Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Air Pollution/analysis* ; Environmental Exposure/analysis* ; China/epidemiology* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Monitoring

Humans ; Whooping Cough/prevention & control* ; Vaccines, Acellular ; China

OBJECTIVE: To evaluate the efficacy and safety of Cidan Capsule combined with adjuvant transarterial chemoembolization (TACE) in patients with a high risk of early recurrence after curative resection of hepatocellular carcinoma (HCC). METHODS: A multicenter, randomized controlled trial was conducted in patients with high-risk recurrence factors after curative resection of HCC from 9 medical centers between July 2014 and July 2018. Totally 249 patients were randomly assigned to TACE with or without Cidan Capsule administration groups by stratified block in a 1:1 ratio. Postoperative adjuvant TACE was given 4-5 weeks after hepatic resection in both groups. Additionally, 125 patients in the TACE plus Cidan group were administrated Cidan Capsule (0.27 g/capsule, 5 capsules every time, 4 times a day) for 6 months with a 24-month follow-up. Primary endpoints included disease-free survival (DFS) and tumor recurrence rate (TRR). Secondary endpoint was overall survival (OS). Any drug-related adverse events (AEs) were observed and recorded. RESULTS: As the data cutoff in July 9th, 2018, the median DFS was not reached in the TACE plus Cidan group and 234.0 days in the TACE group (hazard ratio, 0.420, 95% confidence interval, 0.290-0.608; P<0.01). The 1- and 2-year TRR in the TACE plus Cidan and TACE groups were 31.5%, 37.1%, and 60.8%, 63.4%, respectively (P<0.01). Median OS was not reached in both groups. The 1- and 2-year OS rates in TACE plus Cidan and TACE groups were 98.4%, 98.4%, and 89.5%, 87.9%, respectively (P<0.05). The most common grade 3-4 AEs included fatigue, abdominal pain, lumbar pain, and nausea. One serious AE was reported in 1 patient in the TACE plus Cidan group, the death was due to retroperitoneal mass hemorrhage and hemorrhagic shock, and was not related to study drug. CONCLUSIONS Cidan Capsule in combination with TACE can reduce the incidence of early recurrence in HCC patients at high-risk of recurrence after radical hepatectomy and may be an appropriate option in postoperative anti-recurrence treatment. (Registration No. NCT02253511).

Pregnancy ; Female ; Humans ; Placenta ; Nuclear Transfer Techniques ; Embryo, Mammalian ; Cell Nucleus

The development of chemoresistance which results in a poor prognosis often renders current treatments for colorectal cancer(CRC).In this study,we identified reduced microvessel density(MVD)and vascular immaturity resulting from endothelial apoptosis as therapeutic targets for overcoming chemoresistance.We focused on the effect of metformin on MVD,vascular maturity,and endothelial apoptosis of CRCs with a non-angiogenic phenotype,and further investigated its effect in overcoming chemoresistance.In situ transplanted cancer models were established to compare MVD,endothelial apoptosis and vascular maturity,and function in tumors from metformin-and vehicle-treated mice.An in vitro co-culture system was used to observe the effects of metformin on tumor cell-induced endothelial apoptosis.Transcriptome sequencing was performed for genetic screening.Non-angiogenic CRC developed inde-pendently of angiogenesis and was characterized by vascular leakage,immaturity,reduced MVD,and non-hypoxia.This phenomenon had also been observed in human CRC.Furthermore,non-angiogenic CRCs showed a worse response to chemotherapeutic drugs in vivo than in vitro.By suppressing endo-thelial apoptosis,metformin sensitized non-angiogenic CRCs to chemo-drugs via elevation of MVD and improvement of vascular maturity.Further results showed that endothelial apoptosis was induced by tumor cells via activation of caspase signaling,which was abrogated by metformin administration.These findings provide pre-clinical evidence for the involvement of endothelial apoptosis and subsequent vascular immaturity in the chemoresistance of non-angiogenic CRC.By suppressing endothelial apoptosis,metformin restores vascular maturity and function and sensitizes CRC to chemotherapeutic drugs via a vascular mechanism.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

BACKGROUND: Human umbilical cord mesenchymal stem cells (hUCMSCs) have emerged as promising therapy for immune and inflammatory diseases. However, how to maintain the activity and unique properties during cold storage and transportation is one of the key factors affecting the therapeutic efficiency of hUCMSCs. Schisandrin B (SchB) has many functions in cell protection as a natural medicine. In this study, we investigated the protective effects of SchB on the hypothermic preservation of hUCMSCs. METHODS: hUCMSCs were isolated from Wharton’s jelly. Subsequently, hUCMSCs were exposed to cold storage (4 °C) and 24-h re-warming. After that, cells viability, surface markers, immunomodulatory effects, reactive oxygen species (ROS), mitochondrial integrity, apoptosis-related and antioxidant proteins expression level were evaluated. RESULTS: SchB significantly alleviated the cells injury and maintained unique properties such as differentiation potential, level of surface markers and immunomodulatory effects of hUCMSCs. The protective effects of SchB on hUCMSCs after hypothermic storage seemed associated with its inhibition of apoptosis and the anti-oxidative stress effect mediated by nuclear factor erythroid 2–related factor 2 signaling. CONCLUSION These results demonstrate SchB could be used as an agent for hypothermic preservation of hUCMSCs.