Explore the vast range of titles available.

Bone morphogenetic proteins (BMPs) are associated with the prognosis of various types of adult cancers. However, the expressions and prognosis of BMPs in pediatric neuroblastoma remain unclear. Six publicly available neuroblastoma cohorts were downloaded for bioinformatics analysis. The prognosis of BMPs in neuroblastoma was determined using cox regression analysis and Kaplan-Meier survival analysis. Our study revealed that, compared to other BMP family members, BMP1, BMP7 and BMP8B were highly expressed in neuroblastoma. However, only BMP7 was associated with the prognosis of neuroblastoma in all six neuroblastoma cohorts. Higher BMP7 expression was associated with the shorted event free survival and overall survival of neuroblastoma. The prognosis of BMP7 in neuroblastoma was independent of age and MYCN amplification. The expressions of BMP7 were higher in neuroblastoma patients with MYCN amplification or age ≥ 18months or in stage 4 neuroblastoma. Moreover, higher BMP7 was associated with the shorted event free survival and overall survival of MYCN amplified or stage 4 neuroblastoma. At last, we found that breast cancer metastasis suppressor 1 (BRMS1) was correlated with BMP7 expression. However, in contrast with the suppressor role of BRMS1 in adult cancers, BRMS1 was significantly associated with the unfavorable clinical outcomes of neuroblastoma. Overall, our analysis the demonstrated correlations between BMPs and clinical features of neuroblastoma and provided unique therapeutic targets for neuroblastoma treatments. BMP7 was a prognostic maker of neuroblastoma.

Seneca Valley Virus (SVV) 3C protease is essential for viral polyprotein processing and virion assembly. Meanwhile, it has evolved to cleave and antagonize the multiple innate immune proteins, enabling viral immune evasion. Inhibitors of 3C protease are therefore powerful antiviral agents. Among these, antiviral peptide inhibitors hold particular promise because of their high specificity, strong efficacy, and broad-spectrum activity, and minimal side effects. Here, we developed a dimerization-dependent red fluorescent protein (ddRFP) biosensor system to screen anti-SVV 3C peptides and identified a substrate-competitive decapeptide (P5) that markedly suppresses 3C protease activity. P5 inhibited 3C-mediated cleavage of multiple key immune proteins, including porcine cGAS (pcGAS), porcine Gasdermin A (pGSDMA) and porcine Pro-IL-1β (sPro-IL-1β). Mechanistically, P5 directly interacted with the catalytic His48 site of 3C protease through hydrogen bonding. Remarkably, P5 restored the formation of cGAS-DNA liquid-liquid phase separation (LLPS) by competitively blocking 3C cleavage activity, thereby enhancing cGAS activity and downstream antiviral interferon signaling. Furthermore, P5 demonstrated favorable cellular permeability, low cytotoxicity, good stability and robust antiviral activity. Our findings establish P5 as a highly promising peptide inhibitor of SVV 3C protease with strong translational potential.

PLS1 , encoding an actin-bundling protein Plastin 1, is one of the pathogenic genes responsible for autosomal dominant non-syndromic hearing loss. The cellular damage caused by mutant plastin proteins may arise from loss of function and/or dominant-negative effects. In this study, we investigated the pathogenic mechanism of PLS1 mutations by establishing both in vitro cellular models and in vivo zebrafish models. Immunoprecipitation demonstrated that the ΔExon8 mutation disrupts the intramolecular interaction between the ABD1 and ABD2 domains, while immunofluorescence colocalization revealed that the mutation alters the binding pattern of PLS1 to actin bundles and disturbs cytoskeletal organization. Through zebrafish FM1-43 uptake assays, we showed that the mutant protein impaired the functional opening and permeability of the stereociliary mechanoelectrical transduction (MET) channel. Although both PLS1-knockout zebrafish and zebrafish expressing ΔExon8-PLS1 exhibited behavioral signs of hearing impairment, defects in MET channel uptake were observed only in the mutant-expressing fish. Notably, neither PLS1 deficiency nor re-expression of human wild-type PLS1 in knockout zebrafish reproduces this MET defect. Thus, the mutation not only reduces native PLS1 function but also interferes with normal MET channel activity. In summary, our findings indicate that the DFNA76 phenotype caused by the PLS1ΔExon8 mutation is likely due to a dominant-negative effect with partial loss-of-function.

To investigate the energy-saving advantages of truck platoons, this study focused on two, three, and six-axle trucks as research subjects. A non-uniform platoon model of trucks was constructed based on braking performance differences. The aerodynamic characteristics of the non-uniform platoon fleet were analyzed in depth using numerical simulation methods, and the fuel consumption rate of the fleet was calculated. In a heterogeneous three-vehicle platoon, the 3-2-6 platooning approach exhibited the most effective reduction in fuel consumption, whereas the 3-6-2 platooning approach was the least effective. For a four-vehicle heterogeneous platoon comprising a three-axle truck, a six-axle truck, and two two-axle trucks, the 3-2-2-6 platooning approach yielded the best fuel efficiency. In a four-vehicle heterogeneous platoon with a two-axle truck, a six-axle truck, and two three-axle trucks, the 3-2-3-6 platooning approach proved to be the most effective in reducing fuel consumption. In a four-vehicle heterogeneous fleet composed of two-axle trucks, three-axle trucks, and two six-axle trucks, the 3-2-6-6 platooning approach achieved the best fuel efficiency. The conclusions of this study offer a fundamental scheme for truck formation, laying a crucial theoretical foundation for energy conservation and emissions reduction in transport systems.

Lung cancer is a complex disease with varying subtypes. The genetic architectures and risk factors that are similar or distinct among these subtypes remain unclear. In this work, Genome-wide association studies (GWAS) conducted by the International Lung Cancer Consortium and transdisciplinary Research in Cancer of the Lung were utilized to illustrate the genetic landscapes of different subtypes of lung cancer. GWAS of 942 phenotypes from UK Biobank and 902 phenotypes from FinnGen Biobank were analyzed to identify the genetic risk factors specific or common to each subtype of lung cancer through two sample Mendelian randomization inverse variance weighting method. Multivariable Mendelian randomization was employed to assess the true causals of lung cancer. We found that lung cancer, small cell lung carcinoma, squamous cell lung cancer and lung adenocarcinoma shared similar, yet varied genetic architectures. Genetic risk loci at 15q25 were identified in all types of lung cancer. Yet, genetic risk loci at 5p15 were observed in squamous cell lung cancer and lung adenocarcinoma, but not in small cell lung carcinoma. Out of 942 phenotypes from UK Biobank, smoking, time spent watching television, age first had sexual intercourse, alcohol usually taken with meal and age at first live birth were common risk factors for all types of lung cancer. Moreover, out of 902 traits in FinnGen Biobank, chronic obstructive pulmonary disease (COPD) was positively associated with small cell lung carcinoma, squamous cell lung cancer and lung adenocarcinoma. Angina pectoris and myocardial infarction were negatively associated with lung cancer, squamous cell lung cancer and lung adenocarcinoma. And Alzheimer’s disease was negatively associated with lung cancer, small cell lung carcinoma and squamous cell lung cancer. In further weighted median and weighted mode methods, myocardial infarction, angina pectoris and Alzheimer’s disease also had genetical associations with lung cancer or its subtypes. Even, considering factors such as smoking, COPD, and other risk factors together, myocardial infarction, angina pectoris and Alzheimer’s disease retained the genetical associations with lung cancer and its subtypes. Overall, in a phenome-wide Mendelian randomization analysis, our results have highlighted both similar and distinct risk factors among different subtypes of lung cancer. Additionally, our findings have provided genetic associations linking myocardial infarction, angina pectoris and Alzheimer’s disease with lung cancer or its various subtypes.

The molecular heterogeneity of primary clear cell renal cell carcinoma (ccRCC) has been reported. However, the classifications of Von Hippel–Lindau (VHL) mutant ccRCC are unclear. Here, VHL mutant ccRCC from The Cancer Genome Atlas and E-MTAB-1980 datasets were divided into two sub-clusters through non-negative matrix factorization algorithm. Most VHL mutant ccRCC patients in sub-cluster2 were with pathological T1 stage and VHL mutant ccRCC patients in sub-cluster1 were with decreased overall survival. DNA replication and homologous recombination scores were higher, while, WNT signaling pathway and regulation of autophagy scores were lower in sub-cluster1 VHL mutant ccRCC. Moreover, PBX1 transcriptional scores and mRNA expressions were lower in sub-cluster1 VHL mutant ccRCC patients and were associated with the overall survival of VHL mutant ccRCC. Furthermore, PBX1 associated genes EMCN and ERG were down-regulated in sub-cluster1 VHL mutant ccRCC and overall survival was decreased in EMCN or ERG lowly expressed VHL mutant ccRCC patients. Also, PBX1 and EMCN were down-regulated in ccRCC tissues, compared with normal kidney tissues. At last, we constructed risk models based on PBX1, EMCN and EGR expression features. With the increase of the risk score, the number of death of VHL mutant ccRCC patients was increased.

Background Transforming growth factor β (TGFβ) is a critical regulator of lung metastasis of breast cancer and is correlated with the prognosis of breast cancer. However, not all TGFβ stimulated genes were functional and prognostic in breast cancer lung metastatic progress. In this study, we tried to determine the prognosis of TGFβ stimulated genes in breast cancer. Methods TGFβ stimulated genes in MDA-MB-231 cells and lung metastasis-associated genes in LM2-4175 cells were identified through gene expression microarray. The prognosis of the induced gene (TGFBI) in breast cancer was determined through bioinformatics analysis and validated using tissue microarray. The immune infiltrations of breast cancer were determined through “ESTIMATE” and “TIMER”. Results TGFBI was up-regulated by TGFβ treatment and over-expressed in LM2-4175 cells. Through bioinformatics analysis, we found that higher expression of TGFBI was associated with shorted lung metastasis-free survival, relapse-free survival, disease-free survival, and overall survival of breast cancer. Moreover, the prognosis of TGFBI was validated in 139 Chinese breast cancer patients. Chinese breast cancer patients with higher TGFBI expression had lower overall survival. Correspondingly, breast cancer patients with higher TGFBI methylation had higher overall survival. TGFBI was correlated with the score of the TGFβ signaling pathway and multiple immune-related signaling pathways in breast cancer. The stromal score, immune score, and the infiltrations of immune cells were also correlated with TGFBI expression in breast cancer. Conclusions TGFβ-induced gene TGFBI was correlated with the prognosis and immune infiltrations of breast cancer.

Background Gestational diabetes mellitus (GDM) is a common complication of mid–to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers. Methods Data from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The “rms” package in R was used to explore GDM-associated factors through stepwise regression at P  < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves. Results Data from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband– wife relationship, family history, and parental relationships as predictors. The Hosmer–Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ 2  = 5.964, 3.249, and 12.182, respectively) were all P  > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89–0.97), 0.72 (95% CI: 0.62–0.81), and 0.68 (95% CI: 0.53–0.83), respectively. Conclusion In this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information.

Osteoporosis is a type of bone loss disease characterized by a reduction in bone mass and microarchitectural deterioration of bone tissue. With the intensification of global aging, this disease is now regarded as one of the major public health problems that often leads to unbearable pain, risk of bone fractures, and even death, causing an enormous burden at both the human and socioeconomic layers. Classic anti‐osteoporosis pharmacological options include anti‐resorptive and anabolic agents, whose ability to improve bone mineral density and resist bone fracture is being gradually confirmed. However, long‐term or high‐frequency use of these drugs may bring some side effects and adverse reactions. Therefore, an increasing number of studies are devoted to finding new pathogenesis or potential therapeutic targets of osteoporosis, and it is of great importance to comprehensively recognize osteoporosis and develop viable and efficient therapeutic approaches. In this study, we systematically reviewed literatures and clinical evidences to both mechanistically and clinically demonstrate the state‐of‐art advances in osteoporosis. This work will endow readers with the mechanistical advances and clinical knowledge of osteoporosis and furthermore present the most updated anti‐osteoporosis therapies. Osteoporosis, a type of bone loss disease, causes enormous burden at both the human and socioeconomic layer. In this study, we systematically reviewed according literatures and clinical evidences to both mechanistically and clinically demonstrate the state‐of‐art advances on osteoporosis, and furthermore present the most updated anti‐osteoporosis therapies.

Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of “big data” and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.