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Purpose We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. Methods A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. Results In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P  < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). Conclusion The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy. Key messages What is already known on this topic   The application of artificial intelligence in transrectal ultrasound can assist in the diagnosis of prostate cancer (PCa). What this study adds   Differing from previous studies, our study combined magnetic resonance imaging fusion targeted biopsy and systematic biopsy, in analyzing segmented needle tract ultrasound images of prostate biopsies. Excluding the interference of inaccurate region of interest outlining, a deep learning PCa diagnostic model with better diagnostic efficacy was constructed. How this study might affect research, practice, or policy   Through our model screening, a greater number of suspected PCa patients who need further biopsy can receive the proper diagnosis and treatment, avoiding unnecessary biopsy in patients without PCa. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large power fluctuations and poor stability in photovoltaic and other new energy power generation systems, a step-up multiport converter (MPC) that can simultaneously connect low-voltage photovoltaic cells, batteries, and loads (independent loads or power grids) is proposed in this manuscript. According to the possible operating conditions of the system, the working principles are described in detail. Theoretical analysis based on different working modes is presented and a hybrid modulation control method including pulse width modulation (PWM) and phase shift modulation (PSM) are applied to realize energy transmission between photovoltaics, batteries, and power grids. A simulation model is built in the PSIM environment to validate each working state of the system and mode switching function. Experiments are carried out on an experimental platform using the dsPIC33FJ64GS606 digital microcontroller as the control center, and the experimental results successfully verify the system function and PWM + PSM control efficiency. [ABSTRACT FROM AUTHOR]

This paper analyzed the materials, structure, and functionality of existing biodegradable seedling containers from the viewpoint of the necessity of container seedling cultivation, providing a reference for relevant researches. Finally, the current problems in environmentally friendly seedling containers were summarized and their future development trends were overviewed. [ABSTRACT FROM AUTHOR]

Listeriosis is highly prevalent in the animal farming industry, with Listeria monocytogenes as the causative pathogen. To identify potential therapeutic targets for LM infection, we investigated the mechanisms of LM infection in goat uteri. We inoculated a group of goats with LM via jugular vein injection, isolated and raised them, and subsequently collected sterile samples of their uterine tissue after they exhibited clinical symptoms of LM infection. We used Giemsa staining, immunohistochemical staining, real-time qPCR, and Western blotting as experimental methods.First, we investigated the mechanism of Listeria monocytogenes (LM) infection in the goat uterus by examining the expression levels of listeriolysin O, E-cadherin, and tyrosine kinase c-Met in the uterus.Furthermore, we investigated the impact of LM infection on uterine autophagy and cell apoptosis. The results indicate that the injection of LM into the goats' jugular veins leads to LM infection in the goats' uteri. During LM survival inside the goat uterine cells, there is a significant increase in the expression levels of LLO, E-cadherin, and c-Met in the host uterine tissue. This suggests that LM may potentially infect goat uteri through the InlA/E-cadherin and InlB/c-Met pathways. Furthermore, LM infection increases the levels of apoptosis and autophagy in goat uteri. Apoptosis genes Bcl-2 and Bax, as well as autophagy-related genes LC3B, PINK1, and Parkin, exhibit varying degrees of changes in localization and expression in goat uteri, mediating the occurrence of apoptotic and autophagic responses., Competing Interests: BL was employed by Jiangxi Lvke Agriculture and Animal Husbandry Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hong, Hu, Shi, Liu, Zheng, Bo, Xu, Wu and Cao.)

Background and Objective: The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability around the globe. The goal of this study was to rapidly and accurately identify carotid plaques and automatically quantify plaque burden using our automated tracking and segmentation US-video system., Methods: We collected 88 common carotid artery transection videos (11048 frames) with a history of atherosclerosis or risk factors for atherosclerosis, which were randomly divided into training, test, and validation sets using a 6:3:1 ratio. We first trained different segmentation models to segment the carotid intima and adventitia, and calculate the maximum plaque burden automatically. Finally, we statistically analyzed the plaque burden calculated automatically by the best model and the results of manual labeling by senior sonographers., Results: Of the three Artificial Intelligence (AI) models, the Robust Video Matting (RVM) segmentation model's carotid intima and adventitia Dice Coefficients (DC) were the highest, reaching 0.93 and 0.95, respectively. Moreover, the RVM model has shown the strongest correlation coefficient (0.61±0.28) with senior sonographers, and the diagnostic effectiveness between the RVM model and experts was comparable with paired-t test and Bland-Altman analysis [P= 0.632 and ICC 0.01 (95% CI: -0.24~0.27), respectively]., Conclusion: Our findings have indicated that the RVM model can be used in ultrasound carotid video. The RVM model can automatically segment and quantify atherosclerotic plaque burden at the same diagnostic level as senior sonographers. The application of AI to carotid videos offers more precise and effective methods to evaluate carotid atherosclerosis in clinical practice.

The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames., Competing Interests: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (© 2022 The Author(s).)