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Continuous cropping in greenhouse cultivation often leads to increased pest and disease problems, reducing crop quality and yield. Crop rotation is a common strategy to address these issues. This study compared the growth of Chinese cabbage (Brassica rapa var. chinensis) following rotations with ginger (Zingiber officinale) and sponge gourd (Luffa aegyptiaca). The Chinese cabbage exhibited normal growth following ginger rotation but showed abnormal growth after sponge gourd rotation. The study investigated the underlying causes by analyzing soil physicochemical properties and rhizosphere microbial communities of Chinese cabbage using 16S rRNA and ITS sequencing. The results revealed that soil from ginger–Chinese cabbage rotation had higher levels of soil organic carbon (SOC) and available phosphorus (AP), but lower total nitrogen (TN) and available potassium (AK). Despite similar alpha-diversity for both bacterial and fungal communities, distinct bacterial and fungal community structures between two rotation cropping systems were observed. This suggests that even if the alpha-diversity does not change, the composition of the microbial community can shift in ways that might influence soil health and plant growth. Furthermore, redundancy analysis revealed a significant correlation between microbial community structures and soil physicochemical properties of two rotation cropping systems. The SOC and TN were revealed to be the most significant of the investigated soil physicochemical parameters with respect to the variation of both bacterial and fungal assemblages, respectively. The identified biomarkers in bacterial community composition further emphasize the potential for specific microbes to influence crop health positively or negatively. We found that the indicator genera of the bacterial community composition of the ginger–Chinese cabbage rotation system were Amycolatopsis (genus), Pseudonocardiales (order), Pseudonocardiaceae (family), and Amycolatopsis mediterranei, which are known as producers of secondary metabolites, such as antibiotics. These findings highlight the importance of crop selection in rotation strategies for optimizing agricultural outcomes.

Barth syndrome (BTHS) is a rare X-linked recessive genetic disorder characterized by a broad spectrum of clinical features including cardiomyopathy, skeletal myopathy, neutropenia, growth delay, and 3-methylglutaconic aciduria. This disease is caused by loss-of-function mutations in the TAFAZZIN gene located on chromosome Xq28, resulting in cardiolipin deficiency. Most patients are diagnosed in childhood, and the mortality rate is highest in the early years. We report a case of acute, life-threatening metabolic decompensation occurring one day after birth. A novel TAFAZZIN splice site mutation was identified in the patient, marking the first reported case of such a mutation in BTHS identified in China. The report aims to expand our understanding of the spectrum of TAFAZZIN mutations in BTHS.

Coalbed methane (CBM) is one of the realistic and reliable strategic supplementary resources of conventional natural gas in China, and intelligent calibration of CBM production capacity is of great importance for developing the natural gas industry. Actual geological and production data were collected and preprocessed for CBM wells in a CBM block in the Qinshui Basin, Shanxi Province. For CBM wells that have been exploited for a long time, a calculation formula based on production well history was proposed for single well productivity based on formation pressure in static data, daily gas production, and bottom-hole flow pressure in production data. The accuracy of the calculation formula was determined using the maximum daily gas production curve of CBM wells. Using the preprocessed production and reservoir data, an intelligent algorithm was developed for CBM productivity calibration based on deep neural network (DNN), support vector regression machine, and random forest for predicting the productivity of a single CBM well. A comparison of the prediction results of three machine learning models was performed, and the effect of production data for different discharge days as input parameters on model accuracy was examined. Based on the machine learning model with the best prediction effect, the importance of dynamic parameters (daily gas production, daily water production, and bottom-hole flow pressure in the early stage of drainage and production) and static parameters (coal seam depth, porosity, permeability, coal seam thickness, and gas content) to CBM was explored. The findings revealed that the fluctuation coefficient for 100 CBM wells is approximately zero, and the fluctuation range is small, indicating high calculation accuracy. The average coefficient of determination of CBM well productivity calibrated using the three machine learning models is 0.828, and the coefficient of determination of the DNN model is the highest, attaining 0.923, with mean absolute error and root mean square error values of 194.44 and 214.66 m3·d−1, respectively. With increasing days of production data collection in the early stage, the coefficient of determination clearly increases, and then the growth trend slows down and finally becomes stable. Daily gas production, daily water production, production pressure difference, gas content, and permeability are important factors affecting coalbed methane productivity in the early stage of drainage and production. Productivity is highly sensitive to dynamic and static parameters in the early stage of drainage and production,and these two types of parameters contribute 48% and 52%, respectively, to the capacity prediction model.

Abstract Some noncoding RNAs (ncRNAs) carry open reading frames (ORFs) that can be translated into micropeptides, although noncoding RNAs (ncRNAs) have been previously assumed to constitute a class of RNA transcripts without coding capacity. Furthermore, recent studies have revealed that ncRNA-derived micropeptides exhibit regulatory functions in the development of many tumours. Although some of these micropeptides inhibit tumour growth, others promote it. Understanding the role of ncRNA-encoded micropeptides in cancer poses new challenges for cancer research, but also offers promising prospects for cancer therapy. In this review, we summarize the types of ncRNAs that can encode micropeptides, highlighting recent technical developments that have made it easier to research micropeptides, such as ribosome analysis, mass spectrometry, bioinformatics methods, and CRISPR/Cas9. Furthermore, based on the distribution of micropeptides in different subcellular locations, we explain the biological functions of micropeptides in different human cancers and discuss their underestimated potential as diagnostic biomarkers and anticancer therapeutic targets in clinical applications, information that may contribute to the discovery and development of new micropeptide-based tools for early diagnosis and anticancer drug development.

Abstract Purpose Sequenced Protein–Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. Results In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.

Collisions between ions and neutral particles are often ignored in plasma numerical simulations. However, when studying plasma jets, the collision between neutral particles and ions is significant for ion transport, so calculations cannot ignore this. Therefore, this study adopted a two-fluid model and numerically simulated the process of ion emission from the sheath edge in atmospheric pressure plasma. In this study, particle collisions are treated as Maxwellian particle collisions. We analyze the influence of several parameters, such as sheath thickness and potential drop within the sheath. We also discuss the relaxation phenomenon of relative motion between ions and neutral particles. The research results show a transition from a non-equilibrium state to an equilibrium state in the ion migration region. Based on this change process, the ion migration region can be divided into relaxation and equilibrium zones. The relaxation zone is very thin, where the velocity and temperature of ions vary greatly. In the equilibrium zone, ions and neutral particles can be regarded as a single fluid. This study explores the interaction theory between neutral particles and ions in numerical simulations of plasmas.

Abstract Microchromosome maintenance (MCM) proteins are a number of nuclear proteins with significant roles in the development of cancer by influencing the process of cellular DNA replication. Of the MCM protein family, MCM10 is a crucial member that maintains the stability and extension of DNA replication forks during DNA replication and is significantly overexpressed in a variety of cancer tissues, regulating the biological behaviour of cancer cells. But little is understood about MCM10’s functional role and regulatory mechanisms in a range of malignancies. We investigate the impact of MCM10 in human cancers by analyzing data from databases like the Gene Expression Profiling Interaction Analysis (GEPIA2), Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), among others. Possible relationships between MCM10 and clinical staging, diagnosis, prognosis, Mutation burden (TMB), microsatellite instability (MSI), immunological checkpoints, DNA methylation, and tumor stemness were identified. The findings demonstrated that MCM10 expression was elevated in the majority of cancer types and was connected to tumor dryness, immunocytic infiltration, immunological checkpoints, TMB and MSI. Functional enrichment analysis in multiple tumors also identified possible pathways of MCM10 involvement in tumorigenesis. We also discovered promising MCM10-targeting chemotherapeutic drugs. In conclusion, MCM10 may be a desirable pan-cancer biomarker and offer fresh perspectives on cancer therapy.

Abstract Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.

The Hedgehog (Hh) family is a prototypical morphogen involved in embryonic patterning, multi-lineage differentiation, self-renewal, morphogenesis, and regeneration. There are studies that have demonstrated that the Hh signaling pathway differentiates developing T cells into MHC-restricted self-antigen tolerant T cells in a concentration-dependent manner in the thymus. Whereas Hh signaling pathway is not required in the differentiation of B cells but is indispensable in maintaining the regeneration of hematopoietic stem cells (HSCs) and the viability of germinal centers (GCs) B cells. The Hh signaling pathway exerts both positive and negative effects on immune responses, which involves activating human peripheral CD4+ T cells, regulating the accumulation of natural killer T (NKT) cells, recruiting and activating macrophages, increasing CD4+Foxp3+ regulatory T cells in the inflammation sites to sustain homeostasis. Hedgehog signaling is involved in the patterning of the embryo, as well as homeostasis of adult tissues. Therefore, this review aims to highlight evidence for Hh signaling in the differentiation, function of immune cells and autoimmune disease. Targeting Hh signaling promises to be a novel, alternative or adjunct approach to treating tumors and autoimmune diseases.

Nacre-inspired metal matrix composites have received much attention due to their excellent deformation coordination ability, which can achieve the synergy of strength and ductility. The preparation of nacre-like Al matrix composites by freeze casting has been a promising application, but the continuous ceramic-rich layer affects the corrosion resistance of the composites, facing complex corrosion problems during service. In this work, the microstructure and corrosion behavior of the nacre-inspired (TiBw-TiB2)/Al composites fabricated by freeze casting and squeeze casting were systematically studied. The results indicated that the Al layers and ceramic-rich layers had little change, about 35 μm and 31 μm, respectively, with an increasing ratio of the Ti/TiB2. Meanwhile, a high Ti/TiB2 ratio resulted in an increase in the Fe-Ti intermetallic phases, which was detrimental to the corrosion performance of the composites and was prone to pitting. The electrochemical test results showed that the 3Ti7TiB2 composite had the lowest corrosion current density (15.9 μA) and intergranular corrosion depth (231 μm), indicating that it had the best corrosion resistance, which can be attributable to its stable and dense passivation film. Two different corrosion phenomena during the intergranular corrosion test existed in the present nacre-inspired (TiBw-TiB2)/Al composites: intergranular corrosion in the Al matrix layer and pitting corrosion in the ceramic-rich layer. Among all the composites, the corrosion depth of the 3Ti7TiB2 composite was the smallest and significantly less than that of the 2024Al alloy. In addition, the continuous ceramic-rich layer acted as a corrosion channel during corrosion, significantly degrading the corrosion resistance of the nacre-like Al composites.

Objective To investigate the association of fat and lean mass in specific regions with all-cause and cardiovascular-related mortality.Design Population based cohort study.Setting US National Health and Nutrition Examination Survey (2003–2006 and 2011–2018).Participants 22 652 US adults aged 20 years or older.Exposures Fat and lean mass in specific regions obtained from the whole-body dual-energy X-ray absorptiometry.Main outcome measures All-cause and cardiovascular-related mortality.Results During a median of 83 months of follow-up, 1432 deaths were identified. Associations between body composition metrics and mortality risks were evident above specific thresholds. For all-cause mortality, Android fat mass showed elevated HRs above 2.46 kg (HR: 1.17, 95% CI 1.02 to 1.34), while Android lean mass (ALM) had similar trends above 2.75 kg (HR: 1.17, 95% CI 1.03 to 1.33), and Android total mass above 5.75 kg (HR: 1.08, 95% CI 1.01 to 1.16). Conversely, lower HRs were observed below certain thresholds: Gynoid fat mass (GFM) below 3.71 kg (HR: 0.72, 95% CI 0.56 to 0.93), Gynoid lean mass below 6.44 kg (HR: 0.77, 95% CI 0.64 to 0.92), and Gynoid total mass below 11.78 kg (HR: 0.76, 95% CI 0.70 to 0.84). Notably, below 0.722 kg, the HR of visceral adipose tissue mass (VATM) was 1.25 (95% CI 1.04 to 1.48) for all-cause mortality, and above 3.18 kg, the HR of total abdominal fat mass was 2.41 (95% CI 1.15 to 5.05). Cardiovascular-related mortality exhibited associations as well, particularly for Android fat mass (AFM) above 1.78 kg (HR: 1.22, 95% CI 1.01 to 1.47) and below 7.16 kg (HR: 0.50, 95% CI 0.36 to 0.69). HRs varied for Gynoid total mass below and above 10.98 kg (HRs: 0.70, 95% CI 0.54 to 0.93, and 1.12, 95% CI 1.02 to 1.23). Android per cent fat, subcutaneous fat mass (SFM), AFM/GFM, and VATM/SFM were not statistically associated with all-cause mortality. Android per cent fat, Gynoid per cent fat, AFM/GFM, and VATM/SFM were not statistically associated with cardiovascular-related mortality. Conicity index showed that the ALM/GLM had the highest performance for all-cause and cardiovascular-related mortality with AUCs of 0.785, and 0.746, respectively.Conclusions The relationship between fat or lean mass and all-cause mortality varies by region. Fat mass was positively correlated with cardiovascular mortality, regardless of the region in which they located. ALM/GLM might be a better predictor of all-cause and cardiovascular-related mortality than other body components or body mass index.

BackgroundTransitioning from marriage to widowhood presents inevitable and significant challenges for many older adults. This study explored the impact of widowhood on a range of mental health outcomes, including pulse pressure and fasting blood glucose levels, among older adults in nursing homes.MethodsThis cross-sectional study utilized cluster random sampling to recruit participants, with data analyzed from 388 older Chinese adults. Psychosocial traits were assessed using the Perceived Social Support from Family scale (PSS-Fa) for family support, the Generalized Anxiety Disorder 7-item scale (GAD-7) for anxiety symptoms, and the 9-item Patient Health Questionnaire (PHQ-9) for depressive symptoms and suicidal ideation. Propensity score matching (PSM) was employed to control for confounding factors. A multivariate linear regression analysis was performed to explore the relationship between widowhood, mental health outcomes, pulse pressure, and fasting blood glucose levels.ResultsAfter applying PSM, the sample size was refined to 268 (N = 134 for both married and widowed groups) from the initial 388, excluding 120 unmatched cases. Widowed older adults were found to have notably lower family support (β = −0.81, p = 0.002), increased depressive symptoms (β = 1.04, p = 0.043), elevated pulse pressure (β = 8.90, p

Dendrobium, which belongs to the family of Orchidaceae, is a highly valuable traditional Chinese medicine commonly used in China. It exerts pharmacological activities such as antitumor and hypoglycemia effects, and its main components are alkaloids, polysaccharides, and terpenoids, among others. In recent years, research on the clinical application of Dendrobium in antitumor therapy has gained increasing attention. Accumulating evidence suggests that the active components of Dendrobium possess significant inhibitory effects on the viability of cancer cells as evident from in vivo and in vitro experiments, which indicates that Dendrobium exerts significant anticancer effect in treating and preventing cancer development, inhibiting the underlying potential molecular mechanisms, including suppression of cancer cell growth and proliferation, epithelial-mesenchymal transition (EMT), apoptosis induction, tumor angiogenesis, and reinforcement of cisplatin (DDP) -induced apoptosis. We herein present a review that summarizes the research progress of the application of Dendrobium in cancer therapy and its molecular mechanisms. This review describes the positive aspects of the active ingredients of Dendrobium in the treatment of cancers in various systems of the human body, their inhibitory effects on tumor survival and tumor microenvironment, and their potential mechanisms. Additionally, this review proposes future application prospects of Dendrobium in cancer therapy to promote further research and future extensive clinical applications of Dendrobium in cancer therapy.

Abstract Background Observational studies have shown home hemodialysis (HHD) to be associated with better survival than facility hemodialysis (HD) and peritoneal dialysis (PD). Patients on HHD have reported higher quality of life and independence. HHD is considered to be an economical way to manage end-stage kidney disease (ESKD). The coronavirus disease 2019 pandemic has had a significant impact on patients with ESKD. Patients on HHD may have an advantage over in-center HD patients because of a lower risk of exposure to infection. Participants and methods We enrolled HD patients from our dialysis center. We first established the HHD training center. The training center was approved by the Chinese government. Doctors, nurses and engineers train and assess patients separately. There are three forms of patient monitoring: home visits, internet remote monitoring, and outpatient services. Demographic and medical data included age, sex, blood pressure, and dialysis-related data. Laboratory tests were conducted in our central testing laboratory, including hemoglobin (Hgb), serum creatinine (Cr), urea nitrogen (BUN), uric acid (UA), albumin (Alb), calcium (Ca), phosphorus (P), parathyroid hormone (PTH), and brain natriuretic peptide (BNP) levels. Results Six patients who underwent regular dialysis in the HD center of our hospital were selected for HHD training. We enrolled 6 patients, including 4 males and 2 females. The mean age of the patients was 47.5 (34.7-55.7) years, and the mean dialysis age was 33.5 (11.2-41.5) months. After an average of 16.0 (11.2-25.5) months of training, Alb, P and BNP levels were improved compared with the baseline values. After training, three patients returned home to begin independent HD. During the follow-up, there were no serious adverse events leading to hospitalization or death, but there were several adverse events. They were solved quickly by extra home visits of the technicians or online by remote monitoring. During the follow-up time, the laboratory indicators of all the patients, including Hgb, Alb, Ca, P, PTH, BNP, and β2-MG levels, remained stable before and after HHD treatment. Conclusion HHD is feasible and safe for ESKD in China, but larger-scale and longer-term studies are needed for further confirmation.

Although a growing body of research has recently shown how crucial inflammation and infection are to all major diseases, several of the medications currently available on the market have various unfavourable side effects, necessitating the development of alternative therapeutic choices. Researchers are increasingly interested in alternative medications or active components derived from natural sources. Naringenin is a commonly consumed flavonoid found in many plants, and since it was discovered to have nutritional benefits, it has been utilized to treat inflammation and infections caused by particular bacteria or viruses. However, the absence of adequate clinical data and naringenin's poor solubility and stability severely restrict its usage as a medicinal agent. In this article, we discuss naringenin’s effects and mechanisms of action on autoimmune-induced inflammation, bacterial infections, and viral infections based on recent research. We also present a few suggestions for enhancing naringenin's solubility, stability, and bioavailability. This paper emphasizes the potential use of naringenin as an anti-inflammatory and anti-infective agent and the next prophylactic substance for the treatment of various inflammatory and infectious diseases, even though some mechanisms of action are still unclear, and offers some theoretical support for its clinical application.

Abstract Background Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. Results To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. Conclusion The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.

The fabrication of ZrC–Mo composites with (10–25 vol. %) Mo content using a spark plasma sintering at 1750 °C under 35 MPa was investigated in this research. The effects of adding various Mo contents on the composites were also examined. The microstructure and phase analysis were investigated by scanning electron microscopy, x-ray diffractometer, and energy dispersive spectroscopy. Microscopic analysis revealed that the addition of metal Mo resulted in making the ceramic particles of ZrC finer, the sintered products denser, and the sintering conditions softer. Moreover, a continuous solid solution forms in the composite as the Mo content rises, the (Zr, Mo)C and (Zr, Mo)2C start to generate, and thus solid solution strengthens the material. The addition of metal Mo reduced the grain size of the ceramic composite and also lead to fine-grain strengthening. With a relative density of 97.53%, a Vickers hardness of 23.88 GPa, and a flexural strength of 317.55 MPa, the composite possessed the best properties when the molybdenum volume fraction reaches 15 vol. %. The synergistic effect of fine grain strengthening and solid solution strengthening improved the mechanical behavior of the composite.

ABSTRACT Background and Objective Epidemiological studies suggested that the association between the visceral adiposity index (VAI) and the risk of prediabetes is inconsistent. Whether VAI is a useful predictor of prediabetes remains unclear. Up until April 2021, there had been no systematic review on this topic. In this meta‐analysis, the available observational epidemiological evidence was synthesized to identify the association between VAI and prediabetes risk. Methods PubMed, EMBASE, and Cochrane databases in any language were searched systematically from the earliest available online indexing year to April 2021 for relevant observational studies published on the association between VAI and the risk of prediabetes. A random effects model was used to combine quantitatively the odds ratios (ORs) and 95% confidence intervals (CIs). Results Ten relevant studies (2 cohort study, 2 case‐control studies, and 6 cross‐sectional studies) involving 112,603 participants were identified. Compared with the highest VAI, the lowest level of VAI was associated with an increased risk of prediabetes. The pooled OR of VAI for prediabetes was 1.68 (95% CI: 1.44–1.96), with significant heterogeneity across the included studies (P = 0.000, I2 = 91.4%). Exclusion of any single study did not materially alter the combined risk estimate. Conclusions Integrated epidemiological evidence supports the hypothesis that VAI is a lipid combined anthropometric index and may be a risk factor for prediabetes. VAI may be related to a high risk of prediabetes. However, it should be noted that the included studies have a publication bias and there was significant heterogeneity between our pooled estimate.

The retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) play a crucial role as pattern-recognition receptors within the innate immune system. These receptors, present in various cell and tissue types, serve as essential sensors for viral infections, enhancing the immune system’s capacity to combat infections through the induction of type I interferons (IFN-I) and inflammatory cytokines. RLRs are involved in a variety of physiological and pathological processes, including viral infections, autoimmune disorders, and cancer. An increasing body of research has examined the possibility of RLRs or microRNAs as therapeutic targets for antiviral infections and malignancies, despite the fact that few studies have focused on the regulatory function of microRNAs on RLR signaling. Consequently, our main emphasis in this review is on elucidating the role of microRNAs in modulating the signaling pathways of RLRs in the context of cancer and viral infections. The aim is to establish a robust knowledge base that can serve as a basis for future comprehensive investigations into the interplay between microRNAs and RIG-I, while also facilitating the advancement of therapeutic drug development.

Background. There is controversy regarding whether patients with single hepatocellular carcinoma (HCC) should be offered radiofrequency ablation (RFA) as a first-line treatment option. Thus, this study compared overall survival after surgical resection (SR) and RFA for single HCC. Methods. The Surveillance, Epidemiology, and End Results (SEER) database was used for this retrospective study. The study included 30- to 84-year-old patients diagnosed with HCC from 2000 to 2018. Selection bias was reduced via propensity score matching (PSM). The study compared the overall survival (OS) and cancer-specific survival (CSS) of patients with single HCC who were treated with SR and RFA. Results. Before and after PSM, the median OS and median CSS were significantly longer in the SR group than in the RFA group (p5 cm, age at diagnosis between 60 and 84 years, and grades I–IV tumors were longer than in the SR group than in the RFA group (p

Time-sensitive network can provide deterministic transmission services with bounded low latency for applications.Besides, it brings a unified architecture and standards to heterogeneous real-time Ethernet in the industry.However, the implementation and deployment of time-sensitive network based on IEEE Std 802.1Qcc face many challenges in various application scenarios.The research on the control and management mechanisms in time-sensitive network was summarized.Then the problems to be solved were analyzed and summarized from transmission performance, scalability and technology fusion.The latest research progress of control and management mechanisms was shown.The open issues and future research directions were discussed.

Abstract. The feasibility and safety of intracardiac echocardiography (ICE)-guided catheter ablation for atrial fibrillation (AF) using a minimal/zero-fluoroscopy approach have recently been reported. This approach helps to reduce ionizing radiation exposure and orthopedic complications resulting from using lead aprons. The objectives of this planned prospective, multicenter randomized controlled trial (RCT) (paroxysmal AF (PAF)-ICE trial; ChiCTR2000033624) are to evaluate the efficacy and safety of ICE-guided minimal-fluoroscopy ablation in patients with PAF and the impact on occupational hazards among lab staff. Patients will be randomized in a 1:1 ratio to 2 groups: minimal fluoroscopy group (n = 216) and traditional approach group (n = 216). In the minimal fluoroscopy group, an ICE catheter will be used for geometry/anatomic construction, transseptal puncture, catheter tracking, and effusion monitoring. Pulmonary vein isolation (PVI) will be performed using an open-irrigated radiofrequency SmartTouch Surround Flow or SmartTouch catheter (Biosense Webster, Diamond Bar, California, USA), and confirmed by a multipolar Lasso or PentaRay catheter (Biosense Webster). In the traditional approach group, an ICE catheter will not be used. Transseptal puncture will be performed under fluoroscopic guidance, with all geometries constructed by mapping the catheters. The primary efficacy endpoint is freedom from AF recurrence (without antiarrhythmic medications) at 12 months after ablation. Other endpoints include duration of lead apron use, measures of intra-procedural efficiency, and peri-procedural complications. This RCT will evaluate the efficacy and safety of ICE-guided minimal-fluoroscopy ablation in patients with PAF, also evaluate the benefits to lab staff (regarding reducing occupational hazards) related to this “minimal/zero-fluoroscopy” and “leadless” mode.

Abstract Background Breast cancer imposes a considerable burden on both the health care system and society, and becomes increasingly severe among women in China. To reduce the economic burden of this disease is crucial for patients undergoing the breast cancer surgery, hospital managers, and medical insurance providers. However, few studies have evidenced the prediction of the total hospital expenses (THE) for breast cancer surgery. The aim of the study is to predict THE for breast cancer surgery and identify the main influencing factors. Methods Data were retrieved from the first page of medical records of 3699 patients undergoing breast cancer surgery in one tertiary hospital from 2017 to 2018. Multiple liner regression (MLR), artificial neural networks (ANNs), and classification and regression tree (CART) were constructed and compared. Results The dataset from 3699 patients were randomly divided into training and test sets at a 70:30 ratio (2599 and 1100 records, respectively). The average total hospital expenses were 12520.54 ± 7844.88 ¥ (US$ 1929.20 ± 1208.11). MLR results revealed six factors to be significantly associated with THE: age, LOS, type of disease, having medical insurance, minimally invasive surgery, and receiving general anesthesia. After comparing three models, ANNs was the best model to predict THEs in patients undergoing breast cancer surgery, and its strong predictive performance was also validated. Conclusions To reduce the THEs, more attention should be paid to related factors of LOS, major and minimally invasive surgeries, and general anesthesia for these patient groups undergoing breast cancer surgery. This may reduce the information asymmetry between doctors and patients and provide more reliable cost, practical inpatient medical consumption standards and reimbursement standards reference for patients, hospital managers, and medical insurance providers ,respectively.

Abstract Roles of interstitial tissue in morphogenesis of testicular structures remain less well understood. To analyze the roles of CD34+ cells in the reconstruction of interstitial tissue containing Leydig cells (LCs), and testicular structures, we used 3D-reaggregate culture of dissociated testicular cells from prepubertal mouse. After a week of culture, adult Leydig cells (ALCs) were preferentially incorporated within CD34+ cell-aggregates, but fetal LCs (FLCs) were not. Immunofluorescence studies showed that integrins α4, α9 and β1, and VCAM1, one of the ligands for integrins α4β1 and α9β1, are expressed mainly in CD34+ cells and ALCs, but not in FLCs. Addition of function-blocking antibodies against each integrin and VCAM1 to the culture disturbed the reconstruction of testicular structures. Antibodies against α4 and β1 integrins and VCAM1 robustly inhibited cell-to-cell adhesion between testicular cells and between CD34+ cells. Cell-adhesion assays indicated that CD34+ cells adhere to VCAM1 through the interaction with α4β1 integrin. Live cell imaging showed that CD34+ cells adhered around ALC-aggregates. CD34+ cells on the dish moved toward the aggregates, extending filopodia, and entered into them, which was disturbed by VCAM1 antibody. These results indicate that VCAM1-α4β1 integrin interaction plays pivotal roles in formation of testicular interstitial tissues in vitro and also in vivo.

Understanding the targets and interactions of long non-coding RNAs (lncRNAs) related to the retinoic acid-inducible gene-I (RIG-I) signaling pathway is essential for developing interventions, which would enable directing the host inflammatory response regulation toward protective immunity. In the RIG-I signaling pathway, lncRNAs are involved in the important processes of ubiquitination, phosphorylation, and glycolysis, thus promoting the transport of the interferon regulatory factors 3 and 7 (IRF3 and IRF7) and the nuclear factor kappa B (NF-κB) into the nucleus, and activating recruitment of type I interferons (IFN-I) and inflammatory factors to the antiviral action site. In addition, the RIG-I signaling pathway has recently been reported to contain the targets of coronavirus disease-19 (COVID-19)-related lncRNAs. The molecules in the RIG-I signaling pathway are directly regulated by the lncRNA–microRNAs (miRNAs)–messenger RNA (mRNA) axis. Therefore, targeting this axis has become a novel strategy for the diagnosis and treatment of cancer. In this paper, the studies on the regulation of the RIG-I signaling pathway by lncRNAs during viral infections and cancer are comprehensively analyzed. The aim is to provide a solid foundation of information for conducting further detailed studies on lncRNAs and RIG-I in the future and also contribute to clinical drug development.

BackgroundDextrocardia is a congenital heart malformation with a low incidence that occurs in only 1 in 10,000–12,000 people. Wolff-Parkinson-White (WPW) syndrome is a congenital condition with additional accessory pathways between the atria and the ventricle, which affects up to three in 1,000 people worldwide. Experience of radiofrequency catheter ablation in patients with WPW syndrome and dextrocardia is scarce due to its rare incidence.Case presentationA 39-year-old female was hospitalized due to two episodes of palpitations in the latest 2 months. The morphology of the P-QRS-T complex of lead aVR and aVL, II, and III were presented invertedly as common conditions, and shortened P-R interval and a characteristic “delta” wave were shown on the electrocardiogram (EGM). The patient with dextrocardia and situs invertus malposition was confirmed by chest-X ray, cardiac color Doppler echocardiography. The patient was diagnosed with WPW syndrome with dextrocardia and underwent radiofrequency catheter ablation (RFCA) successfully. In this case, the key to the success of RFCA is to understand the anatomical structure of the heart and the great vessels before the operation and make a personalized operative plan.ConclusionCatheter ablation for tachycardia patients with dextrocardia is efficient and safe. For patients with dextrocardia, the key to successful ablation was adjusting for projection angulation and different catheter manipulation compared with a standard case because of the mirror image of a normal heart.

Abstract Solar cells featuring polythiophenes as donors are one of the optoelectronic devices that hold notable promises for commercial application, profiting from the lowest synthetic complexity and excellent scalability. However, the complex phase behaviors of polythiophenes and their blends put constraints on modulating electrical performance and thus realizing stable performance under thermal stress. In this contribution, we present a multi‐technique approach that combines calorimetry, scattering, spectroscopy, and microscopy to thoroughly probe the thermodynamic mixing, thermal properties of materials, the evolution of nanoscale domain structure, and device performance of poly(3‐hexylthiophene) (P3HT) with a range of nonfullerene acceptors (NFAs) such as ITIC, IDTBR, and ZY‐4Cl. Accordingly, two blending guidelines are established for matching these popular NFAs with P3HT to enable highly efficient and thermally stable cells. First, blend systems with weak vitrification and hypo‐miscibility are excellent candidates for efficient solar cells. Furthermore, high thermal stability can be achieved by selecting NFAs with diffusion‐limited crystallization. The P3HT:ZY‐4Cl blend was found to endow the best performance of over 10% efficiency and an exceptionally high T80 lifetime of >6000 h under continuous thermal annealing, which are among the highest values for P3HT‐based solar cells. This realization of high thermal stability and efficiency demonstrates the remarkable potentials of simple polythiophene :nonfullerene pairs in electronic applications.

Introduction: Differentiating idiopathic premature ventricular contractions (PVCs) originating from the right and left ventricular outflow tracts with a left bundle branch block (LBBB) morphology is relevant to catheter ablation planning and important for lowering the risk of complications. This study established a novel electrocardiographic (ECG) criterion to discriminate PVCs originating from the septum of the right ventricular outflow tract (s-RVOT) and those originating from the aortic sinus cusp of the left ventricular outflow tract (LVOT-ASC).Methods: A total of 259 patients with idiopathic PVCs originating from ventricular outflow tract with a LBBB pattern who underwent successful catheter ablation were retrospectively included. Among them, the PVCs originated from the s-RVOT in 183 patients and from the LVOT-ASC in 76 patients. The surface ECGs of the PVCs and sinus beats were analyzed using an electronic caliper. The R-S difference index in the precordial leads was calculated as V2R + V3R + V4R − V1S.Results: PVCs originating from both the s-RVOT and LVOT-ASC displayed an inferior axis (dominant R waves in leads II, III, and aVF). Compared with the s-RVOT group, the R-wave amplitudes on leads II, III, and aVF were significantly larger in the LVOT-ASC group (p < 0.001, p < 0.003, and p < 0.001, respectively). Compared to the LVOT-ASC group, the s-RVOT group showed smaller R-wave amplitudes on leads V1–V6 (p = 0.021, p < 0.001, p < 0.001, p < 0.001, p < 0.001, and p < 0.001, respectively) and larger S-wave amplitudes on leads V1–V3 (p < 0.001, p < 0.001, and p < 0.001, respectively). Lead V3 was the most common transitional lead in both groups. Analysis of the receiver operating characteristic curve showed that the R-wave amplitude on lead V3 had the largest area under the curve (AUC) of 0.856 followed by the R-wave amplitudes on leads V4 (0.834) and V2 (0.806). The AUC of the R-S difference index was 0.867. An R-S difference index greater than 20.9 predicted an LVOT-ASC origin with 73.7% sensitivity and 86.3% specificity. This index is superior to previous criteria in differentiating PVCs with LBBB morphology and inferior axis originating from s-RVOT vs. LVOT-ASC.Conclusions: The R-S difference index in precordial leads is a useful new ECG criterion for distinguishing LVOT-PVCs from RVOT-PVCs with LBBB morphology.

ObjectivesTo compare the safety and efficacy of completely zero-fluoroscopy radiofrequency ablation (RFA) with that of conventional RFA guided by three-dimensional mapping in Chinese children with paroxysmal supraventricular tachycardia (PSVT).MethodsThe study had a single-center observational design and included 46 children aged 6–14 years who underwent RFA for PSVT at the Second Hospital of Hebei Medical University between March 2019 and September 2021. The children were divided according to whether they underwent zero-fluoroscopy RFA (zero-fluoroscopy group, n = 26) or routine RFA under X-ray guidance (conventional group, n = 20). Three-dimensional mapping was used in both groups. Baseline characteristics, total procedure time, RFA time, volume and duration of X-ray exposure, target mapping time, the immediate RFA success rate, incidence of complications, and recurrence rate were compared between the two groups.ResultsThe children had a median age of 12 years (interquartile range 10, 13), 47.8% (22/46) were boys, and 52.2% (24/46) were girls. The mean body weight was 48.75 ± 15.26 kg. There was no significant between-group difference in the baseline data (P > 0.05). All children were followed up as outpatients at 1, 3, and 6 months postoperatively. The target mapping time was significantly longer in the zero-fluoroscopy group than in the conventional group (12.96 ± 2.24 min vs. 6.65 ± 2.56 min, P < 0.05); however, there was no significant between-group difference in the immediate success rate (100% vs. 100%), success rate at 6 months postoperatively (92.30% vs. 95.00%), complication rate (0% vs. 0.05%), recurrence rate (7.70% vs. 5.00%), RFA time (212.50 s vs. 214.00 s), or total procedure time (78.50 min vs. 74.00 min) (P > 0.05).ConclusionZero-fluoroscopy catheter ablation can completely avoid fluoroscopy exposure in children without affecting the safety and efficacy of RFA.

Two kinds of nitrogen-bridged star-shaped fused-ring electron acceptors based on benzo[1,2-b:3,4-b’-5,6-b’’]tri(4-H-dithieno[3,2-b:2’,3’-d]pyrrole) (BTDTP) core with linear and branched alkyl side chains on bridging nitrogen atoms are developed. Both electron acceptors exhibit strong intramolecular charge transfer absorption bands at 500–800 nm (molar extinction coefficient of 3.0 × 105 M −1 cm−1) with narrow optical bandgaps around 1.58 eV, but reveal different molecular packing and morphology. Compared to acceptor with linear side chains, the acceptor bearing branched side chains exhibits ordered face-on orientation with tight π-π stacking, as well as more favorable phase separation morphology and balanced charge transport when blended with polymer donor PM6. Organic solar cell device based on acceptors bearing branched side chains achieves a power conversion efficiency (PCE) of 9.95% with a fill factor of 74.98% and a short-circuit current density of 14.90 mA cm−2, which is much superior to the device using acceptors bearing linear side chains (a PCE of 6.74%).

Abstract Background The primary aim was to observe the predictive value of P-wave ECG index and left atrial appendage volume (LLAV) for atrial fibrillation recurrence after first radiofrequency catheter ablation. Methods A total of 196 patients with paroxysmal atrial fibrillation were enrolled. The preoperative LLAV was measured by cardiac enhanced CT. The P-wave ECG index including minimum P-wave duration (P-min), maximum P-wave duration (P-max), mean P-wave duration (mPWD), P-wave dispersion (PWD), P-wave terminal force in lead V1 (PtfV1), PR interval prolongation, and interatrial block (IAB) were analyzed and recorded in 12-lead ECG of sinus rhythm. Results According to the follow-up results, the patients were divided into two groups: the non-recurrence group and the recurrence group. P-min, PWD, P-max, PtfV1 ≥ 0.04 mV·s, PR interval prolongation, and the ratio of first and third-degree IAB in the recurrence group were higher than those in the non-recurrence group, with significant statistical differences (P

In real-world production processes, the same enterprise often has multiple factories or one factory has multiple production lines, and multiple objectives need to be considered in the production process. A dual-population genetic algorithm with Q-learning is proposed to minimize the maximum completion time and the number of tardy jobs for distributed hybrid flow shop scheduling problems, which have some symmetries in machines. Multiple crossover and mutation operators are proposed, and only one search strategy combination, including one crossover operator and one mutation operator, is selected in each iteration. A population assessment method is provided to evaluate the evolutionary state of the population at the initial state and after each iteration. Two populations adopt different search strategies, in which the best search strategy is selected for the first population and the search strategy of the second population is selected under the guidance of Q-learning. Experimental results show that the dual-population genetic algorithm with Q-learning is competitive for solving multi-objective distributed hybrid flow shop scheduling problems.

Aiming at the problem of low registration caused by image rotation or background transformation in large-scale image classification processing, a convolutional neural network image classification method based on edge enhancement was proposed.This method extracted image features through the VGG19 network model, and used cosine similarity for image classification judgment, used edge enhancement to highlight the edge features of image subject, and reduced the impact of image rotation or background transformation on the classification performance of the VGG19 network.Experiments prove that this method can effectively improve the similarity between the same subject rotated image and background transformed image and the original image, and is suitable for the classification of various images.

Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms.Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for clinical manifestations. Protein–protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by Cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, the protein expression of hub genes was validated by the Human Protein Atlas database.Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression.Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.

Objectives:Lactobacillus reuteri Fn041 (Fn041) is a probiotic isolated from immunoglobulin A coated microbiota in the human breast milk of Gannan in China with a low incidence of hypercholesterolemia. This study aims to explore the role and mechanism of Fn041 in preventing hypercholesterolemia caused by a high-fat diet in mice.MethodsC57BL/6N mice were fed a low-fat diet or a high-fat diet and gavage with Fn041 and Lactobacillus rhamnosus GG (LGG) for 8 weeks.ResultsBoth Fn041 and LGG prevented the occurrence of hypercholesterolemia, liver and testicular fat accumulation. In addition, a high-fat diet causes intestinal dysbiosis and mucosal barrier damage, which is associated with hypercholesterolemia. Fn041 prevented the high-fat diet-induced reduction in alpha diversity of intestinal microbiota and intestinal mucosal barrier damage. Fn041 treatment significantly increased fecal total cholesterol and total bile acids.ConclusionsFn041 prevented hypercholesterolemia by enhancing cholesterol excretion and mucosal barrier function.

Abstract Driven by industrial development and the rising population, the upward trend of electricity consumption is not going to curb. While the electricity suppliers make every endeavor to satisfy the needs of consumers, they are facing the plight of indirect losses caused by technical or non-technical factors. Technical losses are usually induced by short circuits, power outage, or grid failures. The non-technical losses result from humans’ improper behaviors, e.g., electricity burglars. Due to the restrictions of the detection methods, the detection rate in the traditional power grid is lousy. To provide better electricity service for the customers and minimize the losses for the providers, a leap in the power grid is occurring, which is referred to as the smart grid. The smart grid is envisioned to increase the detection accuracy to an acceptable level by utilizing modern technologies, such as cloud computing. With the aim of obtaining achievements of anomaly detection for electricity consumption with cloud computing, we firstly introduce the basic definition of anomaly detection for electricity consumption. Next, we conduct the surveys on the proposed framework of anomaly detection for electricity consumption and propose a new framework with cloud computing. This is followed by centralized and decentralized detection methods. Then, the applications of centralized and decentralized detection methods for the anomaly electricity consumption are listed. Finally, the open challenges of the accuracy of detection and anomaly detection for electricity consumption with edge computing are discussed.

The demand for miniaturization of electronics has been motivating a growing interest in high-performance molecular electronics. Li, Bandari et al. report a fully integrated molecular rectifier based on a molecular heterojunction and microtubular electrode enabling high frequency operation at more than 10 MHz.

Abstract Background Genetic and epigenetic alterations have been indicated to be closely correlated with the carcinogenesis, DNA methylation is one of most frequently occurring molecular behavior that take place early during this complicated process in gastric cancer (GC). Methods In this study, 398 samples were collected from the cancer genome atlas (TCGA) database and were analyzed, so as to mine the specific DNA methylation sites that affected the prognosis for GC patients. Moreover, the 23,588 selected CpGs that were markedly correlated with patient prognosis were used for consistent clustering of the samples into 6 subgroups, and samples in each subgroup varied in terms of M, Stage, Grade, and Age. In addition, the levels of methylation sites in each subgroup were calculated, and 347 methylation sites (corresponding to 271 genes) were screened as the intrasubgroup specific methylation sites. Meanwhile, genes in the corresponding promoter regions that the above specific methylation sites were located were performed signaling pathway enrichment analysis. Results The specific genes were enriched to the biological pathways that were reported to be closely correlated with GC; moreover, the subsequent transcription factor enrichment analysis discovered that, these genes were mainly enriched into the cell response to transcription factor B, regulation of MAPK signaling pathways, and regulation of cell proliferation and metastasis. Eventually, the prognosis prediction model for GC patients was constructed using the Random Forest Classifier model, and the training set and test set data were carried out independent verification and test. Conclusions Such specific classification based on specific DNA methylation sites can well reflect the heterogeneity of GC tissues, which contributes to developing the individualized treatment and accurately predicting patient prognosis.

Summary: Indoor photovoltaic (IPV) with power output over 100 μW is promising to power the numerous sensor nodes in the Internet of Things (IoT) ecosystem. All polymer photovoltaic has the advantages of excellent thermal stability and superior mechanical properties. In this work, we fabricate the first all-polymer indoor photovoltaic module with the active area of 10 cm2. The module uses polymer donor CD1 and new polymer acceptor PBN-21 with medium optical band gap of 1.9 eV as the active layer. It is processed with eco-friendly solvent tetrahydrofuran and the morphology can be improved by blade coating at 55°C. Under light emitting diode illumination at 1000 lux, the module exhibits a power conversion efficiency of 12.04% and a power output of 367.2 μW. The sufficient power output, high efficiency, excellent stability, and eco-friendly processing indicate that all-polymer indoor photovoltaic is a promising approach to achieve the self-powered of sensor nodes in the IoT ecosystem.

Objective The predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.Design A retrospective cohort study.Setting and participants Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed.Methods Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. Discrimination, calibration and clinical usefulness of the predicting model were assessed using the C-index, calibration plot and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.Results Among the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (GWTG-HF) risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (p

The organic solar cells are typically based on a binary combination of polymeric donor and molecular acceptor. Here Zhang et al. develop alternative combination based on molecular donor and polymeric acceptor featuring 8% efficiency and high stability up to 7 days at 180 °C.

In this work, we studied the effects of different reinforcements on a metal matrix composite (MMC) using molecular dynamics (MD) simulations, where graphene was chosen as the two-dimensional (2D) material and diamond was selected as the three-dimensional (3D) material. Sintering and tensile processes were conducted on the MMC models containing reinforcements of various sizes, and the effects of reinforcements with the same surface area were compared. The results indicated that the 2D material was more beneficial for sintering at the heating stage, producing a higher-density structure. The volume of Al atoms fell from 752 to 736 nm3 as the graphene size in the composite system increased. However, a slight increase from 749 to 755 nm3 was observed when the diamond radius was small. Converted to relevant metrics in the experiments, the density of the composite reached 2.84 g/cc with a 3.3 wt.% addition of single-layer graphene (SLG) and 2.87 g/cc with a 15.4 wt.% addition of diamond, and the results were slightly higher than the experimental reports. Both SLG and diamond could reduce the number of arranged Al atoms from 43,550 to approximately 35,000, and bilayer graphene (BLG) with the largest size could further decrease the number of arranged atoms to nearly 30,000, implying that grain refinement could be obtained by increasing the surface area of reinforcements. Considering the scale of these models, the reinforcement size and pore location in the initial structures were deemed to have an impact on the mechanical properties. The composite with the largest proportion of SLG showed an increase of more than 1.6 GPa in tensile strength; however, BLG showed a significant drop of 1.9 GPa when stretched in the normal direction, as the large interlayer space acted as a large hole in tension. The diamond size did not appear to affect the strengthening effects. Nevertheless, the elongation values of composites with graphene were generally 35% higher than the Al-diamond composites.

In this paper, we consider the difference in the abstraction level of features extracted by different perceptual layers and use a weighted perceptual loss-based generative adversarial network to deblur the UAV images, which removes the blur and restores the texture details of the images well. The perceptual loss is used as an objective evaluation index for training process monitoring and model selection, which eliminates the need for extensive manual comparison of the deblurring effect and facilitates model selection. The UNet jump connection structure facilitates the transfer of features across layers in the network, reduces the learning difficulty of the generator, and improves the stability of adversarial training.

Glioma is the most common malignant tumor in adult brain characteristic with poor prognosis and low survival rate. Despite the application of advanced surgery, chemotherapy, and radiotherapy, the patients with glioma suffer poor treatment effects due to the complex molecular mechanisms of pathological process. In this paper, we conducted the experiments to prove the critical roles TET1 played in glioma and explored the downstream targets of TET1 in order to provide a novel theoretical basis for clinical glioma therapy. RT-qPCR was adopted to detect the RNA level of TET1 and β-catenin; Western blot was taken to determine the expression of proteins. CCK8 assay was used to detect the proliferation of glioma cells. Flow cytometry was used to test cell apoptosis and distribution of cell cycle. To detect the migration and invasion of glioma cells, wound healing assay and Transwell were performed. It was found that downregulation of TET1 could promote the proliferation migration and invasion of glioma cells and the concomitant upregulation of β-catenin, and its downstream targets like cyclinD1 and c-myc were observed. The further rescue experiments were performed, wherein downregulation of β-catenin markedly decreases glioma cell proliferation in vitro and in vivo. This study confirmed the tumor suppressive function of TET1 and illustrated the underlying molecular mechanisms regulated by TET1 in glioma.

Abstract Herein, a simple structure, nonchlorinated solvent processable high mobility donor–acceptor conjugated polymer, poly(2,5‐bis(4‐hexyldodecyl)‐2,5‐dihydro‐3,6‐di‐2‐thienyl‐pyrrolo[3,4‐c]pyrrole‐1,4‐dione‐alt‐thiophene) (PDPPT3‐HDO), is reported. The enhanced solubility in nonchlorinated solvent is realized based on a denser alkyl side chains strategy by incorporating small size comonomer thiophene. An associated benefit of thiophene comonomer is the remarkable structural simplicity of the resulting polymer, which is advantageous for industrial scaling up. The alkyl side chain density and structure of PDPPT3‐HDO can efficiently control the self‐assembly properties in solution and film. By bar coating from o‐xylene solution, PDPPT3‐HDO forms aligned films and exhibits high hole mobility of up to 9.24 cm2 V−1 s−1 in organic thin film transistors (OTFTs). Notably, the bar‐coated OTFT based on PDPPT3‐HDO shows a close to ideal transistor model and a high mobility reliability factor of 87%. The multiple benefits of increased side chain density strategy may encourage the design of high mobility polymers that meet the requirements of mass production of OTFT materials and devices.

Targeted expression of gene technique is an important therapeutic strategy for lung cancer. MicroRNA-7 has been well documented as a promising tumor suppressor but never been test in specific gene-promoter-targeted expression in cancer gene therapy. Here, we first evaluated the efficacy of miR-7 expression operated by the promoter of TTF-1, a lineage-specific oncogene in lung cancer, in vitro using an eukaryotic vector of TTF-1-promoter-operated expression of miR-7 (termed as p-T-miR-7). Interestingly, using a nude mice model, the growth and metastasis of human lung cancer cells in vivo were significantly reduced in remote hypodermic injection of the p-T-miR-7 group, accompanied by increased expression of miR-7 and reduced transduction of the Akt and Erk pathway in situ. Mechanism aspect, global gene expression analysis showed that downregulation of NDUFA4, a novel target of miR-7, contributed to the effects of miR-7 expression operated by TTF-1 promoter on the growth and metastasis of human lung cancer cells, as well as altered transduction of the Akt and Erk pathway. Finally, there was no significant difference in weight or histopathology of other organs. These data provided a basis for development of novel modality of miRNA-based targeted expression therapy against clinical lung cancer. Keywords: miR-7, TTF-1, 95D cells, lung cancer, growth, NDUFA4

N6-methyladenosine (m6A) plays important roles in a branch of biological and physiological processes. Accurate identification of m6A sites is especially helpful for understanding their biological functions. Since the wet-lab techniques are still expensive and time-consuming, it's urgent to develop computational methods to identify m6A sites from primary RNA sequences. Although there are some computational methods for identifying m6A sites, no methods whatsoever are available for detecting m6A sites in microbial genomes. In this study, we developed a computational method for identifying m6A sites in Escherichia coli genome. The accuracies obtained by the proposed method are >90% in both 10-fold cross-validation test and independent dataset test, indicating that the proposed method holds the high potential to become a useful tool for the identification of m6A sites in microbial genomes.

Background: Initial observational studies and a systematic review published recently have suggested that non-steroidal anti-inflammatory drug (NSAID) use has the trend to be associated with reduced risk of Alzheimer's disease (AD), while results remain conflicting. Thus, we performed an updated meta-analysis to reevaluate the evidence on this association.Methods: Data sources from PUBMED, Embase and Cochrane Library from inception through April 2017 were searched by two independent reviewers. Eligible cohort studies were selected according to predefined keywords. We did a meta-analysis of available study data using a random-effects model to calculate overall relative risks (RRs) for associations between NSAID exposure and AD risk.Results: From 121 potentially relevant studies, 16 cohort studies including 236,022 participants, published between 1995 and 2016, were included in this systematic review. Meta-analysis demonstrated that current or former NSAID use was significantly associated with reduced risk of AD (RR, 0.81, 95% CI0.70 to 0.94) compared with those who did not use NSAIDs. This association existed in studies including all NSAID types, but not in aspirin (RR, 0.89, 95% CI 0.70 to 1.13), acetaminophen (RR, 0.87, 95% CI 0.40 to 1.91) or non-aspirin NSAID (RR, 0.84, 95% CI 0.58 to 1.23).Conclusions: Current evidence suggests that NSAID exposure might be significantly associated with reduced risk of AD. However, further large-scale prospective studies are needed to reevaluate this association, especially the associations in individual NSAID type.

Poor homing of systemically infused cells to disease sites may limit the success of exogenous cell-based therapy. In this study, we screened 9,000 signal-transduction modulators to identify hits that increase mesenchymal stromal cell (MSC) surface expression of homing ligands that bind to intercellular adhesion molecule 1 (ICAM-1), such as CD11a. Pretreatment of MSCs with Ro-31-8425, an identified hit from this screen, increased MSC firm adhesion to an ICAM-1-coated substrate in vitro and enabled targeted delivery of systemically administered MSCs to inflamed sites in vivo in a CD11a- (and other ICAM-1-binding domains)-dependent manner. This resulted in a heightened anti-inflammatory response. This represents a new strategy for engineering cell homing to enhance therapeutic efficacy and validates CD11a and ICAM-1 as potential targets. Altogether, this multi-step screening process may significantly improve clinical outcomes of cell-based therapies.