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Background/purpose: Root canal irrigants are difficult to diffuse deep into the dentinal tubules for root canal disinfection. The purpose of this study was to assess the ability of different diffusing enhancers to deliver chlorhexidine (CHX) into dentinal tubules. Materials and methods: The diffusing property of five diffusing enhancers (acetone, DMSO, Triton X-100, JFC-E and azone) into dentinal tubules was firstly assessed using the Rhodamine B. The ability of the diffusing enhancers to deliver CHX into dentinal tubules was then evaluated both qualitatively and quantitatively. A deep dentinal tubule Enterococcus faecalis infection model was further established to test the bactericidal effect of CHX delivered by different diffusing enhancers. Finally, the in vitro cytotoxicity of these diffusing enhancers was tested using the CCK-8 method. Results: Azone, Triton X-100 and JFC-E exhibited the largest maximum diffusing depths at all root canal parts (P

Abstract Background Preserved Ratio Impaired Spirometry (PRISm) is considered to be a precursor of chronic obstructive pulmonary disease. Radiomics nomogram can effectively identify the PRISm subjects from non-COPD subjects, especially when during large-scale CT lung cancer screening. Methods Totally 1481 participants (864, 370 and 247 in training, internal validation, and external validation cohorts, respectively) were included. Whole lung on thin-section computed tomography (CT) was segmented with a fully automated segmentation algorithm. PyRadiomics was adopted for extracting radiomics features. Clinical features were also obtained. Moreover, Spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking and least absolute shrinkage and selection operator (LASSO) classifier were adopted to analyze whether radiomics features could be used to build radiomics signatures. A nomogram that incorporated clinical features and radiomics signature was constructed through multivariable logistic regression. Last, calibration, discrimination and clinical usefulness were analyzed using validation cohorts. Results The radiomics signature, which included 14 stable features, was related to PRISm of training and validation cohorts (p

Abstract Background The objective of the present study was to evaluate the effect of 3D printed teeth and virtual simulation system on the pre-clinical access cavity preparation training of senior dental undergraduates. Methods The 3D printed teeth were manufactured based on the micro-CT data of an extracted lower first molar. Ninety-eight senior dental undergraduate students were required to finish the access cavity preparation of lower first molar within 20 min on plastic and 3D printed teeth on the manikin system as well as on a virtual simulation machine respectively with randomly selected sequences. Expert dentists scored the operated teeth. The scores from the virtual simulation system were also recorded. All the scores were analyzed and compared. Following the procedure, two questionnaires were sent to students to further evaluate the feelings and optimal training sequence. Results No significant differences were found between plastic and 3D printed teeth scores, while virtual simulation achieved a valid/invalid area removal ratio of 96.86% ± 5.08% and 3.97% ± 1.85%, respectively. Most students found plastic teeth training the easiest and favored the three-training combination (36.36%). 71.42% of the students thought the virtual simulation training should be put at the first place of the three trainings. Over 80% of students agreed with incorporating 3D printed teeth and virtual simulation into their routine training courses. In addition, the general advantages and disadvantages of the virtual simulation system and 3D printed teeth training received almost equal recognition by students. Conclusions Virtual simulation system training + plastic teeth training + 3D printed teeth training might be the optimal training sequence. Virtual simulation system training could not completely replace the traditional training methods on the manikin system at the moment. With further modifications, 3D printed teeth could be expected to replace the plastic teeth for the pre-clinical access cavity preparation training.

Microorganisms, physical factors such as temperature or mechanical injury, and chemical factors such as free monomers from composite resin are the main causes of dental pulp diseases. Current clinical treatment methods for pulp diseases include the root canal therapy, vital pulp therapy and regenerative endodontic therapy. Regenerative endodontic therapy serves the purpose of inducing the regeneration of new functional pulp tissues through autologous revascularization or pulp tissue engineering. This article first discusses the current clinical methods and reviews strategies as well as the research outcomes regarding the pulp regeneration. Then the in vivo models, the prospects and challenges for regenerative endodontic therapy were further discussed.

Abstract Background Distinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma. Methods Approximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA). Results Ultimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p

ABSTRACT Weaning weight is a reflection of management during the breastfeeding phase and will influence animal performance in subsequent phases, considered important indicators within production systems. The aims of this study were as follows: (i) to investigate variability in the growth rate among individual lambs from ewes rearing single or twin lambs fed with two different diets and (ii) to explore the molecular mechanisms regulating the growth rate and the potential long-term effects on the host. No significant change in lamb average daily gain (ADG) was observed in litter size and diet treatment, and there were large variations among individual lambs (ranging from 0.13 to 0.41 kg/day). Further analysis was conducted on serum amino acids, rumen fermentation characteristics, rumen metagenomics and transcriptome, and hepatic transcriptome of lambs with extremely high (HA; n = 6) and low (LA; n = 6) ADG. We observed significant increases in serum lysine, leucine, alanine, and phenylalanine in the HA group. The metagenome revealed that the HA group presented a higher rumen propionate molar proportion via increasing gene abundance in the succinate pathway for propionate synthesis. For the rumen transcriptome, higher expressed gene sets in the HA group were mainly related to rumen epithelial growth, including cytokine–cytokine receptor interaction, Jak-STAT signaling pathway, and adherens junction. For the liver transcriptome, the upregulated KEGG pathways in the HA group were primarily associated with fatty acid degradation, glyoxylate and dicarboxylate metabolism, cholesterol metabolism, and the immune system. This research suggests that preweaning lambs with high ADG may benefit from rumen development and enhanced liver metabolic and immune function.IMPORTANCEThere is accumulating evidence indicating that the early-life rumen microbiome plays vital roles in rumen development and microbial fermentation, which subsequently affects the growth of young ruminants. The liver is also vital to regulate the metabolism and distribution of nutrients. Our results demonstrate that lambs with high average daily gain (ADG) enhanced microbial volatile fatty acid (VFA) metabolism toward rumen propionate and serum amino acid (AA) production to support host growth. The study highlights that high ADG in the preweaning period is beneficial for the rumen development and liver energy metabolism, leading to better growth later in life. Overall, this study explores the molecular mechanisms regulating the growth rate and the potential long-term effects of increased growth rate on the host metabolism, providing fundamental knowledge about nutrient manipulation in pre-weaning.

The fragmentation size distribution is an important index to evaluate blasting effect. Based on stress wave theory, a blasting fragmentation distribution model is established, and the key influencing factors were clarified. Then, the distribution characteristics of rock fragmentation in water-coupled blasting and air-coupled blasting were compared and verified by numerical simulation and field test. The results show that the rock blasting fragmentation size is negatively correlated with borehole pressure and unit explosive consumption when blasting rock is determined. The existence of water slows down the attenuation of blasting load, prolongs the duration, and makes the blasting pressure transmitted to hole wall significantly greater than air-coupled one, which is equivalent to increasing the unit explosive consumption. Moreover, the rock fracture development speed and fragmentation degree of water-coupled blasting is significantly higher than air-coupled blasting. Comprehensively determined in same charging parameters, water-coupled blasting compared with air-coupled blasting can improve the degree of rock fragmentation, the average size of rock after blasting is smaller, more uniform particle size distribution. The research results for the control of blasting and optimization of explosive energy utilization have important reference significance.

Abstract Chemical cleaning and disinfection are crucial steps for eliminating infection in root canal treatment. However, irrigant selection or irrigation procedures are far from clear. The vapor lock effect in the apical region has yet to be solved, impeding irrigation efficacy and resulting in residual infections and compromised treatment outcomes. Additionally, ambiguous clinical indications for root canal medication and non-standardized dressing protocols must be clarified. Inappropriate intracanal medication may present side effects and jeopardize the therapeutic outcomes. Indeed, clinicians have been aware of these concerns for years. Based on the current evidence of studies, this article reviews the properties of various irrigants and intracanal medicaments and elucidates their effectiveness and interactions. The evolution of different kinetic irrigation methods, their effects, limitations, the paradigm shift, current indications, and effective operational procedures regarding intracanal medication are also discussed. This expert consensus aims to establish the clinical operation guidelines for root canal irrigation and a position statement on intracanal medication, thus facilitating a better understanding of infection control, standardizing clinical practice, and ultimately improving the success of endodontic therapy.

Abstract Background Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. Methods This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. Results Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869–0.906], 0.874 (95%CI 0.844–0.904) and 0.846 (95%CI 0.822–0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P

Purpose: It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups. Methods: Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). Results: The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79–0.86), 0.77(95 %CI 0.72–0.83) and 0.841(95 %CI 0.78–0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility. Conclusions: The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a thorough overview of this research topic. Web of Science was used to retrieve publications on the application of AI in RA from 2003 to 2022. Bibliometric analysis and visualization were performed using Microsoft Excel (2019), R software (4.2.2) and VOSviewer (1.6.18). The overall distribution of yearly outputs, leading countries, top institutions and authors, active journals, co-cited references and keywords were analyzed. A total of 859 relevant articles were identified in the Web of Science with an increasing trend. USA and China were the leading countries in this field, accounting for 71.59% of publications in total. Harvard University was the most influential institution. Arthritis Research & Therapy was the most active journal. Primary topics in this field focused on estimating the risk of developing RA, diagnosing RA using sensor, clinical, imaging and omics data, identifying the phenotype of RA patients using electronic health records, predicting treatment response, tracking the progression of the disease and predicting prognosis and developing new drugs. Machine learning and deep learning algorithms were the recent research hotspots and trends in this field. AI has potential applications in various fields of RA, including the risk assessment, screening, early diagnosis, monitoring, prognosis determination, achieving optimal therapeutic outcomes and new drug development for RA patients. Incorporating machine learning and deep learning algorithms into real-world clinical practice will be a future research hotspot and trend for AI in RA. Extensive collaboration to improve model maturity and robustness will be a critical step in the advancement of AI in healthcare.

Background and purposeThe objective of this study is to evaluate the risk of secondary autoimmune diseases in multiple sclerosis (MS) patients treated with alemtuzumab (ALZ) through a meta-analysis.MethodsPubMed, Web of Science, OVID, EMBASE, and Cochrane central register of controlled trials were searched. Information and data were screened and extracted by 2 researchers. The obtained data were analyzed using the R software meta package. Quality assessment was conducted using the Newcastle-Ottawa Scale (NOS). The causes of heterogeneity were analyzed using subgroup analysis and sensitivity analysis. Publication bias was evaluated using funnel plots and Egger’s test.ResultsThe search retrieved a total of 3530 papers from the databases. After screening, a total of 37 studies were included in the meta-analysis. The analysis results indicate that the pooled incidence rate of overall secondary autoimmune events (SAEs) in the included studies was 0.2824 [0.2348, 0.3300] (I²=94%, p

Abstract The dental operative microscope has been widely employed in the field of dentistry, particularly in endodontics and operative dentistry, resulting in significant advancements in the effectiveness of root canal therapy, endodontic surgery, and dental restoration. However, the improper use of this microscope continues to be common in clinical settings, primarily due to operators’ insufficient understanding and proficiency in both the features and established operating procedures of this equipment. In October 2019, Professor Jingping Liang, Vice Chairman of the Society of Cariology and Endodontology, Chinese Stomatological Association, organized a consensus meeting with Chinese experts in endodontics and operative dentistry. The objective of this meeting was to establish a standard operation procedure for the dental operative microscope. Subsequently, a consensus was reached and officially issued. Over the span of about four years, the content of this consensus has been further developed and improved through practical experience.

Abstract Objectives To construct and validate unfavorable pathology (UFP) prediction models for patients with the first diagnosis of bladder cancer (initial BLCA) and to compare the comprehensive predictive performance of these models. Materials and Methods A total of 105 patients with initial BLCA were included and randomly enrolled into the training and testing cohorts in a 7:3 ratio. The clinical model was constructed using independent UFP‐risk factors determined by multivariate logistic regression (LR) analysis in the training cohort. Radiomics features were extracted from manually segmented regions of interest in computed tomography (CT) images. The optimal CT‐based radiomics features to predict UFP were determined by the optimal feature filter and the least absolute shrinkage and selection operator algorithm. The radiomics model consist with the optimal features was constructed by the best of the six machine learning filters. The clinic‐radiomics model combined the clinical and radiomics models via LR. The area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive value, calibration curve and decision curve analysis were used to evaluate the predictive performance of the models. Results Patients in the UFP group had a significantly older age (69.61 vs. 63.93 years, p = 0.034), lager tumor size (45.7% vs. 11.1%, p = 0.002) and higher neutrophil to lymphocyte ratio (NLR; 2.76 vs. 2.33, p = 0.017) than favorable pathologic group in the training cohort. Tumor size (OR, 6.02; 95% CI, 1.50–24.10; p = 0.011) and NLR (OR, 1.50; 95% CI, 1.05–2.16; p = 0.026) were identified as independent predictive factors for UFP, and the clinical model was constructed using these factors. The LR classifier with the best AUC (0.817, the testing cohorts) was used to construct the radiomics model based on the optimal radiomics features. Finally, the clinic‐radiomics model was developed by combining the clinical and radiomics models using LR. After comparison, the clinic‐radiomics model had the best performance in comprehensive predictive efficacy (accuracy = 0.750, AUC = 0.817, the testing cohorts) and clinical net benefit among UFP‐prediction models, while the clinical model (accuracy = 0.625, AUC = 0.742, the testing cohorts) was the worst. Conclusion Our study demonstrates that the clinic‐radiomics model exhibits the best predictive efficacy and clinical net benefit for predicting UFP in initial BLCA compared with the clinical and radiomics model. The integration of radiomics features significantly improves the comprehensive performance of the clinical model.

The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In view of this, this paper proposes a data-driven approach to combine an improved deep belief network (DBN) and long short-term memory (LSTM) network model for water quality prediction and assessment, avoiding the complexity of constructing a model of the internal mechanism of water quality. Firstly, using Gaussian Restricted Boltzmann Machines (GRBMs) to construct a DBN, the model has a better ability to extract continuous data features compared to classical DBN. Secondly, the extracted time-series data features are input into the LSTM network to improve predicting accuracy. Finally, due to prediction errors, noise that randomly follows the Gaussian distribution is added to the assessment results based on the predicted values, and the probability of being at the current water quality level in the future is calculated through multiple evolutionary computations to complete the water quality assessment. Numerical experiments have shown that our proposed algorithm has a greater accuracy compared to classical algorithms in challenging scenarios.

Abstract Background Enterococcus faecalis (E. faecalis) is frequently isolated from root canals with failed root canal treatments. Due to the strong ability of E. faecalis to resist many often-used antimicrobials, coping with E. faecalis infections remains a challenge. The aim of this study was to investigate the synergistic antibacterial effect of low-dose cetylpyridinium chloride (CPC) and silver ions (Ag+) against E. faecalis in vitro. Methods The minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and the fractional inhibitory concentration index (FICI) were used to confirm the existence of the synergic antibacterial activity between low-dose CPC and Ag+. Colony-forming unit (CFU) counting, time-killing curve and dynamic growth curve were used to evaluate the antimicrobial effects of CPC and Ag+ combinations against planktonic E. faecalis. Four weeks biofilms were treated with drug-contained gels to determine the antimicrobial effect on biofilm-resident E.faecalis, and the integrity of E.faecalis and its biofilms were observed by FE-SEM. CCK-8 assays was used to test the cytotoxicity of CPC and Ag+ combinations on MC3T3-E1 cells. Results The results confirmed the synergistic antibacterial effect of low-dose CPC and Ag+ against both planktonic and 4-week biofilm E. faecalis. After the addition of CPC, the sensitivity of both planktonic and biofilm-resident E. faecalis to Ag+ improved, and the combination showed good biocompatibility on MC3T3-E1 cells. Conclusions Low-dose CPC enhanced the antibacterial ability of Ag+ against both planktonic and biofilm E.faecalis with good biocompatibility. It may be developed into a novel and potent antibacterial agent against E.faecalis, with low toxicity for root canal disinfection or other related medical applications.

Abstract Objectives To identify the differences of clinical characteristics, laboratory findings, and the long‐term outcomes in patients with anti‐synthetase syndrome (ASS) of different anti‐aminoacyl‐transfer RNA synthetase antibodies. Methods We retrospectively enrolled 119 patients with ASS, and the clinical characteristics and laboratory findings were collected. Additionally, multivariate COX regression analysis was performed to estimate the risk factors of prognosis in patients with ASS. Results The frequency of interstitial lung disease (ILD) reached 93.3% in our cohort, of 28 (23.5%) was classified as rapidly progressive (RP)‐ILD. The highest incidence of RP‐ILD was 36.4% in the PL12 group of ASS patients. The ILD group was characterized by an older age, a lower prevalence of V sign, and a higher prevalence of pulmonary symptoms when contrasted with the non‐ILD group. There were statistical differences of clinical significance in arthritis, myositis, mechanic's hands, triad, shawl sign, V sign, and Raynaud's phenomenon among the four subgroups (all p

Unmanned Aerial Vehicle (UAV) has very wide application prospect in aiding terrestrial cellular network communication, but it remains a challenge to optimize UAV locations and maximize user service rate during deployment. In this paper, a novel network optimization scheme based on anti-flocking model and improved Nash Equilibrium (NE) algorithm is proposed by studying the problem of dynamic UAV deployment and backhaul transmission. Firstly, the UAV-adaptive algorithm based on gray wolf optimization (U-GWO) is used to predeploy UAVs with limited number of UAVs. Secondly, ground mobile users are tracked by building a UAV-based anti-flocking (U-AF) model. Then, during the tracking of ground users by UAV, an improved NE strategy is used to establish the backhaul transmission links between UAVs, ground BSs and other UAVs to ensure that deployed UAVs can maximize the service rate and effective backhaul transmission rate of ground users. Simulation results show that the average service rate of User Equipment (UE) with U-GWO algorithm is improved from 1 % to 5.77 % compared to other different swarm intelligence optimization algorithms. And the service rate obtained with U-AF algorithm is 43.2 % improved compared to the baseline scenario without U-AF algorithm. For UAV backhaul transmission link construction, the simulation results show that the proposed improved NE strategy improves the average effective backhaul transmission rate by 12 %, the minimum backhaul transmission rate by 84 % and the overall iteration number by 5 % on average compared to a pure NE strategy.

Abstract An unbiased and accurate diagnosis of benign and malignant endometrial lesions is essential for the gynecologist, as each type might require distinct treatment. Radiomics is a quantitative method that could facilitate deep mining of information and quantification of the heterogeneity in images, thereby aiding clinicians in proper lesion diagnosis. The aim of this study is to develop an appropriate predictive model for the classification of benign and malignant endometrial lesions, and evaluate potential clinical applicability of the model. 139 patients with pathologically-confirmed endometrial lesions from January 2018 to July 2020 in two independent centers (center A and B) were finally analyzed. Center A was used for training set, while center B was used for test set. The lesions were manually drawn on the largest slice based on the lesion area by two radiologists. After feature extraction and feature selection, the possible associations between radiomics features and clinical parameters were assessed by Uni- and multi- variable logistic regression. The receiver operator characteristic (ROC) curve and DeLong validation were employed to evaluate the possible predictive performance of the models. Decision curve analysis (DCA) was used to evaluate the net benefit of the radiomics nomogram. A radiomics prediction model was established from the 15 selected features, and were found to be relatively high discriminative on the basis of the area under the ROC curve (AUC) for both the training and the test cohorts (AUC = 0.90 and 0.85, respectively). The radiomics nomogram also showed good performance of discrimination for both the training and test cohorts (AUC = 0.91 and 0.86, respectively), and the DeLong test shows that AUCs were significantly different between clinical parameters and nomogram. The result of DCA demonstrated the clinical usefulness of this novel nomogram method. The predictive model constructed based on MRI radiomics and clinical parameters indicated a highly diagnostic efficiency, thereby implying its potential clinical usefulness for the precise identification and prediction of endometrial lesions.

Abstract Background Z-DNA binding protein 1 (ZBP1) is a vital innate immune sensor that regulates inflammation during pathogen invasion. ZBP1 may contribute to pyroptosis, apoptosis and necroptosis in infectious diseases. In this study, Fusobacterium nucleatum (F. nucleatum) infection caused periapical inflammation through proinflammatory cell death and ZBP1 was involved in regulating the inflammatory activities caused by F. nucleatum infection in apical periodontitis (AP). Methods Human periapical tissues were tested by fluorescent in situ hybridization, immunohistochemical staining, immunofluorescence staining, quantitative real-time PCR (qRT‒PCR) and western blotting. F. nucleatum-infected and F. nucleatum extracellular vesicles (F. nucleatum-EVs)-treated RAW264.7 cells were used to detect the expression of inflammatory cytokines and different cell death mechanisms by qRT‒PCR and western blotting. ZBP1 expression in F. nucleatum-infected tissues and RAW264.7 cells was detected by qRT‒PCR, western blotting, and immunohistochemical and immunofluorescence staining. Furthermore, the expression of ZBP1 was inhibited by siRNA and different cell death pathways, including pyroptosis, apoptosis, and necroptosis, and inflammatory cytokines were measured in F. nucleatum-infected RAW264.7 cells. Results F. nucleatum was detected in AP tissues. F. nucleatum-infected RAW264.7 cells polarized to the M1 phenotype, and this was accompanied by inflammatory cytokine production. High levels of ZBP1 and GSDME (gasdermin E)-mediated pyroptosis, caspase-3-mediated apoptosis and MLKL-mediated necroptosis (PANoptosis) were identified in F. nucleatum-infected tissues and RAW264.7 cells. ZBP1 inhibition reduced inflammatory cytokine secretion and the occurrence of PANoptosis. Conclusion The present study identified a previously unknown role of ZBP1 in regulating F. nucleatum-induced proinflammatory cell death and inflammatory activation. Video abstract

Objective: The present study was designed to investigate the expression of miRNA-146a-5p in gastric cancer (GC) tissues and the paired nonmalignant counterparts, to explore the influences of miRNA-146a-5p on the cell biological behavior of MKN-28 cells (highly metastatic human gastric cancer cells), and to identify the function of abnormal expression of its target gene cell division cycle 14 homolog A (CDC14A) in GC.Methods: We detected the expression of miRNA-146a-5p in formalin-fixed and paraffin-embedded (FFPE) GC tissues through microarray and quantitative real-time polymerase chain reaction (qRT-PCR). Then, we employed cell counting kit-8 (CCK-8) assays, cell cycle assays, and apoptosis analysis to uncover the latent function of miRNA-146a-5p in MKN-28 human GC cells. We also validated the target of miRNA-146a-5p via luciferase reporter assays.Results: miRNA-146a-5p levels were examined in the majority of primary GC tissues and several GC cell lines. As a result, miRNA-146a-5p levels were significantly declined in the GC tissues and cells. In addition, miRNA-146a-5p demonstrated a straight act on its 3′-untranslated region (3′-UTR) of CDC14A mRNA, accordingly decreasing the contents of CDC14A mRNA as well as its protein expression. An inverse correlation between CDC14A and miRNA-146a-5p was observed.Conclusion: The data suggest miRNA-146a-5p may contribute to inducing cell cycle arrest as well as prompting GC cell apoptosis via directly targeting CDC14A. Therefore, miRNA-146a-5p may be a potential indicator of the occurrence and development of GC.

ObjectiveThe purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NE-CT) scanning images.Materials and methodsThe computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA).ResultsThe selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA.ConclusionMachine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively.

BackgroundThe goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer.MethodsOne hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness.ResultsMultivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78–0.93) and the testing set (AUC, 0.80; 95% CI, 0.65–0.95).ConclusionThe DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

ObjectiveThis study aims to develop and validate the performance of an unenhanced magnetic resonance imaging (MRI)-based combined radiomics nomogram for discrimination between low-grade and high-grade in chondrosarcoma.MethodsA total of 102 patients with 44 in low-grade and 58 in high-grade chondrosarcoma were enrolled and divided into training set (n=72) and validation set (n=30) with a 7:3 ratio in this retrospective study. The demographics and unenhanced MRI imaging characteristics of the patients were evaluated to develop a clinic-radiological factors model. Radiomics features were extracted from T1-weighted (T1WI) images to construct radiomics signature and calculate radiomics score (Rad-score). According to multivariate logistic regression analysis, a combined radiomics nomogram based on MRI was constructed by integrating radiomics signature and independent clinic-radiological features. The performance of the combined radiomics nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness.ResultsUsing multivariate logistic regression analysis, only one clinic-radiological feature (marrow edema OR=0.29, 95% CI=0.11-0.76, P=0.012) was found to be independent predictors of differentiation in chondrosarcoma. Combined with the above clinic-radiological predictor and the radiomics signature constructed by LASSO [least absolute shrinkage and selection operator], a combined radiomics nomogram based on MRI was constructed, and its predictive performance was better than that of clinic-radiological factors model and radiomics signature, with the AUC [area under the curve] of the training set and the validation set were 0.78 (95%CI =0.67-0.89) and 0.77 (95%CI =0.59-0.94), respectively. DCA [decision curve analysis] showed that combined radiomics nomogram has potential clinical application value.ConclusionThe MRI-based combined radiomics nomogram is a noninvasive preoperative prediction tool that combines clinic-radiological feature and radiomics signature and shows good predictive effect in distinguishing low-grade and high-grade bone chondrosarcoma, which may help clinicians to make accurate treatment plans.

Abstract Background This study aimed to evaluate the synergistic antibacterial activities of silver ions (Ag+) and metformin hydrochloride (Met) against Enterococcus faecalis (E. faecalis) under normal or high-glucose conditions. Results The minimum inhibitory concentration, minimum bactericidal concentration, growth curves, and colony-forming units were used to evaluate the antibacterial effects of Ag+ and Met on planktonic E. faecalis in Brain Heart Infusion broth with or without additional glucose. The influences of Ag+ and Met on four weeks E. faecalis biofilm on human dentin slices was also tested. Cytotoxicity was tested on MC3T3-E1 osteoblastic cells using CCK-8 assays. The results indicated that E. faecalis showed higher resistance to drug treatment under high-glucose conditions. Ag+ (40 μg/mL) plus Met (3.2% or 6.4%) showed enhanced antibacterial activities against both planktonic E. faecalis and biofilm on dentin slices, with low cytotoxicity. Conclusions Met enhanced the bactericidal effects of Ag+ against both planktonic and biofilm E. faecalis under normal or high-glucose conditions with low cytotoxicity. Further molecular studies are needed to be conducted to understand the mechanisms underlying the synergistic activity between Met and Ag+.

Abstract Aim This study has conducted a comparative analysis of common carbapenemases harboring, the expression of resistance-nodulation-cell division (RND) family efflux pumps, and biofilm formation potential associated with carbapenem resistance among Acinetobacter baumannii (A. baumannii) strains with different carbapenem susceptibility. Methods: A total of 90 isolates of A. baumannii from two tertiary hospitals of China were identified and grouped as carbapenem susceptible A. baumannii (CSAB) strains and carbapenem non-susceptible A. baumannii (CnSAB) strains based on the susceptibility to imipenem. Harboring of carbapenemase genes, relative expression of RND family efflux pumps and biofilm formation potential were compared between the two groups. Result: Among these strains, 12 (13.3 %) strains were divided into the CSAB group, and 78 (86.7 %) strains into the CnSAB group. Compared with CSAB strains, CnSAB strains increased distribution of bla OXA−23 (p

ObjectiveThis study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images.Materials and MethodsThe clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis.ResultsMaximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram.ConclusionWe demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Diabetic foot infection (DFI) is a common complication in diabetes patients, with foot infections being the leading cause of amputations. Staphylococcus aureus is frequently found in diabetic foot infections, of which methicillin-resistant Staphylococcus aureus (MRSA) has become a major clinical and epidemiological challenge. Since MRSA strains are resistant to most β-lactam antibiotics, and also partially resistant to other antibiotics, treatment is difficult and costly. The emergence of drug-resistant bacteria often arises from overuse or misuse of antibiotics. Clinically, canagliflozin is commonly used for the treatment of type 2 diabetes. On this basis, we investigated the antibacterial activity and mechanism of canagliflozin against MRSA, with the aim to discover novel functions of canagliflozin and provide new insights for the treatment of MRSA. Using the microbroth dilution method to determine the half maximal inhibitory concentration of drugs, we found that canagliflozin not only can inhibit the growth of methicillin-sensitive Staphylococcus aureus (MSSA) but also exhibits antibacterial activity against MRSA. The IC50 values, at approximately 56.01 μM and 57.60 μM, were almost the same. At 12 h, canagliflozin showed a significant antibacterial effect against MRSA at and above 30 μM. In addition, its combined use with penicillin achieved better antibacterial effects, which were increased by about three times. Additive antibacterial activity (FICI = 0.69) was found between penicillin and canagliflozin, which was better than that of doxycycline and canagliflozin (FICI = 0.95). Canagliflozin also affected bacterial metabolic markers, such as glucose, ATP, and lactic acid. The results of crystal violet staining indicate that canagliflozin disrupted the formation of bacterial biofilm. Our electron microscopy results showed that canagliflozin distorted the bacterial cell wall. The results of RT-PCR suggest that canagliflozin down-regulated the expressions of biofilm-related gene (clfA, cna, agrC, mgrA, hld) and methicillin-resistance gene (mecA), which was related to MRSA. Molecular docking also indicated that canagliflozin affected some interesting targets of MRSA, such as the sarA, crtM and fnbA proteins. In conclusion, canagliflozin exhibits antibacterial activity against MRSA by affecting bacterial metabolism, inhibiting its biofilm formation, distorting the bacterial cell wall, and altering the gene expression of biofilm formation and its virulence. Our study reveals the antibacterial activity of canagliflozin against MRSA, providing a new reference for treating diabetic foot infections.

Abstract To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient’s enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733–0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696–0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: − 3.133, P = 0.008), maximum tumor diameter (Z value: − 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659–0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.

Abstract Background Sphingomyelin (SM) is an essential component of biological lipid rafts, and it plays an indispensable role in maintaining plasma membrane stability and in mediating signal transduction. The ultimate biosynthesis of SM is catalyzed by two sphingomyelin synthases (SMSs) namely SMS1 and SMS2, which are selectively distributed in the trans-Golgi apparatus and the plasma membrane. It has been demonstrated that SMS2 acts as an irreplaceable molecule in the regulation of transmembrane signaling, and loss of SMS2 has been reported to worsen atherosclerosis and liver steatosis. However, the function of SMS2 in platelet activation and its association with the pathological process of thrombosis in acute coronary syndrome (ACS) and portal hypertension (PH) remain unclear. Methods In this study, we tested the role of SMS2 in platelet activation and thrombosis using SMS2 knockout (SMS2 –/–) mice and SMS2-specific inhibitor, D609. Furthermore, we detected SMS2 expression in patients with ACS and PH. Results SMS2 –/– platelets showed significant reduction in platelet aggregation, spreading, clot retraction and in vivo thrombosis. Similar inhibitory effects on platelet activation were detected in D609-treated wild-type platelets. PLCγ/PI3K/Akt signaling pathway was inhibited in SMS2 –/– platelets and D609-treated wild-type platelets. In addition, we discovered that platelet SMS2 expression was remarkably increased in patients with ACS and PH, compared with healthy subjects. Conclusions Our study indicates that SMS2 acts as a positive regulator of platelet activation and thrombosis, and provides a theoretical basis for the potential use of D609 in anti-thrombosis treatment.

Abstract This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.

Selection of a suitable additive manufacturing (AM) machine to manufacture a specific product is one of the important tasks in design for AM. So far, many selection approaches based on multi-attribute decision making have been proposed within academia. Each of these approaches works well in its specific context. However, the approaches are not flexible enough and could produce undesirable results as they are all based on multi-attribute two-way decisions. In this paper, a selection approach based on ontology-supported multi-attribute three-way decisions is presented. Firstly, an ontology for AM machine selection is constructed according to vendor documents, benchmark data, expert experience, and the Senvol database. Supported by this ontology, a selection approach based on multi-attribute three-way decisions is then developed. After that, four AM machine selection examples are introduced to illustrate the application of the developed approach. Finally, the effectiveness and advantages of the approach are demonstrated via a set of comparison experiments. The demonstration results suggest that the presented approach is as effective as the existing approaches and more flexible than them when the information for decision making is insufficient or the cost for undesirable decision results is high.

ObjectiveProstate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI)-derived radiomic nomogram based on T2-weighted images (T2WI) in differentiating prostate cancer and hyperplasia.Materials and MethodsOne hundred forty-six patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected. We manually delineated T2WI of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected using the LASSO algorithm, and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish a radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram. Finally, the clinical application value of the nomogram was evaluated by decision curve analysis.ResultsThe PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC = 0.85; 95% CI: 0.77–0.92) and testing set (AUC = 0.84; 95% CI: 0.72–0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC = 0.91; 95% CI: 0.85–0.97) and testing set (AUC = 0.90; 95% CI: 0.81–0.99). The decision curve showed the clinical application value of the radiomic nomogram.ConclusionThe radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia. It also provides suggestions for further clinical events.

The mechanical strength of the stalk affects the lodging resistance and digestibility of the stalk in maize. The molecular mechanisms regulating the brittleness of stalks in maize remain undefined. In this study, we constructed the maize brittle stalk mutant (bk5) by crossing the W22:Mu line with the Zheng 58 line. The brittle phenotype of the mutant bk5 existed in all of the plant organs after the five-leaf stage. Compared to wild-type (WT) plants, the sclerenchyma cells of bk5 stalks had a looser cell arrangement and thinner cell wall. Determination of cell wall composition showed that obvious differences in cellulose content, lignin content, starch content, and total soluble sugar were found between bk5 and WT stalks. Furthermore, we identified 226 differentially expressed genes (DEGs), with 164 genes significantly upregulated and 62 genes significantly downregulated in RNA-seq analysis. Some pathways related to cellulose and lignin synthesis, such as endocytosis and glycosylphosphatidylinositol (GPI)-anchored biosynthesis, were identified by the Kyoto Encyclopedia of Gene and Genomes (KEGG) and gene ontology (GO) analysis. In bulked-segregant sequence analysis (BSA-seq), we detected 2,931,692 high-quality Single Nucleotide Polymorphisms (SNPs) and identified five overlapped regions (11.2 Mb) containing 17 candidate genes with missense mutations or premature termination codons using the SNP-index methods. Some genes were involved in the cellulose synthesis-related genes such as ENTH/ANTH/VHS superfamily protein gene (endocytosis-related gene) and the lignin synthesis-related genes such as the cytochrome p450 gene. Some of these candidate genes identified from BSA-seq also existed with differential expression in RNA-seq analysis. These findings increase our understanding of the molecular mechanisms regulating the brittle stalk phenotype in maize.

Objective. To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. Method. Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. Results. The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96–1.00) and testing set (AUC = 0.98; 95%CI: 0.95–1.00). The decision curve thus demonstrated the clinical application of the radiomics model. Conclusions. The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.

Poland syndrome is a rare congenital developmental deformity characterized by unilateral agenesis or hypoplasia of thoracic wall soft tissue. We report two adult cases of Poland syndrome detected by computed tomography (CT) images. CT images of the two cases depicted an asymmetric chest wall with the absence of a breast and agenesis of the pectoralis muscles. A physical examination of case 1 showed a thin right chest wall with depression of the right nipple region. Hand deformities were also observed, including brachydactyly and syndactyly. In case 2, hand deformities were not found in a physical examination. Using multi-planar reconstruction, the size, position, origin, and termination of bilateral pectoral muscles could be compared symmetrically. For patients with Poland syndrome, a timely diagnosis and treatment are important. The use of chest CT in clinical practice could play an important role in the early diagnosis and treatment of Poland syndrome.

Zenker’s diverticulum (ZD) is a bag-like pharyngeal pouch that protrudes to the outside of the pharynx. It is thought to be an acquired disease that occurs following the dysfunction of laryngopharynx muscle, and certain body shapes may be predisposed to this condition. We report a 56-year-old female of slim build with ZD. Computed tomography scanning revealed a hypodense lesion on the left posterior side of her upper esophagus that was filled with air and had no obvious wall. To verify this finding, a barium esophagogram was carried out which showed a round pouch at the level of the 6th cervical vertebral body that communicated with the esophagus through a narrow neck. ZD was subsequently confirmed by endoscopy. These findings provide further evidence in support of a body shape predisposition for ZD.

With the exploitation of oil and gas resources, the water environment of high-Ca2+ and high-Cl− stratum puts forward high safety requirements for tubular columns. This paper simulates the underground environment by using high-temperature and high-pressure autoclaves, combines electrochemical research results, and analyzes the effect of flow rate on the corrosion behavior of P110 steel in a water environment of 7.5 g/L Ca2+ + 128 g/L Cl− simulated stratum. The research results show that the presence of Ca2+ promotes the acidification of the solution and accelerates the dissolution of P110 steel. With the increased flow rate of the fluid, the corrosion rate of P110 steel increases, but the increasing trend slows down gradually. At the same time, the flow rate decreases the probability of corrosive pitting on P110 steel. The decreasing is closely related the peeling of earlier CaCO3 precipitation by the fluid.

The prevention and treatment of oral diseases is more difficult in diabetic patients with poorly controlled blood glucose levels. This study aims to explore an effective, low-cytotoxicity medication for root canal treatment in diabetic patients. The antibacterial effect of the combination of Triton X-100 (TX-100) and metformin (Met) on Enterococcus faecalis (E. faecalis) was evaluated by determining the minimum inhibitory concentration (MIC), minimum bactericidal concentration required to kill 99% bacteria (MBC99) and by conducting dynamic time-killing assays. While the antibiofilm activity was measured by crystal violet (CV) assay, field emission scanning electron microscope (FE-SEM), confocal laser scanning microscope (CLSM) and colony-forming unit (CFU) counting assays. The expression of relative genes was evaluated by real-time quantitative polymerase chain reaction (RT-qPCR), and the cytotoxicity of the new combination on MC3T3-E1 cell was also tested. Results showed that the antibacterial and antibiofilm activities of Met could be significantly enhanced by very low concentrations of TX-100 in both normal and high-glucose conditions, with a much lower cytotoxicity than 2% chlorhexidine (CHX). Thus, the TX-100 + Met combination may be developed as a promising and effective root canal disinfectant for patients with diabetes.

Abstract Background Ag+ and Zn2+ have already been used in combinations to obtain both enhanced antibacterial effect and low cytotoxicity. Despite this, it is still unclear how the Zn2+ co-works with Ag+ in the synergistic antibacterial activity. The main purposes of this study were to investigate the co-work pattern and optimum ratio between Ag+ and Zn2+ in their synergistic antibacterial activity against E. faecalis, the possible mechanisms behind this synergy and the primary application of optimum Ag+–Zn2+ co-work pattern against the E. faecalis biofilm on dentin. A serial of Ag+–Zn2+ atomic combination ratios were tested on both planktonic and biofilm-resident E. faecalis on dentin, their antibacterial efficiency was calculated and optimum ratio determined. And the cytotoxicity of various Ag+–Zn2+ atomic ratios was tested on MC3T3-E1 Cells. The role of Zn2+ in Ag+–Zn2+co-work was evaluated using a Zn2+ pretreatment study and membrane potential—permeability measurement. Results The results showed that the synergistically promoted antibacterial effect of Ag+–Zn2+ combinations was Zn2+ amount-dependent with the 1:9 and 1:12 Ag+–Zn2+ atomic ratios showing the most powerful ability against both planktonic and biofilm-resident E. faecalis. This co-work could likely be attributed to the depolarization of E. faecalis cell membrane by the addition of Zn2+. The cytotoxicity of the Ag+–Zn2+ atomic ratios of 1:9 and 1:12 was much lower than 2% chlorhexidine. Conclusions The Ag+–Zn2+ atomic ratios of 1:9 and 1:12 demonstrated similar strong ability against E. faecalis biofilm on dentin but much lower cytotoxicity than 2% chlorhexidine. New medications containing optimum Ag+–Zn2+ atomic ratios higher than 1:6, such as 1:9 or 1:12, could be developed against E. faecalis infection in root canals of teeth or any other parts of human body.

In the traditional investigations on the drag and heat reduction of hypersonic spiked models, only the aerodynamic calculation is performed, and the structural temperature cannot be obtained. This paper adopted the loosely coupled method to study its efficiency of drag and heat reduction, in which the feedback effect of wall temperature rise on aeroheating is considered. The aeroheating and structural temperature were obtained by the CFD and ABAQUS software respectively. The coupling analysis of the hypersonic circular tube was carried out to verify the accuracy of the fluid field, the structural temperature, and the coupled method. Compared with experimental results, the calculated results showed that the relative errors of stagnation heat flux and stagnation temperature were 1.34% and 4.95% respectively, and thus the effectiveness of the coupled method was verified. Installing a spike reduced the total drag of the forebody. The spiked model with an aerodisk reduced the aeroheating of the forebody, while the model without an aerodisk intensified the aeroheating. The spiked model with a planar aerodisk had the best performance on drag and heat reduction among all the models. In addition, increasing the length of the spike reduced the drag and temperature of the forebody. With the increase of the length, the change rates of drag, pressure, heat flux, and temperature decreased gradually. Increasing the diameter of the aerodisk also reduced the temperature of the forebody, while the efficiency of forebody drag reduction first increased and then decreased. In conclusion, the heat and drag reduction must be considered comprehensively for the optimal design of the spike.

Iopromide (IOP), an iodinated X-ray contrast medium (ICM), is identified as a precursor to iodide disinfection byproducts that have high genotoxicity and cytotoxicity to mammals. ICM remains persistent through typical wastewater treatment processes and even through some hydroxyl radical-based advanced oxidation processes. The development of new technologies to remove ICMs is needed. In this work, mackinawite (FeS)-activated sulfite autoxidation was employed for the degradation of IOP-containing water. The experiment was performed in a 500 mL self-made temperature-controlled reactor with online monitoring pH and dissolved oxygen in the laboratory. The effects of various parameters, such as initial pH values, sulfite dosages, FeS dosages, dissolved oxygen, and inorganic anions on the performance of the treatment process have been investigated. Eighty percent of IOP could be degraded in 15 min with 1 g L−1 FeS, 400 μmol L−1 sulfite at pH 8, and high efficiency on the removal of total organic carbon (TOC) was achieved, which is 71.8% via a reaction for 1 h. The generated hydroxyl and oxysulfur radicals, which contributed to the oxidation process, were identified through radical quenching experiments. The dissolved oxygen was essential for the degradation of IOP. The presence of Cl− could facilitate IOP degradation, while NO3− and CO32− could inhibit the degradation process. The reaction pathway involving H-abstraction and oxidative decarboxylation was proposed, based on product identification. The current system shows good applicability for the degradation of IOP and may help in developing a new approach for the treatment of ICM-containing water.

Mesoporous calcium-silicate nanoparticles (MCSNs) are excellent biomaterials for controlled drug delivery and mineralization induction. In this study, MCSNs were loaded with low-dose silver ion (Ag+) and Triton X-100 (TX-100) as the M-AgTX to achieve both enhanced antibacterial properties and low cytotoxicity for dentin disinfection. The physicochemical property, biocompatibility, infiltration ability into dentinal tubules, anti-bacterial ability against both planktonic Enterococcusfaecalis (E. faecalis) and its biofilm on dentin, effects on dentin microhardness and in vitro mineralization property were systematically investigated. Results confirmed that the MCSNs and M-AgTX nanoparticles showed typical morphology of mesoporous materials and exhibited sustained release of chemicals with an alkaline pH value over time. M-AgTX also exhibited excellent biocompatibility on MC3T3-E1 cells and could eliminate 100% planktonic E. faecalis after 48-h treatment. On dentin slices, it could enter dentinal tubules by ultrasonic activation and inhibit the growth of E. faecalis on dentin. M-AgTX could completely inactive 28-day E. faecalis biofilm. TEM confirmed the destruction of cell membrane integrity and Ag+ infiltration into bacteria by M-AgTX. Besides, dentin slices medicated with M-AgTX nanoparticles displayed an increased microhardness. After being immersed in SBF for 7 days, apatite crystals could be observed on the surface of the material tablets. M-AgTX could be developed into a new multifunctional intra-canal medication or bone defect filling material for infected bone defects due to its sustained release profile, low cytotoxicity, infiltration ability, enhanced anti-bacterial and mineralization features.

Abstract Omnidirectional stereo vision systems have been widely used as primary vision sensors in intelligent robot 3D measurement tasks, which require stereo calibration and rectification. Current stereo calibration and rectification methods suffer from complex calculations or a lack of accuracy. This paper establishes a simple and effective equivalency between an omnidirectional stereo vision system and a perspective vision system by studying stereo calibration and rectification methods. First, we improved the stereo calibration method. By applying the essential matrix, the complicated calibration process of the original method is simplified. By using a manual extraction method to extract corner points, noise error is eliminated and high precision is ensured. Second, we propose a new rectification method. By using the proposed simple rectification model and calibration data, the baseline length and an accurate column-aligned image pair are easily obtained, which reduces the computation time. The proposed stereo calibration and rectification method can simply and effectively obtain two key parameters of the triangulation formula for 3D measurement tasks: baseline length and parallax. Using real data captured by equipment, we performed experiments covering all the necessary stages to obtain a high-performance omnidirectional stereo vision system. Statistical analyses of the experimental results demonstrate the effectiveness of the proposed method.

Abstract Sodium hypochlorite is an effective irrigant for chemical debridement of root canals. However, increasing the intracanal pressure during irrigant delivery may result in irrigant extrusion into the bone and soft tissues surrounding the tooth. Because clinicians often encounter teeth with intracanal communications, the objective of the present study was to examine the effects of canal anastomosis on the generation of periapical fluid pressure at different fluid flow rates and insertion depths. Two similar polycarbonate models were used to simulate a single root with double canals, one containing, and the other without communicating channels between the canals. For both models, periapical pressure increased with increasing irrigant flow rates and insertion depths of a 30-gauge side-venting needle. In the presence of communicating channels, the magnitude of pressure build-up decreased by almost 90% irrespective of the fluid flow rate or needle insertion depth. Pressure reduction in anastomoses-containing roots provides an explanation why pressure generation in single roots is considerably higher. Nevertheless, it is still possible in teeth with canal anastomoses for pressure exceeding the intraosseous pressure to be generated when the fluid flow rate is sufficiently high and when the needle tip is close to the apical terminus.

As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method.

AB yerleşik göçmen sayısının en fazla olduğu yerdir; Avrupa dışında doğan insanların nüfuzu 56 milyonu bulmaktadır. Orta Doğu’daki bitmek bilmez savaşlar ve kargaşa yüzünden, bölgeye yerleşen insanların, özellikle de sığınmacıların sayısı AB’yi bu açıdan dünyanın bir numaralı göçmen bölgesine dönüştürmüştür. Dolayısıyla AB ve Orta Doğu’daki nüfuz hareketlerine dair temel hatları açığa çıkarmak, uluslararası göç meseleleriyle ilgili araştırmalar bakımından büyük önem taşımaktadır. AB ülkelerine giden çok sayıda sığınmacı ve mülteci vardır. Bu süreç dört aşamaya ayrılabilir. Arap Baharı’ndan bu yana, özellikle de 2011’de Suriye’de patlak veren iç savaş ve 2013 yılında “İslami Devletin” yükselişiyle birlikte, tüm AB bölgesi II. Dünya Savaşı’ndan bu yana gerçekleşmiş en büyük göç dalgasıyla karşılaşmıştır. Anahtar Kelimeler: AB, Orta Doğu, Göçmen, Sığınmacı, Suriye

Abstract Background In infected periapical tissues, Enterococcus faecalis is one of the most common dominant bacteria. Chlorhexidine has been proved to show strong antibacterial ability against E. faecalis but is ineffective in promoting mineralization for tissues around root apex. Mesoporous calcium-silicate nanoparticles are newly synthesized biomaterials with excellent ability to promote mineralization and carry-release bioactive molecules in a controlled manner. In this study, mesoporous calcium-silicate nanoparticles were functionalized with chlorhexidine and their releasing profile, antibacterial ability, effect on cell proliferation and in vitro mineralization property were evaluated. Results The chlorhexidine was successfully incorporated into mesoporous calcium-silicate nanoparticles by a mixing-coupling method. The new material could release chlorhexidine as well as Ca2+ and SiO3 2− in a sustained manner with an alkaline pH value under different conditions. The antimicrobial ability against planktonic E. faecalis was dramatically improved after chlorhexidine incorporation. The nanoparticles with chlorhexidine showed no negative effect on cell proliferation with low concentrations. On dentin slices, the new synthesized material demonstrated a similar inhibitory effect on E. faecalis as the chlorhexidine. After being immersed in SBF for 9 days, numerous apatite crystals could be observed on surfaces of the material tablets. Conclusions Mesoporous calcium-silicate nanoparticles loaded with chlorhexidine exhibited release of ions and chlorhexidine, low cytotoxicity, excellent antibacterial ability and in vitro mineralization. This material could be developed into a new effective intra-canal medication in dentistry or a new bone defect filling material for infected bone defects.

In energy-constrained wireless sensor networks, low energy utilization and unbalanced energy distribution are seriously affecting the operation of the network. Therefore, efficient and reasonable routing algorithms are needed to achieve higher Quality of Service (QoS). For the Dempster−Shafer (DS) evidence theory, it can fuse multiple attributes of sensor nodes with reasonable theoretical deduction and has low demand for prior knowledge. Based on the above, we propose an energy efficient and reliable routing algorithm based on DS evidence theory (DS-EERA). First, DS-EERA establishes three attribute indexes as the evidence under considering the neighboring nodes’ residual energy, traffic, the closeness of its path to the shortest path, etc. Then we adopt the entropy weight method to objectively determine the weight of three indexes. After establishing the basic probability assignment (BPA) function, the fusion rule of DS evidence theory is applied to fuse the BPA function of each index value to select the next hop. Finally, each node in the network transmits data through this routing strategy. Theoretical analysis and simulation results show that DS-EERA is promising, which can effectively prolong the network lifetime. Meanwhile, it can also reach a lower packet loss rate and improve the reliability of data transmission.

Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RWSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.