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Vasomotor dysfunction is one of the key pathological aspects of shock and heart failure (HF). Shenfu injection (SFI) has been widely used for the treatment of shock and HF in China. Pharmacological studies have suggested that SFI can reduce peripheral circulation resistance and improve microcirculation. However, whether it has a regulatory effect on macrovascular has not been elucidated. In this study, we used thoracic aorta rings isolated from Wistar rats and the human umbilical vein cell line (EA.hy926) to explore the vasodilative activity of SFI and its potential mechanisms. The relaxation due to SFI was measured after pre-treatment with selective soluble guanylate cyclase (sGC) inhibitor or cyclooxygenase (COX) inhibitor and compared with the vasodilation effect of SFI only treated with norepinephrine (NE). The contents of NO, endothelin-1 (ET-1), endothelial nitric oxide synthase (eNOS), COX-1, 6-K-PGF1α, and caveolin-1 were evaluated respectively. Additionally, the level of eNOS mRNA and total eNOS and its phosphorylation were studied to investigate the potential mechanisms involved. Experimental results showed that SFI markedly attenuated NE-induced vasoconstriction but that this effect was significantly eliminated after pre-incubation with the selective sGC inhibitor 1-H-[1, 2, 4] oxadiazolo [4, 3-α] quinoxaline-1-one (ODQ), instead of the COX inhibitor indomethacin (INDO). SFI significantly increased the eNOS content and up-regulated the eNOS mRNA expression, while it did not affect the content of COX-1 and 6-K-PGF1α. SFI also markedly increased NO content but significantly reduced the content of ET-1 and caveolin-1 in the cell supernatant. Furthermore, it promoted the expression of total eNOS and the phosphorylation of eNOS at serine (Ser) 1177 but inhibited the phosphorylation at threonine (Thr) 495, which was significantly reversed by PI3K-specific inhibitor LY294002. In conclusion, our study showed the vasodilation effect of SFI in thoracic aorta is mediated entirely by enhancing eNOS activity through the PI3K/Akt signaling pathway, providing novel knowledge on the effect of SFI on shock and HF for future clinical applications.

Abstract The purpose is to investigate the clinical significance of prostatic calculi in patients with chronic prostatitis and to discuss the possible treatment.The data from 277 young males with CP/CPPS were analyzed prospectively. Symptom severity was measured using the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) and the International Prostatic Symptoms Score (IPSS). Sexual function was assessed by the International Index of Erectile Function (IIEF-5) questionnaire. After four weeks of therapy, the NIH-CPSI, IPSS, and IIEF-5 tests were repeated. The variables were compared between patients with and without prostatic calcifications using the Students t-test or chi-square test. No significant differences were found between CP/CPPS patients with and without prostatic calcifications regarding age, body mass index, prostate volume, CPSI, IPSS and IIEF-5. Men with calcifications endured symptoms significantly longer (37.9 ± 25.2 versus 19.0 ± 16.4 months, P

Abstract Coagulation alterations manifest early after severe burns and are closely linked to mortality outcomes. Nevertheless, the precise characterization of coagulation changes associated with early mortality remains elusive. We examined alterations in indicators linked to mortality outcomes at both the transcriptomic and clinical characteristic levels. At the transcriptomic level, we pinpointed 28 differentially expressed coagulation-related genes (DECRGs) following burn injuries and endeavored to validate their causal relationships through Mendelian randomization. DECRGs tied to survival exhibit a significant association with neutrophil function, wherein the expression of CYP4F2 and P2RX1 serves as robust predictors of fatal outcomes. In terms of clinical indicators, early levels of D-dimer and alterations in serum calcium show a strong correlation with mortality outcomes. Coagulation depletion and fibrinolytic activation, stemming from the hyperactivation of coagulation pathways post-severe burns, are strongly linked to patient mortality. Monitoring these early coagulation markers with predictive value can effectively identify individuals necessitating priority critical care.

IntroductionBurns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient’s prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models.MethodsWe analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR.ResultsIn both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury.ConclusionThis study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated.

Abstract Background Non‐coding RNAs (ncRNAs) are a group of RNAs that cannot synthesize proteins, but are critical in gene expression regulation. Long non‐coding RNAs (lncRNAs) and microRNAs (miRNAs), the two major family members, are intimately involved in controlling immune response, cell proliferation, apoptosis, differentiation and polarization, and cytokine secretion. Their interactions significantly influence lung inflammatory diseases and could be potential therapeutic targets. Objectives The review aims to elucidate the role of ncRNAs, especially the interactions between lncRNA and miRNA in lung diseases, including acute and chronic lung inflammatory diseases, as well as lung cancer. And provide novel insights into disease mechanisms and potential therapeutic methods. Methods We conducted a comprehensive review of the latest studies on lncRNA and miRNA in lung inflammatory diseases. Our research involved searching through electronic databases like PubMed, Web of Science, and Scopus. Results We explain the fundamental characteristics and functions of miRNA and lncRNA, their potential interaction mechanisms, and summarize the newly explorations on the role of lncRNA and miRNA interactions in lung inflammatory diseases. Conclusions Numerous lncRNAs and miRNAs have been found to partipicate in all stages of lung inflammatory diseases. While ncRNA‐based therapies have been validated and developed, there remain challenges in developing more stable and effective drugs for clinical use.

Single-Cell Sequencing (SCS) technology plays an important role in the field of Mesenchymal Stem Cells (MSCs) research. This paper comprehensively describes the application of SCS technology in the field of MSCs research, including (1) SCS enables more precise MSCs characterization and biomarker definition. (2) SCS reveals the prevalent gene expression heterogeneity among different subclusters within MSCs, which contributes to a more comprehensive understanding of MSCs function and diversity in developmental, regenerative, and pathological contexts. (3) SCS provides insights into the dynamic transcriptional changes experienced by MSCs during differentiation and the complex web of important signaling pathways and regulatory factors controlling key processes within MSCs, including proliferation, differentiation and regulation, and interactions mechanisms. (4) The analytical methods underpinning SCS data are rapidly evolving and converging with the field of histological research to systematically deconstruct the functions and mechanisms of MSCs. This review provides new perspectives for unraveling the biological properties, heterogeneity, differentiation potential, biological functions, and clinical potential of MSCs at the single-cell level.

In this paper, considering the aggregation effect and Allee effect of cyanobacteria populations and the harvesting of both cyanobacteria and fish by human beings, a new cyanobacteria-fish model with two harvesting terms and a modified Holling type Ⅳ functional response function is proposed. The main purpose of this paper is to further elucidate the influence of harvesting terms on the dynamic behavior of a cyanobacteria-fish model. Critical conditions for the existence and stability of several interior equilibria are given. The economic equilibria and the maximum sustainable total yield problem are also studied. The model exhibits several bifurcations, such as transcritical bifurcation, saddle-node bifurcation, Hopf bifurcation and Bogdanov-Takens bifurcation. It is concluded from a biological perspective that the survival mode of cyanobacteria and fish can be determined by the harvesting terms. Finally, concrete examples of our model are given through numerical simulations to verify and enrich the theoretical results.

Abstract Background Hepatitis B virus (HBV) reactivation impact negatively the prognosis of patients with HBV-related hepatocellular carcinoma (HCC). This study aimed to observe the effect of antiviral therapy (AVT) on viral reactivation and long-term outcomes after percutaneous radiofrequency ablation (PRFA) for HBV-related HCC. Methods Data on 538 patients between 2009 and 2013 were reviewed. Propensity score matching (PSM) analysis was used to adjust for differences in baseline features between patients who received AVT (AVT group) and did not receive it (non-AVT group). Logistic regression was used to identify the independent factors for viral reactivation. The tumor recurrence and overall survival (OS) rates were analyzed using the Kaplan–Meier method. Recurrence patterns were also investigated. Results HBV reactivation developed in 10.8% (58/538) of patients after PRFA. AVT was associated independently with decreased viral reactivation (odd ratio: 0.061, 95% confidence interval: 0.018–0.200). In 215 pairs of patients obtained after PSM, the AVT group had lower 1-, 3-, and 5-year recurrence rates (24%, 55%, and 67% vs 33%, 75%, and 85%, respectively) and higher 1-, 3-, and 5-year OS rates (100%, 67%, and 59% vs 100%, 52%, and 42%, respectively) than non-AVT group (P

In the paper, under the stress of aggregation and reproduction mechanism of algae, we proposed a modified algae and fish model with aggregation and Allee effect, its main purpose was to further ascertain the dynamic relationship between algae and fish. Several critical conditions were investigated to guarantee the existence and stabilization of all possible equilibrium points, and ensure that the model could undergo transcritical bifurcation, saddle-node bifurcation, Hopf bifurcation and B-T bifurcation. Numerical simulation results of related bifurcation dynamics were provided to verify the feasibility of theoretical derivation, and visually demonstrate the changing trend of the dynamic relationship. Our results generalized and improved some known results, and showed that the aggregation and Allee effect played a vital role in the dynamic relationship between algae and fish.

The role of the gut microbiota in modulating the risk of respiratory infections has garnered increasing attention. However, conventional clinical trials have faced challenges in establishing the precise relationship between the two. In this study, we conducted a Mendelian randomization analysis with single nucleotide polymorphisms employed as instrumental variables to assess the causal links between the gut microbiota and respiratory infections. Two categories of bacteria, family Lactobacillaceae and genus Family XIII AD3011, were causally associated with the occurrence of upper respiratory tract infections (URTIs). Four categories of gut microbiota existed that were causally associated with lower respiratory tract infections (LRTIs), with order Bacillales and genus Paraprevotella showing a positive association and genus Alistipes and genus Ruminococcaceae UCG009 showing a negative association. The metabolites and metabolic pathways only played a role in the development of LRTIs, with the metabolite deoxycholine acting negatively and menaquinol 8 biosynthesis acting positively. The identification of specific bacterial populations, metabolites, and pathways may provide new clues for mechanism research concerning therapeutic interventions for respiratory infections. Future research should focus on elucidating the potential mechanisms regulating the gut microbiota and developing effective strategies to reduce the incidence of respiratory infections. These findings have the potential to significantly improve global respiratory health.

Abstract Background Portal vein tumor thrombosis (PVTT) in hepatocellular carcinoma (HCC) is a sign of advanced stage disease, which is associated with poor prognosis. Liver resection (LR) may provide better prognosis in selected patients. In the present study, we aimed to assess information from HCC patients with PVTT who died within 3 months or 2 years after LR in order to identify preoperative factors correlated to short-term or long-term survival, by which inappropriate selection of patients for LR might be avoided in the future. Methods A retrospective cohort study consisting of 487 consecutive cases of HCC patients with PVTT was performed from 2008 to 2010 at Eastern Hepatobiliary Surgery Hospital. Medical records, including laboratory values, imaging results and treatment information, were obtained from participants. Study endpoints were survival at 3 months and 2 years post-hepatectomy. Logistic regression analysis was utilized to determine the significant pre-operative factors influencing short-term or long-term survival. Results In multivariable analysis, α-fetoprotein, total bilirubin and radiologic ascites were significantly associated with short-term survival, while α-fetoprotein level, clinical significant portal hypertension, extent of PVTT and tumor differentiation were factors significantly associated with long-term survival. Conclusions The independent risk factors of poor short-term survival were the liver function-associated, such as factors radiologic ascites and total bilirubin, while tumor differentiation indicating the tumor biology was associated with longer-term survival. In addition, α-fetoprotein was a risk factor associated with both short-term and longer-term survivals.

The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.