Cox model. In addition, LASSO-Cox regression analysis revealed five prognostic factors (age, WHO grade, surgery, radiotherapy, and chemotherapy), and a LASSO model was constructed. A comparison of the two models revealed that the LASSO model with five variables had better predictive performance than the full Cox model with four variables. Ultimately, five key variables based on LASSO-Cox regression were utilized to develop prognostic nomograms for predicting the 1-, 2-, and 5-year OS and CSS rates. The nomograms exhibited relatively good predictive ability and clinical utility. Moreover, the risk stratification system based on the nomograms effectively divided patients into low-risk and high-risk subgroups.ConclusionVariable screening based on LASSO-Cox regression was used to determine the optimal prediction model in this study. Prognostic nomograms could serve as practical tools for predicting survival probabilities, categorizing these patients into different mortality risk subgroups, and developing personalized decision-making strategies for elderly patients with LGGs. Moreover, the web-based dynamic nomogram could facilitate its use in the clinic.

Abstract Due to the BCS theory, hydrogen, the lightest element, would be the prospect of room-temperature superconductor after metallization, but because of the difficulty of the hydrogen metallization, the theory about hydrogen pre-compression was proposed that the hydrogen-rich compounds could be a great option for the high T c superconductors. The superior properties of TmH6, YbH6 and LuH6 indicated the magnificent potential of heavy rare earth elements for low-pressure stability. Here, we designed XTmH12 (X = Y, Yb, Lu, and La) to obtain higher T c while maintaining low pressure stability. Most prominently, YbTmH12 can stabilize at a pressure of 60 GPa. Compared with binary TmH6 hydride, its T c was increased to 48 K. The results provide an effective method for the rational design of moderate pressure stabilized hydride superconductors.

ObjectiveTo establish an indicator system for multi-point trigger surveillance and early warning of infectious diseases, providing valuable evidence to improve the overall prevention and control framework. MethodsThe pool of indicators was initially developed through a literature review and focus group discussions, followed by two rounds of Delphi expert consultations with 21 experts nationwide. Based on the experts' ratings of the importance, usability and familiarity of the indicators, the indicators were screened, statistically analyzed and their final weights determined. ResultsThe positive coefficients of experts in the two rounds of consultation were both 100%, the authority coefficients of experts were both ≥ 0.79, and the coordination coefficients of expert opinions were 0.34 – 0.46 (all P < 0.001). According to the occurrence and development process of infectious diseases and the requirements of related multi-source data fusion and collision, the indicator system was divided into three dimensions: single-source data, multi-source data fusion and risk factors. Finally, 82 indicators for multi-point trigger surveillance and early warning of infectious diseases were established. ConclusionThe index system developed in this study for multi-point trigger surveillance and early warning of infectious diseases is based on sound scientific principles and reasoning. It has the potential to expand the channels of infectious disease monitoring and achieve multi-point trigger surveillance and early warning.

Early warning of geo-hazards based on meteorological factors has played an important supportive role in disaster prevention and mitigation in China since its inception in 2003. This paper summarizes the the 20-year development process, technical methods, and disaster reduction effects of the early warning works. (1) The development process of early warning work process is divided into three stages: initiation and promotion (2003-2009), deepening cooperation (2010-2017), and reform and enhancement (2018-2022). (2) With 24-hour early warning work as the main content, a progressive warning model and a relatively complete warning service system have been gradually formed. (3) Three sets of early warning model technology and method systems have been gradually developed, including critical precipitation threshold model, the threshold model based on geo-hazards risk, and the dynamic early warning models, with the publication of industry standards for warning. (4) The spatial and temporal accuracy of warning products continues to improve, with the national and 26 provincial warning spatial accuracies exceeding 5 km × 5 km. The focus is on 24-hour warnings, with development towards 72-hour and medium- to long-term forecasts. Over 8 provincial-level and some municipal and county-level authorities have implemented 3-hour short-term warnings, gradually forming a work system to support service short impending warning response, medium-term prevention and long term deployment. (5) Where there is warning, there is response. The Ministry of Natural Resources has taken the national early warning as one of the bases for initiating defense responses, and 18 provinces have clarified the working mechanisms of the early warning response linkage. (6) The awareness of multi-party disaster prevention has been continuously enhanced. With strengthened inspections,evacuations, and successful risk aversion after receiving early warning information, the effectiveness of disaster prevention and mitigation is evident. The experience of early warning works in the past 20-year can provide reference for the next step in promoting the early warning of geo-hazards based on meteorological factors, supporting the enhancement of China’s capability and level of geo-hazards prevention and control work.

Early warning of geo-hazards based on meteorological factors in China began in 2003, and early warning informations are issued to the public every year during the flood season to alert the public to guard against geo-hazards such as landslides, debris flows, and mudslides caused by rainfall. To better understand the current situation and future trends of research for early warning of geo-hazards based on meteorological factors, the visualization tool CiteSpace was utilized to review the domestic and foreign literature on geo-hazards meteorological risk warning published from 2003 to 2023 and construct the scientific knowledge map. The CNKI database and SCI-Expanded databases were used as data sources. The result show that: (1) The study of critical rainfall thresholds based on the relationship between historical rainfall and disaster is in the leading position in research on early warning of geo-hazards based on meteorological factors, and the study of rainfall-related thresholds will continue to be a research hotspot in the future. (2) Keyword cluster analysis shows that a large number of studies have conducted research on early warning and prediction models and critical thresholds of geo-hazards under different formation conditions, meteorological conditions, different types, and genetic models, indicating that refinement is an important direction for the development of early warning of geo-hazards based on meteorological factors. (3) In the international research, Chinese scholars account for 39% of the total number of publications, showing an absolute advantage in research on geo-hazards meteorological risk early warning. The research results objectively demonstrate the development context, domestic and foreign research hotspots, and trends of research on geo-hazards meteorological risk warning in China, aiming to contribute to the progress of geo-hazards research and early warning forcast business in China.

Improving the geo-hazard meteorological risk warning capabilities is an important component of the comprehensive prevention system for geological disasters. Since 2003, Sichuan Province has been carrying out geo-hazard meteorological risk warning work. Building on the previous foundation, research has been focused on the analysis and establishment of warning models, system design and implementation, and graded warning operation modes since 2019. A geo-hazard warning model library, an integrated geological disaster meteorological risk warning interconnection system at the provincial, municipal, and county levels, and a graded warning mechanism have been created. We have achieved closed-loop management of interconnection and graded early warning based on integrated platform such as rainfall data transmission, early warning model analysis and calculation, and early warning information release in province, cities, and counties. This has solved the three problems of lack of technology, capacity, and funding in cities and counties, as well as the interference caused by multiple early warning information releases on grassroots levels. In 2023, 86% of the successful risk avoidance cases in Sichuan Province were affected by meteorological risk warning, effectively reducing casualties and property losses caused by geo-hazard.

Abstract Background Concurrent follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL)was reported in some studies, while the diagnosis of TdT (terminal deoxynucleotydil transferase) positive high grade B cell lymphoma (HGBL) with MYC and BCL2 rearrangements (“double hit”) transformed from FL/DLBCL has been rarely reported. Herein, we described the clinical features and mutation profiles of a case diagnosed with TdT positive “double hit” HGBL following the treatment of FL/DLBCL. Case presentation This is a 43-year-old Chinese man who was diagnosed with low grade FL (account for 80%) combined with DLBCL (20%) at a stage of IVB. The patient presented with BCL2/IGH translocation without MYC rearrangement, as well as the expressions of CD20, CD19, CD10 and BCL2 at the initial diagnosis of FL/DLBCL. MYC rearrangement and TdT expression occurred after the treatment. The targeted sequencing revealed mutations in KMT2D, FOXO1, CREBBP, ATM, STAT6, BCL7A, DDX3X, MUC4, FGFR3, ARID5B, DDX11 and PRKCSH genes were the co-mutations shared by the FL/DLBCL and TdT positive “double hit” HGBL, while CCND3, BIRC6, ROBO1 and CHEK2 mutations specifically occurred after the treatment. The overall survival time was 37.8 and 17.8 months after the initial diagnosis of FL/DLBCL and TdT positive “double hit” HGBL, respectively. Conclusion This study reports a rare case of TdT positive “double hit” HGBL following the treatment of concurrent FL/DLBCL and highlights the mutation characteristics. Collectively, this study will help enrich the knowledge of TdT positive “double hit” HGBL transformed from FL/DLBCL.

River bottom tearing scour (RBTS) has a strong effect on the scouring and moulding of channel in the Yellow River. Due to the special forming conditions, complex influencing factors, and limited observed data, it is difficult to predict whether RBTS will occur accurately. By collecting and disposing of the hydrodynamic, sediment, and initial boundary data of 246 flood events related to RBTS in three typical reaches of the Yellow River basin, the correlation between different characteristic influencing factors and the occurrence and absence of RBTS were analysed, and prediction models based on machine learning algorithms were constructed. The results showed that under the existing data conditions, the maximum sediment concentration Sm, average sediment concentration Sp, flood growth rate ν, and shape coefficient δ were the four key indices to more easily distinguish whether RBTS will occur. The support vector machine algorithm model had the best performance results and exhibited higher accuracy and precision in predicting its occurrence compared with other models under given water and sediment conditions. The method proposed in this study provides a new method for accurately predicting RBTS in the Yellow River. HIGHLIGHTS The maximum sediment concentration, average sediment concentration, flood growth rate, and shape coefficient are the four key indices for distinguishing whether RBTS will occur.; Prediction models of RBTS based on machine learning algorithms were built.; The SVM model showed the best predictive performance in the case study of the middle reach of the Yellow River.;

The rainfall-induced shallow landslides are primarily debris landslides, which features simultaneity with significant hazard, and the hydrological response mechanism of water table and soil moisture content to precipitation of this type of landslide is sophisticated, which makes it difficult to predict the slope stability accurately. To further study the influence of the rainfall-triggered internal hydrological responses on slope-stability, on-site precipitation infiltration monitoring, correlation analysis and mechanical analysis were carried out on the Houshanli landslide in Qingchuan County, Sichuan Province. The relationship between precipitation and water table was proposed based on climate and hydrological monitoring data obtained within three year interval. The response of rainfall infiltration, soil volumetric water content and water table were analyzed. The results indicate that: (1) groundwater exhibits periodic fluctuations throughout the year, characterized by three phases of slow decline, rapid decline, and rapid ascent; a linear negative correlation between precipitation and water table was found, and no significant correlation was observed with the water table increment; (2) through the infinite slope model and the relationship between precipitation and water table, a prediction model for shallow landslide stability was constructed. The precipitation threshold (81.8 mm/d) and water table threshold (0.73 m) were determined which has good agreement with the actual situations. This provides an early warning method for rainfall-induced shallow landslides by monitoring these two factors.

Unfavorable environmental cues severely affect crop productivity resulting in significant economic losses to farmers. In plants, multiple regulatory genes, such as the WRKY transcription factor (TF) family, modulate the expression of defense genes. However, the role of the pineapple WRKY genes is poorly understood. Here, we studied the pineapple WRKY gene, AcWRKY28, by generating AcWRKY28 over-expressing transgenic pineapple plants. Overexpression of AcWRKY28 enhanced the salt stress resistance in transgenic pineapple lines. Comparative transcriptome analysis of transgenic and wild-type pineapple plants showed that “plant-pathogen interaction” pathway genes, including 9 calcium-dependent protein kinases (CPKs), were up-regulated in AcWRKY28 over-expressing plants. Furthermore, chromatin immunoprecipitation and yeast one-hybrid assays revealed AcCPK12, AcCPK3, AcCPK8, AcCPK1, and AcCPK15 as direct targets of AcWRKY28. Consistently, the study of AcCPK12 over-expressing Arabidopsis lines showed that AcCPK12 enhances salt, drought, and disease resistance. This study shows that AcWRKY28 plays a crucial role in promoting salt stress resistance by activating the expression of AcCPK genes.

Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms—Random Forest, Multilayer Perceptron, and Convolutional Neural Network—are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930–0.957 and an AUC value of 0.955. The CNN-based warning model demonstrated an accuracy of 0.945–0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930–0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on 5 August 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multi-classification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this study contribute to valuable attempts in landslide disaster warning model research, with anticipated further improvements through the gradual accumulation of samples and practical application verification.

Abstract Background and Objective High‐grade glioma (HGG) is known to be characterized by a high degree of malignancy and a worse prognosis. The classical treatment is safe resection supplemented by radiotherapy and chemotherapy. Tumor treating fields (TTFields), an emerging physiotherapeutic modality that targets malignant solid tumors using medium‐frequency, low‐intensity, alternating electric fields to interfere with cell division, have been used for the treatment of new diagnosis of glioblastoma, however, their administration in HGG requires further clinical evidence. The efficacy and safety of TTFields in Chinese patients with HGG were retrospectively evaluated by us in a single center. Methods We enrolled and analyzed 52 patients with newly diagnosed HGG undergoing surgery and standard chemoradiotherapy regimens from December 2019 to June 2022, and followed them until June 2023. Based on whether they used TTFields, they were divided into a TTFields group and a non‐TTFields group. Progression‐free survival (PFS) and overall survival (OS) were compared between the two groups. Results There were 26 cases in the TTFields group and 26 cases in the non‐TTFields group. In the TTFields group, the median PFS was 14.2 months (95% CI: 9.50–18.90), the median OS was 19.7 months (95% CI: 14.95–24.25) , the median interval from surgery to the start of treatment with TTFields was 2.47 months (95% CI: 1.47–4.13), and the median duration of treatment with TTFields was 10.6 months (95% CI: 9.57–11.63). 15 (57.69%) patients experienced an adverse event and no serious adverse event was reported. In the non‐TTFields group, the median PFS was 9.57 months (95% CI: 6.23–12.91) and the median OS was 16.07 months (95% CI: 12.90–19.24). There was a statistically significant difference in PFS (p = 0.005) and OS (p = 0.007) between the two groups. Conclusions In this retrospective analysis, TTFields were observed to improve newly diagnosed HGG patients' median PFS and OS. Compliance was much higher than reported in clinical trials and safety remained good.

Summary: The 2022 mpox outbreak led the World Health Organization (WHO) to declare it a public health emergency of international concern (PHEIC). There is a need to develop more effective and safer mpox virus (MPXV)-specific vaccines in response to the mpox epidemic. The mRNA vaccine is a promising platform to protect against MPXV infection. In this study, we construct two bivalent MPXV mRNA vaccines, designated LBA (B6R-A29L) and LAM (A35R-M1R), and a quadrivalent mRNA vaccine, LBAAM (B6R-A35R-A29L-M1R). The immunogenicity and protective efficacy of these vaccines alone or in combination were evaluated in a lethal mouse model. All mRNA vaccine candidates could elicit potential antigen-specific humoral and cellular immune responses and provide protection against vaccinia virus (VACV) infection. The protective effect of the combination of two bivalent mRNA vaccines and the quadrivalent vaccine was superior to that of the individual bivalent mRNA vaccine. Our study provides valuable insights for the development of more efficient and safer mRNA vaccines against mpox.

Landslide disasters occur frequently in Fujian Province, and early warning of landslide disasters on a regional scale is an important means of effective disaster prevention and mitigation. Due to the complex mechanism of landslide disasters, the traditional regional landslide early warning methods have such problems as insufficient accuracy. Deep learning mainly refers to the technology of feature extraction, abstraction, representation and learning by constructing the neural network model, which is a kind of machine learning. As a classical deep learning algorithm, convolutional neural network has more powerful classification and representation ability than traditional machine learning. Taking Fujian Province as the research area, this paper introduces the convolution neural network into the field of landslide disaster early warning and constructs a regional landslide early warning model of Fujian Province. The process is as follows: (1) The SMOTE optimization algorithm is used to optimize the sample database of landslide disasters in Fujian Province from 2010 to 2018, enlarging the number of positive samples and expanding the proportion of positive and negative samples from 1∶3.4 to 1∶2, and the total number of samples reaches 18040. (2) Construct a convolution neural network model structure, which includes an input layer, two convolution layers, two maximum pooling layers, a full connection layer and an output layer. (3) Use the convolution neural network to train the optimized samples (80% of the samples from 2010 to 2018 as the training set), and use the Bayesian optimization algorithm to optimize the model parameters to obtain the regional landslide early warning model of Fujian Province. (4) The model is tested with 20% of the samples from 2010 to 2018 as the test set, and the confusion matrix and ROC curve are used to test the model. The results show that the accuracy of the model ranges from 0.96 to 0.97, the AUC value is 0.977, indicating that the model accuracy and generalization ability are good. (5) The actual situation of the landslide disaster in the flood season of 2019 is taken as a positive sample, negative samples are collected through the method of time-space sampling, and the 2019 regional landslide sample verification set (603 samples) is constructed. The model is further verified by using the confusion matrix and ROC curve. The results show that the accuracy of the model ranges from 0.75 to 0.85, and the AUC value is 0.852. Although only the actual landslide samples in the flood season of 2019 is used for verification, good results is also achieved. In this paper, the convolution neural network algorithm is applied to the regional landslide early warning, which provides a new way to establish the regional landslide early warning model. The preliminary verification shows that the model is effective and will be further applied and verified in Fujian Province in the future.

Abstract Studying complex relaxation behaviors is of critical importance for understanding the nature of glasses. Here we report a Kovacs-like memory effect in glasses, manifested by non-monotonic stress relaxation during two-step high-to-low strains stimulations. During the stress relaxation process, if the strain jumps from a higher state to a lower state, the stress does not continue to decrease, but increases first and then decreases. The memory effect becomes stronger when the atomic motions become highly collective with a large activation energy, e.g. the strain in the first stage is larger, the temperature is higher, and the stimulation is longer. The physical origin of the stress memory effect is studied based on the relaxation kinetics and the in-situ synchrotron X-ray experiments. The stress memory effect is probably a universal phenomenon in different types of glasses.

Abstract Background Neurocognitive and psychiatric disorders have been proved that they can comorbid more often with idiopathic normal pressure hydrocephalus (iNPH) than general population. However, the potential causal association between these disorders and iNPH has not been assessed. Thus, our study aims to investigate the causal relationship between them based on a bidirectional Mendelian randomization (MR) analysis. Methods Random effects of the inverse variance weighted (IVW) method were conducted to obtain the causal association among the neurocognitive disorders, psychiatric disorders, and iNPH. Genome‐wide association studies (GWAS) of 12 neurocognitive and psychiatric disorders were downloaded via the OpenGWAS database, GWAS Catalog, and Psychiatric Genomics Consortium, whereas GWAS data of iNPH were obtained from the FinnGen consortium round 9 release, with 767 cases and 375,610 controls of European ancestry. We also conducted the sensitivity analysis in these significant causal inferences using weighted median model, Cochrane's Q test, MR‐Egger regression, MR Pleiotropy Residual Sum and Outlier detect and the leave‐one‐out analysis. Results For most of the neurocognitive and psychiatric disorders, no causal association was established between them and iNPH. We have found that iNPH (odds ratio [OR] = 1.030, 95% confidence interval [CI]: 1.011–1.048, p = .001) is associated with increased risk for schizophrenia, which failed in validation of sensitivity analysis. Notably, genetically predicted Parkinson's disease (PD) is associated with increased risk of iNPH (OR = 1.256, 95% CI: 1.045–1.511, p = .015). Conclusion Our study has revealed the potential causal effect in which PD associated with an increased risk of iNPH. Further study is warranted to investigate the association between PD and iNPH and the potential underlying mechanism.

Abstract An experimental investigation of circular concrete-filled steel tubular (CFST) columns subjected to very low-elevation lateral impacts was performed. Six circular CFST members were prepared for lateral impact tests according to the typical CFST columns in high-speed railway stations in China, and the impact location was at the height of the 2/9 column. The tests had three variables: the thickness of the steel tube, the impact velocity, and the axial load. The failure modes were determined in the tests, along with the time histories of the impact force and the deflection at the impact location. A finite-element analysis was performed to examine the effects of the axial load and scaling on the maximum deflection. The results show that with the increase of axial compression ratio, the impact resistance of the member first increases and then weakens. According to the travelling plastic hinge theory, a three-stage rigid plastic mechanical model was employed to describe the impact process, in which the impact location was at the non-mid-span, and a deflection calculation method for CFST applicable to any impact position was developed. A comparison with the test results indicated that deflections can be calculated with reasonable accuracy using the proposed method.

Aiming at the problems of low intelligence of current tunneling equipment, difficulty in describing over-excavation, under-excavation and abnormal collision in tunneling process, and difficulty in adapting traditional automatic cutting and memory cutting technology to complex geological conditions, a digital twin-driven virtual teaching memory cutting control method for cantilever roadheader is proposed. By analyzing the research situation of digital twin technology in the field of intelligent coal mining, the overall scheme of memory cutting control system of cantilever roadheader driven by digital twin is designed, and the key technology of memory cutting of cantilever roadheader under complex working conditions is studied. Firstly, the characteristics of digital twin and virtual reality technology are fully utilized to study the virtual teaching strategy under complex working conditions. Based on the Unity3D platform, the virtual twin model of the working face and equipment with the same size of the corresponding entity, the kinematics model of the cutting unit and the virtual collision detection model are established. The virtual model movement is controlled through the intelligent interactive interface at the virtual end, and the teaching trajectory is designed and optimized according to the worker’s experience, so that it can be used as the target expected trajectory of trajectory tracking to make up for the excessive dependence on the worker’s experience caused by the traditional underground manual teaching due to the harsh working conditions. Secondly, in order to improve the quality of section forming, the control method of teaching trajectory tracking and reproduction in memory automatic cutting stage is studied. The dynamic model of cutting part is established by Lagrange method, and the tracking control accuracy of end effector to teaching trajectory is improved by combining iterative learning with sliding mode control. Finally, the simulation control platform of the memory cutting of the cantilever roadheader is built. Through the real-time data transmission and interaction between the virtual space and the physical space and between the modules, the three-dimensional visual simulation of the memory cutting virtual teaching and trajectory tracking control process is completed in the virtual space, and then the memory automatic cutting trajectory tracking control command is generated and sent to the end effector of the physical entity of the cantilever roadheader to drive it to carry out the section forming cutting according to the teaching trajectory. At the same time, the physical sensor collects the pose data of the cantilever roadheader fuselage and the cutting arm, and reversely drives the virtual model to move synchronously. The closed-loop control of robot virtual model and physical entity is realized. On this basis, the virtual and real synchronization of the system, the motion consistency between the virtual prototype and the physical prototype, and the trajectory tracking and reproduction control accuracy are verified. The experimental results show that the system data transmission delay is low, which can ensure the virtual and real consistency and synchronization, and the trajectory tracking control accuracy meets the actual use requirements. This method provides a new idea for memory cutting and intelligent control of tunneling equipment.

Abstract Background Epigenetic clocks constructed from DNA methylation patterns have emerged as excellent predictors of aging and aging-related health outcomes. Iron, a crucial element, is meticulously regulated within organisms, a phenomenon referred as iron homeostasis. Previous researches have demonstrated the sophisticated connection between aging and iron homeostasis. However, their causal relationship remains relatively unexplored. Results Through two-sample Mendelian randomization (MR) utilizing the random effect inverse variance weighted (IVW) method, each standard deviation (SD) increase in serum iron was associated with increased GrimAge acceleration (GrimAA, BetaIVW = 0.27, P = 8.54E−03 in 2014 datasets; BetaIVW = 0.31, P = 1.25E−02 in 2021 datasets), HannumAge acceleration (HannumAA, BetaIVW = 0.32, P = 4.50E−03 in 2014 datasets; BetaIVW = 0.32, P = 8.03E−03 in 2021 datasets) and Intrinsic epigenetic age acceleration (IEAA, BetaIVW = 0.34, P = 5.33E−04 in 2014 datasets; BetaIVW = 0.49, P = 9.94E−04 in 2021 datasets). Similar results were also observed in transferrin saturation. While transferrin manifested a negative association with epigenetic age accelerations (EAAs) sensitivity analyses. Besides, lack of solid evidence to support a causal relationship from EAAs to iron-related biomarkers. Conclusions The results of present investigation unveiled the causality of iron overload on acceleration of epigenetic clocks. Researches are warranted to illuminate the underlying mechanisms and formulate strategies for potential interventions.

Abstract The underlying atomistic mechanism of deformation is a central problem in mechanics and materials science. Whereas deformation of crystalline metals is fundamentally understood, the understanding of deformation of amorphous metals lacks behind, particularly identifying the involved temporal and spatial scales. Here, we reveal that at small scales the size-dependent deformation behavior of amorphous metals significantly deviates from homogeneous flow, exhibiting increasing deformation rate with reducing size and gradually shifted composition. This transition suggests the deformation mechanism changes from collective atomic transport by viscous flow to individual atomic transport through interface diffusion. The critical length scale of the transition is temperature dependent, exhibiting a maximum at the glass transition. While viscous flow does not discriminate among alloy constituents, diffusion does and the constituent element with higher diffusivity deforms faster. Our findings yield insights into nano-mechanics and glass physics and may suggest alternative processing methods to epitaxially grow metallic glasses.

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures.

Pineapple is a globally significant tropical fruit, but its cultivation faces numerous challenges due to abiotic and biotic stresses, affecting its quality and quantity. WRKY transcription factors are known regulators of stress responses, however, their specific functions in pineapple are not fully understood. This study investigates the role of AcWRKY31 by overexpressing it in pineapple and Arabidopsis. Transgenic pineapple lines were obtained using Agrobacterium-mediated transformation methods and abiotic and biotic stress treatments. Transgenic AcWRKY31-OE pineapple plants showed an increased sensitivity to salt and drought stress and an increased resistance to biotic stress from pineapple mealybugs compared to that of WT plants. Similar experiments in AcWRKY31-OE, AtWRKY53-OE, and the Arabidopsis Atwrky53 mutant were performed and consistently confirmed these findings. A comparative transcriptomic analysis revealed 5357 upregulated genes in AcWRKY31-OE pineapple, with 30 genes related to disease and pathogen response. Notably, 18 of these genes contained a W-box sequence in their promoter region. A KEGG analysis of RNA-Seq data showed that upregulated DEG genes are mostly involved in translation, protein kinases, peptidases and inhibitors, membrane trafficking, folding, sorting, and degradation, while the downregulated genes are involved in metabolism, protein families, signaling, and cellular processes. RT-qPCR assays of selected genes confirmed the transcriptomic results. In summary, the AcWRKY31 gene is promising for the improvement of stress responses in pineapple, and it could be a valuable tool for plant breeders to develop stress-tolerant crops in the future.

Terpinen-4-ol (T-4-O) is an important component of tea tree oil and has anti-inflammatory effects. Currently, there are very few studies on the mechanisms by which T-4-O improves lipopolysaccharide (LPS)-induced macrophage inflammation. In this study, LPS-stimulated mouse RAW264.7 macrophages were used as a model to analyze the effects of T-4-O on macrophage inflammatory factors and related metabolic pathways in an inflammatory environment. The results showed that T-4-O significantly decreased the expression levels of inflammatory cytokines induced by LPS. Cellular metabolism results showed that T-4-O significantly decreased the ratio of the extracellular acidification rate and oxygen consumption rate. Non-targeted metabolomics results showed that T-4-O mainly affected glutamine and glutamate metabolism and glycine, serine, and threonine metabolic pathways. qPCR results showed that T-4-O increased the transcript levels of GLS and GDH and promoted glutamine catabolism. Western blotting results showed that T-4-O inhibited the mTOR and IκB, thereby decreasing NF-κB activity. The overall results showed that T-4-O inhibited mTOR phosphorylation to promote glutamine metabolism and increased cell oxidative phosphorylation levels, thereby inhibiting the expression of LPS-induced inflammatory cytokines.

Abstract Background H3K27M mutation status significantly affects the prognosis of patients with diffuse midline gliomas (DMGs), but this tumor presents a high risk of pathological acquisition. We aimed to construct a fully automated model for predicting the H3K27M alteration status of DMGs based on deep learning using whole‐brain MRI. Methods DMG patients from West China Hospital of Sichuan University (WCHSU; n = 200) and Chengdu Shangjin Nanfu Hospital (CSNH; n = 35) who met the inclusion and exclusion criteria from February 2016 to April 2022 were enrolled as the training and external test sets, respectively. To adapt the model to the human head MRI scene, we use normal human head MR images to pretrain the model. The classification and tumor segmentation tasks are naturally related, so we conducted cotraining for the two tasks to enable information interaction between them and improve the accuracy of the classification task. Results The average classification accuracies of our model on the training and external test sets was 90.5% and 85.1%, respectively. Ablation experiments showed that pretraining and cotraining could improve the prediction accuracy and generalization performance of the model. In the training and external test sets, the average areas under the receiver operating characteristic curve (AUROCs) were 94.18% and 87.64%, and the average areas under the precision‐recall curve (AUPRC) were 93.26% and 85.4%. Conclusions The developed model achieved excellent performance in predicting the H3K27M alteration status in DMGs, and its good reproducibility and generalization were verified in the external dataset.

Abstract Approximately 20% of aged captive giant pandas (Ailuropoda melanoleuca) have cataracts that impair their quality of life. To identify potential biomarkers of cataract formation, we carried out a quantitative proteomics analysis of 10 giant pandas to find proteins differing in abundance between healthy and cataract-bearing animals. We identified almost 150 proteins exceeding our threshold for differential abundance, most of which were associated with GO categories related to extracellular localization. The most significant differential abundance was associated with components of the proteasome and other proteins with a role in proteolysis or its regulation, most of which were depleted in pandas with cataracts. Other modulated proteins included components of the extracellular matrix or cytoskeleton, as well as associated signaling proteins and regulators, but we did not find any differentially expressed transcription factors. These results indicate that the formation of cataracts involves a complex post-transcriptional network of signaling inside and outside lens cells to drive stress responses as a means to address the accumulation of protein aggregates triggered by oxidative damage. The modulated proteins also indicate that it should be possible to predict the onset of cataracts in captive pandas by taking blood samples and testing them for the presence or absence of specific protein markers.

Objective To study prevalence characteristics and change pattern of dominant virus strain of influenza epidemics in Guangzhou city and to provide evidence for formulating region-specific prevention and control measures. Methods Weekly influenza surveillance data of Guangzhou city, Guangdong province from 2015 to 2022 were collected from China Influenza Surveillance Information System. The positive rate series of influenza virus and its pathogen subtype were decomposed into trend/season/remainder component by using the seasonal-trend decomposition procedure based on locally weighted regression. By calculating covariance, the contribution rate of the obtained components to the fluctuation of positive rate was analyzed. Results Of 39 198 surveillance specimens sampled in the city during the 8-year period, 3 632 (9.27%) were positive for influenza virus nucleic acid. Trend series analysis showed that the positive rate fluctuated from 4.57% to 16.07%, with an increasing trend, during 2015 – 2019 and varied with a U-shape trajectory during 2020 – 2022. Winter-spring and spring-summer peaks of the positive rate were observed in season series analysis and the remainder component analysis revealed the highest fluctuation range of 48.96% for the positive rate in 2022. The positive rate of influenza A (H3N2) presented a similar season series to that of influenza B (Victoria), with winter and summer peaks, but the summer peaks of influenza A (H3N2) was later than that of influenza B (Victoria); the peak shape and prevalence duration (single peaks mainly in winter-spring season) of influenza A (H1N1) pdm09 positivity were similar to those of influenza B (Yamagata). In general, the remainder component was the main contributor to the fluctuation of the total positive rate, accounting for 59.16% of overall fluctuation (covariance of remainder component versus that of original series: 80.81/136.60), followed by season and trend component, accounting for 25.19% (34.41/136.60) and 15.68% (21.38/136.60) of overall fluctuation, respectively. Conclusion There were virus subtype-specific differences in seasonal peak and prevalence duration of influenza infection and random fluctuation exerted a greater effect than long-term trend and seasonal variation on influenza epidemic pattern in Guangzhou city.

Abstract Background Short-term air pollution exposure and intracerebral hemorrhage (ICH) risk are related. However, the impact of the pollutant levels decline on this relationship, which attributes to clean air policy implementation and the COVID-19 pandemic lockdown, is unclear. In the present research, we explored the influence of different pollutant levels on ICH risk during eight years in a southwestern China megacity. Methods Our research used a time-stratified case-crossover design. We retrospectively analyzed ICH patients in a teaching hospital from January 1, 2014, to December 31, 2021, and divided 1571 eligible cases into two groups (1st group: 2014–2017; 2nd group: 2018–2021). We observed the trend of every pollutant in the entire study period and compared the pollution levels in each group, using air pollutants data (PM2.5, PM10, SO2, NO2, CO, and O3) documented by the local government. We further established a single pollutant model via conditional logistic regression to analyze the association between short-term air pollutants exposure and ICH risk. We also discussed the association of pollution levels and ICH risk in subpopulations according to individual factors and monthly mean temperature. Results We found that five air pollutants (PM2.5, PM10, SO2, NO2, CO) exhibited a continuous downward trend for the whole duration, and the daily concentration of all six pollutants decreased significantly in 2018–2021 compared with 2014–2017. Overall, the elevation of daily PM2.5, SO2, and CO was associated with increased ICH risk in the first group and was not positively associated with risk escalation in the second group. For patients in subgroups, the changes in the influence of lower pollutant levels on ICH risk were diverse. In the second group, for instance, PM2.5 and PM10 were associated with lower ICH risk in non-hypertension, smoking, and alcohol-drinking participants; however, SO2 had associations with increased ICH risk for smokers, and O3 had associations with raised risk in men, non-drinking, warm month population. Conclusions Our study suggests that decreased pollution levels diminish the adverse effects of short-term air pollutants exposure and ICH risk in general. Nevertheless, the influence of lower air pollutants on ICH risk in subgroups is heterogeneous, indicating unequal benefits among subpopulations.

Abstract H3 K27M-mutant diffuse midline glioma (H3 K27M-mt DMG) is a rare, highly invasive tumor with a poor prognosis. The prognostic factors of H3 K27M-mt DMG have not been fully identified, and there is no clinical prediction model for it. This study aimed to develop and validate a prognostic model for predicting the probability of survival in patients with H3 K27M-mt DMG. Patients diagnosed with H3 K27M-mt DMG in the West China Hospital from January 2016 to August 2021 were included. Cox proportional hazard regression was used for survival assessment, with adjustment for known prognostic factors. The final model was established using the patient data of our center as the training cohort and data from other centers for external independent verification. One hundred and five patients were ultimately included in the training cohort, and 43 cases from another institution were used as the validation cohort. The factors influencing survival probability in the prediction model included age, preoperative KPS score, radiotherapy and Ki-67 expression level. The adjusted consistency indices of the Cox regression model in internal bootstrap validation at 6, 12, and 18 months were 0.776, 0.766, and 0.764, respectively. The calibration chart showed high consistency between the predicted and observed results. The discrimination in external verification was 0.785, and the calibration curve showed good calibration ability. We identified the risk factors that affect the prognosis of H3 K27M-mt DMG patients and then established and validated a diagnostic model for predicting the survival probability of these patients.

Glucose transporter 1 (GLUT1) overexpression in tumor cells is a potential target for drug therapy, but few studies have reported screening GLUT1 inhibitors from natural or synthetic compounds. With current analysis techniques, it is difficult to accurately monitor the GLUT1 inhibitory effect of drug molecules in real-time. We developed a cell membrane-based glucose sensor (CMGS) that integrated a hydrogel electrode with tumor cell membranes to monitor GLUT1 transmembrane transport and screen for GLUT1 inhibitors in traditional Chinese medicines (TCMs). CMGS is compatible with cell membranes of various origins, including different types of tumors and cell lines with GLUT1 expression knocked down by small interfering RNA or small molecules. Based on CMGS continuous monitoring technique, we investigated the glucose transport kinetics of cell membranes with varying levels of GLUT1 expression. We used CMGS to determine the GLUT1-inhibitory effects of drug monomers with similar structures from Scutellaria baicalensis and catechins families. Results were consistent with those of the cellular glucose uptake test and molecular-docking simulation. CMGS could accurately screen drug molecules in TCMs that inhibit GLUT1, providing a new strategy for studying transmembrane protein-receptor interactions.

Somaclonal variation in tissue culture is a common phenomenon induced by various external or internal environmental conditions, resulting in heritable or non-heritable alterations in gene expression. One crucial mechanism involved in plant growth and development is epigenetic regulation. A highly dynamic epigenome can respond to environmental changes by regulating gene expression. DNA methylation is one of these epigenetic modifications that can alter gene expression in tissue-cultured pineapple plants. The underlying mechanism of such somaclonal variations in pineapple and the epigenetic regulation involvement in somaclonal variations has not been studied. This study performed DNA methylome and transcriptome sequencing of wild-type (WT) and mutant pineapple plants (WS, HW, and TW). We observed altered DNA methylation patterns in chlorophyll development in the mutants. Specifically, we noticed that the methylation levels in the CHG and CHH contexts were lower in the gene body regions compared to the upstream and downstream regions. We identified several thousand differentially methylated regions (DMRs) located at the gene body regions, some of which overlapped with the differentially expressed genes (DEGs). Functional enrichment analyses suggested that these genes were involved in chlorophyll metabolism. Thus, our results revealed that the transcriptional regulation of many chlorophyll metabolic essential genes could be regulated by DNA methylation caused by somaclonal variations and provided insights into epigenetic mechanisms underlying the regulation of chlorosis in pineapple plants.

Abstract Aim To explore the experiences of healthcare workers (HCWs) following occupational exposure to coronavirus disease 2019 (COVID‐19) during the early stage of the pandemic. Design A Husserl descriptive phenomenological study design was employed. Methods Convenient and snowball sampling was used. In‐depth semi‐structured telephone interviews were conducted from February to March 2020 with the frontline HCWs who were exposed to COVID‐19 during work. Data analysis was conducted following the 7‐step analysis method developed by Colaizzi. Results Fifteen HCWs participated in the study. Four themes were identified, including (1) traumatic experiences since the occupational exposure; (2) getting through the hard time; (3) struggling to return to work; (4) reflections on occupational exposures. Conclusion The HCWs had traumatic and painful experiences after the occupational exposure. But they returned to work with strong resilience, professional obligation and social support. Training and supervision, and adequate supply of personal protective equipment are suggested to prevent professional exposure. Social and organizational support should be provided for the exposed HCWs.

Brain metastasis is the leading cause of death in most cancer patients; thus, anti‐brain metastasis is a crucial step in cancer treatment. The unique microenvironment and pathophysiological characteristics of brain metastases hamper the development of effective treatment methods, while the advances in nanomedicine demonstrate its immense potential for addressing this challenge. In recent years, due to breakthroughs in nanotechnology, functional and complex nanomedicine is prepared and it has been extensively explored to improve the treatment of brain metastasis. Compared to traditional treatment methods, nanomedicine can combine multiple traditional treatment methods into one single medicinal format and target unique characteristics of brain metastases to enhance the treatment efficacy. This review summarizes the main obstacles in the field of brain metastasis treatment and clinical/preclinical studies and surveys the use of a variety of nanomedicine candidates as antibrain metastasis strategies for different types of brain metastases.

ObjectiveAnalyzing the epidemiological characteristics of influenza cases among children aged 0–17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures.MethodsThe influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence.ResultsThe relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0–3 presented protective effects and the 0–3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7–12 years group.ConclusionTemperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0–3 years, but significantly increases the risk for those aged 7–12 years.

Considering the mass reservoirs construction, it is important to study the cascade reservoir construction sequence, which directly affects the water and sediment resource allocation, ecological environment restoration, and socioeconomic development of the basin. Thus, we constructed an evaluation index system by comprehensively considering the basin water and sediment discharge index, basin ecological and environmental maintenance index, and basin social and economic development index. The evaluation model was established to assess the impact of the cascade reservoir construction sequence on basin multifunctional synergy development index. The model was applied to evaluate the cascade reservoir construction sequence in the Yangtze and Yellow River Basin of China. The results revealed that: (1) The basin water and sediment discharge index, basin social and economic development index, and basin multifunctional synergy development index benefits gradually increased, the basin ecological and environmental maintenance index benefits progressively decreased in the Yangtze and Yellow River Basin. (2) The basin water and sediment discharge index, and basin ecological and environmental maintenance index of the planned cascade reservoir construction sequence ranked top in the Yellow River Basin; the basin water and sediment discharge index, and basin social and economic development index of the planned cascade reservoir construction sequence ranked top in the Yangtze River Basin. (3) The cascade reservoir construction sequence focused more on basin social and economic development index benefits when the level of national economic development was low, and more on basin water and sediment discharge index benefits when it was high. (4) Reservoir capacity and installed capacity were the main factors influencing the cascade reservoir construction sequence in the Yangtze and Yellow River Basin, respectively. These findings provide useful information regarding the cascade reservoir construction sequence.

BackgroundPostpartum depression (PPD) is a type of depressive episode related to parents after childbirth, which causes a variety of symptoms not only for parents but also affects the development of children. The causal relationship between potential risk factors and PPD remains comprehensively elucidated.MethodsLinkage disequilibrium score regression (LDSC) analysis was conducted to screen the heritability of each instrumental variant (IV) and to calculate the genetic correlations between effective causal factors and PPD. To search for the causal effect of multiple potential risk factors on the incidence of PPD, random effects of the inverse variance weighted (IVW) method were applied. Sensitivity analyses, including weighted median, MR-Egger regression, Cochrane’s Q test, and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO), were performed to detect potential Mendelian randomization (MR) assumption violations. Multivariable MR (MVMR) was conducted to control potential multicollinearity.ResultsA total of 40 potential risk factors were investigated in this study. LDSC regression analysis reported a significant genetic correlation of potential traits with PPD. MR analysis showed that higher body mass index (BMI) (Benjamini and Hochberg (BH) corrected p = 0.05), major depression (MD) (BH corrected p = 5.04E-19), and schizophrenia (SCZ) (BH corrected p = 1.64E-05) were associated with the increased risk of PPD, whereas increased age at first birth (BH corrected p = 2.11E-04), older age at first sexual intercourse (BH corrected p = 3.02E-15), increased average total household income before tax (BH corrected p = 4.57E-02), and increased years of schooling (BH corrected p = 1.47E-11) led to a decreased probability of PPD. MVMR analysis suggested that MD (p = 3.25E-08) and older age at first birth (p = 8.18E-04) were still associated with an increased risk of PPD.ConclusionIn our MR study, we found multiple risk factors, including MD and younger age at first birth, to be deleterious causal risk factors for PPD.

Abstract Background The current molecular classifications cannot completely explain the polarized malignant biological behavior of low‐grade gliomas (LGGs), especially for tumor recurrence. Therefore, we tried to identify suspicious hub genes related to tumor recurrence in LGGs. Methods In this study, we constructed a gene‐miRNA‐lncRNA co‐expression network for LGGs by a weighted gene co‐expression network analysis (WGCNA). GDCRNATools and the WGCNA R package were mainly used in data analysis. Results Sequencing data from 502 LGG patients were analyzed in this study. Compared with recurrent glioma tissues, we identified 774 differentially expressed (DE) mRNAs, 49 DE miRNAs, and 129 DE lncRNAs in primary LGGs and ultimately determined that the expression of MKLN1 was related to tumor recurrence in LGG. Conclusion This study identified the potential biomarkers for the pathogenesis and recurrence of LGGs and proposed that MKLN1 could be a potential therapeutic target.

Background: Immunosenescence, an age-related deficit in immunity, associated with multiple disorders and making the successful aging a challenge. Although nearly 4000 articles have been published, only few review articles have summarized the research status. In order to better understand the most recent advances, hotspots and development trends in immunosenescence, it is very necessary to conduct a comprehensive bibliometric analysis. Hence, commonly used bibliometric analysis software CiteSpace and VOSviewer were employed to conduct a quantitative analysis and critical evaluation of publications in this study. Methods: Immunosenescence publications were screened from the Web of Science Core Collection (WoSCC). Microsoft Excel 2021, CiteSpace 5.8.R3, and VOSviewer 1.6.17 were used for bibliometric study. Results: A total of 3875 publications were retrieved from WoSCC. After screening by document type (article or review) (352 publications were excluded) and language of English (85 were excluded), 3438 studies were finally used for bibliometric analysis. The literature on immunosenescence had been continuously growing since 1991, and by 2020 it has skyrocketed 312 publications from 240 in 2019. USA (1111 publications, 35.01%) was the leading country of publications, followed by ITALY (379, 11.94%) and ENGLAND (366, 11.53%). Of the authors, Pawelec G from the Tubingen University of GERMANY contributed the greatest articles (93 publications). All the keywords could be divided into five clusters, and additional potent visualization bursts revealed that “gut microbiota,” “health,” “dysfunction,” and “nivolumab” were the active hotspots presently. Conclusion: Based on the current data, we firstly concluded that there will be a dramatically rising publications on immunosenescence, and research teams from USA or GERMANY might be the best chooses for collaboration. Moreover, We particularly emphasized the development potential of mechanism and intervening strateges like “gut microbiota” and “nivolumab” in immunosenescence. We hope to provide new ideas for promoting the basic research and clinical application of immunosenescence.

Abstract Materials genome engineering (MGE) has been successfully applied in various fields, resulting in a series of novel materials with excellent performance. Significant progress has been made in high‐throughput simulation, experimentation, and data‐driven techniques, enabling the effective prediction, rapid synthesis, and characterization of many classes of materials. In this brief review, we introduce the achievements made in the field of metallic glasses (MGs) using MGE, in particular high‐throughput experimentation and data‐driven approaches. High‐throughput experiments help to efficiently synthesize and characterize many materials in a short period of time, enabling the construction of high‐quality material databases for data‐driven methods. Paired with machine learning, potential alloys of desired properties may be revealed and predicted. Along with the progress in computational power and algorithms of machine learning, the complex composition‐structure‐properties relationship is hopefully established, which in turn help efficient and precise prediction of new MGs.

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory disease characterized by a slow progression and caused by the inhalation of harmful particulate matter. Cigarette smoke and air pollutants are the primary contributing factors. Currently, the pathogenesis of COPD remains incompletely understood. The PI3K/Akt signaling pathway has recently emerged as a critical regulator of inflammation and oxidative stress response in COPD, playing a pivotal role in the disease’s progression and treatment. This paper reviews the association between the PI3K/Akt pathway and COPD, examines effective PI3K/Akt inhibitors and novel anti-COPD agents, aiming to identify new therapeutic targets for clinical intervention in this disease.