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Acoustic radiation force impulse (ARFI) imaging is an ultrasound-based elastography method that has been studied in the staging of hepatic fibrosis, especially in chronic hepatitis. However, the diagnostic accuracy of ARFI in non-viral hepatopathies, such as autoimmune hepatitis and non-alcoholic fatty liver disease, has not been systematically determined. To systematically assess the diagnostic accuracy of ARFI in non-viral hepatopathies. The databases of PubMed, Embase, Cochrane Library and clinicaltrials.gov were systematically searched for candidate studies reporting the diagnostic accuracy of ARFI for hepatic fibrosis. The pooled estimates of the sensitivity, specificity, diagnostic odds ratio, and positive and negative likelihood ratios were calculated with the summary receiver operating curve (sROC) performed using STATA software. In detail, a total of 29 diagnostic studies were included for further analysis. The quality of the included studies was relatively high using QUADAS method. The pooled sensitivity and specificity were 0.79 (0.73, 0.83) and 0.81 (0.75, 0.86), with AUROC 0.87 (0.83, 0.89) for the staging of significant fibrosis (F≥2). Meanwhile, for the staging of severe fibrosis (F≥3), the pooled sensitivity and specificity were 0.92 (0.87, 0.95) and 0.85 (0.80, 0.89), with AUROC 0.94 (0.92, 0.96). Furthermore, the pooled sensitivity and specificity were 0.89 (0.79, 0.95) and 0.89 (0.85, 0.92), with AUROC 0.94 (0.92, 0.96) for ARFI in staging cirrhosis (F = 4), which were similar to the data for severe fibrosis. No significant publication bias was present in this study. This meta-analysis demonstrated that ARFI exerted satisfactory diagnostic performance in staging non-viral hepatic fibrosis, especially severe fibrosis (F≥3) and cirrhosis (F = 4).

Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines.

Background Research studies suggest that the host immune system is modulated by gut microbiota (GM), which is crucial for the occurrence and progression of malignancies. Here, immune cell traits (ICTs) as mediating factors in the causative association between basal cell cancer (BCC) and gut microbiome were investigated. Methods A two-sample Mendelian randomization (MR) analysis was undertaken to assess the influence of 412 GMs on BCC using data from a genome-wide association study (GWAS) of individuals with European ancestry. Afterward, the ICTs’ mediation effect on the relationships between BCC and GM was examined using MR. Results Inverse variance weighted method identified 9 genera and 47 ICTs that were genetically associated with BCC. Moreover, the MR demonstrated that CD33-HLA DR-AC (− 0.015, 95% CI  − 0.144, 0.115) mediated a portion of the causative function of p_Firmicutes.c_Clostridia on BCC, it represented − 8.92% of the total effect. Conclusion The current research elucidated the causal association between BCC risk, GM, and certain ICTs. This also explained the mechanism and offered potential BCC therapeutic options and preventive targets against the disease.

The goaf treatment of underground metal mines is an important link in mining, and it is particularly important to master the laws of overlying rock strata and surface movement of goaf. In this paper, Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology is used to monitor the surface subsidence of the Taibao lead-zinc mine, and the surface subsidence laws of goaf-closure, partial-filling, and full-filling treatments are analyzed by the time-series method. The findings indicate that the surface subsidence of the closed goaf is solely governed by the pillars, with the quality of these pillars playing a pivotal role in controlling such subsidence. Factors like stope span also influence the surface subsidence of partially filled goaf. Prior to compaction, it is primarily the pillars that control surface subsidence; however, after compaction, filling and pillars jointly regulate this phenomenon. Notably, in filled goaf, the quality of both roof and pillars significantly impacts surface subsidence. Before compaction occurs, control over surface subsidence is not evident, yet post-compaction, the filling is effective and tends to stabilize this process. The research findings are significant in enhancing goaf’s treatment efficacy, mitigating surface damage and minimizing ecological environmental impact.

In ancient china, according to the local natural environment, a variety of drainage facilities were built in order to excrete rainwater, domestic sewage, production wastewater and so on. These drainage facilities were mainly made of pottery, bricks, wood, stone, etc. For example, ceramic water pipelines, buried in the ground, connect together one by one, and there was a slight drop from one end to the other in favor of drainage. These measures can also be used for reference in today's urban drainage and flood control.

In this study, delta-12 desaturase was overexpressed in Yarrowia lipolytica using the single-copy integrative vector pINA1312 and multicopy integrative vector pINA1292, resulting in the engineered yeast strains 1312-12 and 1292-12, respectively. The content of intracellular linoleic acid (LA) in the 1292-12 strain was much higher than in the 1312-12 strain and the control group. One interesting finding was that the 1292-12 strain showed obvious changes in surface morphology. The 1292-12 colonies were much smaller and smoother, whereas their single cells became much larger compared to the control strain. In addition, the dry cell weight (DCW) of the 1292-12 strain was obviously increased from 8.5 to 12.7 g/L, but the viable cell number sharply decreased from 107 to 105/mL. These results indicated that increased LA content in Yarrowia lipolytica could induce morphological changes or even oxidative stress-dependent cell death. The reactive oxygen species (ROS) and malondialdehyde (MDA) were accumulated in the 1292-12 strain, while the antioxidant activities of intracellular catalase (CAT) and superoxide dismutase (SOD) were significantly decreased by 27.6 and 32.0%, respectively. Furthermore, it was also revealed that these issues could be ameliorated by the exogenous supplementation of vitamin C, fish and colza oil.

Ethical and legal considerations have led to increased use of non-animal methods to evaluate the safety of chemicals for human use. We describe the development and qualification of a physiologically-based kinetics (PBK) model for the cosmetic UV filter ingredient, homosalate, to support its safety without the need of generating further animal data. The intravenous (IV) rat PBK model, using PK-Sim ® , was developed and validated using legacy in vivo data generated prior to the 2013 EU animal-testing ban. Input data included literature or predicted physicochemical and pharmacokinetic properties. The refined IV rat PBK model was subject to sensitivity analysis to identify homosalate-specific sensitive parameters impacting the prediction of C max (more sensitive than AUC (0-∞) ). These were then considered, together with population modeling, to calculate the confidence interval (CI) 95% C max and AUC (0-∞) . Final model parameters were established by visual inspection of the simulations and biological plausibility. The IV rat model was extrapolated to oral administration, and used to estimate internal exposures to doses tested in an oral repeated dose toxicity study. Next, a human PBK dermal model was developed using measured human in vitro ADME data and a module to represent the dermal route. Model performance was confirmed by comparing predicted and measured values from a US-FDA clinical trial (Identifier: NCT03582215, https://clinicaltrials.gov/ ). Final exposure estimations were obtained in a virtual population and considering the in vitro and input parameter uncertainty. This model was then used to estimate the C max and AUC (0–24 h) of homosalate according to consumer use in a sunscreen. The developed rat and human PBK models had a good biological basis and reproduced in vivo legacy rat and human clinical kinetics data. They also complied with the most recent WHO and OECD recommendations for assessing the confidence level. In conclusion, we have developed a PBK model which predicted reasonably well the internal exposure of homosalate according to different exposure scenarios with a medium to high level of confidence. In the absence of in vivo data, such human PBK models will be the heart of future completely non-animal risk assessments; therefore, valid approaches will be key in gaining their regulatory acceptance. Clinical Trial Registration: https://clinicaltrials.gov/ , identifier, NCT03582215

To investigate the role of transrectal ultrasound guidance in interstitial brachytherapy for cervical cancer. Forty-eight patients who underwent interstitial brachytherapy treatment for cervical cancer between January 2017 and January 2018 were enrolled in the study. The distances between each inserted needle and the lesion were measured at seven sites by ultrasound (D1-D7) and compared to the corresponding distances (M1-M7) when visualised with nuclear magnetic resonance imaging (MRI). Measurements were paired on the basis of the observation sites, e.g. D1 and M1, D2 and M2. The statistical differences, intraclass correlation coefficients (ICCs), and linear relationships for the paired measurements were calculated. No significant differences were found between the paired M and D measurements, with all ICCs showing high levels of concordance (0.81-0.93). Transrectal ultrasound showed strong agreement with MRI results in determining the position of the inserted needles. Transrectal ultrasound is a useful tool for guided interstitial brachytherapy and is appropriate for widespread use in the treatment of locally advanced cervical cancer.

Renewable energy resources and electric vehicles give pressure on the smart grid. Once a large number of electric vehicles do batch charging at peak time, the dynamic resource allocation ability of the smart grid appears to be very important. This article proposes one composite game model to describe the interaction process during the charging. The smart grid area covers numbers of charging stations which satisfy spatial non-equilibrium distribution. Each vehicle has enough remaining energy to reach each charging station in the considering field. This paper considers preferential policies to the cost-sensitive drivers. From the view of energy management, preferential policies from charging stations will fluctuate the energy demand. To do dynamic dispatching strategy in power supply, the model describe two type of interactions, one is between stations and vehicles, the other is between vehicles. The model help strategy managers understand more about energy demand which hopefully can assist them do reasonable dynamic dispatching strategy.

In modern clinical medicine, the important information of red blood cells, such as shape and number, is applied to detect blood diseases. However, the automatic recognition problem of single cells and adherent cells always exists in a densely distributed medical scene, which is difficult to solve for both the traditional detection algorithms with lower recognition rates and the conventional networks with weaker feature extraction capabilities. In this paper, an automatic recognition method of adherent blood cells with dense distribution is proposed. Based on the Faster R-CNN, the balanced feature pyramid structure, deformable convolution network, and efficient pyramid split attention mechanism are adopted to automatically recognize the blood cells under the conditions of dense distribution, extrusion deformation, adhesion and overlap. In addition, the Align algorithm for region of interest also contributes to improving the accuracy of recognition results. The experimental results show that the mean average precision of cell detection is 0.895, which is 24.5% higher than that of the original network model. Compared with the one-stage mainstream networks, the presented network has a stronger feature extraction capability. The proposed method is suitable for identifying single cells and adherent cells with dense distribution in the actual medical scene.