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Abstract The photothermal sensitivity of tobacco refers to the degree to which tobacco responds to changes in light and temperature conditions in its growth environment, which is crucial for determining the planting area of cultivars and improving tobacco yield and quality. In order to accurately and effectively evaluate the photothermal sensitivity of tobacco cultivars, this study selected five cultivars and their hybrid combinations with significant differences planted under different ecological conditions from 2021 to 2022 as materials. The experiment was conducted in two locations with significant differences in temperature and light. We measured the agronomic traits and biomass of the experimental materials, and constructed an effective tobacco photothermal sensitivity evaluation model using principal component analysis, membership function, and regression analysis. The reliability of the model was evaluated by utilizing the photosynthetic characteristics, chlorophyll content, and antioxidant enzyme system activity of the experimental materials. The results showed that tobacco biomass is the most important principal component in agricultural traits, and the comprehensive evaluation model for tobacco photothermal sensitivity is: y = 0.4571y1 + 0.2406y2 + 0.1725y3, where the fitting coefficients R2 of y1, y2, and y3 are 0.945, 0.851, and 0.977, respectively; The photothermal sensitivity of the experimental materials was calculated using this model, and the comprehensive ranking of the 11 experimental materials is: G3

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS) is a rapidly progressing infectious disease with a high fatality rate caused by a novel bunyavirus (SFTSV). The role of lipids in viral infections is well-documented; however, the specific alterations in lipid metabolism during SFTSV infection remain elusive. This study aims to elucidate the lipid metabolic dysregulations in the early stages of SFTS patients. Methods This study prospectively collected peripheral blood sera from 11 critical SFTS patients, 37 mild SFTS patients, and 23 healthy controls during the early stages of infection for lipidomics analysis. A systematic bioinformatics analysis was conducted from three aspects integrating lipid differential expressions, lipid differential correlations, and lipid-clinical indices correlations to reveal the serum lipid metabolic dysregulation in SFTSV-infected individuals. Results Our findings reveal significant lipid metabolic dysregulation in SFTS patients. Specifically, compared to healthy controls, SFTS patients exhibited three distinct modes of lipid differential expression: increased levels of lipids including phosphatidylserine (PS), hexosylceramide (HexCer), and triglycerides (TG); decreased levels of lipids including lysophosphatidylcholine (LPC), acylcarnitine (AcCa), and cholesterol esters (ChE); and lipids showing “dual changes” including phosphatidylcholine (PC) and phosphatidylethanolamine (PE). Finally, based on lipid metabolic pathways and literature analysis, we systematically elucidated the potential mechanisms underlying lipid metabolic dysregulation in the early stage of SFTSV infection. Conclusions Our study presents the first global serum lipidome profile and reveals the lipid metabolic dysregulation patterns in the early stage of SFTSV infection. These findings provide a new basis for the diagnosis, treatment, and further investigation of the disease.

Abstract Heterosis, recognized for improving crop performance, especially in the first filial (F1) generation, remains an area of significant study in the tobacco industry. The low utilization of leaf veins in tobacco contributes to economic inefficiency and resource waste. Despite the positive impacts of heterosis on crop genetics, investigations into leaf-vein ratio heterosis in tobacco have been lacking. Understanding the mechanisms underlying negative heterosis in leaf vein ratio at the molecular level is crucial for advancing low vein ratio leaf breeding research. This study involved 12 hybrid combinations and their parental lines to explore heterosis associated with leaf vein ratios. The hybrids displayed diverse patterns of positive or negative leaf vein ratio heterosis across different developmental stages. Notably, the F1 hybrid (G70 × Qinggeng) consistently exhibited substantial negative heterosis, reaching a maximum of -19.79% 80 days after transplanting. A comparative transcriptome analysis revealed that a significant proportion of differentially expressed genes (DEGs), approximately 39.04% and 23.73%, exhibited dominant and over-dominant expression patterns, respectively. These findings highlight the critical role of non-additive gene expression, particularly the dominance pattern, in governing leaf vein ratio heterosis. The non-additive genes, largely associated with various GO terms such as response to abiotic stimuli, galactose metabolic process, plant-type cell wall organization, auxin-activated signaling pathway, hydrolase activity, and UDP-glycosyltransferase activity, were identified. Furthermore, KEGG enrichment analysis unveiled their involvement in phenylpropanoid biosynthesis, galactose metabolism, plant hormone signal transduction, glutathione metabolism, MAPK signaling pathway, starch, and sucrose metabolism. Among the non-additive genes, we identified some genes related to leaf development, leaf size, leaf senescence, and cell wall extensibility that showed significantly lower expression in F1 than in its parents. These results indicate that the non-additive expression of genes plays a key role in the heterosis of the leaf vein ratio in tobacco. This study marks the first exploration into the molecular mechanisms governing leaf vein ratio heterosis at the transcriptome level. These findings significantly contribute to understanding leaf vein ratios in tobacco breeding strategies.

Abstract The metastasis-associated protein (MTA) family plays a crucial role in the development of breast cancer, a common malignancy with a high incidence rate among women. However, the mechanism by which each member of the MTA family contributes to breast cancer progression is poorly understood. In this study, we aimed to investigate the roles of MTA1, MTA3, and tripartite motif-containing 21 (TRIM21) in the proliferation, invasion, epithelial-mesenchymal transition (EMT), and stem cell-like properties of breast cancer cells in vivo and in vitro. The molecular mechanisms of the feedback loop between MTA1 and MTA3/TRIM21 regulated by estrogen were explored using Chromatin immunoprecipitation (ChIP), luciferase reporter, immunoprecipitation (IP), and ubiquitination assays. These findings demonstrated that MTA1 acts as a driver to promote the progression of breast cancer by repressing the transcription of tumor suppressor genes, including TRIM21 and MTA3. Conversely, MTA3 inhibited MTA1 transcription and TRIM21 regulated MTA1 protein stability in breast cancer. Estrogen disrupted the balance between MTA1 and MTA3, as well as between MTA1 and TRIM21, thereby affecting stemness and the EMT processes in breast cancer. These findings suggest that MTA1 plays a vital role in stem cell fate and the hierarchical regulatory network of EMT through negative feedback loops with MTA3 or TRIM21 in response to estrogen, supporting MTA1, MTA3, and TRIM21 as potential prognostic biomarkers and MTA1 as a treatment target for future breast cancer therapies.

Abstract Low‐light image enhancement is a longstanding challenge in low‐level vision, as images captured in low‐light conditions often suffer from significant aesthetic quality flaws. Recent methods based on deep neural networks have made impressive progress in this area. In contrast to mainstream convolutional neural network (CNN)‐based methods, an effective solution inspired by the transformer, which has shown impressive performance in various tasks, is proposed. This solution is centred around two key components. The first is an image synthesis pipeline, and the second is a powerful transformer‐based pre‐trained model, known as the low‐light image enhancement transformer (LIET). The image synthesis pipeline includes illumination simulation and realistic noise simulation, enabling the generation of more life‐like low‐light images to overcome the issue of data scarcity. LIET combines streamlined CNN‐based encoder‐decoders with a transformer body, efficiently extracting global and local contextual features at a relatively low computational cost. The extensive experiments show that this approach is highly competitive with current state‐of‐the‐art methods. The codes have been released and are available at LIET.

Honey is not equivalent to sugar and possess a worldwide health promoting effects such as antioxidant, antibacterial, anti-inflammatory, and hepatoprotective activities. Nevertheless, the potential impacts of honey on high-fat diet induced chronic kidney disease (CKD) and gut microbiota remain to be explored. Herein a high-fat diet was used to induce a mouse CKD model, and analysis was conducted on liver, kidney, spleen indices, tissue morphology, biochemical parameters, CKD related genes, and gut microbial diversity. The results indicated that significant inhibitory effects on renal damage caused by a high-fat diet in mice and improvement in disease symptoms were observed upon honey treatment. Significant changes were also found in serum TC, TG, UA, and BUN as well as the inflammation-related protein TNF-α and IL-6 levels in renal tissues. Gene expression analysis revealed that honey intake closely relates to gut microbiota diversity, which can regulate the composition of gut microbiota, increase microbial diversity, especially Bifidobacteriales and S24_7 and promote the synthesis of short chain fatty acids (SCFAs). In summary, this study suggests that honey has both preventive and therapeutic effects on CKD, which may be associated with its ability to improve microbial composition, increase microbial diversity, and regulate SCFAs levels.

BackgroundColorectal cancer (CRC) is an aggressive primary intestinal malignancy with the third-highest incidence and second-highest mortality among all cancer types worldwide. Transcription factors (TFs) regulate cell development and differentiation owing to their ability to recognize specific DNA sequences upstream of genes. Numerous studies have demonstrated a strong correlation between TFs, the etiology of tumors, and therapeutic approaches. Here, we aimed to explore prognosis-related TFs and comprehend their carcinogenic mechanisms, thereby offering novel insights into the diagnosis and management of CRC.Materials and MethodsDifferentially expressed TFs between CRC and normal tissues were identified leveraging The Cancer Genome Atlas database, Weighted correlation network analysis and Cox regression analysis were performed to identify prognosis-related TFs. The cellular functions of hub TF zinc finger E-box binding homeobox 1 (ZEB1) were determined using by 5-ethynyl-2′-deoxyuridine and cell invasion assays in CRC cells. RNA-sequencing, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses were used to identify the cellular processes in which ZEB1 participates. Immunoaffinity purification, silver staining mass spectrometry, and a chromatin immunoprecipitation assay were conducted to search for proteins that might interact with ZEB1 and the target genes they jointly regulate.ResultsThirteen central TFs related to prognosis were identified through bioinformatics analysis techniques. Among these TFs, ZEB1 emerged as the TF most closely associated with CRC, as determined through a combination of regulatory network diagrams, survival curves, and phenotype analyses. ZEB1 promotes CRC cell growth by recruiting the NuRD(MTA1) complex, and the ZEB1/NuRD(MTA1) complex transcriptionally represses glycolysis-associated tumor suppressor genes.ConclusionOur study not only identified a hub biomarker related to CRC prognosis but also revealed the specific molecular mechanisms through which ZEB1 affects cancer progression. These insights provide crucial evidence for the diagnosis of CRC and potential treatment opportunities.

BackgroundCancers arise from genetic and epigenetic abnormalities that affect oncogenes and tumor suppressor genes, compounded by gene mutations. The N6-methyladenosine (m6A) RNA modification, regulated by methylation regulators, has been implicated in tumor proliferation, differentiation, tumorigenesis, invasion, and metastasis. However, the role of m6A modification patterns in the tumor microenvironment of gastric cancer (GC) remains poorly understood.Materials and methodsIn this study, we analyzed m6A modification patterns in 267 GC samples utilizing 31 m6A regulators. Using consensus clustering, we identified two unique subgroups of GC. Patients with GC were segregated into high- and low-infiltration cohorts to evaluate the infiltration proportions of the five prognostically significant immune cell types. Leveraging the differential genes in GC, we identified a “green” module via Weighted Gene Co-expression Network Analysis. A risk prediction model was established using the LASSO regression method.ResultsThe “green” module was connected to both the m6A RNA methylation cluster and immune infiltration patterns. Based on “Module Membership” and “Gene Significance”, 37 hub genes were identified, and a risk prediction model incorporating nine hub genes was established. Furthermore, methylated RNA immunoprecipitation and RNA Immunoprecipitation assays revealed that YTHDF1 elevated the expression of DNMT3B, which synergistically promoted the initiation and development of GC. We elucidated the molecular mechanism underlying the regulation of DNMT3B by YTHDF1 and explored the crosstalk between m6A and 5mC modification.Conclusionm6A RNA methylation regulators are instrumental in malignant progression and the dynamics of tumor microenvironment infiltration of GC. Assessing m6A modification patterns and tumor microenvironment infiltration characteristics in patients with GC holds promise as a valuable prognostic biomarker.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains the leading cause of mortality by a single infectious agent in the world. M. tuberculosis infection could also result in clinical chronic infection, known as latent TB infection (LTBI). Compared to the current limited treatment, several subunit vaccines showed immunotherapeutic effects and were included in clinical trials. In this study, a subunit vaccine of Ag85B with a novel mucosal adjuvant c-di-AMP (Ag85B:c-di-AMP) was delivered intranasally to a persistent M. tuberculosis H37Ra infection mouse model, which also presented the asymptomatic characteristics of LTBI. Compared with Ag85B immunization, Ag85B:c-di-AMP vaccination induced stronger humoral immune responses, significantly higher CD4+ T cells recruitment, enhanced Th1/Th2/Th17 profile response in the lung, decreased pathological lesions of the lung, and reduced M. tuberculosis load in mice. Taken together, Ag85B:c-di-AMP mucosal route immunization provided an immunotherapeutic effect on persistent M. tuberculosis H37Ra infection, and c-di-AMP, as a promising potential mucosal adjuvant, could be further used in therapeutic or prophylactic vaccine strategies for persistent M. tuberculosis infection as well as LTBI.

Abstract Background Sarcopenia is a senile syndrome of age-related muscle loss. It is thought to affect the development of chronic kidney disease and has a serious impact on the quality of life of the elder adults. Little is known about the association between sarcopenia and new-onset chronic kidney disease in middle-aged and elder adults. Using nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), we conducted a longitudinal analysis to investigate the association between sarcopenia status and new-onset chronic kidney disease in middle-aged and elder adults in China. Methods The study population consisted of 3676 participants aged 45 or older selected from 2011 CHARLS database who had no history of chronic kidney disease at the baseline and completed the follow-up in 2015. A multivariate cox regression model was employed to examine the association between sarcopenia and the incidence of new-onset chronic kidney disease. Results Followed up for 4 years, a total of 873 (22.5%) new cases of chronic kidney disease occurred. Among them, participants diagnosed with sarcopenia (HR1.45; 95% CI 1.15–1.83) were more likely to develop new-onset chronic kidney disease than those without sarcopenia. Similarly, patients with sarcopenia were more likely to develop new-onset chronic kidney disease than those with possible sarcopenia (HR 1.27; 95%CI 1.00-1.60). Subgroup analysis revealed that elder adults aged between 60 and 75 years old (HR 1.666; 95%CI 1.20-22.28), with hypertension (HR 1.57; 95%CI 1.02–2.40), people with sarcopenia had a significantly higher risk of developing new-onset chronic kidney disease than those without sarcopenia (all P

The deployment of multi-agent systems (MASs) is widely used in the fields of unmanned agricultural machineries, unmanned aerial vehicles, intelligent transportation, etc. To make up for the defect that the existing PDE-based results are overly idealistic in terms of system models and control strategies, we study the PDE-based deployment of clustered nonlinear first-order and second-order MASs over a finite-time interval (FTI). By designing special communication protocols, the collective dynamics of numerous agents are modeled by simple fist-order and second-order PDEs. Two practical factors, external disturbance and Markov switching topology, are considered in this paper to better match actual situations. Besides, T–S fuzzy technology is used to approximate the unknown nonlinearity of MASs. Then, by using boundary control scheme with collocated measurements, two theorems are obtained to ensure the finite-time $ H_\infty $ deployment of first-order and second-order agents, respectively. Finally, numerical examples are provided to illustrate the effectiveness of the proposed approaches.

This paper presents an algorithmic approach for the conceptual design of architectural surfaces represented by triangulated meshes. Specifically, we propose a method to optimise a surface according to user-specified geometric properties including the distribution of the Gaussian curvature and preferable boundary location. Designing a surface manually with specific Gaussian curvatures can be a time-consuming task, and the proposed method automates this task. Also, in the proposed approach, the resulting mesh could be encouraged to form a regular tessellation or kept close to those of the initial one. Our method relies on the idea in computational conformal geometry called circle packing and the discrete Ricch energy, which have been used for surface modelling. We develop a least-squares-based optimisation scheme by introducing a variant of the Ricci energy to accommodate flexibility in specifying design constraints such as boundary locations and convexity of the spanned surface, which are essential to architectural applications. We provide an open-source implementation of our method in Python.

Abstract Background Bone mineral density (BMD) is important for the outcome of cervical spine surgery. As the gold standard of assessing BMD, dual-energy X-ray absorptiometry scans are often not ordered or go unreviewed in patients’ charts. As the supplement, MRI-based vertebral bone quality (VBQ) was found to accurately predict osteopenia/osteoporosis and postoperative complications in lumbar spine. However, discussion of the efficiency of VBQ in cervical spine is lacking. And measurement methods of VBQ in cervical spine are diverse and not universally acknowledged like lumbar spine. We aimed to compare the predictive performance of three kinds of different Cervical-VBQ (C-VBQ) scores for bone mineral density assessment in patients undergoing cervical spine surgery. HU value of cervical spine was set as a reference. Methods Adult patients receiving cervical spine surgery for degenerative diseases were retrospectively included between Jan 2015 and Dec 2022 in our hospital. The VBQ scores and HU value were measured from preoperative MRI and CT. The correlation between HU value/C-VBQs (named C-VBQ1/2/3 according to different calculating methods) and DEXA T-score was analyzed using univariate linear correlation and Pearson’s correlation. We evaluated the predictive performance of those two parameters and achieved the most appropriate cutoff value by comparing the receiver operating characteristic (ROC) curves. Results 106 patients (34 patients with T ≥ − 1.0 vs 72 patients with T

Abstract Background Proteins containing the Jumonji C (JmjC) domain participated in tumorigenesis and cancer progression. However, the mechanisms underlying this effect are still poorly understood. Our objective was to investigate the role of Jumonji and the AT‐rich interaction domain‐containing 2 (JARID2) — a JmjC family protein — in breast cancer, as well as its latent association with obesity. Methods Immunohistochemistry, The Cancer Genome Atlas, Gene Expression Omnibus, and other databases were used to analyze the expression of JARID2 in breast cancer cells. Growth curve, 5‐ethynyl‐2‐deoxyuridine (EdU), colony formation, and cell invasion experiments were used to detect whether JARID2 affected breast cancer cell proliferation and invasion. Spheroidization‐based experiments and xenotumor transplantation in NOD/SCID mice were used to examine the association between JARID2 and breast cancer stemness. RNA‐sequencing, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the cell processes in which JARID2 participates. Immunoaffinity purification and silver staining mass spectrometry were conducted to search for proteins that might interact with JARID2. The results were further verified using co‐immunoprecipitation and glutathione S‐transferase (GST) pull‐down experiments. Using chromatin immunoprecipitation (ChIP) sequencing, we sought the target genes that JARID2 and metastasis‐associated protein 1 (MTA1) jointly regulated; the results were validated by ChIP‐PCR, quantitative ChIP (qChIP) and ChIP‐reChIP assays. A coculture experiment was used to explore the interactions between breast cancer cells and adipocytes. Results In this study, we found that JARID2 was highly expressed in multiple types of cancer including breast cancer. JARID2 promoted glycolysis, lipid metabolism, proliferation, invasion, and stemness of breast cancer cells. Furthermore, JARID2 physically interacted with the nucleosome remodeling and deacetylase (NuRD) complex, transcriptionally repressing a series of tumor suppressor genes such as BRCA2 DNA repair associated (BRCA2), RB transcriptional corepressor 1 (RB1), and inositol polyphosphate‐4‐phosphatase type II B (INPP4B). Additionally, JARID2 expression was regulated by the obesity‐associated adipokine leptin via Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) pathway in the breast cancer microenvironment. Analysis of various online databases also indicated that JARID2/MTA1 was associated with a poor prognosis of breast cancer. Conclusion Our data indicated that JARID2 promoted breast tumorigenesis and development, confirming JARID2 as a target for cancer treatment.

The transition of maternal to zygotic gene expression regulation is critical for human preimplantation embryo development. In recent years, single-cell RNA sequencing (scRNA-seq) had been applied to detect the factors that regulate human oocyte maturation and early embryo development. Here, the evaluation of transcriptomes in single blastomere from the embryo collected from patients by scRNA-seq was performed. There were 20 blastomeres biopsied from 8-cell embryos of seven patients who received more than two ART cycles due to low embryo competence. Meanwhile, ten cells were collected from 8-cell embryos of four patients who received ART treatment due to male or tubal factors. The blastomeres were then evaluated using the previously established scRNA-seq method to determine the associations between their gene expression and developmental competence. The total number of genes detected in 8-cell embryos that failed to form blastocyst including maternal and zygotic mRNAs was reduced. There were 324 differently expressed genes detected among the 8-cell embryos including 65 genes that were significantly suppressed in the 8-cell embryos that failed to form blastocyst. Further analysis found these 8-cell embryos arrested at the cleavage stage due to the dysfunction of the cell cycle, DNA transcription activity, histone methylation, and cell division-related genes such as SMCO- 1, ZNF271P, ZNF679, ASF1b, BEX3, DPPA2, and ORC4. The alterations of gene expression detected in human 8-cell embryos are tightly associated with its developmental competence and could be used as targets to enhance embryo development or parameters to predict the embryo’s development outcomes.

Peptides are increasingly important resources for biological and therapeutic development, however, their intrinsic susceptibility to proteolytic degradation represents a big hurdle. As a natural agonist for GLP-1R, glucagon-like peptide 1 (GLP-1) is of significant clinical interest for the treatment of type-2 diabetes mellitus, but its in vivo instability and short half-life have largely prevented its therapeutic application. Here, we describe the rational design of a series of α/sulfono-γ-AA peptide hybrid analogues of GLP-1 as the GLP-1R agonists. Certain GLP-1 hybrid analogues exhibited enhanced stability (t1/2 > 14 days) compared to t1/2 (

Abstract Background Breast cancer (BC) metastasis is the leading cause of poor prognosis and therapeutic failure. However, the mechanisms underlying cancer metastasis are far from clear. Methods We screened candidate genes related to metastasis through genome-wide CRISPR screening and high-throughput sequencing of patients with metastatic BC, followed by a panel of metastatic model assays. The effects of tetratricopeptide repeat domain 17 (TTC17) on migration, invasion, and colony formation ability together with the responses to anticancer drugs were investigated in vitro and in vivo. The mechanism mediated by TTC17 was determined by RNA sequencing, Western blotting, immunohistochemistry, and immunofluorescence. The clinical significance of TTC17 was evaluated using BC tissue samples combined with clinicopathological data. Results We identified the loss of TTC17 as a metastasis driver in BC, and its expression was negatively correlated with malignancy and positively correlated with patient prognosis. TTC17 loss in BC cells promoted their migration, invasion, and colony formation capacity in vitro and lung metastasis in vivo. Conversely, overexpressing TTC17 suppressed these aggressive phenotypes. Mechanistically, TTC17 knockdown in BC cells resulted in the activation of the RAP1/CDC42 pathway along with a disordered cytoskeleton in BC cells, and pharmacological blockade of CDC42 abolished the potentiation of motility and invasiveness caused by TTC17 silencing. Research on BC specimens demonstrated reduced TTC17 and increased CDC42 in metastatic tumors and lymph nodes, and low TTC17 expression was linked to more aggressive clinicopathologic characteristics. Through screening the anticancer drug library, the CDC42 inhibitor rapamycin and the microtubule-stabilizing drug paclitaxel showed stronger inhibition of TTC17-silenced BC cells, which was confirmed by more favorable efficacy in BC patients and tumor-bearing mice receiving rapamycin or paclitaxel in the TTC17Low arm. Conclusions TTC17 loss is a novel factor promoting BC metastasis, that enhances migration and invasion by activating RAP1/CDC42 signaling and sensitizes BC to rapamycin and paclitaxel, which may improve stratified treatment strategies under the concept of molecular phenotyping-based precision therapy of BC.

Two-dimensional transition metal carbides/nitrides (MXenes) are emerging members of the two-dimensional material family, obtained by removing the A layer of the MAX phase through methods such as liquid-phase etching. This article summarizes the structure and properties of MXenes, as well as several preparation methods, including etching with hydrofluoric acid and fluoride salts, alkali-based etching, electrochemical etching, Lewis acid molten salt etching, and direct synthesis. Due to their unique two-dimensional structure and surface chemistry, MXenes exhibit good metallic conductivity, hydrophilicity, excellent flexibility, and ion intercalation properties, showing great potential in the research and application of supercapacitors and attracting widespread attention. The combination of MXene with other types of materials, including polymers, metal hydroxides, metal oxides, and carbon materials, takes advantage of composites to improve energy storage performance and shows great potential in the research and application of supercapacitors. This article provides a detailed summary of MXene composite materials and capacitor performance and introduces the research progress of MXene materials in the field of supercapacitor energy storage applications, aiming to provide references for the preparation of high-performance MXene supercapacitor electrode materials.

Background: To develop the preoperative prediction of ovarian lesions using regression-based statistics analyses and machine learning methods based on multiple serological biomarkers in China. Methods: 1137 patients with ovarian lesions in Zhujiang Hospital and 518 patients in others hospital in China were randomly assigned to training, test and external validation cohorts. Five machine learning classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Classifier (SVC), K-nearest Neighbor (KN), Multi-Layer Perceptron (MLP) and the Lasso-Logistics prediction model (LLRM) were used to derive diagnostic information from 23 predictors. Results: The RF model had a high diagnostic value (AUC = 0.968) in predicting benign and malignant ovarian disease. Age and MLR were also potential diagnostic indicators for predicting ovarian disease except tumor indicators. The RF model well distinguished borderline ovarian tumors (AUC = 0.742). The RFM had a high predictive power to identify ovarian serous adenocarcinoma (AUC = 0.943) and ovarian endometriosis cysts (AUC = 0.914). Conclusions: The RF models can effectively predict adnexal lesions, promising to be adjuncts to the preoperative prediction of ovarian cancer.

Abstract Background Mesenchymal stem cells (MSCs) therapy for sepsis has been extensively studied in the past decade; however, the treatment regimen and mechanism of action of MSCs remain elusive. Here, we attempted to understand the efficacy and mechanism of action of MSCs on rescuing mice with sepsis. Methods A mouse model of sepsis was produced by cecal ligation and puncture (CLP). Allogeneic adipose-derived MSCs (ADSCs) were administered by intravenous infusion at 6 h after CLP, and dose-related effects of ADSCs on these mice were determined by survival rate, histopathological changes, biochemical and coagulation parameters, bacterial load, and plasma levels of endotoxin and inflammatory cytokines. The tissue distribution of intravenously infused ADSCs in septic mice was investigated by pre-labeling ADSCs with the lipophilic membrane dye PKH26. RNA sequencing analysis was performed to assess the transcriptional changes in peripheral blood mononuclear cells (PBMCs) and the liver. Results A significant therapeutic effect of ADSCs at a dose of 2 × 107 cells/kg in septic mice was evidenced by a remarkable reduction in mortality (35.89% vs. 8.89% survival rate), blood bacterial burden, systemic inflammation, and multiple organ damage. In contrast, ADSCs at a lower dose (1 × 107 cells/kg) failed to achieve any beneficial outcomes, while ADSCs at a higher dose (4 × 107 cells/kg) caused more early death within 24 h after CLP, retaining a steady survival rate of 21.42% thereafter. PKH26-labeled ADSCs were predominantly localized in the lungs of septic mice after intravenous infusion, with only a smaller proportion of PKH26-positive signals appearing in the liver and spleen. RNA sequencing analysis identified that insufficient phagocytic activity of PBMCs in addition to a hyperactivation of the hepatic immune response was responsible for the ineffectiveness of low-dose ADSCs therapy, and acute death caused by high-dose ADSCs infusion was associated with impaired coagulation signaling in PBMCs and exacerbated hepatic hypoxic injury. Conclusions Our findings demonstrate a dose-specific effect of ADSCs on the treatment of sepsis due to dose-related interactions between exogenous stem cells and the host’s microenvironment. Therefore, a precise dosing regimen is a prerequisite for ADSCs therapy for sepsis.

Abstract Background Hypoxia inducible factor-1 (HIF-1) plays a key role in modulating post-infarct healing after myocardial ischemic injury through transcriptional regulation of hundreds of genes involved in diverse cardiac remodeling processes. However, the dynamic changes in HIF-1 target gene expression in the ischemic heart after myocardial infarction (MI) have not been well characterized. Methods We employed a rhesus monkey model of MI induced by left anterior descending artery ligation and examined the expression pattern of HIF-1 target genes in the ischemic heart at 1, 7, and 28 days after injury by bulk RNA-sequencing analysis. Results Myocardial transcriptomic analysis demonstrated a temporal-specific regulation of genes associated with the inflammatory response, cell proliferation, fibrosis and mitochondrial metabolism during the pathological progression of MI. HIF-1 target genes involved in processes related to glycolysis, angiogenesis, and extracellular matrix (ECM) remodeling also exhibited distinct expression patterns during MI progression. Copper concentrations were gradually decreased in the heart after ischemic injury, which was positively correlated with the expression of HIF-1-mediated angiogenic and glycolytic genes but negatively correlated with the expression of HIF-1-mediated ECM remodeling genes. Moreover, genes related to intracellular copper trafficking and storage were suppressed along with the loss of myocardial copper in the ischemic heart. Conclusions This study demonstrated a dynamic, functional-specific regulation of HIF-1 target gene expression during the progression of MI. The fine-tuning of HIF-1 signaling in the ischemic heart may be relate to the alteration in myocardial copper homeostasis. These findings provide transcriptomic insights into the distinct roles of HIF-1 signaling in the heart after ischemic injury, which will help determine the beneficial cutoff point for HIF-1 targeted therapy in ischemic heart diseases.

Abstract Lysine‐specific histone demethylase 1 (LSD1) is an attractive target for malignancies therapy. Nevertheless, its role in hepatocellular carcinoma (HCC) progression and the potential of its inhibitor in HCC therapy remains unclear. Here, we show that LSD1 overexpression in human HCC tissues is associated with HCC progression and poor patient survival. ZY0511, a highly selective and potent inhibitor of LSD1, suppressed human HCC cell proliferation in vitro and tumor growth in cell‐derived and patient‐derived HCC xenograft models in vivo. Mechanistically, ZY0511 induced mRNA expression of growth arrest and DNA damage‐inducible gene 45beta (GADD45B) by inducing histone H3 at lysine 4 (H3K4) methylation at the promoter of GADD45B, a novel target gene of LSD1. In human HCC tissues, LSD1 level was correlated with a decreased level of GADD45B, which was associated with HCC progression and predicted poor patient survival. Moreover, co‐administration of ZY0511 and DTP3, which specifically enhanced the pro‐apoptotic effect of GADD45B, effectively inhibited HCC cell proliferation both in vitro and in vivo. Collectively, our study revealed the potential value of LSD1 as a promising target of HCC therapy. ZY0511 is a promising candidate for HCC therapy through upregulating GADD45B, thereby providing a novel combinatorial strategy for treating HCC.

Abstract Previous studies have demonstrated dysregulated mitochondrial dynamics in fibrotic livers and hepatocytes. Little is currently known about how mitochondrial dynamics are involved, nor is it clear how mitochondrial dynamics participate in hepatic stellate cell (HSC) activation. In the present study, we investigated the role of mitochondrial dynamics in HSC activation and the underlying mechanisms. We verified that mitochondrial fission was enhanced in human and mouse fibrotic livers and active HSCs. Moreover, increased mitochondrial fission driven by fis1 overexpression could promote HSC activation. Inhibiting mitochondrial fission using mitochondrial fission inhibitor-1 (Mdivi-1) could inhibit activation and induce apoptosis of active HSCs, indicating that increased mitochondrial fission is essential for HSC activation. Mdivi-1 treatment also induced apoptosis in active HSCs in vivo and thus ameliorated CCl4-induced liver fibrosis. We also found that oxidative phosphorylation (OxPhos) was increased in active HSCs, and OxPhos inhibitors inhibited activation and induced apoptosis in active HSCs. Moreover, increasing mitochondrial fission upregulated OxPhos, while inhibiting mitochondrial fission downregulated OxPhos, suggesting that mitochondrial fission stimulates OxPhos during HSC activation. Next, we found that inhibition of oxidative stress using mitoquinone mesylate (mitoQ) and Tempol inhibited mitochondrial fission and OxPhos and induced apoptosis in active HSCs, suggesting that oxidative stress contributes to excessive mitochondrial fission during HSC activation. In conclusion, our study revealed that oxidative stress contributes to enhanced mitochondrial fission, which triggers OxPhos during HSC activation. Importantly, inhibiting mitochondrial fission has huge prospects for alleviating liver fibrosis by eliminating active HSCs.

Branching structures are mechanically efficient in supporting large-span structures, such as free-form roofs. To support a roof with a specified geometry, we present a novel shape and topology optimization method to find the optimal branching structure in this paper. In the proposed method, the branching structure is modelled as a cable-net, while the reaction forces from the roof are taken as external loads. The force densities of the members are the design variables. The optimal branching structure can be obtained by minimizing one of the several proposed objective functions. The shape of the branching structure represented by the nodal coordinates is determined by solving the linear equilibrium equations. The topology is optimized by removing the members with small axial forces and incorporating the closely spaced nodes. The cross-sectional areas can be easily calculated, if the allowable stress is assigned. Hence, it is very convenient to simultaneously optimize the cross-section, shape, and topology of a branching structure. Numerical examples show that this method can be easily applied to a 2D problem. For a 3D problem, the constraints on the reaction forces should be relaxed. Considering the roof supports as variables is also an effective solution for 3D problems.

Abstract Circadian rhythms, as physiological systems with self‐regulatory functions in living organisms, are controlled by core clock genes and are involved in tumor development. The protein arginine methyltransferase 6 (PRMT6) serves as an oncogene in a myriad of solid tumors, including breast cancer. Hence, the primary aim of the current study is to investigate the molecular mechanisms by which the PRMT6 complex promotes breast cancer progression. The results show that PRMT6, poly(ADP‐ribose) polymerase 1 (PARP1), and the cullin 4 B (CUL4B)‐Ring E3 ligase (CRL4B) complex interact to form a transcription‐repressive complex that co‐occupies the core clock gene PER3 promoter. Moreover, genome‐wide analysis of PRMT6/PARP1/CUL4B targets identifies a cohort of genes that is principally involved in circadian rhythms. This transcriptional‐repression complex promotes the proliferation and metastasis of breast cancer by interfering with circadian rhythm oscillation. Meanwhile, the PARP1 inhibitor Olaparib enhances clock gene expression, thus, reducing breast carcinogenesis, indicating that PARP1 inhibitors have potential antitumor effects in high‐PRMT6 expression breast cancer.

IntroductionLung cancer is one of the most common cancers and a significant cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer cases. Therefore, it is crucial to identify effective diagnostic and therapeutic methods. In addition, transcription factors are essential for eukaryotic cells to regulate their gene expression, and aberrant expression transcription factors are an important step in the process of oncogenesis in NSCLC.MethodsDifferentially expressed transcription factors between NSCLC and normal tissues by analyzing mRNA profiling from The Cancer Genome Atlas (TCGA) database program were identified. Weighted correlation network analysis (WGCNA) and line plot of least absolute shrinkage and selection operator (LASSO) were performed to find prognosis-related transcription factors. The cellular functions of transcription factors were performed by 5-ethynyl-2'-deoxyuridine (EdU) assay, wound healing assay, cell invasion assay in lung cancer cells.ResultsWe identified 725 differentially expressed transcription factors between NSCLC and normal tissues. Three highly related modules for survival were discovered, and transcription factors highly associated with survival were obtained by using WGCNA. Then line plot of LASSO was applied to screen transcription factors related to prognosis and build a prognostic model. Consequently, SETDB2, SNAI3, SCML4, and ZNF540 were identified as prognosis-related transcription factors and validated in multiple databases. The low expression of these hub genes in NSCLC was associated with poor prognosis. The deletions of both SETDB2 and SNAI3 were found to promote proliferation, invasion, and stemness in lung cancer cells. Furthermore, there were significant differences in the proportions of 22 immune cells between the high- and low-score groups.DiscussionTherefore, our study identified the transcription factors involved in regulating NSCLC, and we constructed a panel for the prediction of prognosis and immune infiltration to inform the clinical application of transcription factor analysis in the prevention and treatment of NSCLC.

Seizures are life-threatening complications of neuropsychiatric systemic lupus erythematosus (NPSLE) and are often associated with poor outcomes. Cyclophosphamide immunotherapy is the mainstay of NPSLE treatment. We report the unique case of a patient with NPSLE who developed seizures soon after her first and second doses of low-dose cyclophosphamide. The exact pathophysiological mechanism underlying cyclophosphamide-induced seizures is not well understood. However, this unusual drug-associated side effect of cyclophosphamide is thought to be due to the drug’s unique pharmacology. Clinicians should be aware of this complication to make a correct diagnosis and adjust the immunosuppressive regimens very carefully.

Abstract Trees are thought to have acquired a mechanically optimized shape through evolution, but a scientific methodology to investigate the mechanical rationality of tree morphology remains to be established. The aim of this study was to develop a new method for 3D reconstruction of actual tree shape and to establish a theoretical formulation for elucidating the structure and function of tree branches. We obtained 3D point cloud data of tree shape of Japanese zelkova (Zelkova serrata) and Japanese larch (Larix kaempferi) using the NavVis Lidar scanner, then applied a cylinder structure extraction from point cloud data with error estimation. We then formulated the mechanical stress of branches under gravity using the elastic theory, and performed finite element method simulations to evaluate the mechanical characteristics. Subsequently, we constructed a mechanics-based theoretical formulation of branch development that ensures constant bending stress produces various branching patterns depending on growth properties. The derived theory recapitulates the trade-off among branch growth anisotropy, stress-gravity length, and branch shape, which may open the quantitative way to evaluate mechanical and morphological rationality of tree branches.

Background: This study aimed to investigate the effect of increased HER-2 expression on tumor-infiltrating lymphocytes (TILs) and determine its impact on the prognosis of colorectal cancer (CRC) patients; Methods: HER-2, CD4, CD8, CD19, LY6G, CD56, CD68, CD11b, and EpCam expression in CRC tissues and adjacent paracancerous tissues were assessed using multiplex fluorescence immunohistochemical staining. The correlation between HER-2 expression and the number of TILs in CRC tissues was analyzed. Kaplan–Meier and Cox proportional hazards models were used to analyze survival outcomes; Results: The expression of HER-2 in tumor tissues was higher than that in paracancerous tissues (1.31 ± 0.45 vs. 0.86 ± 0.20, p < 0.05). Additionally, there was an increase in the numbers of CD4+, CD8+, CD19+, and CD68+ cells in CRC tissues (14.11 ± 1.10 vs. 3.40 ± 0.18, p < 0.005; 0.16 ± 0.12 vs. 0.04 ± 0.04, p < 0.005; 0.71 ± 0.46 vs. 0.25 ± 0.13, p < 0.0005; 0.27 ± 0.24 vs. 0.03 ± 0.11, p < 0.05). An increase in HER-2 expression was positively correlated with an increase in CD4, CD8, and CD19 (p < 0.0001). In HER-2-positive CRC tissues, CD68 expression was increased (0.80 ± 0.55 vs. 0.25 ± 0.22, p < 0.05). In HER-2-upregulated CRC tissues, CD4, CD8, CD19, CD68, CD11b, Ly6G, and CD56 expressions were elevated (0.70 ± 0.37 vs. 0.32 ± 0.17, p = 0.03; 0.22 ± 0.13 vs. 0.09 ± 0.06, p = 0.03; 0.31 ± 0.19 vs. 0.12 ± 0.08, p = 0.02; 1.05 ± 0.62 vs. 0.43 ± 0.21, p < 0.01; 1.34 ± 0.81 vs. 0.53 ± 0.23, p < 0.01; 0.50 ± 0.31 vs. 0.19 ± 0.10, p < 0.01; 1.26 ± 0.74 vs. 0.52 ± 0.24, p < 0.01). Furthermore, increased HER-2 expression was an independent risk factor for recurrence-free survival (RFS) in patients (p < 0.01, HR = 3.421); Conclusions: The increased expression of HER-2 and its relationship with immune cells will provide new insights for immunotherapy in CRC patients.

Abstract This paper presents an approach for the design of discrete architectural surfaces that are globally developable; that is, having zero Gaussian curvature at every interior node. This kind of architectural surface is particularly suitable for fast fabrication at a low cost, since their curved geometry can be developed into a plane. This highly non‐linear design problem is broken down into two sub‐problems: (1) find the member lengths of a triangular mesh that lead to zero Gaussian curvature, by employing the discrete surface Ricci flow developed in the field of discrete differential geometry; (2) realize the final geometry by solving an optimization problem, subject to the constraints on member lengths as well as the given boundary. It is demonstrated by the numerical examples that both of these two sub‐problems can be solved with small computational costs and sufficient accuracy. In addition, the Ricci flow algorithm has an attractive feature—the final design is conformal to the initial one. Conformality could result in higher structural performance, because the shape of each panel is kept as close as possible to its initial design, suppressing possible distortion of the panels. This paper further presents an improved circle packing scheme implemented in the discrete surface Ricci flow to achieve better conformality, while keeping its simplicity in algorithm implementation as in the existing Thurston's scheme .

Antibiotic resistance genes (ARGs) are a new environmental contaminant that poses a major hazard to humans and the environment. This research discusses the methods and drawbacks of two ARG removal approaches, constructed wetlands (CWs) and photocatalysis. CWs primarily rely on the synergistic effects of substrate adsorption, plant uptake, and microbial processes to remove ARGs. The removal of ARGs can be influenced by wetland plants, substrate type, wetland type, and hydraulic conditions. The absolute abundance of ARGs in effluent decreased, but their relative abundance increased. Photocatalysis deactivates ARGs predominantly through reactive oxygen species, with removal effectiveness determined by catalyst type, radiation type, and radiation intensity. The drawback is that it exposes intracellular resistance genes, perhaps increasing the risk of ARG spread. To address the current shortcomings, this paper proposes the feasibility of combining a constructed wetland with photocatalysis technology, which provides a novel strategy for ARG removal.

IntroductionRecent research has confirmed the critical role that epigenetic factors play in regulating the immune response. Nonetheless, what role m6A methylation modification might play in the immune response of non-small cell lung cancer (NSCLC) remains vague.MethodsHerein, the gene expression, copy number variations (CNVs), and somatic mutations of 31 m6A regulators in NSCLC and adjacent control samples from the GEO and TCGA databases were comprehensively explored. Using consensus clustering, m6A modification patterns were identified. Correlations between m6A modification patterns and immune cell infiltration traits in the tumor immune microenvironment (TME) were systematically analyzed. Differentially expressed genes were verified and screened by random forest and cox regression analysis by comparing different m6A modification patterns. Based on the retained gene panel, a risk model was built, and m6Ascore for each sample was calculated. The function of m6Ascore in NSCLC prognosis, tumor somatic mutations, and chemotherapy/immunotherapy response prediction were evaluated.ResultsConsensus clustering classified all NSCLC samples into two m6A clusters (m6A_clusterA and m6A_clusterB) according to the expression levels of 25 m6A regulator genes. Hierarchical clustering further divides the NSCLC samples into two m6A gene clusters: m6AgeneclusterA and m6AgeneclusterB. A panel of 83 genes was screened from the 194 differentially expressed genes between m6A gene clusters. Based on this, a risk score model was established. m6A modification clusters, m6A gene clusters, and m6Ascore calculated from the risk model were able to predict tumor stages, immune cell infiltration, clinical prognosis, and tumor somatic mutations. NSCLC patients with high m6Ascore have poor drug resistance to chemotherapy drugs (Cisplatin and Gemcitabine) and exhibit considerable therapeutic benefits and favorable clinical responses to anti-PD1 or anti-CTLA4 immunotherapy.DiscussionIn conclusion, methylation modification patterns mediated by the m6A regulators in individuals play a non-negligible role in prognosis prediction and immunotherapy response, which will facilitate personalized treatment and immunotherapeutic strategies for NSCLC patients in the future.

BackgroundIn sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.MethodsA total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.ResultsIn sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).ConclusionThe nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.

BackgroundIn recent years, the number of elderly patients undergoing cardiac surgery has rapidly increased and is associated with poor outcomes. However, there is still a lack of adequate models for predicting the risk of death after cardiac surgery in elderly patients. This study sought to identify independent risk factors for 1-year all-cause mortality in elderly patients after cardiac surgery and to develop a predictive model.MethodsA total of 3,752 elderly patients with cardiac surgery were enrolled from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and randomly divided into training and validation sets. The primary outcome was the all-cause mortality at 1 year. The Least absolute shrinkage and selection operator (LASSO) regression was used to decrease data dimensionality and select features. Multivariate logistic regression was used to establish the prediction model. The concordance index (C-index), receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram.ResultsOur results demonstrated that age, sex, Sequential Organ Failure Assessment (SOFA), respiratory rate (RR), creatinine, glucose, and RBC transfusion (red blood cell) were independent factors for elderly patient mortality after cardiac surgery. The C-index of the training and validation sets was 0.744 (95%CI: 0.707–0.781) and 0.751 (95%CI: 0.709–0.794), respectively. The area under the curve (AUC) and decision curve analysis (DCA) results substantiated that the nomogram yielded an excellent performance predicting the 1-year all-cause mortality after cardiac surgery.ConclusionsWe developed a novel nomogram model for predicting the 1-year all-cause mortality for elderly patients after cardiac surgery, which could be an effective and useful clinical tool for clinicians for tailored therapy and prognosis prediction.

In this work, the removal efficiency and mechanism of various constructed wetlands microcosm systems on antibiotic wastewater, as well as the biological community response of microalgae and microorganisms were explored. Overall, the algal-bacteria symbiosis in conjunction with the gravel matrix had the most comprehensive treatment efficiency for antibiotic wastewater. However, pollutants such as high-concentration antibiotics impaired the biological community and functions. In the systems fed with microorganisms, both abundance and diversity of them were significantly reduced comparing with the initial value. According to the correlation analysis revealed that the pollutants removal rate increased with the addition of the relative abundance of some bacterial genera, while decreased with the addition of relative abundance of other bacterial genera. The presence of gravel matrix could lessen the stressful effect of antibiotics and other pollutants on the growth of microalgae and microorganisms, as well as improved treatment efficiency of antibiotic wastewater. Based on the findings of the study, the combination of gravel matrix and algal-bacteria symbiosis can considerably increase the capacity of constructed wetlands to treat antibiotic wastewater and protect biological community, which is an environmentally friendly way.

N,N-dimethylformamide (DMF) has excellent chemical stability and is widely used as an aprotic polar solvent. In order to reduce production costs and reduce pollution to the surrounding environment, it is necessary to recycle and reuse DMF. Previous research has found that the thin film composite nanofiltration membrane prepared from liquefied walnut shells exhibited a high rejection rate in DMF, but relatively low permeance and mechanical strength. In order to increase permeance without compromising the separation performance, ethylenediamine (EDA) is used as a modifier to graft onto the structure of liquefied walnut shell through the Mannich reaction. Then, modified liquefied walnut shell as an aqueous monomer reacts with trimesoyl chloride (TMC) via the interfacial polymerization method on the EDA-crosslinked polyetherimide (PEI) membrane. The results show that the permeance of the prepared membrane is significantly improved by an order of magnitude, demonstrating a rejection rate of 98% for crystal violet (CV), and a permeance of 3.53 L m−2 h−1 bar−1 in DMF. In conclusion, this study reveals the potential of utilizing liquefied walnut shells as raw materials for preparing high-performance separation membranes and demonstrates that surface modification is a feasible approach to enhance permeance of membranes without sacrificing the rejection rate.

The selective expression of hypoxia‐inducible factor (HIF) target genes in different physiological and pathological environments forms the basis for cellular adaptation to hypoxia in development and disease. Several E26 transformation‐specific (ETS) transcription factors have been shown to specifically regulate the expression of a subset of HIF‐2 target genes. However, it is unknown whether there are ETS factors that specifically regulate hypoxia‐induced HIF‐1 target genes. The present study was undertaken to explore whether friend leukemia integration 1 (FLI1), an ETS transcription factor, regulates the expression of HIF‐1 target genes. To investigate this possibility, EA.hy926 cells were exposed to 20% O2 (normoxia) or 1% O2 (hypoxia). Western blotting, immunofluorescence staining, and RT‐qPCR revealed that FLI1 mRNA and protein levels increased slightly and that the FLI1 protein co‐localized with HIF‐1α in the nucleus under hypoxic conditions. Further analysis showed that, in the absence of FLI1, the hypoxia‐mediated induction of HIF‐1 target genes was selectively inhibited. The results from immunoprecipitation and luciferase reporter assays indicated that FLI1 cooperates with HIF‐1α and is required for the transcriptional activation of a subset of HIF‐1 target genes with a core promoter region containing FBS in proximity to a functional hypoxia response element (HRE). Furthermore, ChIP analysis further confirmed the direct interaction between FLI1 and the promoter region of FLI1‐dependent HIF‐1 target genes under hypoxia. Together, this study demonstrates that FLI1 is involved in the transactivation of certain HIF‐1 target genes in endothelial cells under hypoxic conditions.

Steel slag substrate constructed wetlands (SSCWs) can effectively remove phosphorus (P) from sewage through Ca-P precipitation and adsorption. Nonetheless, the disadvantages of a high pH value of the effluent and low nitrogen (N) removal efficiency limit the practical application of SSCWs. To improve these shortcomings, plant cultivation and combining steel slag with other substrate materials have been applied in SSCWs. However, related studies have not obtained a unanimous consensus elucidating such improvements. To accurately evaluate improvements, we statistically analyzed the experimental data reported in 27 related papers and found that combining steel slag with other substrate materials in SSCWs significantly increased the removal amount of total nitrogen (TN) (51.58 mg TN/L) and ammonium nitrogen (NH4+-N) (74.15 mg NH4+-N/L) but reduced the removal amount of total phosphorus (TP) (7.76 mg TP/L). In these combined substrate SSCWs, plant cultivation could compensate for the decline in TP removal amount and improve upon the simultaneous removal of N and P (6.02 mg TP/L, 62.18 mg TN/L, and 69.16 mg NH4+-N/L). Moreover, compared with vertical flow SSCWs, horizontal flow enables plant-cultivated and combined substrate SSCWs to achieve a higher TP removal capacity (6.38 mg TP/L). In addition, operational parameters, including temperature, hydraulic retention time (HRT), pH value, and influent concentration, significantly affected the N and P removal capacity of SSCWs. Our research results provide a theoretical reference for the design and operation of SSCWs for efficient N and P removal.

Abstract Recently it has been proposed that methane might be produced by all living organisms via a mechanism driven by reactive oxygen species that arise through the metabolic activity of cells. Here, we summarise details of this novel reaction pathway and discuss its potential significance for clinical and health sciences. In particular, we highlight the role of oxidative stress in cellular methane formation. As several recent studies also demonstrated the anti‐inflammatory potential for exogenous methane‐based approaches in mammalians, this article addresses the intriguing question if ROS‐driven methane formation has a general physiological role and associated diagnostic potential.

The analysis of variance-projected difference resolution (ANOVA-PDR) was proposed and compared with multivariate classification for its potential in detecting possible food adulteration in extra virgin olive oils (EVOOs) by UV-Vis spectra. Three factors including origin, adulteration level, and adulteration type were systematically examined by the ANOVA-derived methods. The ANOVA-PDR quantitatively presented the separation of the internal classes according to the three main factors. Specifically, the average ANOVA-derived PDRs of the EVOO origination and adulteration level, respectively, is 4.01 and 1.78, while the conventional PDRs of the three factors are all less than 1.5. Furthermore, the partial least-squares-discriminant analysis (PLS-DA) and the PLS regression (PLSR) modeling with the selected sub-datasets from different origins were used to verify the results. The resulting models suggested that the three main factors and their interactions were all important sources of spectral variations.

Background: NSAIDs are one of the most frequently used medications and a risk factor for AKI. However, the optimal time of NSAIDs in patients with AKI is unknown.Methods: A secondary analysis of a multicenter, randomized clinical trial including adult inpatients with acute kidney injury was performed. Univariate, multivariate, and subgroup analyses were used to explore the impact of NSAIDs during the early onset of AKI on the outcome of patients with AKI.Results: A total of 6,030 patients with AKI were enrolled in the study. Following are the findings of the multi-factor analysis: NSAID treatments within 72 and 24 h before the onset of AKI were not associated with AKI progression, dialysis, or discharge from dialysis; only NSAID treatment within the 24-h onset of AKI was associated with these outcomes, and their OR values were independently 1.50 (95% CI: 1.02–2.19, p = 0.037), 4.20 (95% CI: 1.47–11.97, p = 0.007), and 0.71 (95% CI: 0.54–0.92, p = 0.011); only NSAID treatment within the 24-h onset of AKI would decrease the 14-day mortality, and the OR value was 0.52 (95% CI: 0.33–0.82, p = 0.005). The subgroup analysis revealed that in patients with age ≥65 years, CKD (chronic kidney disease), congestive heart failure, hypertension, and liver disease, NSAID treatments within the 24-h onset of AKI would deteriorate the outcome of patients with AKI.Conclusion: Before an early onset of AKI, NSAID treatment might be safe, but during the onset of AKI, even early NSAID treatment would deteriorate the outcome of patients with AKI.

Dsyregulation of mitosis-related alternative splicing in cancer cells is not well understood. Here, the authors show that cancer metastasis-associated antigen 1 (MTA1), an oncogenic chromatin associated protein, is an RNA-binding protein that regulates the alternative splicing and transcript abundance of mitosis regulators.

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

Epigenetic modifications and metabolism are two fundamental biological processes. During tumorigenesis and cancer development both epigenetic and metabolic alterations occur and are often intertwined together. Epigenetic modifications contribute to metabolic reprogramming by modifying the transcriptional regulation of metabolic enzymes, which is crucial for glucose metabolism, lipid metabolism, and amino acid metabolism. Metabolites provide substrates for epigenetic modifications, including histone modification (methylation, acetylation, and phosphorylation), DNA and RNA methylation and non-coding RNAs. Simultaneously, some metabolites can also serve as substrates for nonhistone post-translational modifications that have an impact on the development of tumors. And metabolic enzymes also regulate epigenetic modifications independent of their metabolites. In addition, metabolites produced by gut microbiota influence host metabolism. Understanding the crosstalk among metabolism, epigenetic modifications, and gene expression in cancer may help researchers explore the mechanisms of carcinogenesis and progression to metastasis, thereby provide strategies for the prevention and therapy of cancer. In this review, we summarize the progress in the understanding of the interactions between cancer metabolism and epigenetics.

Introduction: COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment. However, there is still a lack of a simple and effective tool to identify these patients early.Methods: A retrospective cohort study was conducted to develop an early warning model for predicting the death risk of COVID-19. Seventy-five percent of the cases were used to construct the prediction model, and the remaining 25% were used to verify the prediction model based on data immediately available on admission.Results: From March 1, 2020, to April 16, 2020, a total of 4,711 COVID-19 patients were included in our study. The average age was 63.37 ± 16.70 years, of which 1,148 (24.37%) died. Finally, age, SpO2, body temperature (T), and mean arterial pressure (MAP) were selected for constructing the model by univariate analysis, multivariate analysis, and a review of the literature. We used five common methods for constructing the model and finally found that the full model had the best specificity and higher accuracy. The area under the ROC curve (AUC), specificity, sensitivity, and accuracy of full model in train cohort were, respectively, 0.798 (0.779, 0.816), 0.804, 0.656, and 0.768, and in the validation cohort were, respectively, 0.783 (0.751, 0.815), 0.800, 0.616, and 0.755. Visualization tools of the prediction model included a nomogram and an online dynamic nomogram (https://wanghai.shinyapps.io/dynnomapp/).Conclusion: We developed a prediction model that might aid in the early identification of COVID-19 patients with a high probability of mortality on admission. However, further research is required to determine whether this tool can be applied for outpatient or home-based COVID-19 patients.

Objectives This study aimed to determine the relationship between the body mass index (BMI) and short-term mortality of patients with intra-abdominal infection (IAI) using the Medical Information Mart for Intensive Care (MIMIC-III) database.Design Retrospective cohort study.Setting Adult intensive care units (ICUs) at a tertiary hospital in the USA .Participants Adult IAI ICU patients from 2001 to 2012 in the MIMIC-III database.Interventions In univariate analysis, we compared the differences in the characteristics of patients in each BMI group. Cox regression models were used to evaluate the relationships between BMI and short-term prognosis.Primary and secondary outcome measures 90-day survival.Results In total, 1161 patients with IAI were included. There were 399 (34.4%) patients with a normal BMI (30 kg/m2) who tended to be younger (p

Current management of liver ischemia-reperfusion (I/R) injury is mainly based on supportive care and no specific treatment is available. Irisin, a recently identified hormone, plays pivotal roles in energy expenditure and oxidative metabolism; however, it remains unknown whether irisin has any protective effects on hepatic I/R injury. In this study, we found that serum and liver irisin levels were markedly decreased at 24 h after hepatic I/R. Treatment with exogenous irisin improved liver function, reduced liver necrosis and cell apoptosis, and relieved inflammatory response after hepatic I/R. Meanwhile, exogenous irisin markedly inhibited mitochondrial fission related protein dynamin related protein 1 (drp-1) and fission 1 (Fis-1) expression in hepatic I/R. Additionally, treatment with exogenous irisin increased mitochondrial content and increased mitochondrial biogenesis related peroxisome proliferative activated receptor-γ (PPARγ) co-activator 1α (PGC-1α) and mitochondrial transcription factor (TFAM) expression. Furthermore, irisin decreased oxidative stress by upregulating uncoupling proteins (UCP) 2 expression in hepatic I/R. The results reveal that treatment with exogenous irisin alleviated hepatic I/R injury by restraining mitochondrial fission, promoting mitochondrial biogenesis and relieving oxidative stress. Irisin treatment appears to be a novel and promising therapeutic approach for hepatic I/R injury. Keywords: Hepatic I/R, Irisin, Mitochondrial homeostasis, Oxidative stress

Abstract Cullin4B (CUL4B) is a scaffold protein of the CUL4B‐Ring E3 ligase (CRL4B) complex. However, the role of CUL4B in the development of breast cancer remains poorly understood. Here it is shown that CRL4B interacts with multiple histone deacetylase (HDAC)‐containing corepressor complexes, including MTA1/NuRD, SIN3A, CoREST, and NcoR/SMRT complexes. It is demonstrated that CRL4B/NuRD(MTA1) complexes cooccupy the E‐cadherin and AXIN2 promoters, and could be recruited by transcription factors including Snail and ZEB2 to promote cell invasion and tumorigenesis both in vitro and in vivo. Remarkably, CUL4B responded to transformation and migration/invasion stimuli and is essential for multiple epithelial‐mesenchymal transition (EMT) signaling pathways such as hypoxia. Furthermore, the transcription of CUL4B is directedly activated by hypoxia‐inducible factor 1α (HIF1α) and repressed by the ERα‐GATA3 axis. Overexpressing of CUL4B successfully induced CSC‐like properties. Strikingly, CUL4B expression is markedly upregulated during breast cancer progression and correlated with poor prognosis. The results suggest that CUL4B lies at a critical crossroads between EMT and stem cell properties, supporting CUL4B as a potential novel target for the development of anti‐breast cancer therapy.

Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.