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.

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.

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.

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.

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.

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.

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.

Integrative pharmacology reveals the mechanisms of Erzhi Pill, a traditional Chinese formulation, against diabetic cardiomyopathy.

ETHNOPHARMACOLOGICAL RELEVANCE: Erzhi Pill (EZP) is a traditional Chinese prescription that has marked effects in treating type 2 diabetes mellitus and diabetic nephropathy. However, its underlying pharmacological mechanisms in the treatment of diabetic cardiomyopathy (DCM), remain to be elucidated. AIM OF THE STUDY: This study aimed to apply an integrative pharmacological strategy to systematically evaluate the pharmacological effects and molecular mechanisms of EZP, and provide a solid theoretical basis for the clinical application of EZP in the treatment of DCM. MATERIALS AND METHODS: In this study, the potential targets and key pathways of EZP were predicted and validated using network pharmacology and molecular docking, respectively. Changes in cardiac metabolites and major metabolic pathways in rat heart samples were examined using 1H-nuclear magnetic resonance (NMR) metabolomics. Finally, biochemical analysis was conducted to detect the protein expression levels of key pathways. RESULTS: We found that EZP decreased fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), and low-density lipoprotein (LDL) levels, increased high-density lipoprotein (HDL) levels in the serum, and alleviated the morphological abnormalities of the heart tissue in diabetic rats. Furthermore, EZP effectively restored superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), caspase-3, caspase-8, and caspase-9 activity levels, as well as the levels of reactive oxygen species (ROS), malondialdehyde (MDA), B-cell lymphoma (Bcl)-2, and Bcl-2-associated X protein (Bax) in the heart tissue. Network pharmacology prediction results indicated that the mechanism of EZP in treating DCM was closely related to apoptosis, oxidative stress, and the HIF-1, PI3K-Akt, and FoxO signaling pathways. In addition, 1H-NMR metabolomics confirmed that EZP primarily regulated both energy metabolism and amino acid metabolism, including the tricarboxylic acid (TCA) cycle, ketone bodies metabolism, glutamine and glutamate metabolism, glycine metabolism, and purine metabolism. Finally, immunohistochemistry results indicated that EZP reduced the expression levels of p-AMPK, p-PI3K, p-Akt, and p-FoxO3a proteins, in the heart tissue of DCM rats. CONCLUSION: The results confirmed that the overall therapeutic effect of EZP in the DCM rat model is exerted via inhibition of oxidative stress and apoptosis, alongside the regulation of energy metabolism and amino acid metabolism, as well as the AMPK and PI3K/Akt/FoxO3a signaling pathways. This study provides an experimental basis for the use of EZP in DCM treatment.

Identification of active compounds and molecular mechanisms of Dalbergia tsoi Merr.et Chun to accelerate wound healing.

As a traditional Chinese medicine, Dalbergia tsoi Merr.et Chun (JZX) has been used for the treatment of wounds since ancient times. However, the active compounds and molecular mechanisms of JZX in the acceleration of wound healing are still unknown. Herein, we explored the main active compounds and key molecular mechanisms by which JZX accelerates wound healing. The ethanol extract of JZX was subjected to UPLC-Q-Orbitrap HRMS analysis to identify the main compounds. The pharmacological effect of JZX on wound healing was evaluated using a mouse excision wound model. Network pharmacology was utilized to predict the effective compounds and related signal transduction pathways of JZX that were involved in accelerating wound healing. The predicted key signaling pathways were then validated by immunohistochemical analysis. Interactions between the active compounds and therapeutic targets were confirmed by molecular docking analysis. JZX accelerated wound healing, improved tissue quality, and inhibited inflammation and oxidative stress. Moreover, our results suggested that the active components of JZX, such as butin, eriodyctiol, and formononetin, are the key compounds that facilitate wound treatment. Our studies also indicated that JZX accelerated wound healing by regulating the PI3K/Akt signaling pathway and inducing the expression of TGF-ß1, FGF2, VEGFA, ECM1, and α-SMA at different stages of skin wound healing. The JZX extract accelerates wound healing by reducing inflammation and inhibiting oxidative stress, regulating the PI3K/Akt signaling pathway, and promoting the expression of growth factors, suggesting that JZX has potential clinical applicability in wound treatment.

A Novel Quantification System Combining iTRAQ Technology and Multi-Omics Assessment to Predict Prognosis and Immunotherapy Efficacy in Colon Cancer.

Background: Colon cancer is one of the most common cancer types, although it has certain unique genetic features. This study aimed to develop a unique score for assessing prognosis and immunotherapy efficacy using integrated multi-omics analysis. Methods: Isobaric tagging for relative and absolute quantification (iTRAQ) based proteomic analysis was used to screen differentially expressed proteins (DEP) between tumor and normal samples. DEP mRNA obtained from TCGA were clustered into different categories to show landscape-related prognosis and function. Following that, DEG was extracted from DEP mRNA, and the DEP-related score (DEPRS) was constructed to investigate the difference in immunotherapy prognosis and sensitivity. Finally, WCGNA, random forest, and artificial neural networks were used to screen for key genes. The prognostic value and protein level of these genes were validated. Results: A total of 243 DEPs were identified through iTRAQ analysis, and the corresponding DEP mRNA was clustered into three. Following a series of tests, 1,577 DEGs were identified from overlapped DEP mRNA clusters and were classified into three gene clusters. The two types of clusters described above shared comparable characteristics in terms of prognosis and function. Then, it was established that a high DEPRS indicated a poor prognosis and DEPRS had significant associations with TMB, MSI status, and immunotherapeutic response. Finally, the key genes HART3 and FBLN2 were identified and were found to be implicated in immunotherapy and prognosis. Conclusion: The development of a DEPRS based on multi-omics analysis will aid in improving our understanding of colon cancer and guiding a more effective immunotherapy strategy. DEPRS and key genes are used as biomarkers in the clinical evaluation of patients.

Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.

Purpose: The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a non-invasive radiogenomics approach to identify a hypoxia pattern with potential application in patient prognostication. Methods: Specific hypoxia-related genes (sHRGs) were identified based on RNA-seq of OC cell lines cultured with different oxygen conditions. Meanwhile, multiple hypoxia-related subtypes were identified by unsupervised consensus analysis and LASSO-Cox regression analysis. Subsequently, diversified bioinformatics algorithms were used to explore the immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms. Results: One hundred forty sHRGs and three types of hypoxia-related subtypes were identified. Among them, hypoxia-cluster-B, gene-cluster-B, and high-risk subtypes had poor survival outcomes. The subtypes were closely related to each other, and hypoxia-cluster-B and gene-cluster-B had higher hypoxia risk scores. Notably, the low-risk subtype had an active immune microenvironment and may benefit from immunotherapy. Finally, a four-feature radiogenomics model was constructed to reveal hypoxia risk status, and the model achieved area under the curve (AUC) values of 0.900 and 0.703 for the training and testing cohorts, respectively. Conclusion: As a non-invasive approach, computed tomography-based radiogenomics biomarkers may enable the pretreatment prediction of the hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in patients with OC.

Physalin B ameliorates inflammatory responses in lipopolysaccharide-induced acute lung injury mice by inhibiting NF-κB and NLRP3 via the activation of the PI3K/Akt pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Physalin B (PB) is an active constituent of Physalis alkekengi L. var. Franchetii, which is a traditional medicine for clearing heat and detoxification, resolving phlegm, and diuresis. It has been commonly applied to treat sore throat, phlegm-heat, cough, dysuria, pemphigus, and eczema. AIM OF STUDY: Physalin B has shown efficacy as an anti-acute lung injury (ALI) agent previously; however, its mechanisms of action remain unclear. In the present study, we established a lipopolysaccharide-induced septic ALI model using BALB/c mice to further confirm the therapeutic potential of PB and to assess the underlying molecular mechanisms. MATERIALS AND METHODS: We used 75% ethanol and macroporous resin for extraction, separation, and enrichment of PB. The LPS-induced ALI mouse model was used to determine anti-inflammatory effects of PB. The severity of acute lung injury was evaluated by hematoxylin and eosin staining, wet/dry lung ratio, and myeloperoxidase (MPO) activity in lung tissue. An automatic analyzer was used to measure the arterial blood gas index. Protein levels of pro-inflammatory cytokines in serum, bronchoalveolar lavage fluid (BALF), and lung tissue was measured using an ELISA. Quantitative RT-PCR was used to measure changes in RNA levels of pro-inflammatory cytokines in the lungs. A fluorometric assay kit was used for determination of apoptosis-related factors to assess anti-apoptotic effects of PB. Western blotting was used to assess levels of key pathway proteins and apoptosis-related proteins. Connections between the pathways were tested through inhibitor experiments. RESULTS: Pretreatment with PB (15 mg kg-1 d-1, i.g.) significantly reduced lung wet/dry weight ratios and MPO activity in blood and BALF of ALI mice, and it alleviated LPS-induced inflammatory cell infiltration in lung tissue. The levels of pro-inflammatory factors TNF-α, IL-6, and IL-1ß and their mRNA levels in blood, BALF, and lung tissue were reduced following PB pretreatment. PB pretreatment also downregulated the apoptotic factors caspase-3, caspase-9, and apoptotic protein Bax, and it upregulated apoptotic protein Bcl-2. The NF-κB and NLRP3 pathways were inhibited through activation of the PI3K/Akt pathway due to PB pretreatment, whereas administration of PI3K inhibitors increased activation of these pathways. CONCLUSIONS: Taken together, our results suggest that the anti-ALI properties of PB are closely associated with the inactivation of NF-κB and NLRP3 by altering the PI3K/Akt pathway. Furthermore, our findings provide a novel strategy for application of PB as a potential agent for treating patients with ALI. To the best of our knowledge, this is the first study to elucidate the underlying mechanism of action of PB against ALI.

Prognostic Immunity and Therapeutic Sensitivity Analyses Based on Differential Genomic Instability-Associated LncRNAs in Left- and Right-Sided Colon Adenocarcinoma.

Background: The purpose of our study was to develop a prognostic risk model based on differential genomic instability-associated (DGIA) long non-coding RNAs (lncRNAs) of left-sided and right-sided colon cancers (LCCs and RCCs); therefore, the prognostic key lncRNAs could be identified. Methods: We adopted two independent gene datasets, corresponding somatic mutation and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Identification of differential DGIA lncRNAs from LCCs and RCCs was conducted with the appliance of "Limma" analysis. Then, we screened out key lncRNAs based on univariate and multivariate Cox proportional hazard regression analysis. Meanwhile, DGIA lncRNAs related prognostic model (DRPM) was established. We employed the DRPM in the model group and internal verification group from TCGA for the purpose of risk grouping and accuracy verification of DRPM. We also verified the accuracy of key lncRNAs with GEO data. Finally, the differences of immune infiltration, functional pathways, and therapeutic sensitivities were analyzed within different risk groups. Results: A total of 123 DGIA lncRNAs were screened out by differential expression analysis. We obtained six DGIA lncRNAs by the construction of DRPM, including AC004009.1, AP003555.2, BOLA3-AS1, NKILA, LINC00543, and UCA1. After the risk grouping by these DGIA lncRNAs, we found the prognosis of the high-risk group (HRG) was significantly worse than that in the low-risk group (LRG) (all p < 0.05). In all TCGA samples and model group, the expression of CD8+ T cells in HRG was lower than that in LRG (all p < 0.05). The functional analysis indicated that there was significant upregulation with regard to pathways related to both genetic instability and immunity in LRG, including cytosolic DNA sensing pathway, response to double-strand RNA, RIG-Ⅰ like receptor signaling pathway, and Toll-like receptor signaling pathway. Finally, we analyzed the difference and significance of key DGIA lncRNAs and risk groups in multiple therapeutic sensitivities. Conclusion: Through the analysis of the DGIA lncRNAs between LCCs and RCCs, we identified six key DGIA lncRNAs. They can not only predict the prognostic risk of patients but also serve as biomarkers for evaluating the differences of genetic instability, immune infiltration, and therapeutic sensitivity.

Arc concave sign on thin-section computed tomography：A novel predictor for invasive pulmonary adenocarcinoma in pure ground-glass nodules.

OBJECTIVE: We aimed to investigate the risk factors of invasive pulmonary adenocarcinoma, especially to report and validate the use of our newly identified arc concave sign in predicting invasiveness of pure ground-glass nodules (pGGNs). METHODS: From January 2015 to August 2018, we retrospectively enrolled 302 patients with 306 pGGNs ≤ 20 mm pathologically confirmed (141 preinvasive lesions and 165 invasive lesions). Arc concave sign was defined as smooth and sunken part of the edge of the lesion on thin-section computed tomography (TSCT). The degree of arc concave sign was expressed by the arc chord distance to chord length ratio (AC-R); deep arc concave sign was defined as AC-R larger than the optimal cut-off value. Logistic regression analysis was used to identify the independent risk factors of invasiveness. RESULTS: Arc concave sign was observed in 65 of 306 pGGNs (21.2 %), and deep arc concave sign (AC-R > 0.25) were more common in invasive lesions (P = 0.008). Under microscope, interlobular septal displacements were found at tumour surface. Multivariate analysis indicated that irregular shape (OR, 3.558; CI: 1.374-9.214), presence of deep arc concave sign (OR, 3.336; CI: 1.013-10.986), the largest diameter > 10.1 mm (OR, 4.607; CI: 2.584-8.212) and maximum density > -502 HU (OR, 6.301; CI: 3.562-11.148) were significant independent risk factors of invasive lesions. CONCLUSIONS: Arc concave sign on TSCT is caused by interlobular septal displacement. The degree of arc concave sign can reflect the invasiveness of pGGNs. Invasive lesions can be effectively distinguished from preinvasive lesions by the presence of deep arc concave sign, irregular shape, the largest diameter > 10.1 mm and maximum density > -502 HU in pGGNs ≤ 20 mm.

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges.

Stroke is a leading cause of disability and mortality worldwide, resulting in substantial economic costs for post-stroke care each year. Neuroimaging, such as cranial computed tomography or magnetic resonance imaging, is the backbone of stroke management strategies, which can guide treatment decision-making (thrombolysis or hemostasis) at an early stage. With advances in computational technologies, particularly in machine learning, visual image information can now be converted into numerous quantitative features in an objective, repeatable, and high-throughput manner, in a process known as radiomics. Radiomics is mainly used in the field of oncology, which remains an area of active research. Over the past few years, investigators have attempted to apply radiomics to stroke in the hope of gaining benefits similar to those obtained in cancer management, i.e., in promoting the development of personalized precision medicine. Currently, radiomic analysis has shown promise for a variety of applications in stroke, including the diagnosis of stroke lesions, early prediction of outcomes, and evaluation for long-term prognosis. In this article, we elaborate the contributions of radiomics to stroke, as well as the subprocesses and techniques involved in radiomics studies. We also discuss the potential challenges facing its widespread implementation in routine practice and the directions for future research.