6:2 Cl-PFESA, a proposed safe alternative for PFOS, diminishes the gemcitabine effectiveness in the treatment of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC)/pancreatic cancer, is a highly aggressive malignancy with poor prognosis. Gemcitabine-based chemotherapy remains the cornerstone of PDAC treatment. Nonetheless, the development of resistance to gemcitabine among patients is a major factor contributing to unfavorable prognostic outcomes. The resistance exhibited by tumors is modulated by a constellation of factors such as genetic mutations, tumor microenvironment transforms, environmental contaminants exposure. Currently, comprehension of the relationship between environmental pollutants and tumor drug resistance remains inadequate. Our study found that PFOS/6:2 Cl-PFESA exposure increases resistance to gemcitabine in PDAC. Subsequent in vivo trials confirmed that exposure to PFOS/6:2 Cl-PFESA reduces gemcitabine's efficacy in suppressing PDAC, with the inhibition rate decreasing from 79.5 % to 56.7 %/38.7 %, respectively. Integrative multi-omics sequencing and molecular biology analyses have identified the upregulation of ribonucleotide reductase catalytic subunit M1 (RRM1) as a critical factor in gemcitabine resistance. Subsequent research has demonstrated that exposure to PFOS and 6:2 Cl-PFESA results in the upregulation of the RRM1 pathway, consequently enhancing chemotherapy resistance. Remarkably, the influence exerted by 6:2 Cl-PFESA exceeds that of PFOS. Despite 6:2 Cl-PFESA being regarded as a safer substitute for PFOS, its pronounced effect on chemotherapeutic resistance in PDAC necessitates a thorough evaluation of its potential risks related to gastrointestinal toxicity.

Evidence of promoting effects of 6:2 Cl-PFESA on hepatocellular carcinoma proliferation in humans: An ideal alternative for PFOS in terms of environmental health?

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are synthetic chemicals, encompassing compounds like perfluorooctane sulfonate (PFOS), which have widespread applications across various industries, including food packaging and firefighting. In recent years, China has increasingly employed 6:2 Cl-PFESA as an alternative to PFOS. Although the association between PFAS exposure and hepatocellular carcinoma (HCC) has been demonstrated, the underlying mechanisms that promote HCC proliferation are uncleared. Therefore, we aimed to investigate the effects and differences of PFOS and 6:2 Cl-PFESA on HCC proliferation through in vivo and in vitro tumor models. Our results reveal that both PFOS and 6:2 Cl-PFESA significantly contribute to HCC proliferation in vitro and in vivo. Exposure led to reduced population doubling times, enlarged cell colony sizes, enhanced DNA synthesis efficiency, and a higher proportion of cells undergoing mitosis. Furthermore, both PFOS and 6:2 Cl-PFES) have been shown to activate the PI3K/AKT/mTOR signaling pathway and inhibit necroptosis. This action consequently enhances the proliferation of HCC cells. Our phenotypic assay findings suggest that the tumorigenic potential of 6:2 Cl-PFESA surpasses that of PFOS; in a subcutaneous tumor model using nude mice, the mean tumor weight for the 6:2 Cl-PFESA-treated cohort was 2.33 times that observed in the PFOS cohort (p < 0.01). Despite 6:2 Cl-PFESA being considered a safer substitute for PFOS, the pronounced effects of this chemical on HCC cell growth warrant a thorough assessment of hepatotoxicity risks linked to its usage.

Environment relevant exposure of perfluorooctanoic acid accelerates the growth of hepatocellular carcinoma cells through mammalian target of rapamycin (mTOR) signal pathway.

Perfluorooctanoic acid (PFOA), a synthetic alkyl chain fluorinated compound, has emerged as a persistent organic pollutant of grave concern, casting a shadow over both ecological integrity and humans. Its insidious presence raises alarms due to its capacity to bioaccumulate within the human liver, potentially paving the treacherous path toward liver cancer. Yet, the intricate mechanisms underpinning PFOA's role in promoting the growth of hepatocellular carcinoma (HCC) remain shrouded in ambiguity. Here, we determined the proliferation and transcription changes of HCC after PFOA exposure through integrated experiments including cell culture, nude mice tests, and colony-forming assays. Based on our findings, PFOA effectively promotes the proliferation of HCC cells within the experimental range of concentrations, both in vivo and in vitro. The proliferation efficiency of HCC cells was observed to increase by approximately 10% due to overexposure to PFOA. Additionally, the cancer weight of tumor-bearing nude mice increased by 87.0% (p < 0.05). We systematically evaluated the effects of PFOA on HCC cells and found that PFOA's exposure can selectively activate the PI3K/AKT/mTOR/4E-BP1 signaling pathway, thereby playing a pro-cancer effect on HCC cells Confirmation echoed through western blot assays and inhibitor combination analyses. These insights summon a response to PFOA's dual nature as both an environmental threat and a promoter of liver cancer. Our work illuminates the obscured domain of PFOA-induced hepatoxicity, shedding light on its ties to hepatocellular carcinoma progression.

Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision.

The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. This study aimed to differentiate between XGC and GBC using a deep learning nomogram model built from contrast enhanced computed tomography (CT) scans. 297 patients were included with confirmed XGC (94) and GBC (203) as the training and internal validation cohort from 2017 to 2021. The deep learning model Resnet-18 with Fourier transformation named FCovResnet18, shows most impressive potential in distinguishing XGC from GBC using 3-phase merged images. The accuracy, precision and area under the curve (AUC) of the model were then calculated. An additional cohort of 74 patients consisting of 22 XGC and 52 GBC patients was enrolled from two subsidiary hospitals as the external validation cohort. The accuracy, precision and AUC achieve 0.98, 0.99, 1.00 in the internal validation cohort and 0.89, 0.92, 0.92 in external validation cohort. A nomogram model combining clinical characteristics and deep learning prediction score showed improved predicting value. Altogether, FCovResnet18 nomogram has demonstrated its ability to effectively differentiate XGC from GBC preoperatively, which significantly aid surgeons in making informed and accurate surgical decisions for XGC and GBC patients.

Effect of Intensive Glycemic Control on Myocardial Infarction Outcome in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.

Background: The effect of intensive glucose-lowering treatment on the risk of cardiovascular events in type 2 diabetes remains uncertain, especially the effect on the occurrence of myocardial infarction in patients with type 2 diabetes is still unclear. The purpose of this study was to conduct a systematic review and meta-analysis of relevant RCTs. Methods: We performed a systematic review of randomized clinical trials (RCTS) and observational studies relevant to this study question. We searched the PubMed and Cochrane databases until June 2022. Results: We included data on 14 RCTs and 144,334 patients, all of whom had type 2 diabetes. When all studies were considered, intensive glucose-lowering treatment significantly reduced the incidence of MI compared with conventional therapy and the total OR value is 0.90 (CI 0.84, 0.97; P = 0.004) when considering all the studies. When the target value of intensive glucose-lowering treatment was considered as HbA1c decrease of more than 0.5%, there was no significant protective effect on MI, the total OR value is 0.88 (CI 0.81, 0.96; P = 0.003). When considering all available RCTS, the intensive glucose-lowering treatment group had a protective effect for MACE compared to the conventional treatment group, and the total OR value is 0.92 (CI 0.88, 0.96; P < 0.00001). In the available RCTs, for the patients with a history of prior CAD, the total OR value is 0.94 (CI 0.89, 0.99; P = 0.002). And there was no difference in the incidence of hypoglycemic events between the intensive and conservative treatment groups. Conclusion: Our data support the positive protective effect of glucose-lowering therapy on MI in patients with T2DM, but there is no significant effect of intensive glucose-lowering. In addition, we found no greater protective effect of enhanced glucose control in the HbA1c reduction of more than 0.5%, and no difference in the incidence of adverse events compared with the HbA1c reduction of less than 0.5%.

MVI-TR: A Transformer-Based Deep Learning Model with Contrast-Enhanced CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venous phase (VP) of contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital of Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in this study. All preoperative CECT were collected, and the patients were randomly divided into training and validation cohorts at a ratio of 4:1. We proposed a novel transformer-based end-to-end DL model, named MVI-TR, which is a supervised learning method. MVI-TR can capture features automatically from radiomics and perform MVI preoperative assessments. In addition, a popular self-supervised learning method, the contrastive learning model, and the widely used residual networks (ResNets family) were constructed for fair comparisons. With an accuracy of 99.1%, a precision of 99.3%, an area under the curve (AUC) of 0.98, a recalling rate of 98.8%, and an F1-score of 99.1% in the training cohort, MVI-TR achieved superior outcomes. Additionally, the validation cohort's MVI status prediction had the best accuracy (97.2%), precision (97.3%), AUC (0.935), recalling rate (93.1%), and F1-score (95.2%). MVI-TR outperformed other models for predicting MVI status, and showed great preoperative predictive value for early-stage HCC patients.

Multi-omics insights into potential mechanism of SGLT2 inhibitors cardiovascular benefit in diabetic cardiomyopathy.

Background: Metabolic and energy disorders are considered central to the etiology of diabetic cardiomyopathy (DCM). Sodium-glucose cotransporter-2 inhibitors (SGLT2i) can effectively reduce the risk of cardiovascular death and heart failure in patients with DCM. However, the underlying mechanism has not been elucidated. Methods: We established a DCM rat model followed by treatment with empagliflozin (EMPA) for 12 weeks. Echocardiography, blood tests, histopathology, and transmission electron microscopy (TEM) were used to evaluate the phenotypic characteristics of the rats. The proteomics and metabolomics of the myocardium in the rat model were performed to identify the potential targets and signaling pathways associated with the cardiovascular benefit of SGLT2i. Results: The diabetic rat showed pronounced DCM characterized by mitochondrial pleomorphic, impaired lipid metabolism, myocardial fibrosis, and associated diastolic and systolic functional impairments in the heart. To some extent, these changes were ameliorated after treatment with EMPA. A total of 43 proteins and 34 metabolites were identified as targets in the myocardium of diabetic rats treated with EMPA. The KEGG analysis showed that arachidonic acid is associated with the maximum number of related pathways and may be a potential target of EMPA treatment. Fatty acid (FA) metabolism was enhanced in diabetic hearts, and the perturbation of biosynthesis of unsaturated FAs and arachidonic acid metabolism was a potential enabler for the cardiovascular benefit of EMPA. Conclusion: SGLT2i ameliorated lipid accumulation and mitochondrial damage in the myocardium of diabetic rats. The metabolomic and proteomic data revealed the potential targets and signaling pathways associated with the cardiovascular benefit of SGLT2i, which provides a valuable resource for the mechanism of SGLT2i.

RNA Sequencing of Cardiac in a Rat Model Uncovers Potential Target LncRNA of Diabetic Cardiomyopathy.

Background: Diabetic cardiomyopathy (DCM) is one of the major causes of heart failure in diabetic patients; however, its pathogenesis remains unclear. Long non-coding RNAs (lncRNAs) are involved in the development of various cardiovascular diseases, but little is known in DCM. Objective: The present study was conducted to investigate the altered expression signature of lncRNAs and mRNAs by RNA-sequencing and uncovers the potential targets of DCM. Methods: A DCM rat model was established, and the genome-wide expression profile of cardiac lncRNAs and mRNAs was investigated in the rat model with and without DCM by RNA-sequencing. Bioinformatics analysis included the co-expression, competitive endogenous RNA (ceRNA) network, and functional enrichment analysis of deregulated lncRNAs and mRNAs. Results: A total of 355 lncRNA transcripts and 828 mRNA transcripts were aberrantly expressed. The ceRNA network showed that lncRNA XR_351927.3, ENSRNOT00000089581, XR_597359.2, XR_591602.2, and XR_001842089.1 are associated with the greatest number of differentially expressed mRNAs and AURKB, MELK, and CDK1 may be the potential regulatory targets of these lncRNAs. Functional analysis showed that these five lncRNAs are closely associated with fibration, cell proliferation, and energy metabolism of cardiac myocytes, indicating that these core lncRNAs have high significance in DCM. Conclusions: The present study profiled the DCM-specific lncRNAs and mRNAs, constructed the lncRNA-related ceRNA regulatory network, and identified the potential prognostic biomarkers, which provided new insights into the pathogenesis of DCM.