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BACKGROUND: Extending the lifespan of subcutaneous insulin administration sets and infusion pumps requires overcoming unreliable insulin delivery induced by dermal reactions. All commercially available insulin formulations contain insulin phenolic preservatives (IPP), which stabilize the insulin molecule but result in unwanted cell and tissue toxicity. Mast cells, which are the first line of defense once the epithelium is breached, are particularly abundant beneath the skin surface. Thus, we hypothesize a sequence of events initiated by device insertion that activates skin mast cells (MC) that subsequently trigger neutrophil and monocyte/macrophage recruitment. The ensuing inflammatory response compromises effective insulin infusion therapy. METHODS: We employed a non-genetic, pharmacological approach to MC membrane stabilization using Cromolyn sodium (CS), which inhibits MC degranulation. These studies were conducted in our modified air pouch mouse model using non-diabetic and streptozotocin induced diabetic mice. We evaluated the impact of systemic CS through intraperitoneal injections, as well as the impact of local CS through co-infusion, on infusion catheter insertion and IPP-induced inflammation. RESULTS: CS at a concentration of 50mg/kg minimized inflammation triggered in response to insulin phenolic preservatives present in standard insulin formulations. The resultant degree of tissue inflammation was comparable to that observed with saline injections. CONCLUSION: Targeting MC has the potential to extend the longevity of insulin infusion sets by mitigating the inflammatory response. Future studies should be directed at employing other MC models, such as newer Cre/loxP mouse strains, to confirm the sentinel role of MC in insulin infusion therapy.

Summary Background Icotinib has provided survival benefits for patients with advanced, epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC). We aimed to compare icotinib with chemotherapy in patients with EGFR-mutant stage II–IIIA NSCLC after complete tumour resection. Here, we report the results from the preplanned interim analysis of the study. Methods In this multicentre, randomised, open-label, phase 3 trial done at 29 hospitals in China, eligible patients were aged 18–70 years, had histopathogically confirmed stage II–IIIA NSCLC, had complete resection up to 8 weeks before random assignment, were treatment-naive, and had confirmed activation mutation in exon 19 or exon 21 of the EGFR gene. Participants were randomly assigned (1:1) with an interactive web-based response system to receive either oral icotinib 125 mg thrice daily for 2 years or four 21-day cycles of intravenous chemotherapy (vinorelbine 25 mg/m2 on days 1 and 8 of each cycle plus cisplatin 75 mg/m2 on day 1 of each cycle for adenocarcinoma or squamous carcinoma; or pemetrexed 500 mg/m2 plus cisplatin 75 mg/m2 on day 1 every 3 weeks for non-squamous carcinoma). The primary endpoint was disease-free survival assessed in the full analysis set. Secondary endpoints were overall survival assessed in the full analysis set and safety assessed in all participants who received study drug. This trial is registered with ClinicalTrials.gov , NCT02448797 . Findings Between June 8, 2015, and August 2, 2019, 322 patients were randomly assigned to icotinib (n=161) or chemotherapy (n=161); the full analysis set included 151 patients in the icotinib group and 132 in the chemotherapy group. Median follow-up in the full analysis set was 24·9 months (IQR 16·6–36·4). 40 (26%) of 151 patients in the icotinib group and 58 (44%) of 132 patients in the chemotherapy group had disease relapse or death. Median disease-free survival was 47·0 months (95% CI 36·4–not reached) in the icotinib group and 22·1 months (16·8–30·4) in the chemotherapy group (stratified hazard ratio [HR] 0·36 [95% CI 0·24–0·55]; p Interpretation Our results suggest that compared with chemotherapy, icotinib significantly improves disease-free survival and has a better tolerability profile in patients with EGFR-mutant stage II–IIIA NSCLC after complete tumour resection. Funding Betta Pharmaceuticals Translation For the Chinese translation of the abstract see Supplementary Materials section.

In the early December 2019, a novel coronavirus named severe acute respiratory syndrome coronavirus 2 was first reported in Wuhan, China, followed by an outbreak that spread around the world. Numerous studies have shown that liver injury is common in patients with coronavirus disease 2019 (COVID-19), and may aggravate the severity of the disease. However, the exact cause and specific mechanism of COVID-associated liver injury needs to be elucidated further. In this review, we present an analysis of the clinical features, potential mechanisms, and treatment strategies for liver injury associated with COVID-19. We hope that this review would benefit clinicians in devising better strategies for management of such patients.

Background Alectinib is approved for the treatment of advanced non-small-cell lung cancer (NSCLC) harboring ALK rearrangements. Although generally well tolerated, alectinib can cause serious or life-threatening side effects. Case Presentation Here, we report a case of a patient with NSCLC with an EML4-ALK fusion and was treated with alectinib but who developed grade 4 hyperbilirubinemia after five months on therapy. Alectinib was discontinued, and an artificial liver support system (ALSS) was used with an impressive decline in bilirubin levels. After two months drug-free, the patient experienced disease progression. Ensartinib was initiated as second-line treatment with a best response of stable disease after three months of therapy with no evidence of hyperbilirubinemia. Conclusion This is the first report of ensartinib treatment after alectinib-induced hyperbilirubinemia which was successfully relieved by ALSS treatment and targeted drug cessation.

By implementing dynamic circulating tumor DNA (ctDNA) analysis, we explored the impact of TP53 mutations on tumor evolution and resistance mechanisms to ensartinib in patients with ALK-positive NSCLC.In a multicenter phase 2 trial, patients with ALK-positive NSCLC who progressed on crizotinib were treated with ensartinib. Blood samples for ctDNA analysis were collected at baseline, cycle 3 day 1, and progression disease (PD) and analyzed with a 212-gene panel.A total of 440 samples were collected from 168 patients. Baseline TP53 mutations (20.2%) significantly correlated with inferior progression-free survival (4.2 mo versus 11.7 mo, p0.0001). Patients with TP53 mutations had higher mutation load than those without TP53 mutations at baseline (13.79 ± 3.72 versus 4.67 ± 0.39, p0.001). Although there was no significant difference in mutation load between these groups at cycle 3 day 1 (5.89 ± 2.25 versus 3.72 ± 0.62, p = 0.425), patients with mutated TP53 developed more mutations at PD (7.07 ± 1.25 versus 3.20 ± 0.33, p = 0.003). Frequency and abundance of secondary ALK mutations G1269A, G1202R, and E1210K increased markedly at PD than baseline. In patients without secondary ALK mutations, we identified ALK-independent resistance mechanisms including bypass signaling activation, downstream effector protein reactivation, epithelial-mesenchymal transformation, and epigenetic dysregulation.Our study highlighted the advantage of ctDNA analysis for monitoring tumor evolution. TP53 mutations promoted genetic evolution and accelerated occurrence of resistance. We also unveiled ALK-dependent resistance mechanisms, mainly by G1269A, G1202R, and E1210K mutations, and ALK-independent resistance mechanisms to ensartinib.

In order to improve the performance of entrepreneurship and innovation education in colleges and universities, this study attempts to build an evaluation system and model of innovation and entrepreneurship in colleges and universities to provide a complete and practical tool for government education authorities and universities to evaluate the implementation of innovation and entrepreneurship education. In this research, decision tree and fuzzy mathematics are used as the basis of the model algorithm, and the algorithm is improved based on the analysis of traditional algorithms. Moreover, based on the improved decision tree algorithm, an evaluation index system for university innovation and entrepreneurship education is constructed. After determining the evaluation indicators of innovation and entrepreneurship education in colleges and universities, this study uses several universities as examples to analyze and define the definitions of various indicators. In addition, this study statistically analyzes the results of entrepreneurship and innovation education in colleges and universities through simulation. The research shows that the model proposed in this paper has a certain practical effect, and based on the simulation results, this study makes several suggestions.

Background Millions of human beings have suffered in the epidemic of coronavirus disease 2019 (COVID-19), but until now the effective treatment methods have been limited. Aim This study aimed to evaluate the efficacy and safety of short-wave diathermy (SWD) treatment for moderate COVID-19 patients. Design A prospective, double-blind, randomized controlled clinical study. Setting Inpatients unit of a COVID-19 specialized hospital. Population Forty-two patients with moderate COVID-19 were randomly allocated at a 2:1 ratio to two groups: the SWD group and the control group. Methods Participants of the SWD group received SWD treatment, and participants of the control group received placebo SWD treatment for one session per day, 10 minutes per session, for no more than 14 days. Both groups were given standard care treatment. Primary outcome was the rate of clinical improvement according to a sevencategory ordinal scale. Secondary outcomes included the rate of computed tomography (CT) improvement and the rate of potential adverse events. Results Clinical improvement occurred in 92.6% of patients in the SWD group by day 14 compared with 69.2% of patients in the control group (P = 0.001). The Cox model indicated that the SWD group had a higher clinical improvement probability than the control group (Hazard Ratio, 3.045; 95% CI, 1.391-6.666; P = 0.005). Similarly, CT improvement occurred in 85.2 % of patients in the SWD group and 46.2 % of patients in the control group respectively by day 14 (P = 0.001). The Cox model indicated SWD group had a higher CT improvement probability than control group (Hazard Ratio, 3.720; 95% CI, 1.486-9.311; P = 0.005). There was no significant difference in adverse events between the SWD group and the control group (2 of 27 [7.4%] SWD vs. 1 of 13 [7.7%] control, P = 1.000), the most frequent of which were headache (1 of 27 [3.7%] SWD vs. 1 of 13 [7.7%] control patients) and dizziness (1 of 27 [3.7%] SWD vs. 0 of 13 [0%] control patients). Conclusions SWD is a valid and reliable adjuvant therapy with a favorable safety profile for moderate COVID-19 patients. Clinical rehabilitation impact Clinically relevant information is lacking regarding the efficacy and safety of SWD for patients with COVID-19. This study provides the first evidence that SWD is a promising adjuvant therapy for COVID-19.

Ensartinib is a novel, potent and highly selective inhibitor of anaplastic lymphoma kinase (ALK) that has promising clinical activity and low toxicity in patients with ALK-positive non-small cell lung cancer. This study was conducted to investigate the pharmacokinetics, metabolism and excretion of ensartinib following a single 200 mg/100 μCi oral dose of radiolabeled ensartinib to healthy subjects. Six healthy male subjects were enrolled and administrated an oral suspension in a fasted state. Blood, urine and feces were collected. Radioactivity concentrations were measured by liquid scintillation counting and plasma concentrations of ensartinib by liquid chromatography-tandem mass spectrometry. Both techniques were applied for metabolite profiling and characterization. The mean total recovery was 101.21% of the radiolabeled dose with 91.00% and 10.21% excreted in feces and urine, respectively. Unchanged ensartinib was the predominant drug-related component in urine and feces, representing 4.39% and 38.12% of the administered dose, respectively. Unchanged ensartinib and its metabolite M465 were the major circulating components, accounting for the same 27.45% of the plasma total radioactivity (AUC0–24h pool), while other circulating metabolites were minor, accounting for less than 10%. Mean Cmax, AUC0–∞, T1/2 and Tmax values for ensartinib in plasma were 185 ng/mL, 3827 h ng/mL, 18.3 h and 3.25 h, respectively. The total radioactivity in plasma was cleared with terminal half-life of 27.2 h. Treatment with ensartinib was well tolerated, and no serious adverse events were reported. It was well tolerated in the six healthy male subjects following a single oral administration of 200 mg/100 μCi dose of ensartinib. Besides unchanged ensartinib, metabolite of M465 was the predominant circulating drug-related component. The drug was primarily eliminated in feces. ClinicalTrials.gov NCT03804541.

Although endoscopic resection (ER) is already established as a minimally invasive technique for small (

To develop and validate a deep learning model to discriminate transient from persistent subsolid nodules (SSNs) on baseline CT. A cohort of 1414 SSNs, consisting of 319 transient SSNs in 168 individuals and 1095 persistent SSNs in 816 individuals, were identified on chest CT. The cohort was assigned by examination date into a development set of 996 SSNs, a tuning set of 212 SSNs, and a validation set of 206 SSNs. Our model was built by transfer learning, which was transferred from a well-performed deep learning model for pulmonary nodule classification. The performance of the model was compared with that of two experienced radiologists. Each nodule was categorized by Lung CT Screening Reporting and Data System (Lung-RADS) to further evaluate the performance and the potential clinical benefit of the model. Two methods were employed to visualize the learned features. Our model achieved an AUC of 0.926 on the validation set with an accuracy of 0.859, a sensitivity of 0.863, and a specificity of 0.858, and outperformed the radiologists. The model performed the best among Lung-RADS 2 nodules and maintained well performance among Lung-RADS 4 nodules. Feature visualization demonstrated the model’s effectiveness in extracting features from images. The transfer learning model presented good performance on the discrimination between transient and persistent SSNs. A reliable diagnosis on nodule persistence can be achieved at baseline CT; thus, an early diagnosis as well as better patient care is available. • Deep learning can be used for the discrimination between transient and persistent subsolid nodules. • A transfer learning model can achieve good performance when it is transferred from a model with a similar task. • With the assistance of deep learning model, a reliable diagnosis on nodule persistence can be achieved at baseline CT, which can bring a better patient care strategy.

Purpose: Our primary purpose is to explore safety and efficacy of high-dose icotinib in comparison with routine-dose icotinib in patients with non–small cell lung cancer (NSCLC) harboring 21-L858R mutation. Patients and Methods: Patients with treatment-naïve, EGFR-mutant (21-L858R or exon 19 deletion at 2:1) NSCLC were enrolled. Patients with 21-L858R mutation were randomized to receive routine-dose icotinib (125 mg, thrice daily; L858R-RD) or high-dose icotinib (250 mg, thrice daily; L858R-HD), whereas patients with exon 19 deletion received only routine-dose icotinib (19-Del-RD) until progression, death, or unacceptable toxicity. The primary endpoint was median progression-free survival (mPFS), assessed by an independent review committee. Results: From May 2015 to November 2017, 253 patients (86 in L858R-RD; 90 in L858R-HD; and 77 in 19-Del-RD) were enrolled. The mPFS in L858R-HD group was similar to that in 19-Del-RD group (12.9 months and 12.5 months, respectively) and was significantly longer than that in L858R-RD group [12.9 months vs. 9.2 months, hazard ratio (HR): 0.75; 95% confidence interval (CI), 0.53–1.05]. A longer but statistically nonsignificant mPFS was observed between 19-Del-RD and L858R-RD groups (12.5 months vs. 9.2 months, HR: 0.80; 95% CI, 0.57–1.13). A higher objective response rate (ORR) was observed in L858R-HD group compared with L858R-RD group (73% vs. 48%), also between 19-Del-RD and L858R-RD groups (75% vs. 48%). Similar incidences of grade 3/4 toxicities were observed among the three treatment groups. Conclusions: High-dose icotinib improved mPFS and ORR in patients with NSCLC harboring 21-L858R mutation with acceptable tolerability, which could be a new therapeutic option for this patient population.

To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets.Patients with intracranial aneurysms diagnosed by computed tomography angiography and confirmed by invasive cerebral angiograph or clipping surgery were included. The prediction models were developed based on clinical, aneurysm morphological, and hemodynamic parameters by conventional LR and ML methods.The training, internal validation, and external validation cohorts were composed of 807 patients, 200 patients, and 108 patients, respectively. The area under curves (AUCs) of conventional LR models 1 (clinical), 2 (clinical and aneurysm morphological), and 3 (clinical, aneurysm morphological and hemodynamic characteristics) were 0.608, 0.765, and 0.886, respectively (all p 0.05). The AUCs of ML models using random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM) were 0.871, 0.851, and 0.863, respectively. There were no difference among AUCs of conventional LR, RF, and SVM (all p 0.05/6), while the AUC of MLP was lower than that of conventional LR (p = 0.0055).Hemodynamic parameters play an important role in the prediction performance of the models. ML methods cannot outperform conventional LR in prediction models for rupture status of UIAs integrating clinical, aneurysm morphological, and hemodynamic parameters.• The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management.

Summary Background Ensartinib is a potent new-generation ALK inhibitor with high activity against a broad range of known crizotinib-resistant ALK mutations and CNS metastases. We aimed to assess the efficacy and safety of ensartinib in ALK-positive patients with non-small-cell lung cancer (NSCLC), in whom crizotinib therapy was unsuccessful. The associations between ensartinib efficacy and crizotinib-resistant mutations were also explored. Methods We did a single-arm, open-label, phase 2 study at 27 centres in China. Patients were aged 18 years or older, had stage IIIb or stage IV ALK-positive NSCLC that had progressed while they were on crizotinib therapy, an Eastern Cooperative Oncology Group performance status of 2 or less, had measurable disease, and had received fewer than three previous treatments. Patients with CNS metastases were included if these metastases were asymptomatic and did not require steroid therapy. All patients received 225 mg ensartinib orally once daily on a continuous dosing schedule. The primary outcome was the proportion of patients with an objective response according to the Response Evaluation Criteria in Solid Tumors (version 1.1), as assessed by an independent review committee in all patients who received at least one dose of ensartinib with no major violations of the inclusion criteria (ie, the full analysis set). Safety was assessed in all enrolled patients who received at least one dose of ensartinib. This trial was registered with ClinicalTrials.gov , NCT03215693 . Findings Between Sept 28, 2017, and April 11, 2018, 160 patients were enrolled and had at least one dose of ensartinib (safety analysis set). Four patients had inclusion violations and were excluded from the efficacy analysis, which thus included 156 patients (full analysis set). 97 (62%) patients in the full analysis set had brain metastases. 76 (52% [95% CI 43–60]) of 147 patients in the full analysis set, with responses that could be assessed by the independent review committee, had an objective response. 28 (70% [53–83]) of 40 patients with measurable brain metastases as assessed by the independent review committee had an intracranial objective response. 145 (91%) of 160 patients had at least one treatment-related adverse event, which were mostly grade 1 or 2. The most common treatment-related adverse events were rash (89 [56%]), increased alanine aminotransferase concentrations (74 [46%]), and increased aspartate aminotransferase concentrations (65 [41%]). Interpretation Ensartinib has activity and is well tolerated in patients with crizotinib-refractory, ALK-positive NSCLC, including those with brain metastases. The role of ensartinib in patients in whom other second-generation ALK inhibitors have been unsuccessful warrants further studies. Funding Betta Pharmaceuticals.

Deep learning has gained immense attention from researchers in medicine, especially in medical imaging. The main bottleneck is the unavailability of sufficiently large medical datasets required for the good performance of deep learning models. This paper proposes a new framework consisting of one variational autoencoder (VAE), two generative adversarial networks, and one auxiliary classifier to artificially generate realistic-looking skin lesion images and improve classification performance. We first train the encoder-decoder network to obtain the latent noise vector with the image manifold’s information and let the generative adversarial network sample the input from this informative noise vector in order to generate the skin lesion images. The use of informative noise allows the GAN to avoid mode collapse and creates faster convergence. To improve the diversity in the generated images, we use another GAN with an auxiliary classifier, which samples the noise vector from a heavy-tailed student t-distribution instead of a random noise Gaussian distribution. The proposed framework was named TED-GAN, with T from the t-distribution and ED from the encoder-decoder network which is part of the solution. The proposed framework could be used in a broad range of areas in medical imaging. We used it here to generate skin lesion images and have obtained an improved classification performance on the skin lesion classification task, rising from 66% average accuracy to 92.5%. The results show that TED-GAN has a better impact on the classification task because of its diverse range of generated images due to the use of a heavy-tailed t-distribution.

Background: Diffuse large B-cell lymphoma (DLBCL) is a common aggressive B-cell non-Hodgkin lymphoma (B-NHL). While combined chemotherapy has improved the outcomes of DLBCL, it remains a highly detrimental disease. Pyroptosis, an inflammatory programmed cell death, is considered to have both tumor-promoting and tumor-suppressing effects. The role of pyroptosis in DLBCL has been gradually appreciated, but its value needs further investigation.Methods: We analyzed mutations and copy number variation (CNV) alterations of pyroptosis-related genes (PRGs) from The Cancer Genome Atlas (TCGA) cohort and evaluated the differences in expression in normal B cells and DLBCL patients in two Gene Expression Omnibus (GEO) datasets (GSE12195 and GSE56315). Based on the expression of 52 PRGs, we divided 421 DLBCL patients from the GSE31312 dataset into distinct clusters using consensus clustering. The Kaplan-Meier method was used to prognosis among the three clusters, and GSVA was used to explore differences in the biological functions. ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used to analyze the tumor immune microenvironment (TME) in different clusters. A risk score signature was developed using a univariate survival analysis and multivariate regression analysis, and the reliability and validity of the signature were verified. By combining the signature with clinical factors, a nomogram was established to predict the prognosis of DLBCL patients. The alluvial diagram and correlation matrix were used to explore the relationship between pyroptosis risk score, clinical features and TME.Results: A large proportion of PRGs are dysregulated in DLBCL and associated with the prognosis. We found three distinct pyroptosis-related clusters (cluster A, B, and C) that differed significantly with regard to the prognosis, biological process, clinical characteristics, chemotherapeutic drug sensitivity, and TME. Furthermore, we developed a risk score signature that effectively differentiates high and low-risk patients. The nomogram combining this signature with several clinical indicators showed an excellent ability to predict the prognosis of DCBCL patients.Conclusions: This work demonstrates that pyroptosis plays an important role in the diversity and complexity of the TME in DLBCL. The risk signature of pyroptosis is a promising predictive tool. A correct and comprehensive assessment of the mode of action of pyroptosis in individuals will help guide more effective treatment.

ObjectiveThis study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.MethodsThis retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.ResultsThe AUCs of the combined model reached 0.789 (95%CI, 0.579–0.999) in the internal validation cohort and 0.730 (95%CI, 0.563–0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.ConclusionsThe proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

Liver disease is a severe health problem that endangers human health worldwide. Mesenchymal stem cell (MSC) therapy is a novel treatment for patients with different liver diseases due to its vast expansion potential and distinctive immunomodulatory properties. Despite several preclinical trials having confirmed the considerable efficacy of MSC therapy in liver diseases, the questionable safety and efficacy still limit its application. As a precursor cell, MSCs can adjust their characteristics in response to the surrounding microenvironment. The microenvironment provides physical and chemical factors essential for stem cell survival, proliferation, and differentiation. However, the mechanisms are still not completely understood. We, therefore, summarized the mechanisms underlying the MSC immune response, especially the interaction between MSCs and the liver microenvironment, discussing how to achieve better therapeutic effects.

The prevalence of diabetes in China has increased by nearly 18 times from 0.67% in 1980 to 12.8% in 2020. The incidence has occurred with regional diversity, following different climates, diet, and lifestyle. This study aimed to explore the glucose metabolism status and analyze the risk factors for diabetes among over 45-year-old inhabitants from the Shanghai Songjiang district.A total of 1,213 subjects without diabetes history, thyroid dysfunction history, or other diagnosed diseases were enrolled in this cross-sectional study, all of whom were over 45 years old and from the Shanghai Songjiang district. All subjects participated in a complete physical examination and clinical history collection including name, gender, age, history of drinking and smoking, and presence of other disease. Fasting glucose, postprandial glucose, biochemical, and other metabolic parameters were measured in all subjects.According to the WHO [1990] Standard, the normal glucose regulation (NGR), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), combined glucose impaired (CGI), and newly diagnosis diabetes mellitus (NDDM) were defined and grouped. Among 1,213 subjects in this area, 50.2% had abnormal glucose, including 36.5% with pre-diabetes (PD) and 13.7% with NDDM. We substantiated that hyper-glucose was positively associated with age, body mass index, waist-hip ratio, fat mass, fat percentage, total triglycerides, total cholesterol, low-density lipoprotein, systolic blood pressure, and heart rate in this study. We also found the prevalence of hyper-glucose occurred mainly in women in the 56-60-year-old age group and in men in the 61-65-year-old age group in this area. The overweight rate of male subjects with abnormal glucose was 61.9%, while in females this was 56.3%, especially the central obesity ratio, which reached 91.4% among the female hyper-glucose subjects.In the Shanghai Songjiang district, 56-60-year-old female subjects who were overweight and with central obesity were apt to abnormal glucose regulation. Community-based diabetes management is an important approach for screening diabetes and high-risk factors to reduce the risk and financial burden of individuals and the society.

For the uncertainty and complexity in object decision making and the differences of decision makers ' reliabilities, an object decision making method based on deep learning theory is proposed. However, traditional deep learning approaches optimize the parameters in an "end-to-end" mode by annotating large amounts of data to propagate the errors backwards. The learning method could be considered to be as a "black box", which is weak in explainability. Explainability refers to an algorithm that gives a clear summary of a particular task and connects it to defined principles or principles in the human world. This paper proposes an explainable attention model consisting of channel attention module and spatial attention module. The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Experimental results show that our algorithm is superior in both accuracy and robustness compared with the state of the arts.

Background and aims Hepatocellular carcinoma (HCC) is one of the most common malignancy with poor prognosis, and the mortality rate remains high. More than 70% of HCC patients have recurrence within 5 years after treatment. The purpose of this study is to evaluate the prognostic values of serum markers with retrospective data. Methods We applied real‐world data (RWD) to analyze the prognostic values of six serum markers for HCC patients after treatment, including α‐fetoprotein (AFP), α‐fetoprotein‐L3 (AFP‐L3), Golgi protein73 (GP73), alanine aminotransferase (ALT), albumin (ALB), and total bilirubin (TBil). A total of 268 cases were enrolled to analyze recurrence‐free survival (RFS), and 104 cases were used to analyze overall survival (OS). Results Our results demonstrated that patients with higher AFP and AFP‐L3 had shorter RFS (p = 0.016 and 0.004), while higher GP73, ALT, and TBil experienced longer RFS (p = 0.000, 0.020, and 0.019). Patients with high‐level GP73, ALT, TBil, and low‐level ALB had significantly higher mortality rate (p=0.035, 0.008, 0.010, and 0.005). Multivariate analysis revealed that GP73 (HR = 1.548, p = 0.001) and ALT (HR = 1.316, p = 0.046) were identified as independent prognostic factors for RFS, ALB (HR = 0.127, p = 0.007), and ALT (HR = 0.237, p = 0.01) were identified as independent prognostic factors for OS. Subgroups analysis showed that GP73 had better prognostic values than other serum markers in early‐stage HCC (p = 0.023). Conclusions Our study demonstrates that AFP, AFP‐L3, and GP73 can be used as prognostic indicators for predicting the recurrence of HCC, while liver function tests have better survival prediction values. GP73 can act as a promising prognostic marker for early‐stage HCC., Prior to nasotracheal intubation (NTI), topical nasal vasoconstrictors are used to prevent NTI‐related epistaxis (NTIRE). Our study shows that well‐lubricated nasotracheal intubation does not require pretreatment with ephedrine to reduce NTIRE.

Background: Histiocytic sarcoma (HS) is a rare hematolymphoid neoplasms whose cells show morphologic and immunophenotypic features of mature tissue histiocytes. We herein report a HS case without nodules or lymphadenectasis confirmed with the help of immunohistochemistry (IHC) on bone marrow (BM) biopsy and smear.Case presentation: A 63-year-old female patient with a history of cerebral infarction presented with fever, retching and hypodynamic sign for three weeks. The peripheral blood examination showed aggressive pancytopenia accompanying with the positivity for Epstein-Barr virus (EBV) antibody. The computed tomography and abdomen ultrasound scan didn’t reveal any nodules or lymphadenectasis other than hypersplenotrophy. Significantly, the BM aspirate shows vast pleomorphic tumor cells atypically distributed in both forms of single diffuse and cohesive. The hemophagocyte phagocytized granulocytes on BM smear exhibited the lymphoma related hemophagocytic syndrome. Immunohistochemically, neoplastic cells were immunopositively for macrophage-associated antigen cluster of CD4, CD68, CD163, but negative for the T-cell, B-cell and myeloid lineage markers of CD15, CD20, PAX-5, CD5, CD30, CD3, CD56, CD38, CD138, ALK and MPO, confirming the hypothesis of HS in BM. Conclusion: The rare HS case of bone marrow with atypically partial cohesive pleomorphic tumor cells had the hemophagocytic syndrome, presented highly aggressive clinical course and challenged to the diagnoses.

Backgroud and study aims Esophageal stricture is a serious adverse event occurring after circular endoscopic submucosal dissection (ESD) involving the whole esophagus. However, there is still a lack of effectively preventive methods. The main purpose of this study is to evaluate the efficacy of application of acellularized dermis matrix (ADM) for the prevention of post-ESD esophageal stricture. The main objective of this study was to evaluate the use of decellularized dermal matrix (ADM) in the prevention of post-esophageal ESD strictures. Patients and methods A pilot, single-center, prospective study was conducted. The study enrolled seven patients who had high-risks with extended resection of developing post-ESD esophageal stricture. After undergoing ESD, we attached different size of ADM patches to the mucosal defects using titanium clips then fixed with a metal mesh stent. The stent covered with metal mesh was removed at the median time of 27 days after the endoscopic procedure. Follow-up and repeated outpatient endoscopic screening were performed at appropriate scheduled times. Results The average longitudinal diameter of the resected specimens was 58.3 mm (range 38-90 mm). There were three patients developing strictures postoperatively at a mean time of 87 days (range 42-140). The median number of postoperative endoscopic balloon dilatation (EBD) in patients with stenosis was 2 (range 2-9). There were no deaths during a median follow-up period of 6 moths (range 1-12). Conclusions This study was performed to assess the efficacy and safe method of relieving the severity of esophageal stricture after ESD through transplantation of ADM.

With the rapid development of science and technology, artificial intelligence (AI) systems are becoming ubiquitous, and their utility in gastroenteroscopy is beginning to be recognized. Digestive endoscopy is a conventional and reliable method of examining and diagnosing digestive tract diseases. However, with the increase in the number and types of endoscopy, problems such as a lack of skilled endoscopists and difference in the professional skill of doctors with different degrees of experience have become increasingly apparent. Most studies thus far have focused on using computers to detect and diagnose lesions, but improving the quality of endoscopic examination process itself is the basis for improving the detection rate and correctly diagnosing diseases. In the present study, we mainly reviewed the role of AI in monitoring systems, mainly through the endoscopic examination time, reducing the blind spot rate, improving the success rate for detecting high-risk lesions, evaluating intestinal preparation, increasing the detection rate of polyps, automatically collecting maps and writing reports. AI can even perform quality control evaluations for endoscopists, improve the detection rate of endoscopic lesions and reduce the burden on endoscopists.

Background Endoscopic resection of duodenal subepithelial lesions (SELs) is a difficult procedure with a high risk of perforation. At present, dealing with perforation after endoscopic resection of duodenal SELs is still considered a great challenge. Aim To evaluate the effectiveness and safety of an over-the-scope clip (OTSC) in the treatment of perforation post-endoscopic resection of duodenal SELs. Methods From May 2015 to November 2019, 18 patients with perforation following endoscopic resection of duodenal SELs were treated with OTSCs. Data comprising the rate of complete resection, closure of intraprocedural perforation, delayed bleeding, delayed perforation, and postoperative infection were extracted. Results The rate of complete removal of duodenal SELs and successful closure of the perforation was 100%. The median perforation size was 1 cm in diameter. Seventeen patients had minor intraoperative bleeding, while the remaining 1 patient had considerable amount of bleeding during the procedure. Seven patients had postoperative abdominal infections, of which 1 patient developed an abscess in the right iliac fossa and another patient developed septic shock. All 18 patients recovered and were discharged. No delayed bleeding or perforation was reported. The mean time taken to resume normal diet after the procedure was 6.5 d. The mean postoperative hospital stay was 9.5 d. No residual or recurrent lesions were detected during the follow-up period (15-66 mo). Conclusion Closing a perforation after endoscopic resection of duodenal SELs with OTSCs seems to be an effective and reasonably safe therapeutic method.

Objective: Increased hemoglobin (Hb) levels are known to be associated with increased cardiovascular events and mortalities. Therefore, we assumed that high Hb levels were associated with arterial stiffness. Pulse wave velocity (PWV) is a simple and noninvasive method for measuring arterial stiffness to assess cardiovascular disease in general populations. Accordingly, we conducted a cross-sectional study to explore the association of Hb with PWV. Methods: A total of 6642 adults aged 54.5 ± 11.2 years undergoing physical examinations were enrolled, 71.7% of whom were males. Arterial stiffness was evaluated by carotid-femoral PWV (cfPWV). Multivariable regression analyses were performed to determine the relationship between Hb and increased cfPWV. Results: In this study, the mean Hb (per 10 g/L increase) was 144.7 ± 13.9 g/L, and the mean cfPWV was 15.1 ± 3.1 m/s. cfPWV was significantly higher in high hemoglobin groups ≥15.4 g/L (Quartile 4) than in the lowest hemoglobin group (Quartile 1 ≤ 13.6 g/L; P < 0.001). Multiple linear regression analysis revealed that Hb positively correlated with cfPWV (β = 0.16, P < 0.01). Univariate Logistic regression analysis revealed that Hb was associated with increased cfPWV, with an odd ratio (OR) of 1.46 (95% confidence interval [CI], 1.39-1.54). After adjusting for potential confounders, Hb and the highest Hb quartile group were also independently associated with increased cfPWV, with a fully adjusted OR of 1.11 (95% CI, 1.02-1.20) and 1.45 (95% CI, 1.01-2.08), respectively. Conclusion: This study demonstrated that Hb levels significantly correlate with increased cfPWV. Key words: Hemoglobin; Pulse wave velocity; Arterial stiffness; Cardiovascular diseases

Background: The dysregulation of microRNAs has been implicated in the progression of different malignancies. Herein, we sought to identify the precise roles of miR-155-5p in the progression of cervical cancer. Materials and methods: The expressions of miR-155-5p in cervical carcinoma cells and clinical tissues were assessed using qRT-PCR analysis. The functions of miR-155-5p on the growth of cervical cancer cell were investigated using MTT and colony formation. The Transwell and wound closure assays were selected to explore the influence of miR-155-5p on the invasion and migration of cervical cancer cell. The effect of miR-155-5p on cervical carcinoma cell growth and metastasis in vivo was investigated using xenograft model and experimental lung metastasis model. Bioinformatics analysis and luciferase reporter assay were applied to identify that tumor protein p53-inducible nuclear protein 1 (TP53INP1) was the target of miR-155-5p. Results: MiR-155-5p was significantly upregulated in cervical cancer tissue than that in control normal tissue. Downexpression of miR-155-5p decreased the growth, migration as well as invasiveness abilities of cervical cancer cell in vitro whereas overregulation of miR-155-5p caused the opposite outcomes. In addition, the in vivo mice xenograft model suggested that downexpression of miR-155-5p restrained the progression of cervical cancer cell whereas overexpression of miR-155-5p caused opposite outcomes. Furthermore, we revealed that TP53INP1 was the target of miR-155-5p and the level of TP53INP1 was inversely associated with miR-155-5p level in cervical carcinoma. Furthermore, TP53INP1 knockdown mimicked the influence of miR-155-5p on cervical cancer proliferation, migration and invasion phenotypes. Finally, overexpression of TP53INP1 impaired the promote effect of miR-155-5p on cervical cancer cell and downregulation of TP53INP1 counteracted the suppressive impact of miR-155-5p on the aggressiveness of cervical cancer cell. Conclusion: Our study indicated that miR-155-5p regulated the development of cervical cancer cell by regulating the expression of TP53INP1.

In order to solve the low accuracy of the classic spectral matching algorithms in matching the spectral data of different pigment materials in the homochromatic similar system, this paper proposed a spectral matching algorithm based on adaptive threshold levenshtein distance. The levenshtein distance was researched to improve the matching accuracy by using its characteristics of to be sensitive to the spectral reflectance difference. At the same time, by adaptively setting the judgment condition of levenshtein distance, the error of this algorithm in matching the spectral data of the same pigment material under different conditions is reduced. And the identification accuracy of different pigment materials in the homochromatic similar system is improved. The experimental results show that compared with the traditional spectral matching algorithm, the matching accuracy of the adaptive levenshtein distance algorithm is higher, and the identification result for the pigment is better.

Currently, the security of the Internet of Things (IoT) has aroused great concern. Face detection under arbitrary occlusion has become a key problem affecting social security. This paper designs a novel face occlusion recognition framework in the security scene of IOT, which is used to detect some crime behaviors. Our designed framework utilizes the gradient and shape cues in a deep learning model, and it has been demonstrated to be robust for its superiority to detect faces with severe occlusion. Our contributions contain three main aspects: Firstly, we present a new algorithm based on energy function for face detection; Secondly, we use the CNN models to create deep features of occluded face; Finally, to check whether the detected face is occluded, novel sparse classification model with deep learning scheme is constructed. Statistical results demonstrate that, compared with the state of the arts, our algorithm is superior in both accuracy and robustness. Our designed head detection algorithm can achieve 98.89% accuracy rate even though there are various types of severe occlusions in faces, and our designed occlusion verification scheme can achieve 97.25% accuracy rate, at a speed of 10 frames per second.

In the field of port and shipping, the mutual interest relationship between the port and the shipping company has always been a hot topic.On the one hand, the port needs the shipping company to park its port operations to obtain economic benefits and social benefits.On the other hand, shipping companies have their own considerations when facing different types of ports, and must consider them from the perspectives of maximizing their own interests and optimizing service levels.In this paper, the Stackelberg model and the Nash equilibrium model in game theory are used to analyze the basis of the choice of the interests of the port and shipping enterprises to establish a multi-influence factor and multi-targeted model of the profit distribution of the port and shipping enterprises.According to the research in this paper, the appropriate cooperation between the two parties can further enhance the efforts of the port and shipping companies, and realize the rational distribution of interests between the port and the shipping company, so as to achieve a win-win situation in the entire port supply chain.

Nowadays, there have been many studies on evaluating the scientific impact of scholars. However, we still lack effective methods to predict long-term impact, especially 10 years in the future. Therefore, we propose a long-term scientific impact prediction model based on multi-field feature extraction. The workflow of our proposed model consists of feature engineering and model ensemble. In feature engineering, we extract attribute feature, time-series feature, and heterogeneous network feature based on three different fields. Moreover, when extracting heterogeneous network feature, we propose a scientific impact evaluation method based on heterogeneous academic network, which considers both the time of publication and author order factors. In the model ensemble, we adjust the basic model and noise model to the different training set to make full use of the information from the original dataset. The experiment results demonstrate that the proposed model can stably improve the accuracy of scholars' scientific impact prediction, and it also offers a prediction pattern for long-term prediction problem.

Rapid development of Liquefied Natural Gas (LNG) industry and LNG vapor's flammable nature have brought up many safety issues. Computational fluid dynamics (CFD) modeling provides a powerful tool for studying the LNG spill, vapor dispersion, as well as other related safety assessments; however, traditional single phase model is unable to describe the LNG pool formation and phase change, which are essential to risk management. In this paper, we propose an integrated multiphase CFD model to simulate the complete spill process (including LNG release, LNG-water interaction, LNG evaporation and vapor dispersion) based on Falcon-1 and Burro-8 test. The computed results are compared with the experimental data and other simulation results. It is demonstrated that the multiphase CFD model provides better results than other approaches in accuracy and fully describing the LNG spill process. In addition, the effect of an impoundment on LNG spill and dispersion mitigation is also investigated. The results show that impoundment can significantly control the vapor's dispersion and shorten the hazard area, especially on lateral direction.

Background and Objectives: The adjuvant treatment of stem cell therapy for acute myocardial infarction (AMI) has been studied in multiple clinical trials, but many questions remain to be addressed, such as efficacy, safety, identification of the optimal cell type, tractable route of delivery, transplant dosage, and transplant timing. The current meta-analysis aimed to explore the issues of mesenchymal stem cells (MSCs) transplantation in patients with AMI based on published randomized controlled trials (RCTs) and guide the design of subsequent clinical trials of MSCs therapy for AMI. Methods: The Cochrane Library, PubMed, EMBASE databases were searched for relevant clinical trials from January 1, 2000, to January 23, 2021. Results from RCTs involving MSCs transplantation for the treatment of AMI were identified. According to the Cochrane systematic review method, the literature quality, including studies, was evaluated and valid data was extracted. Rev- Man 5.3 and Stata 15.1 software were used for Meta-analysis. Results: After literature search and detailed evaluation, 9 randomized controlled trials enrolling 460 patients were included in the quantitative analysis. Pooled analyses indicated that MSCs therapy was associated with a significantly greater improvement in overall left ventricular ejection fraction (LVEF), and the effect was maintained for up to 24 months. No significant difference in favor of MSCs treatment in left ventricular (LV) volume and in the risk of rehospitalization as a result of heart failure (HF) was noted, compared with the controls. For transplantation dose, the LVEF of patients accepting a MSCs dose of 107-108 cells was significantly increased by 2.62% (95% CI 1.54 to 3.70; P < 0.00001; I2 =0%), but this increase was insignificant in the subgroup that accepted a MSCs dose of < 107 cells (1.65% in LVEF, 95% CI, 0.03 to 3.27; P =0.05; I2 =75%) or >108 cells (4.65% in LVEF, 95% CI, -4.55 to 13.48; P =0.32; I2 =95%), compared with the controls. For transplantation timing, a significant improvement of LVEF of 3.18% was achieved in the group of patients accepting a MSCs infusion within 2 to 14 days after percutaneous coronary intervention (PCI) (95% CI, 2.89 to 3.47; P Conclusion: Results from our systematic review suggest that MSCs therapy for patients with AMI appears to be safe and might induce a significant increase in LVEF with a limited impact on LV volume and rehospitalization caused by HF. The effect was maintained for up to 24 months. MSCs dose of 107-108 cells was more likely to achieve better clinical endpoints than 108 cells. The optimal time window for cell transplantation might be within 2-14 days after PCI. This meta-analysis was registered with PROSPERO, number CRD 42021241104.

MOOC is a typical representative of education informatization, which has caused fundamental changes in the educational model. Online and offline blended teaching is gradually emerging in colleges and universities as a product of teaching applications in the information age. On the basis of clarifying the evolution of the concept of blended teaching, this paper proposes a method of blended teaching design based on MOOC. The article mainly elaborates on the selection of blended forms, the determination of course content and the implementation of blended teaching, which will provide a certain reference and reference for the innovative reform of blended teaching.

Objective This study evaluated the safety and preliminary efficacy of vorolanib, a novel tyrosine kinase inhibitor, for treatment of patients with advanced solid tumors. Methods During dose escalation, patients received increasing doses of oral vorolanib (50-250 mg once daily) in cycles of four weeks for up to one year. During dose expansion, patients received recommended doses (100 and 200 mg) in 4-week cycles. The primary endpoint was to determine the safety and maximum tolerated dose and/or the recommended phase II dose (RP2D). The severity and type of adverse drug reactions (ADRs) were assessed using the Common Terminology Criteria for Adverse Events version 4.0. The second endpoint was preliminary efficacy in terms of objective response and progression-free survival (PFS). Results No dose-limiting toxicity occurred during dose escalation (50-250 mg). Five (26.3%) patients in the escalation cohort (n=19) and 12 (48.0%) in the expansion cohort (n=25) experienced grade 3 ADRs. The most common ADRs were hair color changes, fatigue, portal hypertension, hypertriglyceridemia, and proteinuria. During dose expansion, the patients treated with 200 mg and 100 mg (once daily) showed an objective response rate of 22.2% and 5.9%, respectively; the disease control rate was 88.9% and 73.3%, respectively; the median PFS was 9.9 [95% confidence interval (95% CI): 7.4-not reached] months and 3.8 (95% CI: 1.9-not reached) months, respectively. Conclusions Oral vorolanib at a dose of 200 mg (once daily) exhibited an acceptable safety profile and favorable clinical benefit for patients with advanced solid tumors. The RP2D for vorolanib was determined to be 200 mg as a daily regimen.

Liver fibrosis refers to the process underlying the development of chronic liver diseases, wherein liver cells are repeatedly destroyed and regenerated, which leads to an excessive deposition and abnormal distribution of the extracellular matrix such as collagen, glycoprotein and proteoglycan in the liver. Liver fibrosis thus constitutes the pathological repair response of the liver to chronic injury. Hepatic fibrosis is a key step in the progression of chronic liver disease to cirrhosis and an important factor affecting the prognosis of chronic liver disease. Further development of liver fibrosis may lead to structural disorders of the liver, nodular regeneration of hepatocytes and the formation of cirrhosis. Hepatic fibrosis is histologically reversible if treated aggressively during this period, but when fibrosis progresses to the stage of cirrhosis, reversal is very difficult, resulting in a poor prognosis. There are many causes of liver fibrosis, including liver injury caused by drugs, viral hepatitis, alcoholic liver, fatty liver and autoimmune disease. The mechanism underlying hepatic fibrosis differs among etiologies. The establishment of an appropriate animal model of liver fibrosis is not only an important basis for the in-depth study of the pathogenesis of liver fibrosis but also an important means for clinical experts to select drugs for the prevention and treatment of liver fibrosis. The present study focused on the modeling methods and fibrosis characteristics of different animal models of liver fibrosis, such as a chemical-induced liver fibrosis model, autoimmune liver fibrosis model, cholestatic liver fibrosis model, alcoholic liver fibrosis model and non-alcoholic liver fibrosis model. In addition, we also summarize the research and application prospects concerning new organoids in liver fibrosis models proposed in recent years. A suitable animal model of liver fibrosis and organoid fibrosis model that closely resemble the physiological state of the human body will provide bases for the in-depth study of the pathogenesis of liver fibrosis and the development of therapeutic drugs.

BackgroundClinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa.MethodsA total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC) curves were performed and the area under the ROC curve (AUC) for discrimination between muscle-invasive BCa and non-muscle-invasive BCa was calculated. The performance of the model was evaluated and compared with that of the subjective assessment by two radiologists.ResultsThe DL model exhibited relatively good performance in all cohorts [AUC: 0.861 in the internal validation cohort, 0.791 in the external validation cohort] and outperformed the two radiologists. The model yielded a sensitivity of 0.733, a specificity of 0.810 in the internal validation cohort and a sensitivity of 0.710 and a specificity of 0.773 in the external validation cohort.ConclusionThe proposed DL model based on CT images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa.

Although liver transplantation is considered to be the best choice for patients with end-stage liver diseases, postoperative immune rejection still cannot be overlooked. Patients with liver transplantation have to take immunosuppressive drugs for a long time or even their entire lives, in which heavy economic burden and side effects caused by the drugs have become the major impediment for liver transplantation. There is a growing body of evidences indicating that mesenchymal stem cell (MSC) transplantation, a promising tool in regenerative medicine, can be used as an effective way to induce immune tolerance after liver transplantation based on their huge expansion potential and unique immunomodulatory properties. MSCs have been reported to inhibit innate immunity and adaptive immunity to induce a tolerogenic microenvironment. In in vitro studies, transplanted MSCs show plasticity in immune regulation by altering their viability, migration, differentiation, and secretion in the interactions with the surrounding host microenvironment. In this review, we aim to provide an overview of the current understanding of immunomodulatory properties of MSCs in liver transplantation, to elucidate the potential mechanisms behind MSCs regulating immune response, especially in vivo and the influence of the microenvironment, and ultimately to discuss the feasible strategies to improve the clinical prognosis of liver transplantation. Only after exhaustive understanding of potential mechanisms of the MSC immunomodulation can we improve the safety and effectiveness of MSC treatment and achieve better therapeutic effects.

Background: Identification of the Aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists is lack of sensitivity and suboptimal at present. Methods: A total of 452 patients underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form an internal cohort (341 patients, 139 AD, 202 non-AD) and an external testing cohort (111 patients, 46 AD, 65 non-AD). The internal cohort was divided into the training cohort (n=238), validation cohort (n=35) and internal testing cohort (n=68). The morphologic characteristics were extracted from the aortic segmentation. A Gaussian Naive Bayes model integrating the deep-learning model score and the morphologic characteristics were built to differentiate AD from non-AD. The areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists. Findings: After the combination of all morphologic characteristics, our proposed model significantly outperformed the deep-learning model (AUC: 0. 948 vs. 0.803 on the internal testing cohort, 0.969 vs. 0.814 on the external testing cohort, both p 0.05). Interpretation: The proposed model presented good performance on the AD detection on non-contrast CT scans, thus early diagnosis as well as prompt treatment would be available. Funding Information: CAMS Innovation Fund for Medical Sciences (CIFMS); the Major International (Regional) Joint Research Project of National Natural Science Foundation of China; the National Natural Science Foundation of China; the National Key Research and Development Program of China; the China Postdoctoral Science Foundation. Declaration of Interests: All the other authors have no conflict of interest to disclose. Ethics Approval Statement: This study was reviewed and approved by the local clinical institutional ethics committees of the two centers involved, and informed consent was waived because of the retrospective nature of this study.

Computer digital technology has now flooded People's Daily life, with the continuous development and innovation of digital technology, has been widely used in various disciplines, especially in the theoretical research and practical application of environmental design. The progress of digital technology still needs to be combined with innovative ideas and applied in practical operation.Taking eye movement tracking technology as an example, this paper introduces the application and innovation of digital technology in landscape environment design in detail, and analyzes the changing law of people's line of sight in landscape environment of different animal exhibition areas through eye movement experiment, and summarizes the psychological characteristics of people. The results of eye movement experiment evaluation were as follows: in the landscape abundance environment of different animal types exhibition area, the visual line of subjects did not change, and the visual line of subjects was more focused on the environmental abundance facilities, water environment and vegetation.The evaluation results reveal the elements of landscape design of zoo environment abundance through eye movement characteristics, and at the same time fully reflect the innovation of digital technology applied to landscape design.

Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients’ care in comparison to clinicians’ assessment., Interpretation of Computed Tomography Angiography for intracranial aneurysm diagnosis can be time-consuming and challenging. Here, the authors present a deep-learning-based framework achieving improved performance compared to that of radiologists and expert neurosurgeons.

It has been reported that sevoflurane induces neurotoxicity in the developing brain. Dexmedetomidine is an α2 adrenoceptor agonist used for the prevention of sevoflurane‑induced agitation in children in clinical practice. The aim of the present study was to determine whether dexmedetomidine could prevent sevoflurane‑induced neuroapoptosis, neuroinflammation, oxidative stress and neurocognitive impairment. Additionally, the involvement of α2 adrenoceptors in the neuroprotective effect of dexmedetomidine was assessed. Postnatal day (P)6 C57BL/6 male mice were randomly divided into four groups (n=6 in each group). Mice were pretreated with dexmedetomidine, either alone or together with yohimbine, an α2 adrenoceptor inhibitor, then exposed to 3% sevoflurane in 25% oxygen. Control mice either received normal saline alone or with sevoflurane exposure. Following sevoflurane exposure, the expression of cleaved caspase‑3 was detected by immunohistochemistry in hippocampal tissue sections. In addition, the levels of tumor necrosis factor‑α (TNF‑α), interleukin (IL)‑1β, IL‑6 and malondialdehyde, as well as superoxide dismutase (SOD) activity in the hippocampus were measured. At P35, the learning and memory abilities were assessed in each mouse using a Morris water maze test. Dexmedetomidine significantly decreased the expression of activated caspase‑3 following sevoflurane exposure. Moreover, dexmedetomidine significantly decreased the levels of TNF‑α, IL‑1β and IL‑6 in the hippocampus. SOD activity also increased in a dose‑dependent manner in dexmedetomidine‑treated mice. MDA decreased in a dose‑dependent manner in dexmedetomidine‑treated mice. Lastly, sevoflurane‑induced learning and memory impairment was reversed by dexmedetomidine treatment. By contrast, co‑administration of yohimbine significantly attenuated the neuroprotective effects of dexmedetomidine. These findings suggested that dexmedetomidine exerted a neuroprotective effect against sevoflurane‑induced apoptosis, inflammation, oxidative stress and neurocognitive impairment, which was mediated, at least in part, by α2 adrenoceptors.

Endoscopic submucosal dissection (ESD) has been widely accepted as an effective treatment for early esophageal cancer. However, post-ESD esophageal stricture remains a thorny issue. We herein review many strategies for preventing post-ESD esophageal stricture, as well as discuss their strengths and weaknesses. These strategies include pharmacological prophylaxis, esophageal stent and tissue engineering and regenerative medicine treatment. In this review, we summarize these studies and discuss the underlying progress and future directions of tissue engineering and regenerative medicine treatment.

Cholelithiasis refers to the gallstone that occurs in the gallbladder. The main component is cholesterol...

The regimens containing levofloxacin (LVX) have been recommended as an alternate to standard triple therapy to treat Helicobacter pylori infections and H pylori mixed infection always lead to H pylori chronic infection. Although the molecular mechanism of LVX resistance with gyrA gene mutation has been clearly understood in H pylori, other genes involved in antibiotic resistance remain unclear. Efflux pump plays an important role in clinically relevant multidrug resistance. Furthermore, the relationship between the strains with different LVX level-resistances from individuals is also unknown. Helicobacter pylori monoclonal strains were isolated from patients with eradication failure. E test was used to detect the minimal inhibitory concentration of LVX. One lower-level LVX-resistant clone and 2 higher-level LVX-resistant clones from the same patient were selected to sequence the complete genomes. Single-nucleotide variants (SNVs) and mutations were extracted and analyzed from gryA and resistance-nodulation-division family efflux genes. Two clones with higher-level resistance had the mutation pattern of Asn87Lys and one lower-level LVX-resistant clone had an Asp91Asn mutation. Compared to clones with higher-level resistance, the higher genetic variations were found in genes belonging to the resistance-nodulation-division family in H pylori strains with lower-level resistance to LVX. There were significantly more SNVs of Hp0970 (hefE) and Hp1329 (hefI) in the lower-level LVX-resistant clone than those in the higher-level LVX-resistant clones (P = .044). The mutation pattern of the Asn87Lys of the gyrA gene confers a higher resistance to LVX than that of the Asp91Asn in H pylori. Increase in the number of SNVs of the Hp0970 (hefE) and Hp1329 (hefI) genes change the resistance to LVX. Twelve mutations verified by Sanger sequencing in Hp0970 (hefE) and Hp1329 (hefI) may decrease resistant levels to LVX.

Purpose To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods In this retrospective study, a machine learning-based CT radiomics model was developed to extract features from chest CT exams for the detection of COVID-19. Other viral-pneumonia CT exams of the corresponding period were also included. The radiomics features extracted from the region of interest (ROI), the radiological features evaluated by the radiologists, the quantity features calculated by the AI segmentation and evaluation, and the clinical parameters including clinical symptoms, epidemiology history and biochemical results were enrolled in this study. The SVM model was built and the performance on the testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results For the SVM model that built on the radiomics features only, it reached an AUC of 0.688(95% CI 0.496 to 0.881) on the testing cohort. After the radiological features were enrolled, the AUC achieved 0.696(95% CI 0.501 to 0.892), then the AUC reached 0.753(95% CI 0.596 to 0.910) after the quantity features were included. Our final model employed all the features, reached the per-exam sensitivity and specificity for differentiating COVID-19 was 29 of 38 (0.763, 95% CI: 0.598 to 0.886]) and 12 of 13 (0.923, 95% CI: 0.640 to 0.998]), respectively, with an AUC of 0.968(95% CI 0.911 to 1.000). Conclusion The machine learning-based CT radiomics models may accurately detect COVID-19 and differentiate it from other viral pneumonia.

We show here that breath figure–derived semiconducting films are suitable and universal for porous organic electronics., Porous semiconductor film morphologies facilitate fluid diffusion and mass transport into the charge-carrying layers of diverse electronic devices. Here, we report the nature-inspired fabrication of several porous organic semiconductor-insulator blend films [semiconductor: P3HT (p-type polymer), C8BTBT (p-type small-molecule), and N2200 (n-type polymer); insulator: PS] by a breath figure patterning method and their broad and general applicability in organic thin-film transistors (OTFTs), gas sensors, organic electrochemical transistors (OECTs), and chemically doped conducting films. Detailed morphological analysis of these films demonstrates formation of textured layers with uniform nanopores reaching the bottom substrate with an unchanged solid-state packing structure. Device data gathered with both porous and dense control semiconductor films demonstrate that the former films are efficient TFT semiconductors but with added advantage of enhanced sensitivity to gases (e.g., 48.2%/ppm for NO2 using P3HT/PS), faster switching speeds (4.7 s for P3HT/PS OECTs), and more efficient molecular doping (conductivity, 0.13 S/m for N2200/PS).

Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features. Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test. Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83–0.88, P = 0.01), but not in the test cohort (AUC = 0.80–0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma. Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

To build a CT-based radiomics model to predict the pathological grade of bladder cancer (BCa) preliminarily. Patients with surgically resected and pathologically confirmed BCa and who received CT urography (CTU) in our institution from October 2014 to September 2017 were retrospectively enrolled and randomly divided into training and validation groups. After feature extraction, we calculated the linear dependent coefficient between features to eliminate the collinearity. F-test was then used to identify the best features related to pathological grade. The logistic regression method was used to build the prediction model, and diagnostic performance was analyzed by plotting receiver operating characteristic (ROC) curve and calculating area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Out of 145 included patients, 108 constituted the training group and 37 the validation group. The AUC value of the radiomics prediction model to diagnose the pathological grade of BCa was 0.950 (95% confidence interval [CI] 0.912–0.988) in the training group and 0.860 (95% CI 0.742–0.979) in the validation group, respectively. In the validation group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 83.8%, 88.5%, 72.7%, 88.5%, and 72.7%, respectively. CT-based radiomics model can differentiate high-grade from low-grade BCa with a fairly good diagnostic performance. •CT-based radiomics model can predict the pathological grade of bladder cancer. •This model has good diagnostic performance to differentiate high-grade and low-grade bladder cancer. •This preoperative and non-invasive prediction method might become an important addition to biopsy.