A dose-response correlation between smoking and severity of acute pancreatitis: a propensity score-matched study.

Background: Acute pancreatitis, among the most prevalent gastrointestinal disorders, exhibits a continual rise in its incidence recent years. This study endeavor to explore the correlation between smoking exposure and the severity of acute pancreatitis (AP). Methods: Five hundred and eight patients diagnosed as acute pancreatitis (AP) were included in our data analysis. Patients were categorized based on their smoking pack-years into four groups: light, moderate, heavy, and non-smokers. Outcomes were classified as two: "mild acute pancreatitis (MAP)" and "moderately severe acute pancreatitis (MSAP) or severe acute pancreatitis (SAP)". We conducted propensity score matching (PSM) to adjust confounding factors and multivariable logistic regression analysis to determine adjusted odds ratios and 95% confidence intervals. Additionally, a dose-dependent association analysis between smoking exposure and the incidence rate of "MSAP or SAP" was performed. Results: Smokers exhibited a higher risk of "MSAP or SAP" compared to non-smokers, both before (17.1 vs. 54.9%, p < 0.001) and after (9.4 vs. 24.7%, p < 0.001) PSM. With an area under the ROC curve of 0.708, smoking showed a moderate level of predictive ability. Furthermore, propensity score matching analysis showed that patients who smoked compared to non-smokers had significantly higher risks of "MSAP or SAP" for light smoking (OR 3.76, 95% CI 1.40-10.07, p = 0.008), moderate smoking (OR 4.94, 95% CI 2.23-10.92, p < 0.001), and heavy smoking (OR 8.08, 95% CI 3.39-19.25, p < 0.001). Conclusion: Smoking is an independent risk factor that can raise the severity of pancreatitis. Moreover, the severity of acute pancreatitis escalates in tandem with the accumulation of pack-years of smoking.

Different neurotoxicity and seeding activity between α-synuclein oligomers formed in plasma of patients with Parkinson's disease and multiple system atrophy.

Previous studies have shown that α-synuclein (α-Syn) aggregates derived from the brains of patients with Parkinson's disease (PD) and multiple system atrophy (MSA) exhibit different phosphorylation, cytotoxicity, and seeding activity. However, the mechanism underlying the differences remains poorly understood. Here, recombinant human α-Syn was incubated in the plasma of patients with PD and MSA, and the oligomers formed in the plasma (PD-O-α-Syn and MSA-O-α-Syn) were purified and analyzed for their phosphorylation, cytotoxicity and seeding activity. In vitro assays revealed that both PD-O-α-Syn and MSA-O-α-Syn were phosphorylated at serine 129. However, the phosphorylation degree of MSA-O-α-Syn was significantly higher than that of PD-O-α-Syn. In addition, MSA-O-α-Syn exhibited stronger cytotoxicity and seeding activity compared with PD-O-α-Syn. In vivo experiments showed that mice receiving intrastriatal inoculation of MSA-O-α-Syn developed more severe motor dysfunction and dopaminergic degeneration than mice receiving intrastriatal inoculation of PD-O-α-Syn. Compared with the mice inoculated with PD-O-α-Syn, the mice inoculated with MSA-O-α-Syn accumulated more phosphorylated and oligomerized α-Syn in the striatum and brain regions (substantia nigra, hippocampus and prefrontal cortex) away from the inoculated site. The results obtained suggest that α-Syn oligomers formed in PD and MSA plasma are different in phosphorylation, cytotoxicity, and seeding activity.

Correlations of erythrocytic oligomer α-synuclein levels with age, sex and clinical variables in patients with Parkinson's disease.

Introduction: Oligomeric alpha-synuclein in red blood cells (RBC-o-α-Syn) has been shown to be increased in patients with Parkinson's disease (PD). However, factors that affect RBC-o-α-Syn levels remain to be elucidated. The aim of this study is to analyze the correlations between RBC-o-α-Syn levels and the age, sex and different clinical variables of patients with PD. Methods: 167 patients with PD and 119 healthy controls (HC) were enrolled in this study. The patients with PD were diagnosed based on the MDS clinical diagnostic criteria for PD. All participants were evaluated for their clinical characteristics. Western blot analysis was used to examine the molecular sizes of RBC-o-α-Syn. A newly established chemiluminescent immunoassay was used to measure RBC-o-α-Syn levels. Results: Higher RBC-o-α-Syn levels were detected in PD patients than in HC subjects. The receiver operating characteristic (ROC) curve indicated that a cut off value of 55.29 ng/mg discriminated well between PD patients and HC subjects, with a sensitivity of 67.66% (95% CI: 60.24-74.29%), a specificity of 88.24% (95% CI: 81.22-92.86%), and an area under the curve (AUC) of 0.857. The levels of RBC-o-α-Syn were higher in female than male patients (p = 0.033). For different subtypes, the levels of RBC-o-α-Syn were higher in the MIX subtype than the tremor-dominant (TD) PD. In addition, the levels of RBC-o-α-Syn were higher in patients with than without cognitive impairment (p = 0.016), and negatively correlated with Mini-Mental State Examination (MMSE) scores (r = -0.156, p = 0.044). Conclusion: Our study demonstrates that RBC-o-α-Syn levels in patients with PD are higher than those in HC subjects and affected by the sex and the severity of cognitive impairment.

Total astragalus saponins attenuate primary sclerosing cholangitis in mice by upregulation of TGR5.

Total astragalus saponins (TAS) are the main active components of astragali radix, and have potent anti-hepatic fibrosis effect. However, the therapeutic efficacy of TAS and their potential mechanisms in the treatment of primary sclerosing cholangitis (PSC) remain unclear. In this study, two mouse models of PSC, including 3,5-Diethoxycarbonyl-1,4-Dihydro-2,4,6-Collidine (DDC)-induced PSC and Mdr2-/- spontaneous PSC, and the Tgr5-/- mice were used to investigate the therapeutic effect and mechanisms of TAS. Treatment with TAS, particularly with a dose of 56 mg/kg, significantly ameliorated the PSC-related liver injury, cholestasis, collagen deposition, ductular reaction (DR), and fibrosis in the DDC-induced and Mdr2-/-spontaneous PSC mice. Furthermore, treatment with TAS significantly mitigated the PSC-related inflammatory responses in vivo and HIBEpiC cells by inhibiting the expression of TNF-α, IL-6, and IL-1ß. Mechanistically, treatment with TAS rescued the PSC-decreased hepatic TGR5 expression to attenuate the NF-κB p65 phosphorylation. Notably, the therapeutic efficacy of TAS on PSC in DDC-induced mice was abrogated in Tgr5-/- mice, suggesting the anti-PSC effect of TAS may depend on enhancing TGR5 expression. In conclusion, TAS ameliorated DR, inflammation and liver fibrosis in both models of PSC mice by rescuing TGR5 expression. Our findings may aid in the design of new therapeutic strategies for the treatment of PSC.

Construction of a novel nomogram for predicting overall survival in patients with Siewert type II AEG based on LODDS: a study based on the seer database and external validation.

Background: In recent years, the incidence of adenocarcinoma of the esophagogastric junction (AEG) has been rapidly increasing globally. Despite advances in the diagnosis and treatment of AEG, the overall prognosis for AEG patients remains concerning. Therefore, analyzing prognostic factors for AEG patients of Siewert type II and constructing a prognostic model for AEG patients is important. Methods: Data of primary Siewert type II AEG patients from the SEER database from 2004 to 2015 were obtained and randomly divided into training and internal validation cohort. Additionally, data of primary Siewert type II AEG patients from the China Medical University Dandong Central Hospital from 2012 to 2018 were collected for external validation. Each variable in the training set underwent univariate Cox analysis, and variables with statistical significance (p < 0.05) were added to the LASSO equation for feature selection. Multivariate Cox analysis was then conducted to determine the independent predictive factors. A nomogram for predicting overall survival (OS) was developed, and its performance was evaluated using ROC curves, calibration curves, and decision curves. NRI and IDI were calculated to assess the improvement of the new prediction model relative to TNM staging. Patients were stratified into high-risk and low-risk groups based on the risk scores from the nomogram. Results: Age, Differentiation grade, T stage, M stage, and LODDS (Log Odds of Positive Lymph Nodes)were independent prognostic factors for OS. The AUC values of the ROC curves for the nomogram in the training set, internal validation set, and external validation set were all greater than 0.7 and higher than those of TNM staging alone. Calibration curves indicated consistency between the predicted and actual outcomes. Decision curve analysis showed moderate net benefit. The NRI and IDI values of the nomogram were greater than 0 in the training, internal validation, and external validation sets. Risk stratification based on the nomogram's risk score demonstrated significant differences in survival rates between the high-risk and low-risk groups. Conclusion: We developed and validated a nomogram for predicting overall survival (OS) in patients with Siewert type II AEG, which assists clinicians in accurately predicting mortality risk and recommending personalized treatment strategies.

Cannabidiol alleviates suture-induced corneal pathological angiogenesis and inflammation by inducing myeloid-derived suppressor cells.

BACKGROUND: Currently, no perfect treatment for neovascularization and lymphangiogenesis exist, and each treatment method has its complications and side effects. This study aimed to investigate the anti-angiogenic and anti-inflammatory effects of cannabidiol and its mechanism of action. METHOD: An in vivo corneal neovascularization (CNV) model was established using the suture method to investigate the inhibitory effects of CBD on suture-induced corneal inflammation, pathological blood vessel formation, and lymphangiogenesis. Additionally, the impact of CBD on immune cells was studied. In vitro methodologies, including cell sorting and co-culture, were employed to elucidate its mechanism of action. RESULTS: Compared with the CNV group, CBD can inhibit CNV, lymphangiogenesis, and inflammation induced via the suture method. In addition, CBD specifically induced CD45+CD11b+Gr-1+ cell upregulation, which significantly inhibited the proliferation of CD4+ T lymphocytes in vitro and exhibited a CD31+ phenotype, proving that they were myeloid-derived suppressor cells (MDSCs). We administered anti-Gr-1 to mice to eliminate MDSCs in vivo and found that anti-Gr-1 partially reversed the anti-inflammatory and angiogenic effects of CBD. Furthermore, we found that compared with MDSCs in the normal group, CBD-induced MDSCs overexpress peroxisome proliferator-activated receptor-gamma (PPAR-γ). Administering PPAR-γ inhibitor in mice almost reversed the induction of MDSCs by CBD, demonstrating the role of PPAR-γ in the function of CBD. CONCLUSION: This study indicates that CBD may induce MDSCs upregulation by activating the nuclear receptor PPAR-γ, exerting anti-inflammatory, antiangiogenic, and lymphangiogenic effects, and revealing potential therapeutic targets for corneal neovascularization and lymphangiogenesis.

DyCR: A Dynamic Clustering and Recovering Network for Few-Shot Class-Incremental Learning.

Few-shot class-incremental learning (FSCIL) aims to continually learn novel data with limited samples. One of the major challenges is the catastrophic forgetting problem of old knowledge while training the model on new data. To alleviate this problem, recent state-of-the-art methods adopt a well-trained static network with fixed parameters at incremental learning stages to maintain old knowledge. These methods suffer from the poor adaptation of the old model with new knowledge. In this work, a dynamic clustering and recovering network (DyCR) is proposed to tackle the adaptation problem and effectively mitigate the forgetting phenomena on FSCIL tasks. Unlike static FSCIL methods, the proposed DyCR network is dynamic and trainable during the incremental learning stages, which makes the network capable of learning new features and better adapting to novel data. To address the forgetting problem and improve the model performance, a novel orthogonal decomposition mechanism is developed to split the feature embeddings into context and category information. The context part is preserved and utilized to recover old class features in future incremental learning stages, which can mitigate the forgetting problem with a much smaller size of data than saving the raw exemplars. The category part is used to optimize the feature embedding space by moving different classes of samples far apart and squeezing the sample distances within the same classes during the training stage. Experiments show that the DyCR network outperforms existing methods on four benchmark datasets. The code is available at: https://github.com/zichengpan/DyCR.

A novel anoikis-related gene signature can predict the prognosis of hepatocarcinoma patients.

Background: Hepatocellular carcinoma (HCC) is a major health problem with more than 850,000 cases per year worldwide. This cancer is now the third leading cause of cancer-related deaths worldwide, and the number is rising. Cancer cells develop anoikis resistance which is a vital step during cancer progression and metastatic colonization. However, there is not much research that specifically addresses the role of anoikis in HCC, especially in terms of prognosis. Methods: This study obtained gene expression data and clinical information from 371 HCC patients through The Cancer Genome Atlas (TCGA) Program and The Gene Expression Omnibus (GEO) databases. A total of 516 anoikis-related genes (ANRGs) were retrieved from GeneCard database and Harmonizome portal. Differential expression analysis identified 219 differentially expressed genes (DEGs), and univariate Cox regression analysis was utilized to select 99 ANRGs associated with the prognosis of HCC patients. A risk scoring model with seven genes was established using the least absolute shrinkage and selection operator (LASSO) regression model, and internal validation of the model was performed. Results: The identified 99 ANRGs are closely associated with the prognosis of HCC patients. The risk scoring model based on seven characteristic genes demonstrates excellent predictive performance, further validated by receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. The study reveals significant differences in immune cell infiltration, gene expression, and survival status among different risk groups. Conclusions: The prognosis of HCC patients can be predicted using a unique prognostic model built on ANRGs in HCC.

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture.

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

A predictive model for intraabdominal infection after radical gastrectomy in elderly patients.

Gastric cancer (GC) is one of the most common malignant tumors worldwide and the fourth leading cause of cancer-related deaths, with a relatively high incidence among the elderly population. Surgical resection is the mainstay treatment for GC and is currently the only cure. However, the incidence of postoperative intraabdominal infections remains high and seriously affects the prognosis. This study aimed to explore the risk factors for intraabdominal infections after radical gastrectomy in elderly patients and to establish and validate a risk prediction model. We collected the clinical data of 322 GC patients, who underwent radical gastrectomy at the General Surgery Department of China Medical University Dandong Central Hospital from January 2016 to January 2023. The patients were divided into an infected group (n = 27) and a noninfected group (n = 295) according to whether intraabdominal infections occurred postoperatively. A nomogram risk prediction model for the occurrence of postoperative intraabdominal infections was developed. All patients were randomized into a training set (n = 225) and a validation set (n = 97) in a 7:3 ratio, and the model was internally validated. Of the 322 patients, 27 (8.3%) experienced postoperative intraabdominal infections. Single-factor analysis revealed associations of intraabdominal infection with body mass index, glucose, hemoglobin, albumin, and other factors. The multifactorial analysis confirmed that body mass index, glucose, hemoglobin, albumin, surgical duration, and bleeding volume were independent risk factors for intraabdominal infections. The nomogram constructed based on these factors demonstrated excellent performance in both the training and validation sets. A nomogram model was developed and validated to predict the risk of intraabdominal infection after radical gastrectomy. The model has a good predictive performance, which could help clinicians prevent the occurrence of intraabdominal infections after radical gastrectomy in elderly patients.

Beam-shaped femtosecond laser printing of quasi-capsule-shaped holographic terahertz metasurfaces.

Terahertz (THz) metasurfaces have opened up a new avenue for the THz wavefront modulation. However, high-efficient and low-cost fabrication of THz metasurfaces remains a great challenge today. Here, quasi-capsule-shaped polarization-multiplexed holographic THz metasurfaces were printed by a beam-shaped femtosecond laser. The laser beam was spatially modulated by holograms of optimized cylindrical lens loaded on a spatial light modulator (SLM). The size of quasi-capsule apertures can be exquisitely and flexibly controlled by adjusting the focal length in holograms, pulse energy, and pulse number. Based on near-field diffraction and Burch encoding, an array of 100 × 100 basic unit apertures were initially designed, and a polarization-multiplexed THz metasurface was finally printed with a dimension of 8 mm × 8 mm. The function of polarization multiplexing was demonstrated, by which two kinds of images were reconstructed in response to X and Y-polarization THz waves, respectively. The present work highlights a great leap in fabrication method for THz metasurfaces and hopefully stimulates the development of miniaturized and integrated THz systems.

A study on the treatment of brain tumors with BNCT using several moderators with different thicknesses.

Boron neutron capture therapy (BNCT) is an effective binary radiation therapy that depends on nuclear capture reactions. In recent years, BNCT can be performed without a reactor owing to the development of accelerator-based neutron sources. A new BNCT irradiation facility is proposed, which is based on a 15 mA 2.5 MeV proton accelerator with a 100 µm thickness natural lithium target as a neutron converter. A great quantity of studies has shown that neutron beams with different spectra have unique therapeutic effects on tumors. An appropriate neutron beam for BNCT is obtained by Beam Shaping Assembly (BSA) and the moderator plays a main role in determining the BSA outlet beam spectrum. To figure out the dose distribution in phantom with various kinds of neutron spectrum modes during BNCT, a series of cases are calculated by MCNPX code. The results give a database for treatment of brain tumors with BNCT by using different moderators.

Bioinformatics identifies key genes and potential drugs for energy metabolism disorders in heart failure with dilated cardiomyopathy.

Background: Dysfunction in myocardial energy metabolism plays a vital role in the pathological process of Dilated Cardiomyopathy (DCM). However, the precise mechanisms remain unclear. This study aims to investigate the key molecular mechanisms of energy metabolism and potential therapeutic agents in the progression of dilated cardiomyopathy with heart failure. Methods: Gene expression profiles and clinical data for patients with dilated cardiomyopathy complicated by heart failure, as well as healthy controls, were sourced from the Gene Expression Omnibus (GEO) database. Gene sets associated with energy metabolism were downloaded from the Molecular Signatures Database (MSigDB) for subsequent analysis. Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis were employed to identify key modules and genes related to heart failure. Potential biological mechanisms were investigated through Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the construction of a competing endogenous RNA (ceRNA) network. Molecular docking simulations were then conducted to explore the binding affinity and conformation of potential therapeutic drugs with hub genes. Results: Analysis of the left ventricular tissue expression profiles revealed that, compared to healthy controls, patients with dilated cardiomyopathy exhibited 234 differentially expressed genes and 2 genes related to myocardial energy metabolism. Additionally, Benzoylaconine may serve as a potential therapeutic agent for the treatment of dilated cardiomyopathy. Conclusion: The study findings highlight the crucial role of myocardial energy metabolism in the progression of Dilated Cardiomyopathy. Notably, Benzoylaconine emerges as a potential candidate for treating Dilated Cardiomyopathy, potentially exerting its therapeutic effects by targeted modulation of myocardial energy metabolism through NRK and NT5.

Octylammonium Iodide Induced in-situ Healing Behavior at Perovskite / Carbon Interface: the "Slow-Release Effect" Caused by Carbon Black Adsorption.

"Perovskite / Carbon" interface has remained a key bottleneck for the hole-conductor-free perovskite solar cells based on carbon-electrode (CPSCs), due to problems like loose physics contact, defects, energy mismatch, poor chemical coupling, etc. A previous study shows that octylammonium iodide (OAI) blending in carbon paste induced a kind of "in-situ healing" effect for "perovskite / carbon" interface, and improved power conversion efficiency from ≈13% to >19%. Here the beneath mechanism is further explored by careful examination of the interaction between OAI molecule and carbon black (CB) nanoparticles. It comes to show that, the famous "CB adsorption" plays a key role during the "healing" processes. Due to CB adsorption behavior, the mass ratio between OAI and CB influences much on the healing effect. By suitably adjusting the mass ratio between OAI and CB, and increasing the light harvest of perovskite, an efficiency of 19.41% is achieved for the hole-conductor-free CPSCs. Device efficiency and the charge-extraction and recombination process are tracked with the storage period, continuous improvement appears for devices assembled by relatively higher CB mass. A kind of "slow-release effect" is revealed during the OAI-induced "in-situ healing" process, which is caused by the famous "CB adsorption" behavior.

Coverage enhancement accelerates acidic CO2 electrolysis at ampere-level current with high energy and carbon efficiencies.

Acidic CO2 electroreduction (CO2R) using renewable electricity holds promise for high-efficiency generation of storable liquid chemicals with up to 100% CO2 utilization. However, the strong parasitic hydrogen evolution reaction (HER) limits its selectivity and energy efficiency (EE), especially at ampere-level current densities. Here we present that enhancing CO2R intermediate coverage on catalysts promotes CO2R and concurrently suppresses HER. We identified and engineered robust Cu6Sn5 catalysts with strong *OCHO affinity and weak *H binding, achieving 91% Faradaic efficiency (FE) for formic acid (FA) production at 1.2 A cm-2 and pH 1. Notably, the single-pass carbon efficiency reaches a new benchmark of 77.4% at 0.5 A cm-2 over 300 hours. In situ electrochemical Fourier-transform infrared spectroscopy revealed Cu6Sn5 enhances *OCHO coverage ~2.8× compared to Sn at pH 1. Using a cation-free, solid-state-electrolyte-based membrane-electrode-assembly, we produce 0.36 M pure FA at 88% FE over 130 hours with a marked full-cell EE of 37%.

Dynamic changes of microbiota and metabolite of traditional Hainan dregs vinegar during fermentation based on metagenomics and metabolomics.

Hainan dregs vinegar (HNDV) is a traditional fermented food in China that is renowned for its unique flavor. HNDV is one of the most popular vinegars in Southeast Asia. However, research on the microorganisms and characteristic metabolites specific to HNDV is lacking. This study investigated the changes in microbial succession, volatile flavor compounds and characteristic non-volatile flavor compounds during HNDV fermentation based on metagenomics and metabolomics. The predominant microbial genera were Lactococcus, Limosilactobacillus, Lactiplantibacillus, and Saccharomyces. Unlike traditional vinegar, l-lactic acid was identified as the primary organic acid in HNDV. Noteworthy flavor compounds specific to HNDV included 3-methylthiopropanol and dl-phenylalanine. Significant associations were observed between six predominant microorganisms and six characteristic volatile flavor compounds, as well as seven characteristic non-volatile flavor compounds. The present results contribute to the development of starter cultures and the enhancement of HNDV quality.

The role of differentially expressed genes and immune cell infiltration in the progression of nonalcoholic steatohepatitis (NASH) to hepatocellular carcinoma (HCC): a new exploration based on bioinformatics analysis.

Nonalcoholic fatty liver disease (NAFLD) is a spectrum of chronic liver disease characterized. The condition ranges from isolated excessive hepatocyte triglyceride accumulation and steatosis (nonalcoholic fatty liver (NAFL), to hepatic triglyceride accumulation plus inflammation and hepatocyte injury (nonalcoholic steatohepatitis (NASH)) and finally to hepatic fibrosis and cirrhosis and/or hepatocellular carcinoma (HCC). However, the mechanism driving this process is not yet clear. Obtain sample microarray from the GEO database. Extract 6 healthy liver samples, 74 nonalcoholic hepatitis samples, 8 liver cirrhosis samples, and 53 liver cancer samples from the GSE164760 dataset. We used the GEO2R tool for differentially expressed genes (DEGs) analysis of disease progression (nonalcoholic hepatitis healthy group, cirrhosis nonalcoholic hepatitis group, and liver cancer cirrhosis group) and necroptosis gene set. Gene set variation analysis (GSVA) is used to evaluate the association between biological pathways and gene features. The STRING database and Cytoscape software were used to establish and visualize protein-protein interaction (PPI) networks and identify the key functional modules of DEGs, drawn factor-target genes regulatory network. Gene Ontology (GO) and KEGG pathway enrichment analyses of DEGs were also performed. Additionally, immune infiltration patterns were analyzed using the cibersort, and the correlation between immune cell-type abundance and DEGs expression was investigated. We further screened and obtained a total of 152 intersecting DEGs from three groups. 23 key genes were obtained through the MCODE plugin. Transcription factors regulating common differentially expressed genes were obtained in the hTFtarget database, and a TF target network diagram was drawn. There are 118 nodes, 251 edges, and 4 clusters in the PPI network. The key genes of the four modules include METAP2, RPL14, SERBP1, EEF2; HR4A1; CANX; ARID1A, UBE2K. METAP2, RPL14, SERBP1 and EEF2 was identified as the key hub genes. CREB1 was identified as the hub TF interacting with those gens by taking the intersection of potential TFs. The types of key gene changes were genetic mutations. It can be seen that the incidence of key gene mutations is 1.7% in EEF2, 0.8% in METAP2, and 0.3% in RPL14, respectively. Finally, We found that the most significant expression differences of the immune infiltrating cells among the three groups, were Tregs and M2, M0 type macrophages. We identified four hub genes METAP2, RPL14, SERBP1 and EEF2 being the most closely with the process from NASH to cirrhosis to HCC. It is beneficial to examine and understand the interaction between hub DEGs and potential regulatory molecules in the process. This knowledge may provide a novel theoretical foundation for the development of diagnostic biomarkers and gene-related therapy targets in the process.

Self-Supervised Lie Algebra Representation Learning via Optimal Canonical Metric.

Learning discriminative representation with limited training samples is emerging as an important yet challenging visual categorization task. While prior work has shown that incorporating self-supervised learning can improve performance, we found that the direct use of canonical metric in a Lie group is theoretically incorrect. In this article, we prove that a valid optimization measurement should be a canonical metric on Lie algebra. Based on the theoretical finding, this article introduces a novel self-supervised Lie algebra network (SLA-Net) representation learning framework. Via minimizing canonical metric distance between target and predicted Lie algebra representation within a computationally convenient vector space, SLA-Net avoids computing nontrivial geodesic (locally length-minimizing curve) metric on a manifold (curved space). By simultaneously optimizing a single set of parameters shared by self-supervised learning and supervised classification, the proposed SLA-Net gains improved generalization capability. Comprehensive evaluation results on eight public datasets show the effectiveness of SLA-Net for visual categorization with limited samples.

Schisantherin A protects hepatocyte via upregulating DDAH1 to ameliorate liver fibrosis in mice.

BACKGROUND: Hepatic fibrosis is the pivotal determinant in the progression of chronic liver diseases towards cirrhosis or advanced stages. Studies have shown that Schisantherin A (Sin A), the primary active compound from Schizandra chinensis (Turcz.) Baill., exhibits anti-hepatic fibrosis effects. However, the mechanism of Sin A in liver fibrosis remain unclear. PURPOSE: To examine the effects and underlying mechanism of Sin A on hepatic fibrosis. STUDY DESIGN AND METHODS: The effects and mechanism of Sin A were investigated using liver fibrosis mouse models induced by carbon tetrachloride (CCl4) or dimethylnitrosamine (DMN), as well as H2O2-induced hepatocyte injury in vitro. RESULTS: Sin A treatment ameliorated hepatocyte injury, inflammation, hepatic sinusoidal capillarization, and hepatic fibrosis in both CCl4-induced and DMN-induced mice. Sin A effectively reversed the reduction of DDAH1 expression, the p-eNOS/eNOS ratio and NO generation and attenuated the elevation of hepatic ADMA level induced by CCl4 and DMN. Knockdown of DDAH1 in hepatocytes not only triggered hepatocyte damage, but it also counteracted the effect of Sin A on protecting hepatocytes in vitro. CONCLUSION: Our findings indicate that Sin A ameliorates liver fibrosis by upregulating DDAH1 to protect against hepatocyte injury. These results provide compelling evidence for Sin A treatment in liver fibrosis.