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Abstract Streptococcus suis infection represents a major challenge in pig farming and public health due to its zoonotic potential and diverse serotypes, while existing vaccines lack effective cross-protection. This study employed reverse vaccinology and immunoinformatics to identify 8 conserved proteins across 11 prevalent serotypes of S. suis. 16 candidate epitopes were selected to design three multi-epitope antigens against S. suis (designated as MEASs), which fused with a dendritic cell-targeting peptide to improve antigen presentation in host. Purified MEASs displayed favorable cross-reactogenicity against 29 serotype-specific antiserums. Robust humoral and cellular immune responses can be induced by MEAS 1 and MEAS 3 in a mouse model, which provided substantial protection against virulent strains from two different serotypes. In particular, their immune serums exhibited positive opsonization effects within bloodstream and macrophage phagocytosis. Taken together, we identified two promising MEASs with excellent cross-protection, offering potential in preventing S. suis infections in a mouse model.

Abstract Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

Abstract As a critical technology, anomaly detection ensures the smooth operation of cloud systems while maintaining the market competitiveness of cloud service providers. However, the resource data in real-world cloud systems is predominantly unannotated, leading to insufficient supervised signals for anomaly detection. Moreover, complicated topological associations existed between cloud servers (e.g., computation, storage, and communication). While acquiring resource information, correlating the system topology is challenging. To this end, we propose the GCAD for cloud system anomaly detection, which integrates data augmentation, GraphGRU, contrastive learning, and reconstruction. First, GCAD constructs positive and negative sample pairs through the masking and Gaussian noise data augmentation. Then, the GraphGRU processes extended temporal graph data, extracting and fusing spatiotemporal features from resource status and system topology. In addition, GCAD introduces linear attention for encoding spatiotemporal representations to capture their global correlation information. The weight parameters of the encoder are optimized using a contrastive learning mechanism. Finally, GCAD utilizes a reconstruction technique to calculate anomaly scores, facilitating the evaluation of the state of the cloud system at each time point. Experimental results indicate that GCAD outperforms state-of-the-art compared methods on two real-world datasets that contain topology information.

Abstract Background In May 2024, a chickenpox outbreak was reported at Xiasha Primary School located in Nanshan District, Shenzhen City, China, with a total of 12 cases identified. Despite thorough on-site investigations, the source of infection remained undetected. The purpose of our study was to infer the timing and duration of the infectious period of the initial case using modeling techniques, thereby deducing the identity of the source. Methods We conducted an individual contact survey within the class affected by the epidemic and utilized an agent-based model (ABM) to estimate the key parameters related to the timing of the infectious source’s emergence and the duration of its infectiousness. The point estimates derived from the ABM served as prior information for a subsequent Bayesian analysis, which in turn provided the posterior distribution for these parameters. Results Our models suggested the infection source entered the classroom around April 24th (95% credible interval: April 22nd to April 26th), with an infectious period of approximately two days. Based on these findings, we should aim to detect students who may have been absent due to atypical chickenpox symptoms during this period and closely examine teachers who were present for two consecutive days for any indication of potential infection. Conclusion This study demonstrates the efficacy of combining contact surveys with mathematical modeling for outbreak source tracing, offering a novel approach to supplement field epidemiological surveys. Clinical trial number Not applicable.

Abstract In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

Abstract Background and aims Diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN) both have microcirculation dysfunction. Urinary albumin-to-creatinine ratio (UACR) is a biomarker for DN. We aimed to explore the links between DPN and UACR in patients with type 2 diabetes mellitus (T2DM). Methods A total of 195 T2DM patients were defined as Control or DPN group. Clinical parameters were compared, and the association between HbA1c (or UACR) and DPN was analyzed. Risk factors for DPN were observed, and the diagnostic values of HbA1c and UACR were assessed. Results Compared with 104 participants without DPN, 91 individuals with DPN exhibited higher HbA1c and UACR levels. In all patients, increased HbA1c and UACR were identified as risk factors for DPN in individuals with T2DM. Moreover, increased HbA1c was a risk factor for DPN in volunteers without DN, whereas elevated UACR was determined as a risk factor for DPN in participants with DN. The cut-off point for HbA1c (7.65%) in patients without DN had a sensitivity of 86.0% and specificity of 44.6%, while the cut-off point for UACR (196.081 mg/g) in patients with DN had a sensitivity of 52.9% and specificity of 76.2%. Conclusion Elevated HbA1c and UACR levels are risk factors for DPN and may serve as potential biomarkers for DPN in T2DM patients.

The sink strength of developing ovaries in wheat determines the grain weight potential. The period from booting to the grain setting stage is critical for ovary growth and development and potential sink capacity determination. However, the underlying regulatory mechanism during this period by which the wheat plant balances and coordinates the floret number and ovary/grain weight under water stress has not been clarified. Therefore, we designed two irrigation treatments of W0 (no seasonal irrigation) and W1 (additional 75 mm of irrigation at the jointing stage) and analyzed the responses of the ovary/grain weight to water stress at the phenotypic, metabolomic, and transcriptomic levels. The results showed that the W0 irrigation treatment reduced the soil water content, plant height, and green area of the flag leaf, thus reducing grain number, especially for the inferior grains. However, it improved the grain weight of the superior and inferior grains as well as average grain weight at maturity, while the average ovary/grain weight and volume during –3 to 10 days after anthesis (DAA) also increased. Transcriptomic analysis indicated that the genes involved in both sucrose metabolism and phytohormone signal transduction were prominently accelerated by the W0 treatment, accompanied by greater enzymatic activities of soluble acid invertase (SAI) and sucrose synthase (Sus) and elevated abscisic acid (ABA) and indole-3-acetic acid (IAA) levels. Thus, the sucrose content decreased, while the glucose and fructose contents increased. In addition, several TaTPP genes (especially TaTPP-6) were down-regulated and the IAA biosynthesis genes TaTAR1 and TaTAR2 were up-regulated under the W0 treatment before anthesis, which further increased the IAA level. Collectively, water stress reduced the growth of vegetative organs and eliminated most of the inferior grains, but increased the ABA and IAA levels of the surviving ovaries/grains, promoting the enzymatic activity of Sus and degrading sucrose into glucose and fructose. As a result, the strong sucrose utilization ability, the enhanced enzymatic activity of SAI and the ABA- and IAA-mediated signaling jointly increased the weight and volume of the surviving ovaries/grains, and ultimately achieved the trade-off between ovary/grain weight and number in wheat under water stress.

In this study, we investigated the evolution of grain structures and second phases of a Mg-9.5Gd-4Y–2Zn-0.3Zr alloy during two-stage deformation that took place in the metal during hot extrusion, as well as their effects on its mechanical properties. We found that the alloy exhibited a bimodal grain structure consisting of equiaxed dynamic recrystallized (DRXed) grains and elongated unDRXed grains. As the extrusion process progressed, DRX occurred on a large scale, resulting in an increase in the proportion of DRXed grains from 17.8% to 93.2%, and a reduction in average grain size from 80.3 μm to 9.41 μm. The grain orientation also underwent a transition from a single orientation of an extrusion direction (ED) parallel to (i.e. ED//) to mixed orientations of ED// and ED//. The primary mechanism of dynamic recrystallization (DRX) during extrusion involved particle-stimulated nucleation (PSN) accompanied by both continuous and discontinuous DRX. The Mg-9.5Gd-4Y–2Zn-0.3Zr alloy mainly contained intergranular 18R-long period stacking order (LPSO) phases and intragranular 14H-LPSO. As the extrusion process progressed, 18R-LPSO changed from blocky to lamellar to accommodate deformation, and β-Mg5(Gd,Y) phase particles dynamically precipitated along DRXed grain boundaries. The 14H-LPSO phase was found to be primarily distributed within coarse unDRXed grains. As deformation via extrusion progressed, the quantity of 14H-LPSO gradually decreased. Ultimately, with the gradual dissolution of Mg5(Gd,Y), 14H-LPSO reprecipitated within the recrystallized grain boundaries. As the extrusion process progressed, both the yield strength (YS) and elongation (EL) increased from 132.6 MPa to 287.6 MPa and from 10.8% to 15.5%, respectively. This was mainly attributed to the significant reduction in grain size and second phase size.

The accurate description of anisotropic plastic deformation is key to accurately predicting the stamping forming of metal sheets. The anisotropic yield criterion, when based on the assumption of isotropic hardening, often leads to significant inaccuracies. To address this issue, this paper observes the anisotropic hardening phenomenon through non-associated flow rules and evaluates the anisotropy of 7075-O aluminum alloy. Through a series of tensile tests, we determined the mechanical properties of 7075-O aluminum alloy in three distinct orientations. To describe the metal hardening behavior, we employed the Swift-Voce hardening criterion. From the hardening curves in three different directions, it was found that AA7075-O exhibits plastic anisotropy. Based on the VUMAT subroutine, finite element simulation of AA7075-O tensile tests was conducted through Abaqus. Compared with the Hill48 model, it was found that the simulated values of the S–Y2009 anisotropic hardening model have a higher degree of agreement with the experimental curves. The S–Y2009 anisotropic hardening model was adopted to predict the earing behavior of AA7075-O during circular cup deep drawing. The root mean square error between the predicted values of the S–Y2009 model and the experimental values was only 0.1795, which is far smaller than that of the Hill48 yield model. Therefore, the SY2009 model has important guiding significance for the stamping forming of metal sheets.

Among various metal oxide nanomaterials, manganese oxides, which can exist in different structures and valence states, are considered highly promising anode materials for lithium-ion batteries (LIBs). However, conventional manganese oxides, such as MnO and MnO2, face significant challenges during cycling process. Specifically, poor electronic conductivity and large volume changes result in low specific capacity during high current charging and discharging, as well as poor fast-charging performance. This work presents an approach to synthesizing porous hexagonal Mn5O8 nanosheets via hydrothermal and annealing methods and applies them as anode materials for LIBs. The Mn5O8 nanomaterials exhibit a thin plate morphology, which effectively reduces the distance for ion/electron transmission and mitigates the phenomenon of volume expansion. Additionally, the large pore size of Mn5O8 results in abundant interlayer and intralayer defects, which further increase the rate of ion transmission. These unique characteristics enable Mn5O8 to demonstrate excellent electrochemical performance (938.7 mAh·g−1 after 100 cycles at 100 mA·g−1) and fast charging performance (675.7 mAh·g−1 after 1000 cycles at 3000 mA·g−1), suggesting that Mn5O8 nanosheets have the potential to be an ideal fast-charging anode material for LIBs.

This is an article in the field of materials science. Magnetic LSX molecular sieves were prepared by colloidal directing agent and Fe3O4. After silver ion exchange and hydrazine hydrate reduction, magnetic LSX molecular sieves containing silver atoms were prepared, the surface morphology, magnetic properties, phase structure, elemental composition and specific surface area of the samples were characterized by scanning electron microscopy, vibrating sample magnetometer, X-ray diffraction analyzer, photoelectron spectroscopy and specific surface adsorption instrument. The results showed that magnetic LSX molecular sieve is the composite material of LSX molecular sieve and Fe3O4. The micropore specific surface area was 679.38 m2/g. The optimum dosage of colloidal guide agent was 0.5%. The magnetic properties increased with the increase of Fe3O4 content. The specific surface area of magnetic LSX molecular sieve reduced after silver exchange with hydrazine hydrate.

Abstract Background Increased awareness of the factors contributing to the diagnostic disparities seen in bipolar disorder between individuals of different heritage is needed to achieve equity in diagnosis and treatment. One such inequity is the provision of earlier treatment. Earlier treatment of patients diagnosed with bipolar disorder may prolong time to recurrence of mood episodes and reduce functional impairment and other poor outcomes associated with disease progression. The aim of this post hoc analysis was to study the efficacy and safety of long-acting injectable aripiprazole once-monthly 400 mg (AOM 400) in patients with earlier-stage bipolar I disorder (BP-I). Data from a 52-week multicenter, double-blind, placebo-controlled, randomized withdrawal trial of AOM 400 versus placebo in patients with BP‑I (NCT01567527) were analyzed. Those patients in the lowest quartiles for age (18–≤32 years; n = 70) or disease duration (0.13–≤4.6 years; n = 67) at baseline were categorized with earlier-stage BP-I. The primary endpoint was time from randomization to recurrence of any mood episode. Other endpoints included proportion of patients with recurrence of any mood episode, and change from baseline in Young Mania Rating Scale (YMRS) and Montgomery–Åsberg Depression Rating Scale (MADRS) total scores. Results Maintenance treatment with AOM 400 significantly delayed time to recurrence of any mood episode versus placebo in patients aged 18–≤32 years (hazard ratio [HR]: 2.46 [95% confidence interval (CI) 1.09, 5.55]; p = 0.0251) or with disease duration 0.13–≤4.6 years (HR: 3.21 [95% CI 1.35, 7.65]; p = 0.005). This was largely driven by a lower proportion of patients in the AOM 400 group with YMRS total score ≥15 or clinical worsening. Changes from baseline in MADRS total score in both earlier-stage groups indicated AOM 400 did not worsen depression versus placebo. The safety profile of AOM 400 was consistent with the original study. Note that the original study included patients who had previously been stabilized on AOM 400 monotherapy, which may have enriched the population with patients who respond to and tolerate AOM 400. Conclusions In this post hoc analysis, AOM 400 prolonged time to recurrence of any mood episode versus placebo in earlier-stage BP-I. These findings support early initiation of maintenance treatment with AOM 400.

Abstract High-grade serous ovarian carcinoma (HGSOC) is one of the most lethal gynecological cancer. Genetic studies have revealed gene copy number alterations (CNAs) frequently occurred in HGSOC pathogenesis, however the function and mechanism of CNAs for microRNAs are still not fully understood. Here, we show the dependence on gene copy number amplification of MIR937 that enhances cell autophagy and dictates HGSOC proliferative activity. Data mining of TCGA database revealed MIR937 amplification is correlated with increased MIR937 expression and cell proliferation of HGSOC. Deletion of MIR937 in HGSOC cells led to impaired autophagy and retarded cell proliferation, and the extent for its inhibitory effects scaled with the degree of MIR937 copy loss. Rescue assay confirmed miR-937-5p, a mature product of MIR937, was sufficient to restore its oncogenic function. Mechanistically, MIR937 amplification raised the expression of miR-937-5p, enhanced its binding to 3′ UTR of FBXO16 transcript, and thereby restricting FBXO16 degradative effects on ULK1. Our results demonstrate that MIR937 amplification augments cell autophagy and proliferation, and suggest an alternative strategy of MIR937/FBXO16/ULK1 targeting for HGSOC treatment.

Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency. Doi: 10.28991/HIJ-2024-05-03-018 Full Text: PDF

Abstract Background Neurocognitive disorders frequently occur in patients with cyanotic congenital heart disease (CCHD) because of the hemodynamic abnormalities induced by preoperative cardiac structural changes. We aimed to evaluate subcortical nuclei volume changes and cognition in postoperative tetralogy of Fallot (TOF) children, and analyze their relationship with preoperative cardiac structural changes. Methods This case-control study involved thirty-six children with repaired TOF and twenty-nine healthy controls (HCs). We utilized three-dimensional (3D) T1-weighted high-resolution structural images alongside the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) to evaluate the cognitive differences between the TOF and HC group. Results We observed notable differences in subcortical nuclei volume between the TOF and HC group, specifically in the left amygdala nucleus (LAM, TOF: 1292.60 ± 155.57; HC: 1436.27 ± 140.62, p

Abstract Background Guinea pigs exhibit numerous physiological similarities to humans, yet the details of their preimplantation embryonic development remain largely unexplored. Results To address this, we conducted single-cell sequencing on the transcriptomes of cells isolated from the zygote stage through preimplantation stages in guinea pigs. This study identified seven distinct cell types within guinea pig preimplantation embryos and pinpointed the timing of zygotic gene activation (ZGA). Trajectory analysis revealed a bifurcation into two lineage-specific branches, accompanied by alterations in specific pathways, including oxidative phosphorylation and vascular endothelial growth factor (VEGF). Additionally, co-expressed gene network analysis highlighted the most enriched functional modules for the epiblast (EPI), primitive endoderm (PrE), and inner cell mass (ICM). Finally, we compared the similarities and differences between human and guinea pig epiblasts (EPIs). Conclusion This study systematically constructs a cell atlas of guinea pig preimplantation embryonic development, offering fresh insights into mammalian embryonic development and providing alternative experimental models for studying human embryonic development.

Abstract Two-dimensional (2D) materials, as adsorbents, have garnered great attention in removing heavy metal ions (HMIs) from drinking water due to their extensive exposed adsorption sites. Nevertheless, there remains a paucity of experimental research to remarkably unlock their adsorption capabilities and fully elucidate their adsorption mechanisms. In this work, exceptional lead ion (Pb2+) (a common HMI) removal capacity (up to 758 mg g−1) is achieved using our synthesized metallic 1T/1T′ phase 2D transition metal dichalcogenide (TMD, including MoS2, WS2, TaS2, and TiS2) nanosheets, which hold tremendous activated S chemisorption sites. The residual Pb2+ concentration can be reduced from 2 mg L−1 to 2 μg L−1 within 0.5 min, meeting the drinking water standards following World Health Organization guideline (Pb2+ concentrations

Abstract Necroptosis is intimately associated with the initiation and progression of colon adenocarcinoma (COAD). However, studies on necroptosis-related genes (NRGs) and the regulating long non-coding RNAs (NRGlncRNAs) in the context of COAD are limited. We retrieved the cancer genome atlas (TCGA) to collect datasets of NRGs and NRGlncRNAs on COAD patients. The risk model constructed using Cox and least absolute shrinkage and selection operator (LASSO) regression was then employed to identify NRGs and NRGlncRNAs with prognostic significance. Subsequently, we validated the results using gene expression omnibus (GEO) datasets from different populations, conducted Mendelian randomization (MR) analysis to explore the potential causal relationships between prognostic NRGs and COAD, and conducted cell experiments to verify the expression of prognostic NRGlncRNAs in COAD. Furthermore, we explored potential pathways and regulatory mechanisms of these prognostic NRGlncRNAs and NRGs in COAD through enrichment analysis, immune cell correlation analysis, tumor microenvironment analysis, immune checkpoint analysis, tumor sample clustering, and so on. We identified eight NRGlncRNAs (AC245100.5, AP001619.1, LINC01614, AC010463.3, AL162595.1, ITGB1-DT, LINC01857, and LINC00513) used for constructing the prognostic model and nine prognostic NRGs (AXL, BACH2, CFLAR, CYLD, IPMK, MAP3K7, ATRX, BRAF, and OTULIN) with regulatory relationships with them, and their validation was performed using GEO and GWAS datasets, as well as cell experiments, which showed largely consistent results. These prognostic NRGlncRNAs and NRGs modulate various biological functions, including immune inflammatory response, oxidative stress, immune escape, telomere regulation, and cytokine response, influencing the development of COAD. Additionally, stratified analysis of the high-risk and low-risk groups based on the prognostic model revealed elevated expression of immune cells, increased expression of tumor microenvironment cells, and upregulation of immune checkpoint gene expression in the high-risk group. Finally, through cluster analysis, we identified tumor subtypes, and the results of cluster analysis were essentially consistent with the analysis between risk groups. The prognostic NGRlncRNAs and NRGs identified in our study serve as prognostic indicators and potential therapeutic targets for COAD, providing a theoretical basis for the clinical diagnosis and treatment of COAD and offering guidance for further research.

Mg-Gd-Y-Zn-Zr alloy is an essential high strength weight ratio material in the aerospace field. It has been fabricated gradually using wire-arc additive manufacturing (WAAM), a key integrated preparation technology for large-scale parts. In this investigation, a Mg-9.4Gd-3.3Y-1.75Zn-0.38Zr (wt.%) alloy was prepared by WAAM based in the cold metal transfer (CMT) process. Investigations were conducted into the microstructural evolution and mechanical properties during the solution and aging heat treatment. With an average grain size of 18.30 ± 9.89 μm, the as-deposited Mg-Gd-Y-Zn-Zr alloy is mainly composed of α-Mg matrix, (Mg,Zn)3(Gd,Y) eutectic phase, RE-rich particles, and rare-earth (RE) segregation zone. Following a 12-h heat treatment at 500 °C, the average grain size of the α-Mg matrix showed no appreciable changes. The α-Mg matrix also dissolved the (Mg, Zn)3(Gd, Y) phases, which led to the production of long period stacking ordered (LPSO) phases and the disappearance of the RE-segregation zone. The appearance of the nanoscale β′ phase following aging heat treatment. The deposited alloys had the following tensile properties: 196.5 ± 24 MPa for ultimate tensile strength (UTS), 157.2 ± 14 MPa for yield strength (YS), and 2.08 ± 0.8% for elongation (EL). The formation of LSPO phase can improve the ductility while the formation of nanoscale β′ phase can improve the strength of Mg-Gd-Y-Zn-Zr alloy. Thus, after heat treatment at 500 °C for 12 h, the alloy's EL rose to 9.27 ± 0.94%. After further aging heat treatment for 225 °C at 24h, the alloy's YS rose to 234±3 MPa and its EL was 4.56 ± 0.25%.

Abstract Regulated cell death (RCD) plays a crucial role in the immune microenvironment, development, and progression of hepatocellular carcinoma (HCC). However, reliable immune-related cell death signatures have not been explored. In this study, we collected 12 RCD modes (e.g., apoptosis, ferroptosis, and cuproptosis), including 1078 regulators, to identify immune-related cell death genes based on HCC immune subgroups. Using a developed competitive machine learning framework, nine genes were screened to construct the immune-related cell death index (IRCDI), which is available for online application. Multi-omics data, along with clinical features, were analyzed to explore the HCC malignant heterogeneity. To validate the efficacy of this model, more than 18 independent cohorts, including survival and diverse treatment cohorts and datasets, were utilized. These findings were further validated using in-house samples and molecular biological experiments. Overall, the IRCDI may have a wide application in individual therapeutic decision-making and improving outcomes for HCC patients.

Abstract Neurodevelopmental disorders are currently one of the major complications faced by patients with congenital heart disease (CHD). Chronic hypoxia in the prenatal and postnatal preoperative brain may be associated with neurological damage and impaired long-term cognitive function, but the exact mechanisms are unknown. In this study, we find that delayed neuronal migration and impaired synaptic development are attributed to altered Atoh1 under chronic hypoxia. This is due to the fact that excessive Atoh1 facilitates expression of Kif21b, which causes excess in free-state α-tubulin, leading to disrupted microtubule dynamic stability. Furthermore, the delay in neonatal brain maturation induces cognitive disabilities in adult mice. Then, by down-regulating Atoh1 we alleviate the impairment of cell migration and synaptic development, improving the cognitive behavior of mice to some extent. Taken together, our work unveil that Atoh1 may be one of the targets to ameliorate hypoxia-induced neurodevelopmental disabilities and cognitive impairment in CHD.

Abstract Background Cotton is an important economic crop and a host of Liriomyza sativae. Pectin methylesterase (PME)-mediated pectin metabolism plays an indispensable role in multiple biological processes in planta. However, the pleiotropic functions of PME often lead to unpredictable effects on crop resistance to pests. Additionally, whether and how PME affects susceptibility to Liriomyza sativae remain unclear. Results Here, we isolated GhPME36, which is located in the cell wall, from upland cotton (Gossypium hirsutum L.). Interestingly, the overexpression of GhPME36 in cotton caused severe susceptibility to Liriomyza sativae but increased leaf biomass in Arabidopsis. Cytological observations revealed that the cell wall was thinner with more demethylesterified pectins in GhPME36-OE cotton leaves than in WT leaves, whereas the soluble sugar content of GhPME36-OE cotton leaf cell walls was accordingly higher; both factors attracted Liriomyza sativae to feed on GhPME36-OE cotton leaves. Metabolomic analysis demonstrated that glucose was significantly differentially accumulated. Transcriptomic analysis further revealed DEGs enriched in glucose metabolic pathways when GhPME36 was overexpressed, suggesting that GhPME36 aggravates susceptibility to Liriomyza sativae by affecting both the structure and components of cell wall biosynthesis. Moreover, GhPME36 interacts with another pectin-modifying enzyme, GhC/VIF1, to maintain the dynamic stability of pectin methyl esterification. Conclusions Taken together, our results reveal the cytological and molecular mechanisms by which GhPME36 aggravates susceptibility to Liriomyza sativae. This study broadens the knowledge of PME function and provides new insights into plant resistance to pests and the safety of genetically modified plants.

This study aimed to investigate the effects of solid-state fermentation products of yeast (SFPY) on liver and intestinal health and disease resistance of common carp (Cyprinus carpio). A total of 200 common carp with an initial average weight of 2.55 ± 0.004 g were divided into 5 groups (4 replications per group and 10 fish per replication), and were fed with one of five diets, including a control diet and 4 diets supplemented with 2‰ (Y2), 3‰ (Y3), 4‰ (Y4), or 5‰ (Y5) SFPY, respectively, for 8 weeks. Results indicated that, the addition of SFPY to the diet of common carp did not affect the growth performance or survival rate of fish (P = 0.253). Interestingly, with the addition of SFPY, the triacylglycerol (TAG) content of the liver presented a linear decreasing tendency (P = 0.004), with significantly decreased in Y4 and Y5 groups (P = 0.035) compared with control. Serum lipopolysaccharide (LPS) content and diamine oxidase (DAO) activity presented a negative linear relationship with the addition of SFPY (P = 0.015, P = 0.030), while serum lipopolysaccharide binding protein (LBP) content first decreased and then increased (P

Abstract Microbes have been demonstrated to be closely linked to diseases that pose a major threat to human health. Computing technologies can help researchers find potential microbe-drug associations more quickly and precisely. In this study, we introduced a novel computational prediction model called GLNNMDA based on global and local features of microbes and drugs to infer possible microbe-drug correlations. In GLNNMDA, we first constructed a heterogeneous network based on known microbe-drug relationships by integrating multiple similarity metrics of drugs and microbes. Subsequently, low-dimensional features will be extracted for nodes in the heterogeneous network by adopting the graph attention encoder. Next, based on combining these low-dimensional features with multiple properties of microbes and drugs to form a new comprehensive feature matrix, we would utilize the GLF module to extract the global and local features for microbes and drugs respectively, and then, we would further fuse these global and local features to come up with predictions of possible microbe-drug associations. Moreover, in order to evaluate the prediction performance of GLNNMDA, under the framework of fivefold cross-validation, intensive comparative experiments and case studies were done on different well-known public databases. The results showed that GLNNMDA obtained the highest AUC values as well as AUPR values of 0.9802 ± 0.0011, 0.9773 ± 0.0021 and 0.8586 ± 0.0004, 0.8008 ± 0.0031 in the two databases, MDAD and aBiofilm, respectively, compared to the state-of-the-art competing prediction methods. In addition, case studies of well-known microorganisms and drugs have demonstrated the effectiveness of GLNNMDA in inferring potential microbial drug associations, which implies that GLNNMDA may be a useful tool for microbe-drug association prediction in the future. The source code is available at: “ https://github.com/KuangHaiYue/GLNNMDA.git ”.

Abstract Paired immunoglobin-like type 2 receptor beta (PILRB) mainly plays a crucial role in regulating innate immunity, but whether PILRB is involved in cancer is poorly understood. Here, we report that PILRB potentiates the PI3K/AKT pathway to drive gastric tumorigenesis by binding and stabilizing IRS4, which could hyperactivate the PI3K/AKT pathway. Firstly, the levels of PILRB are upregulated in human gastric cancer (GC) specimens and associated with poor prognosis in patients with GC. In addition, our data show that PILRB promotes cell proliferation, colony formation, cell migration and invasion in GC cells in vitro and in vivo. Mechanistically, PILRB recruits the deubiquitination enzymes OTUB1 to IRS4 and relieves K48-linked ubiquitination of IRS4, protecting IRS4 protein from proteasomal-mediated degradation and subsequent activation of the PI3K/AKT pathway. Importantly, the levels of PILRB are positively correlated with IRS4 in GC specimens. Meanwhile, we also found that PILRB reprogrammed cholesterol metabolism by altering ABCA1 and SCARB1 expression levels, and PILRB-expression confers GC cell resistance to statin treatment. Taken together, our findings illustrate that the oncogenic role of PILRB in gastric tumorigenesis, providing new insights into the regulation of PI3K/AKT signaling in GC and establishing PILRB as a biomarker for simvastatin therapy resistance in GC.

Fluid shortening is an important ingredient in the production of sponge cake. Peanut oil with 0, 43% and 85% of diacylglycerol content was used as the base oil. Different emulsifiers, such as glycerol monostearate, soy lecithin and sucrose ester, and their respective amounts, were investigated. It was found that the addition of emulsifiers had a positive effect on water-absorbing capacity, air-absorbing capacity and viscosity of the oils. Glycerol monostearate was the preferred emulsifier for fluid shortening with a recommended addition of 1.5%. The effects of different diacylglycerol content on fluid shortening and their impact on sponge cake production was also investigated. The onset oxidation temperature of the oil could be increased from 253.21 °C for PO-TAG-based fluid shortening to 263.70 °C for PO-DAG85-based fluid shortening. And the increase in diacylglycerol content leading to a lower specific gravity of the batter, which was 1.06 g/mL, 1.02 g/mL and 0.98 g/mL prepared by PO-DAG, PO-DAG43 and PO-DAG85 shortening, respectively. The results showed that diacylglycerols can be used as base oils in fluid shortening to improve the crystal network and stability of fluid shortenings, thereby reducing the specific gravity of the batter and improving the structural properties of the cake. This will extend the potential applications of diacylglycerols and increase the variety of base oils available for fluid shortening preparation.

Magnesium alloys have received a lot of attention in aerospace, automotive, electronic communications and other fields due to their low density, high specific strength and damping performance. However, magnesium alloy sheets prepared by conventional thermomechanical processing usually have a strong basal texture, resulting in poor formability. In this paper, Mg–Mn–Ce alloys with good plastic forming ability were prepared by studying and optimizing the annealing process. Under the low-temperature annealing condition, it can promote the static recrystallization and grain refinement of Mg–Mn–Ce alloy, and also eliminate the internal stress. With the increase of annealing temperature, a large amount of dispersed Mg12Ce as a stable second phase effectively hindered the grain boundary migration, while the nanoscale α-Mn forms a fine second phase to inhibit grain growth, and both of them together greatly stabilize the deformation structure of the alloy, and maintain a good grain organization under the condition of annealing temperature of 300 °C and holding time of 30 min. The present work also improves the Arrhenius constitutive equation for the Mg–Mn–Ce alloy with respect to the degree of deviation, which significantly improves the accuracy of the model in predicting the thermoforming behavior. Meanwhile, to address the shortcomings of the traditional forming method of magnesium alloy, the processing process of spreading the centrifugal casting tube flat is proposed, the forming process is simulated, and the location of easy breakage in the forming process is predicted. These findings provide microanalytical support for the improvement of forming properties of magnesium alloys by annealing.

Abstract We investigated changes in physiology and mechanical properties of diatoms exposed to chemical cues released by copepods Pseudodiaptomus annandalei. Our results showed that the diatoms Phaeodactylum tricornutum, Cylindrotheca closterium, Thalassiosira weissflogii, and Amphora coffeaeformis exhibited elevated growth rates and a substantial 2‐ to 50‐fold increase in biogenic silica (BSi) content increase when exposed to the chemical cues except for Cyclotella sp. Atomic force microscopy and X‐ray photoelectron spectroscopy analyses revealed that diatom frustules exhibited a remarkable 3‐ to 10‐fold increase in modulus and a substantial 2‐ to 5‐fold increase in hardness when they received grazing signals. The increase in the proportion of condensed silicon in the frustules could be the major reason for the more mechanically robust cells. Our results indicate that diatoms simultaneously increase their growth rate and robustness when exposed to copepod chemical cues. This study at the nanoscale enhanced our understanding of how diatoms respond to zooplankton predation in marine ecosystems.

By integrating an established rock damage model into the LS-DYNA computer code and using a widely used method for equivalently simulating multiple blastholes detonation, the damage processes of surrounding rock with different properties under the bench blasting of a large cross-section tunnel are comparatively studied. The damage distribution across the excavation cross-section is characterized by greater damage at the upper and lower ends, with lesser damage in the middle (junction between adjacent benches). For grade III surrounding rock excavated with two-bench blasting, the maximum damage depth of 3.6 m occurs in the middle floor. For grades IV and V surrounding rock excavated with three-bench blasting, the maximum damage depths both occur at the vault, with 3.6 m and 2.5 m, respectively. In bench blasting, the blasting of breaking holes closest to the excavation boundary significantly impacts surrounding rock, whereas the damage caused by smooth holes blasting is minor. The surrounding rock with poorer mechanical properties tends to exhibit greater damage under the same blasting operations. According to the proposed method, the critical Peak Particle Velocity (PPV) for cumulative damage in grades III, IV, and V surrounding rock of the Longnan tunnel is 16.8 cm/s, 13.7 cm/s, and 11.5 cm/s, respectively.

Thrombosis and infection are pivotal clinical complications associated with interventional blood-contacting devices, leading to significant morbidity and mortality. To address these issues, we present a stepwise metal-catechol-(amine) (MCA) surface engineering strategy that efficiently integrates therapeutic nitric oxide (NO) gas and antibacterial peptide (ABP) onto catheters, ensuring balanced anti-thrombotic and anti-infective properties. First, copper ions were controllably incorporated with norepinephrine and hexanediamine through a one-step molecular/ion co-assembly process, creating a NO-generating and amine-rich MCA surface coating. Subsequently, azide-polyethylene glycol 4-N-hydroxysuccinimidyl and dibenzylcyclooctyne modified ABP were sequentially immobilized on the surface via amide coupling and bioorthogonal click chemistry, ensuring the dense grafting of ABP while maintaining the catalytic efficacy for NO. This efficient integration of ABP and NO-generating ability on the catheter surface provides potent antibacterial properties and ability to resist adhesion and activation of platelets, thus synergistically preventing infection and thrombosis. We anticipate that this synergistic modification strategy will offer an effective solution for advancing surface engineering and enhancing the clinical performance of biomedical devices.

Mesenchymal stem cells (MSCs) have demonstrated significant therapeutic potential in heart failure (HF) treatment. However, their clinical application is impeded by low retention rate and low cellular activity of MSCs caused by high inflammatory and reactive oxygen species (ROS) microenvironment. In this study, monascus pigment (MP) nanoparticle (PPM) was proposed for improving adverse microenvironment and assisting in transplantation of bone marrow-derived MSCs (BMSCs). Meanwhile, in order to load PPM and reduce the mechanical damage of BMSCs, injectable hydrogels based on Schiff base cross-linking were prepared. The PPM displays ROS-scavenging and macrophage phenotype-regulating capabilities, significantly enhancing BMSCs survival and activity in HF microenvironment. This hydrogel demonstrates superior biocompatibility, injectability, and tissue adhesion. With the synergistic effects of injectable, adhesive hydrogel and the microenvironment-modulating properties of MP, cardiac function was effectively improved in the pericardial sac of rats. Our results offer insights into advancing BMSCs-based HF therapies and their clinical applications.

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

In this paper, let $ (\mathscr{A}, \mathscr{C}, \mathscr{B}) $ be a recollement of extriangulated categories. We construct a silting object and a tilting object from the two end terms of a recollement. We also show that the reverse direction holds under natural assumptions. Moreover, we show that our gluing preserves cotorsion pairs.

Abstract Abnormal cardiac development has been observed in individuals with Cornelia de Lange syndrome (CdLS) due to mutations in genes encoding members of the cohesin complex. However, the precise role of cohesin in heart development remains elusive. In this study, we aimed to elucidate the indispensable role of SMC3, a component of the cohesin complex, in cardiac development and its underlying mechanism. Our investigation revealed that CdLS patients with SMC3 mutations have high rates of congenital heart disease (CHD). We utilized heart-specific Smc3-knockout (SMC3-cKO) mice, which exhibit varying degrees of outflow tract (OFT) abnormalities, to further explore this relationship. Additionally, we identified 16 rare SMC3 variants with potential pathogenicity in individuals with isolated CHD. By employing single-nucleus RNA sequencing and chromosome conformation capture high-throughput genome-wide translocation sequencing, we revealed that Smc3 deletion downregulates the expression of key genes, including Ets2, in OFT cardiac muscle cells by specifically decreasing interactions between super-enhancers (SEs) and promoters. Notably, Ets2-SE-null mice also exhibit delayed OFT development in the heart. Our research revealed a novel role for SMC3 in heart development via the regulation of SE-associated genes, suggesting its potential relevance as a CHD-related gene and providing crucial insights into the molecular basis of cardiac development.

Abstract Background Nonalcoholic fatty liver disease (NAFLD) shares common pathogenic mechanisms of type 2 diabetes mellitus (T2DM) with upregulated advanced glycation end products (AGEs). Here, we aim to investigate the effect of FPS-ZM1, an inhibitor for receptor for AGEs (RAGE), on lipid deposition in the liver of mice. Methods KK-Ay mice were used as models of T2DM with NAFLD, while C57BL/6j mice were controls. Additionally, KK-Ay mice were treated with DMSO (with a concentration of 1%), with or without FPS-ZM1 (3 mg/kg/day, i.p). Lipid deposition in hepatocytes was observed using oil red O stain. Levels of AGEs and RAGE were measured. Sterol regulatory element-binding protein-1c (SREBP-1c), as well as nuclear factor κB p65 (p65 nfκb) and mitogen-activated protein kinase p38 (p38 MAPK), were also detected. Results Lipid deposition is increased in the hepatocytes of KK-Ay mice compared to C57BL/6j mice. In addition, not only were the levels of AGEs elevated in plasma, but also the levels of RAGE in liver tissue. Although total SREBP-1c levels did not change in the liver of diabetic mice, mature SREBP-1c increased in KK-Ay mice with diabetes mellitus. Moreover, diabetic mice showed increased levels of phosphorylated-p65 nfκb (p-p65 nfκb) and phosphorylated-p38 MAPK (p-p38 MAPK). On the contrary, FPS-ZM1 decreased lipid deposition in liver cells, as well as mature SREBP-1c, p-p65 nfκb and p-p38 MAPK levels in liver tissue. Conclusion Generally, FPS-ZM1 may attenuate lipid deposition in hepatocytes of diabetic mice via SREBP-1c down-regulation. This may depend on the downregulation of p65 nfκb and p38 MAPK phosphorylation.

Abstract Objective The objective of this study was to investigate the clinical characteristics and analysis of related factors associated with early death in newly diagnosed patients with acute promyelocytic leukemia (APL). Methods This retrospective study included patients who visited our hospital between January 2010 and August 2022 and were diagnosed with APL for the first time. We analyzed their clinical and laboratory characteristics and analysis of related factors associated with early death. Results A total of 269 patients with a primary diagnosis of APL were collected. The male to female ratio was 6:5, and the median age was 42 years (range 7–80). Among patients with initial APL diagnosis, there were 34 early deaths, resulting in an early mortality rate of 13%. The median time from diagnosis to death was 8.5 days (range 3–24). Comparative analysis of the clinical characteristics between patients who died early and those who did not, using a logistic regression model, revealed that age, white blood cell count (WBC) at initial diagnosis, and prolongation time of prothrombin time (PT) were independent risk factors for early death in patients with primary APL (P

Abstract Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud’s cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods.

Abstract Background Atrial fibrillation (AF) is acknowledged as a disease continuum. Despite catheter ablation being recommended as a primary therapy for AF, the high recurrence rates have tempered the initial enthusiasm. Insulin resistance (IR) has been established as an independent predictor for the onset of AF. However, the correlation between non-insulin-based IR indices and late AF recurrence in patients undergoing radiofrequency catheter ablation remains unknown. Methods A retrospective cohort of 910 AF patients who underwent radiofrequency catheter ablation was included in the analysis. The primary endpoint was late AF recurrence during the follow-up period after a defined blank period. The relationship between non-insulin-based IR indices and the primary endpoint was assessed using multivariate Cox hazards regression models and restricted cubic splines (RCS). Additionally, the net reclassification improvement and integrated discrimination improvement index were calculated to further evaluate the additional predictive value of the four IR indices beyond established risk factors for the primary outcome. Results During a median follow-up period of 12.00 months, 189 patients (20.77%) experienced late AF recurrence, which was more prevalent among patients with higher levels of IR. The multivariate Cox hazards regression analysis revealed a significant association between these IR indices and late AF recurrence. Among the four indices, METS-IR provided the most significant incremental effect on the basic model for predicting late AF recurrence. Multivariable-adjusted RCS curves illustrated a nonlinear correlation between METS-IR and late AF recurrence. In subgroup analysis, METS-IR exhibited a significant correlation with late AF recurrence in patients with diabetes mellitus (HR: 1.697, 95% CI 1.397 − 2.063, P

Abstract Immune checkpoint inhibitor (ICI)-induced myocarditis involves intensive immune/inflammation activation; however, its molecular basis is unclear. Here, we show that gasdermin-E (GSDME), a gasdermin family member, drives ICI-induced myocarditis. Pyroptosis mediated by GSDME, but not the canonical GSDMD, is activated in myocardial tissue of mice and cancer patients with ICI-induced myocarditis. Deficiency of GSDME in male mice alleviates ICI-induced cardiac infiltration of T cells, macrophages, and monocytes, as well as mitochondrial damage and inflammation. Restoration of GSDME expression specifically in cardiomyocytes, rather than myeloid cells, in GSDME-deficient mice reproduces ICI-induced myocarditis. Mechanistically, quantitative proteomics reveal that GSDME-dependent pyroptosis promotes cell death and mitochondrial DNA release, which in turn activates cGAS-STING signaling, triggering a robust interferon response and myocardial immune/inflammation activation. Pharmacological blockade of GSDME attenuates ICI-induced myocarditis and improves long-term survival in mice. Our findings may advance the understanding of ICI-induced myocarditis and suggest that targeting the GSDME-cGAS-STING-interferon axis may help prevent and manage ICI-associated myocarditis.

Abstract Type II collagen (CII), a key component of cartilaginous tissues, offers an attractive option for fabricating biomimetic scaffolds that provide mechanical support and biochemical signals to facilitate cell adhesion, proliferation, and differentiation. Advances in fabrication technologies have enabled the creation of intricate and multicellular CII-based scaffolds that more accurately replicate the natural structure of tissues. Here, we review the application of CII in tissue engineering and highlight its crucial contributions to the regeneration of hyaline cartilage and intervertebral discs. Additionally, we discuss the versatile roles of CII in other biomedical applications, including disease modeling, bone regeneration, and the development of artificial immune organs.

Abstract Background High levels of lactate are positively associated with prognosis and mortality in pulmonary hypertension (PH). Lactate dehydrogenase A (LDHA) is a key enzyme for the production of lactate. This study is undertaken to investigate the role and molecular mechanisms of lactate and LDHA in PH. Methods Lactate levels were measured by a lactate assay kit. LDHA expression and localization were detected by western blot and Immunofluorescence. Proliferation and migration were determined by CCK8, western blot, EdU assay and scratch-wound assay. The right heart catheterization and right heart ultrasound were measured to evaluate cardiopulmonary function. Results In vitro, we found that lactate promoted proliferation and migration of pulmonary artery smooth muscle cells (PASMCs) in an LDHA-dependent manner. In vivo, we found that LDHA knockdown reduced lactate overaccumulation in the lungs of mice exposed to hypoxia. Furthermore, LDHA knockdown ameliorated hypoxia-induced vascular remodeling and right ventricular dysfunction. In addition, the activation of Akt signaling by hypoxia was suppressed by LDHA knockdown both in vivo and in vitro. The overexpression of Akt reversed the inhibitory effect of LDHA knockdown on proliferation in PASMCs under hypoxia. Finally, LDHA inhibitor attenuated vascular remodeling and right ventricular dysfunction in Sugen/hypoxia mouse PH model, Monocrotaline (MCT)-induced rat PH model and chronic hypoxia-induced mouse PH model. Conclusions Thus, LDHA-mediated lactate production promotes pulmonary vascular remodeling in PH by activating Akt signaling pathway, suggesting the potential role of LDHA in regulating the metabolic reprogramming and vascular remodeling in PH.

Abstract The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD–GAT–BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

Abstract Objective This study aims to conduct an extensive analysis of autoimmune bullous diseases, particularly pemphigus vulgaris and bullous pemphigoid, in Shanghai, China, from 2016 to 2023. It seeks to understand the demographic profiles, comorbidities, mortality rates, risk factors, and socioeconomic impacts associated with autoimmune bullous disease. Methods A cross-sectional study design was employed, enrolling 1,072 patients. Diagnostic measures included clinical manifestations, histopathology, direct immunofluorescence, and serologic tests. The study also involved a detailed socioeconomic analysis and evaluation of occupational risks. Results The findings highlight a significant occupational risk in industries requiring enhanced safety measures, with a notable prevalence of autoimmune bullous disease among workers in these sectors. A considerable portion of the patients were from low-income backgrounds with limited literacy, indicating the economic burden of autoimmune bullous disease. A key discovery of the study is the potential pathological link between autoimmune bullous disease and interstitial lung disease. Conclusion This research, one of the first comprehensive studies on autoimmune bullous disease in China, underscores the need for targeted healthcare strategies and further investigation into autoimmune bullous disease, particularly its relationship with interstitial lung disease.

Abstract Globally, breast cancer continues to be the leading cause of cancer-related incidence and mortality among females. Research has shown that sleep patterns significantly influence tumor onset and progression. In this research, the association was examined through the application of a two-sample Mendelian randomization (MR) approach. For the analysis of seven sleep patterns, genetic tools were sourced from both the UK Biobank and 23andMe, including morning/evening person (chronotype) n = 177,604, morning person (chronotype) n = 248,094, daytime dozing/sleepiness n = 193,472, getting up in the morning n = 193,717, and sleeplessness n = 193,987; sleep duration n = 192,810; and nap during the day n = 166,853. The Breast Cancer Association Consortium (BCAC) supplied genome-wide association studies (GWAS) data, including 133,384 breast cancer cases and 113,789 controls, alongside subtype-specific data with 106,278 cases and 91,477 controls. We discovered that chronotype encompasses both morning and evening types contributes to the risk of overall breast cancer. While daytime dozing and morning person (chronotype) are linked to a lower risk of breast cancer in general, In subtype-specific analyses, morning person (chronotype) was negatively associated with luminal B, HER2-negative-like, and daytime dozing was negatively correlated with luminal A-like, luminal B-like, and HER2-enriched-like. The study corroborates that chronotype is a danger element for breast cancer, aligning with previous observational findings. The association between being a morning person (chronotype) or having daytime dozing and a decreased risk of breast cancer underscores the significance of sleep patterns in formulating strategies for cancer prevention.

Abstract Glioma, a malignant and infiltrative neoplasm of the central nervous system, poses a significant threat due to its high mortality rates. Branched-chain amino acid transaminase 1 (BCAT1), a key enzyme in branched-chain amino acid (BCAA) catabolism, exhibits elevated expression in gliomas and correlates strongly with poor prognosis. Nonetheless, the regulatory mechanisms underlying this increased BCAT1 expression remains incompletely understood. In this study, we reveal that ubiquitination at Lys360 facilitates BCAT1 degradation, with low ubiquitination levels contributing to high BCAT1 expression in glioma cells. The Carboxyl terminus of Hsc70-interacting protein (CHIP), an E3 ubiquitin ligase, interacts with BCAT1 via its coiled-coil (CC) domain, promoting its K48-linkage ubiquitin degradation through proteasomal pathway. Moreover, CHIP-mediated BCAT1 degradation induces metabolic reprogramming, and impedes glioma cell proliferation and tumor growth both in vitro and in vivo. Furthermore, a positive correlation is observed between low CHIP expression, elevated BCAT1 levels, and unfavorable prognosis among glioma patients. Additionally, we show that the CHIP/BCAT1 axis enhances glioma sensitivity to temozolomide by reducing glutathione (GSH) synthesis and increasing oxidative stress. These findings underscore the critical role of CHIP/BCAT1 axis in glioma cell proliferation and temozolomide sensitivity, highlighting its potential as a diagnostic marker and therapeutic target in glioma treatment.

Abstract Purpose To build an Mult-Task Learning (MTL) based Artificial Intelligence(AI) model that can simultaneously predict clinical stage, histology, grade and LNM for cervical cancer before surgery. Methods This retrospective and prospective cohort study was conducted from January 2001 to March 2014 for the training set and from January 2018 to November 2021 for the validation set at Beijing Chaoyang Hospital, Capital Medical University. Preoperative clinical information of cervical cancer patients was used. An Artificial Neural Network (ANN) algorithm was used to build the MTL-based AI model. Accuracy and weighted F1 scores were calculated as evaluation indicators. The performance of the MTL model was compared with Single-Task Learning (STL) models. Additionally, a Turing test was performed by 20 gynecologists and compared with this AI model. Results A total of 223 cervical cancer cases were retrospectively enrolled into the training set, and 58 cases were prospectively collected as independent validation set. The accuracy of this cervical cancer AI model constructed with ANN algorithm in predicting stage, histology, grade and LNM were 75%, 95%, 86% and 76%, respectively. And the corresponding weighted F1 score were 70%, 94%, 86%, and 76%, respectively. The average time consumption of AI simultaneously predicting stage, histology, grade and LNM for cervical cancer was 0.01s (95%CI: 0.01–0.01) per 20 patients. The mean time consumption doctor and doctor with AI were 581.1s (95%CI: 300.0-900.0) per 20 patients and 534.8s (95%CI: 255.0-720.0) per 20 patients, respectively. Except for LNM, both the accuracy and F-score of the AI model were significantly better than STL AI, doctors and AI-assisted doctors in predicting stage, grade and histology. (P

Abstract Objectives This study aimed to investigate the association of cystatin C, serum creatinine and sarcopenia index with cardiovascular and all-cause death in general population. Methods Data of participants from the National Health and Nutrition Examination Surveys (NHANES) from 1999 to 2004 were used and all participants were followed up regularly until December 31, 2019. Multivariable Cox analysis was used to investigate the association of cystatin C, serum creatinine and sarcopenia index with cardiovascular and all-cause death. Restricted cubic spline was conducted to evaluate the nonlinear association. Results A total of 9894 participants with a mean age of 45.64 years were enrolled and followed up for a mean duration of 15.62 ± 4.68 years. Approximately 50.3% were male and there were a total of 2681 all-cause deaths and 691 cardiovascular deaths recorded during the follow-up period. In final adjusted model, compared with the first quartile of cystatin C (

Abstract Background There is sufficient evidence that women with atrial fibrillation (AF) have a greater symptom burden than men with AF and are more likely to experience recurrence after catheter ablation. However, the mechanisms underlying these sex differences are unclear. Methods We prospectively enrolled 125 consecutive patients, including 40 non-AF patients and 85 AF patients, who underwent high-density voltage mapping during sinus rhythm and AF patients who underwent first ablation. Results Overall, 37 (44%) female patients with AF and 24 (60%) female non-AF patients with a mean age of 61.7 ± 11.6 years and 53.6 ± 16.7 years, respectively, were enrolled in this study. The results showed that the atrial voltage of female AF patients was significantly lower than that of male AF patients (1.11 ± 0.58 mV vs. 1.53 ± 0.65 mV; P = 0.003), while there were no significant sex differences in non-AF patients (3.02 ± 0.86 mV vs. 3.21 ± 0.84 mV; P = 0.498). Multiple linear regression analysis revealed that female sex (− 0.29, 95% confidence interval [CI] − 0.64 to − 0.13, P = 0.004) and AF type (− 0.32, 95% CI − 0.69 to − 0.13, P = 0.004) were the only factors independently associated with voltage. Compared with men, women in the paroxysmal AF group had a 3.5-fold greater incidence of recurrence (adjusted hazard ratio 4.49; 95% CI 1.101–18.332, P = 0.036). Both globally and regionally, the results showed that sex-related differences in voltage values occurred prominently in paroxysmal AF patients but not in nonparoxysmal AF patients. Conclusion Sex-related differences in atrial substrates and arrhythmia-free survival were found in paroxysmal AF patients, suggesting the existence of sex-related pathophysiological factors.

Abstract Photothermal CO2 conversion to ethanol offers a sustainable solution for achieving net-zero carbon management. However, serious carrier recombination and high C-C coupling energy barrier cause poor performance in ethanol generation. Here, we report a Cu/Cu2Se-Cu2O heterojunction-nanosheet array, showcasing a good ethanol yield under visible–near-infrared light without external heating. The Z-scheme Cu2Se-Cu2O heterostructure provides spatially separated sites for CO2 reduction and water oxidation with boosted carrier transport efficiency. The microreactors induced by Cu2Se nanosheets improve the local concentration of intermediates (CH3* and CO*), thereby promoting C-C coupling process. Photothermal effect of Cu2Se nanosheets elevates system’s temperature to around 200 °C. Through synergizing electron and heat flows, we achieve an ethanol generation rate of 149.45 µmol g−1 h−1, with an electron selectivity of 48.75% and an apparent quantum yield of 0.286%. Our work can serve as inspiration for developing photothermal catalysts for CO2 conversion into multi-carbon chemicals using solar energy.