Baseline 25(OH)D level is a prognostic indicator for bariatric surgery readmission: a matched retrospective cohort study.

Introduction: Managing postsurgical complications is crucial in optimizing the outcomes of bariatric surgery, for which preoperative nutritional assessment is essential. In this study, we aimed to evaluate and validate the efficacy of vitamin D levels as an immunonutritional biomarker for bariatric surgery prognosis. Methods: This matched retrospective cohort study included adult patients who underwent bariatric surgery at a tertiary medical center in China between July 2021 and June 2022. Patients with insufficient and sufficient 25(OH)D (< 30 ng/mL) were matched in a 1:1 ratio. Follow-up records of readmission at 3 months, 6 months, and 1 year were obtained to identify prognostic indicators. Results: A matched cohort of 452 patients with a mean age of 37.14 ± 9.25 years and involving 69.47% females was enrolled. Among them, 94.25 and 5.75% underwent sleeve gastrectomy and gastric bypass, respectively. Overall, 25 patients (5.54%) were readmitted during the 1-year follow-up. The prognostic nutritional index and controlling nutritional status scores calculated from inflammatory factors did not efficiently detect malnourishment. A low 25(OH)D level (3.58 [95% CI, 1.16-11.03]) and surgery season in summer or autumn (2.68 [95% CI, 1.05-6.83]) increased the risk of 1-year readmission in both the training and validation cohorts. The area under the receiver operating characteristic curve was 0.747 (95% CI, 0.640-0.855), with a positive clinical benefit in the decision curve analyses. The relationship between 25(OH)D and 6-month readmission was U-shaped. Conclusion: Serum 25(OH)D levels have prognostic significance in bariatric surgery readmission. Hence, preferable 25(OH)D levels are recommended for patients undergoing bariatric surgery.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder.

BACKGROUND: Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance. RESULTS: To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy. CONCLUSIONS: Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk.

Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.

Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma.

Background & Aims: Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Methods: Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. Results: The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05). Conclusions: The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. Impact and implications: MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Physalis alkekengi L. var. franchetii combined with hormone therapy for atopic dermatitis.

Atopic dermatitis (AD) is a common, chronic, and recurring inflammatory skin disease. Physalis alkekengi L. var. franchetii (Mast) Makino (PAF), a traditional Chinese medicine, is primarily used for the clinical treatment of AD. In this study, a 2,4-dinitrochlorobenzene-induced AD BALB/c mouse model was established, and a comprehensive pharmacological method was used to determine the pharmacological effects and molecular mechanisms of PAF in the treatment of AD. The results indicated that both PAF gel (PAFG) and PAFG+MF (mometasone furoate) attenuated the severity of AD and reduced the infiltration of eosinophils and mast cells in the skin. Serum metabolomics showed that PAFG combined with MF administration exerted a synergistic effect by remodeling metabolic disorders in mice. In addition, PAFG also alleviated the side effects of thymic atrophy and growth inhibition induced by MF. Network pharmacology predicted that the active ingredients of PAF were flavonoids and exerted therapeutic effects through anti-inflammatory effects. Finally, immunohistochemical analysis confirmed that PAFG inhibited the inflammatory response through the ERß/HIF-1α/VEGF signaling pathway. Our results revealed that PAF can be used as a natural-source drug with good development prospects for the clinical treatment of AD.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

Prevalence and associated factors of drug-drug interactions in elderly outpatients in a tertiary care hospital: a cross-sectional study based on three databases.

Background: Drug-drug interactions (DDIs) are factors of adverse drug reactions and are more common in elderly patients. Identifying potential DDIs can prevent the related risks. Fewer studies of potential DDIs in prescribing for elderly patients in outpatient clinics. This study aimed to investigate the prevalence and associated factors with potential DDIs and potentially clinically significant DDIs (csDDIs) among elderly outpatients based on 3 DDIs databases. Methods: A cross-sectional study was carried out on outpatients (≥65 years old) of a tertiary care hospital in China between January and March 2022. Patients' prescriptions, including at least 1 systemic drug, were consecutively collected. The potential DDIs were identified by Lexicomp®, Micromedex®, and DDInter. Patient-related clinical parameter recorded at the prescriptions and DDIs with higher risk rating was analyzed. Variables showing association in univariate analysis (P<0.2) were included in logistic regression analysis. Weighted kappa analysis was used to analyze the consistencies of different databases. Results: A total of 19,991 elderly outpatients were involved in the study, among whom 21,527 drug combinations including 486 drugs occurred. Lexicomp®, Micromedex®, and DDInter respectively identified 32.22%, 32.93%, and 22.62% of patients have at least one potential DDIs, meanwhile, 9.16%, 14.53%, and 4.56% of patients have at least one potential csDDIs. Under any evaluation criteria, polypharmacy and neurology visits were risk factors for csDDIs. Lexicomp® has the highest coverage rate (87.86%) for drugs. Micromedex® identified the most csDDIs (740 drug combinations). Drugs used in diabetes and psycholeptics were frequently found in the csDDIs of 2 commercial databases. The consistency between Lexicomp® and Micromedex® was moderate (weighted kappa 0.473). DDInter had fair consistencies with the other databases. Conclusions: This study showed the prevalence of potential DDIs is high in elderly outpatients and potential csDDIs were prevalent. Considering the relative risk, pre-warning of potential DDIs before outpatient prescribing is necessary. As the consistencies among identification criteria are not good, more research is needed to focus on actual adverse outcomes to promote accurate prevention of csDDIs.

Quality of Low-Allergy Wheat ('O-Free') Flour and Optimization of Its Bread-Baking Performance.

This study explored the quality of hypoallergenic wheat ('O-free') developed in Korea and optimized the basic ingredients and processing conditions for making 'O-free' bread using response surface methodology. Water and yeast amounts and mixing and fermentation times were selected as factors, and each factor's tested range was set by a central composite design using Design Experts: water 52-60 g, yeast 1.5-4.5 g, mixing time 2.5-5 min, and fermentation time 50-70 min. Bread height, volume, and firmness were analyzed to determine bread quality. Flour quality analysis showed that 'O-free' flour's gluten strength was weak. 'O-free' flour exhibited inferior bread-making performance compared to representative bread flour. Water and yeast amounts and mixing time, except for fermentation time, affected bread quality significantly. The interaction between yeast and fermentation also affected bread quality significantly. The optimized condition for making bread using 'O-free' flour is 60 g of water, 2.6 g of yeast, 2.5 min of mixing time, and 70.0 min of fermentation time. In conclusion, 'O-free' flour with the changed gluten composition showed poor gluten strength and bread-making performance. However, modifying the formulation of the basic ingredients and processing conditions could significantly improve the production of high-quality hypoallergenic bread.

Inhibition of inflammasome activation via sphingolipid pathway in acute lung injury by Huanglian Jiedu decoction: An integrative pharmacology approach.

BACKGROUND: Acute lung injury (ALI) is a serious health issue which causes significant morbidity and mortality. Inflammation is an important factor in the pathogenesis of ALI. Even though ALI has been successfully managed using a traditiomal Chinese medicine (TCM), Huanglian Jiedu Decoction (HLD), its mechanism of action remains unknown. PURPOSE: This study explored the therapeutic potential of HLD in lipopolysaccharide (LPS)-induced ALI rats by utilizing integrative pharmacology. METHODS: Here, the therapeutic efficacy of HLD was evaluated using lung wet/dry weight ratio (W/D), myeloperoxide (MPO) activity, and levels of tumor necrosis factor (TNF-α), interleukin (IL)-1ß and IL-6. Network pharmacology predictd the active components of HLD in ALI. Lung tissues were subjected to perform Hematoxylin-eosin (H&E) staining, metabolomics, and transcriptomics. The acid ceramidase (ASAH1) inhibitor, carmofur, was employedto suppress the sphingolipid signaling pathway. RESULTS: HLD reduced pulmonary edema and vascular permeability, and suppressed the levels of TNF-α, IL-6, and IL-1ß in lung tissue, Bronchoalveolar lavage fluid (BALF), and serum. Network pharmacology combined with transcriptomics and metabolomics showed that sphingolipid signaling was the main regulatory pathway for HLD to ameliorate ALI, as confirmed by immunohistochemical analysis. Then, we reverse verified that the sphingolipid signaling pathway was the main pathway involed in ALI. Finally, berberine, baicalein, obacunone, and geniposide were docked with acid ceramidase to further explore the mechanisms of interaction between the compound and protein. CONCLUSION: HLD does have a better therapeutic effect on ALI, and its molecular mechanism is better elucidated from the whole, which is to balance lipid metabolism, energy metabolism and amino acid metabolism, and inhibit NLRP3 inflammasome activation by regulating the sphingolipid pathway. Therefore, HLD and its active components can be used to develop new therapies for ALI and provide a new model for exploring complex TCM systems for treating ALI.

Traditional Chinese medicines as effective agents against influenza virus-induced pneumonia.

Influenza virus-induced pneumonia (IVP) is a high morbidity and contagiousness pulmonary infectious disease caused by invasion of the influenza virus into the lower respiratory tract. Currently, the treatment of IVP is mainly based on an anti-influenza virus infection strategy, which includes the use of anti-influenza vaccines and drugs. However, the clinical use of these treatment options is limited as the influenza virus has a high level of variability and drug resistance may occur. Traditional Chinese medicines (TCMs) for the treatment of IVP have unique advantages, a variety of precise curative effects and have been widely used in clinical practice in China both historically and in the present day. However, there are only few literature reviews on the prevention and treatment of IVP using TCMs. Therefore, we conducted a review of relevant literature from the past 10 years and a comprehensive analysis of various databases containing reports on TCMs used for IVP prevention and treatment to provide basic data for future research and development of drugs against IVP. Herein, we summarize research progress on the pathogenesis of IVP, the TCMs effective in prevention or treatment of IVP, their underlying molecular mechanisms and active components. Overall, we provide a theoretical basis for the clinical use of TCM in the prevention and treatment of IVP. Furthermore, we provide a reference for the development of new multi-component, multi-target, low-toxicity drugs, which is of great academic and clinical significance.

Bisphenol A Promotes the Progression of Colon Cancer Through Dual-Targeting of NADPH Oxidase and Mitochondrial Electron-Transport Chain to Produce ROS and Activating HIF-1α/VEGF/PI3K/AKT Axis.

Bisphenol A (BPA) is a high-production-volume industrial chemical. Despite recent research conducted on its carcinogenicity, its role in the development of colon cancer (CC) has been rarely studied. This study aims to evaluate the effects of BPA on the migration and invasion of CC cells. First, we clinically verified that patients with CC exhibit higher serum BPA level than healthy donors. Subsequently, different CC cell lines were exposed to a series of BPA concentrations, and the migration and invasion of cells were detected by the wound healing test and transwell assay. Finally, N-acetyl-L-cysteine (NAC) and siHIF-1α intervention was used to explore the effects of ROS and HIF-1α on cell migration and invasion, respectively. The results demonstrated that the occurrence of BPA-induced migration and invasion were dependent on the dose and time and was most pronounced in DLD1 cells. ROS production was jointly driven by NADPH oxidase (NOX) and mitochondrial electron-transport chain (ETC). Furthermore, the intervention of NAC and siHIF-1α blocked the HIF-1α/VEGF/PI3K/AKT axis and inhibited cell migration and invasion. In conclusion, our results suggest that BPA exposure promotes the excessive production of ROS induced by NOX and ETC, which in turn activates the HIF-1α/VEGF/PI3K/AKT axis to promote the migration and invasion of CC cells. This study provides new insights into the carcinogenic effects of BPA on CC and warns people to pay attention to environmental pollution and the harm caused to human health by low-dose BPA.

Physalis alkekengi L. var. franchetii (Mast.) Makino: A review of the pharmacognosy, chemical constituents, pharmacological effects, quality control, and applications.

BACKGROUND: Physalis alkekengi L. var. franchetii (Mast.) Makino (PAF) (Chinese name Jin-Deng-Long) from the Solanaceae family is a traditional Chinese medicine with various pharmacological effects, such as removing heat, detoxification, improving throat conditions, removing phlegm, and ameliorating diuresis. PURPOSE: This paper reviews the existing literature and patents and puts forward some suggestions for future PAF research. METHODS: Using the PubMed, Google Scholar, Web of Science, and China National Knowledge Infrastructure databases, we performed comprehensive search of literature and patents published before April 2022 on PAF and its active ingredients. RESULTS: We comprehensively reviewed the research progress of PAF from aspects of the traditional application, botany, chemical composition, pharmacological effects, and toxicology, and first discussed quality control and modern applications, which have not been explored in previous reviews. Thereafter, we reviewed the limitations of pharmacological mechanism and quality control studies and proposed appropriate solutions, which is of great practical significance to subsequent studies. CONCLUSION: In this review, we present a comprehensive overview on PAF, and put forward new insights on studies regarding quality control, material basis, and mechanisms in classical prescription, providing theoretical guidance for the clinical application and development of Chinese medicine.

Integrated Pharmacology Reveals the Molecular Mechanism of Gegen Qinlian Decoction Against Lipopolysaccharide-induced Acute Lung Injury.

ALI is a severe inflammatory disease of the lungs. In previous studies, we found that GQD was effective against ALI, but specific molecular mechanism is still unclear. Therefore, this study was to examine effect of GQD on LPS-induced ALI rats and underlying mechanisms using multi-omics and molecular methods. The results showed that GQD significantly improved lung tissue damage, reduced pulmonary edema, inhibited MPO activity, and improved respiratory function in ALI rat. Additionally, GQD significantly reduced the levels of TNF-α, IL-1ß, and IL-6 in serum and BALF. Furthermore, metabolomic analysis showed that GQD reduced pulmonary inflammation by improving metabolic remodeling. Moreover, transcriptomic analysis showed that GQD inhibited the activation of complement pathway and regulated Th17 and Treg cells balance. Additionally, GQD inhibited the expression of C3, C5a, and IL-17, and promoted the expression of TGF-ß and CYP1A1 at the mRNA and protein levels. Gut microbial assay showed that GQD treatment increased the relative abundance of Firmicutes and their genera in intestinal microbiota, and increased short-chain fatty acids concentration. Overall, GQD treated ALI by improving metabolic remodeling, affecting immune-related pathways and regulating intestinal microbiota. This study provides a solid scientific basis for promoting the clinical use of GQD in treating ALI.