Improved sports image classification using deep neural network and novel tuna swarm optimization.

Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (DNN) with a modified metaheuristic algorithm known as novel tuna swarm optimization (NTSO) for the purpose of sports image classification. The DNN is a potent technique capable of extracting high-level features from raw images, while the NTSO algorithm optimizes the hyperparameters of the DNN, including the number of layers, neurons, and activation functions. Through the application of NTSO to the DNN, a finely-tuned network is developed, exhibiting exceptional performance in sports image classification. Rigorous experiments have been conducted on an extensive dataset of sports images, and the obtained results have been compared against other state-of-the-art methods, including Attention-based graph convolution-guided third-order hourglass network (AGTH-Net), particle swarm optimization algorithm (PSO), YOLOv5 backbone and SPD-Conv, and Depth Learning (DL). According to a fivefold cross-validation technique, the DNN/NTSO model provided remarkable precision, recall, and F1-score results: 97.665 ± 0.352%, 95.400 ± 0.374%, and 0.8787 ± 0.0031, respectively. Detailed comparisons reveal the DNN/NTSO model's superiority toward various performance metrics, solidifying its standing as a top choice for sports image classification tasks. Based on the practical dataset, the DNN/NTSO model has been successfully evaluated in real-world scenarios, showcasing its resilience and flexibility in various sports categories. Its capacity to uphold precision in dynamic settings, where elements like lighting, backdrop, and motion blur are prominent, highlights its utility. The model's scalability and efficiency in analyzing images from live sports competitions additionally validate its suitability for integration into real-time sports analytics and media platforms. This research not only confirms the theoretical superiority of the DNN/NTSO model but also its pragmatic effectiveness in a wide array of demanding sports image classification assignments.

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ.

Background: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods: The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results: In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions: Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.

Diagnostic and prognostic significance of miR-486-5p in patients who underwent minimally invasive surgery for lumbar spinal stenosis.

BACKGROUND: This study aimed to investigate the expression and clinical value of microRNA miR-486-5p in diagnosing lumbar spinal stenosis (LSS) patients and predicting the clinical outcomes after minimally invasive spinal surgery (MISS) in LSS patients, and the correlation of miR-486-5p with inflammatory responses in LSS patients. METHODS: This study included 52 LSS patients, 46 patients with lumbar intervertebral disk herniation (LDH) and 42 healthy controls. Reverse transcription quantitative PCR was used to detect miR-486-5p expression. The ability of miR-486-5p to discriminate between different groups was evaluated by receiver-operating characteristic analysis. The visual analogue scale (VAS), Oswestry Disability Index (ODI) and Japanese Orthopaedic Association (JOA) scores at 6 months postoperatively were used to reflect the clinical outcomes of LSS patients. Enzyme-linked immunosorbent assay was used to measure the levels of inflammatory factor [interleukin-1ß (IL-1ß) and tumor necrosis factor-α (TNF-α)]. The correlation of miR-486-5p with continuous variables in LSS patients was evaluated by the Pearson correlation coefficient. RESULTS: Expression of serum miR-486-5p was upregulated in LSS patients and had high diagnostic value to screen LSS patients. In addition, serum miR-486-5p could predict the 6-month clinical outcomes after MISS therapy in LSS patients. Moreover, serum miR-486-5p was found to be positively correlated with the levels of IL-1ß and TNF-α in patients with LSS. CONCLUSION: miR-486-5p, increased in LSS patients, can function as an indicator to diagnose LSS and a predictive indicator for the clinical outcomes after MISS therapy in LSS patients. In addition, miR-486-5p may regulate LSS progression by modulating inflammatory responses.

Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a significant health concern with a variable global incidence and is linked to regional lifestyle factors and HPV infections. Despite treatment advances, patient prognosis remains variable, necessitating an understanding of its molecular mechanisms and the identification of reliable prognostic biomarkers. METHODS: We analyzed 959 HNSCC samples and employed batch correction to obtain consistent transcriptomic data across cohorts. We examined 79 disulfidptosis-related genes to determine consensus clusters and utilized high-throughput sequencing to identify genetic heterogeneity within tumors. We established a disulfidptosis prognostic signature (DSPS) using least absolute shrinkage and selection operator (LASSO) regression and developed a prognostic nomogram integrating the DSPS with clinical factors. Personalized chemotherapy prediction was performed using the "pRRophetic" R package. RESULTS: Batch corrections were used to harmonize gene expression data, revealing two distinct disulfidptosis subtypes, C1 and C2, with differential gene expression and survival outcomes. Subtype C1, characterized by increased expression of the MYH family genes ACTB, ACTN2, and FLNC, had a mortality rate of 48.4%, while subtype C2 had a mortality rate of 38.7% (HR = 0.77, 95% CI: 0.633-0.934, P = 0.008). LASSO regression identified 15 genes that composed the DSPS prognostic model, which independently predicted survival (HR = 2.055, 95% CI: 1.420-2.975, P < 0.001). The prognostic nomogram, which included the DSPS, age, and tumor stage, predicted survival with AUC values of 0.686, 0.704, and 0.789 at 3, 5, and 8 years, respectively, indicating strong predictive capability. In the external validation cohort (cohort B), the DSPS successfully identified patients at greater risk, with worse overall survival outcomes in the high-DSPS subgroup (HR = 1.54, 95% CI: 1.17-2.023, P = 0.002) and AUC values of 0.601, 0.644, 0.636, and 0.748 at 3, 5, 8, and 10 years, respectively, confirming the model's robustness. CONCLUSION: The DSPS provides a robust prognostic tool for HNSCC, underscoring the complexity of this disease and the potential for tailored treatment strategies. This study highlights the importance of molecular signatures in oncology, offering a step toward personalized medicine and improved patient outcomes in HNSCC management.

Ozone, hydrogen peroxide, and peroxymonosulfate disinfection of MS2 coliphage in water.

The control of viruses in water is critical to preventing the spread of infectious viral diseases. Many oxidants can inactivate viruses, and this study aims to systematically compare the disinfection effects of ozone (O3), peroxymonosulfate (PMS), and hydrogen peroxide (H2O2) on MS2 coliphage. The effects of oxidant dose and contact time on disinfection were explored, as were the disinfection effects of three oxidizing agents in secondary effluent. The 4-log inactivation of MS2 coliphage required 0.05 mM O3, 0.5 mM PMS, or 25 mM H2O2 with a contact time of 30 min. All three oxidants achieved at least 4-log disinfection within 30 min, and O3 required only 0.5 min. In secondary effluent, all three oxidants also achieved 4-log inactivation of MS2 coliphage. Excitation-emission matrix (EEM) results indicate that all three oxidants removed dissolved organic matter synchronously and O3 oxidized dissolved organic matter more thoroughly while maintaining disinfection efficacy. Considering the criteria of oxidant dose, contact time, and disinfection efficacy in secondary effluent, O3 is the best choice for MS2 coliphage disinfection among the three oxidants.

A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical image segmentation applications. However, CNNs have limitations in learning long-range spatial dependencies due to the locality of the convolutional layers. Transformers were introduced to address this challenge. In transformers with self-attention mechanism, even the first layer of information processing makes connections between distant image locations. Our paper presents a novel framework that bridges these two unique techniques, CNNs and transformers, to segment the gross tumor volume (GTV) accurately and efficiently in computed tomography (CT) images of non-small cell-lung cancer (NSCLC) patients. METHODS: Under this framework, input of multiple resolution images was used with multi-depth backbones to retain the benefits of high-resolution and low-resolution images in the deep learning architecture. Furthermore, a deformable transformer was utilized to learn the long-range dependency on the extracted features. To reduce computational complexity and to efficiently process multi-scale, multi-depth, high-resolution 3D images, this transformer pays attention to small key positions, which were identified by a self-attention mechanism. We evaluated the performance of the proposed framework on a NSCLC dataset which contains 563 training images and 113 test images. Our novel deep learning algorithm was benchmarked against five other similar deep learning models. RESULTS: The experimental results indicate that our proposed framework outperforms other CNN-based, transformer-based, and hybrid methods in terms of Dice score (0.92) and Hausdorff Distance (1.33). Therefore, our proposed model could potentially improve the efficiency of auto-segmentation of early-stage NSCLC during the clinical workflow. This type of framework may potentially facilitate online adaptive radiotherapy, where an efficient auto-segmentation workflow is required. CONCLUSIONS: Our deep learning framework, based on CNN and transformer, performs auto-segmentation efficiently and could potentially assist clinical radiotherapy workflow.

Robot-assisted percutaneous vertebroplasty for osteoporotic vertebral compression fracture treatment and risk factor screening for postoperative refracture.

Osteoporotic vertebral compression fracture (OVCF) is a serious complication of osteoporosis, and percutaneous vertebroplasty (PVP) is a major therapeutic method for OVCF. This study aimed to evaluate the clinical efficacy and postoperative complications of robot-assisted targeted PVP for the treatment of OVCF. The data from 202 OVCF patients were analyzed in this study, including 72 cases received traditional PVP (PVP group), 68 cases received robot-assisted PVP (R-PVP group), and 62 cases underwent robot-assisted PVP combined with targeted plugging (R-PVP + TP group). The fluoroscopic exposure conditions, operative duration, lengths of stay, postoperative bone cement leakage, refracture, Visual Analog Scale (VAS) score, and Oswestry Disability Index (ODI) score were obtained and compared between the three groups. The Kaplan-Meier method and logistic regression model were adopted to screen the risk factors related with postoperative refracture. R-PVP and R-PVP + TP group had significantly reduced fluoroscopic frequency and radiation dose, and reduced cement leakage compared with PVP group. R-PVP + TP not only showed more obvious advantages in these aspects, but also had a lower probability of postoperative refracture. In addition, BMD, fracture vertebral distribution, cement leakage, and surgery methods were independent related with refracture. All the results demonstrated robot assistance could improve the application of PVP in the treatment of OVCF, and robot-assisted PVP combined with targeted plugging showed significantly reduced fluoroscopic exposure, bone cement leakage, and rate of postoperative refracture. BMD, fracture vertebral distribution, cement leakage, and operation methods were identified as four risk factors for the onset of refracture after PVP.

Evaluation of standard breast ultrasonography by adding two-dimensional and three-dimensional shear wave elastography: a prospective, multicenter trial.

OBJECTIVE: To reduce the number of biopsies performed on benign breast lesions categorized as BI-RADS 4-5, we investigated the diagnostic performance of combined two-dimensional and three-dimensional shear wave elastography (2D + 3D SWE) with standard breast ultrasonography (US) for the BI-RADS assessment of breast lesions. METHODS: A total of 897 breast lesions, categorized as BI-RADS 3-5, were subjected to standard breast US and supplemented by 2D SWE only and 2D + 3D SWE analysis. Based on the malignancy rate of less than 2% for BI-RADS 3, lesions assessed by standard breast US were reclassified with SWE assessment. RESULTS: After standard breast US evaluation, 268 (46.1%) participants underwent benign biopsies in BI-RADS 4-5 lesions. By using separated cutoffs for upstaging BI-RADS 3 at 120 kPa and downstaging BI-RADS 4a at 90 kPa in 2D + 3D SWE reclassification, 123 (21.2%) participants underwent benign biopsy, resulting in a 54.1% reduction (123 versus 268). CONCLUSION: Combining 2D + 3D SWE with standard breast US for reclassification of BI-RADS lesions may achieve a reduction in benign biopsies in BI-RADS 4-5 lesions without sacrificing sensitivity unacceptably. CLINICAL RELEVANCE STATEMENT: Combining 2D + 3D SWE with US effectively reduces benign biopsies in breast lesions with categories 4-5, potentially improving diagnostic accuracy of BI-RADS assessment for patients with breast lesions. TRIAL REGISTRATION: ChiCTR1900026556 KEY POINTS: • Reduce benign biopsy is necessary in breast lesions with BI-RADS 4-5 category. • A reduction of 54.1% on benign biopsies in BI-RADS 4-5 lesions was achieved using 2D + 3D SWE reclassification. • Adding 2D + 3D SWE to standard breast US improved the diagnostic performance of BI-RADS assessment on breast lesions: specificity increased from 54 to 79%, and PPV increased from 54 to 71%, with slight loss in sensitivity (97.2% versus 98.7%) and NPV (98.1% versus 98.7%).

Vacuum ultraviolet irradiation for reduction of the toxicity of wastewater towards mammalian cells: Removal mechanism, changes in organic compounds, and toxicity alternatives.

Vacuum ultraviolet (VUV, 185 + 254 nm) irradiation performs well for oxidation of model pollutants. However, oxidation of pollutants does not necessarily lead to a reduction in toxicity. Currently, a comprehensive understanding of the effect of VUV irradiation on the toxicity of real wastewater is still lacking. In this study, the influence of VUV irradiation on the toxicity of secondary effluents to Chinese hamster ovary (CHO) cells was investigated. The induction units of endogenous reactive oxygen species (ROS) and 8-hydroxyguanosine (8-OHdG) in cells continuously decreased with prolonged irradiation time. After 36 min of irradiation, the cytotoxicity and the genotoxicity of the secondary effluents were reduced by 57%-63% and 56%-61%, respectively. The UV (254 nm), •OH, and other substances generated during the VUV irradiation directly drive toxicity changes of wastewater. The contribution of •OH generated during VUV irradiation to the reductions in cytotoxicity and genotoxicity of the secondary effluents reached 72%-78% and 77%-84%, respectively. Hydroxyl radicals generated during VUV irradiation played an important role in the detoxification. The relative signal intensity of dissolved organic carbon (DOC) > 500 Da was partially removed, whereas that of DOC < 500 Da was small changed. Since the content of DOC > 500 Da in the samples was much lower than that of DOC < 500 Da, the removal of total DOC was only 15.8%-20.0% after 36 min of irradiation. The UV254 values and the fluorescence intensity values for different molecular weights (MWs) were all reduced effectively by VUV irradiation. Electron-rich organic compounds of all MWs were all sensitive to VUV irradiation. There were mono-linear relationships between changes in chemical indexes and changes in cytotoxicity or genotoxicity. The total fluorescence intensity (Ex: 220-420 nm, Em: 280-560 nm) was identified as the best indicator of the reduction in toxicity.

2S-BUSGAN: A Novel Generative Adversarial Network for Realistic Breast Ultrasound Image with Corresponding Tumor Contour Based on Small Datasets.

Deep learning (DL) models in breast ultrasound (BUS) image analysis face challenges with data imbalance and limited atypical tumor samples. Generative Adversarial Networks (GAN) address these challenges by providing efficient data augmentation for small datasets. However, current GAN approaches fail to capture the structural features of BUS and generated images lack structural legitimacy and are unrealistic. Furthermore, generated images require manual annotation for different downstream tasks before they can be used. Therefore, we propose a two-stage GAN framework, 2s-BUSGAN, for generating annotated BUS images. It consists of the Mask Generation Stage (MGS) and the Image Generation Stage (IGS), generating benign and malignant BUS images using corresponding tumor contours. Moreover, we employ a Feature-Matching Loss (FML) to enhance the quality of generated images and utilize a Differential Augmentation Module (DAM) to improve GAN performance on small datasets. We conduct experiments on two datasets, BUSI and Collected. Moreover, results indicate that the quality of generated images is improved compared with traditional GAN methods. Additionally, our generated images underwent evaluation by ultrasound experts, demonstrating the possibility of deceiving doctors. A comparative evaluation showed that our method also outperforms traditional GAN methods when applied to training segmentation and classification models. Our method achieved a classification accuracy of 69% and 85.7% on two datasets, respectively, which is about 3% and 2% higher than that of the traditional augmentation model. The segmentation model trained using the 2s-BUSGAN augmented datasets achieved DICE scores of 75% and 73% on the two datasets, respectively, which were higher than the traditional augmentation methods. Our research tackles imbalanced and limited BUS image data challenges. Our 2s-BUSGAN augmentation method holds potential for enhancing deep learning model performance in the field.

Study on the clinical efficacy of painting therapy of patients with anxiety disorders.

AIMS: This study analyzed the application value of drug combined painting therapy for patients with anxiety disorder by observing the changes in patients' mental and social functions. METHODS: 400 cases with anxiety disorder were recruited, and randomly divided into the experimental group and the control group with 200 cases in each group. Patients in the control group received drug therapy alone, while experimental group was combined with painting treatment on the basis of control group. The Nurses Observation Scale for Inpatient Evaluation (NOSIE) was used for the evaluation of mental and social functioning. Clinical efficacy was evaluated according to the degree of reduction in the Hamilton Depression Scale (HAMD) score. RESULTS: After 8 weeks of treatment, the experimental group had a lower HAMD score than those in the control group. After 8 weeks of treatment, mental and social functions in both groups improved significantly. And the social competence, social interest and personal cleanliness of the experimental group were better than those of the control group, and the degree of irritability, retardation and depression were lower than those of the control group. In comparison with the control group, the experimental group had a higher cure rate and remarkable response rate. CONCLUSIONS: Painting therapy combined with drug therapy can relieve anxiety symptoms of patients with anxiety disorder, improve their mental and social functions, and improve clinical efficacy.

Corrigendum: Impact of destination image formation on tourist trust: mediating role of tourist satisfaction.

[This corrects the article DOI: 10.3389/fpsyg.2022.845538.].

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video.

With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds.

Impact of Destination Image Formation on Tourist Trust: Mediating Role of Tourist Satisfaction.

Tourist destinations with cultural heritage have arisen as a prominent issue in tourism literature. Creating a positive image of the destination can influence tourists' satisfaction and willingness to return. The goal of this research is to investigate the relationship between destination image formation (DIF), tourist satisfaction (TS), and tourist trust (TT). As a result, the structural relationships between local community participation (LCP), authenticity (A), access to local products (ALP), TS, and TT were investigated in this study. This study used a quantitative approach based on a survey of 644 domestic and foreign tourists visiting the Guangdong cities of Guangzhou, Foshan, and Shenzhen. The statistical software SmartPLS 3.3.3 was used to determine the relationship between variables in the research model using structural equation modeling. The outcomes show a positive correlation between LCP, A, and ALP, which led to tourist satisfaction and, eventually, tourist trust. It is concluded that the DIF and TS may result in increased tourist trust. There is also a discussion of additional theoretical contributions, practical implications, and limitations. The outcomes of this study will help to shed light on the variables that encourage and promote tourism in developing countries.

Exploring the Relationship of Image Formation on Tourist Satisfaction and Loyalty: Evidence From China.

Cultural heritage tourist destinations have emerged as a hot topic in tourism literature, but there have been relatively few studies that determine the role of the involvement of local participation in either tourism planning or the decision-making processes of tourists. The connection between local community participation (LCP), authenticity, access to local products, destination visit image, tourist satisfaction, and tourist loyalty is thus relatively unexplored in the literature. This study used a quantitative approach based on a survey with 406 respondents visiting the city of Kaiping in Guangdong, China. The proposed hypotheses were empirically tested with SPSS and Analysis of Moment Structures. The resulting outcomes indicated a positive correlation between LCP, authenticity, and access to local and destination visit image, which led to tourist satisfaction and ultimately resulted in tourist loyalty. Additional theoretical contributions, practical implications, and limitations were also discussed.

Metastases to the Breast from Extramammary Nonhematological Malignancies: Case Series.

OBJECTIVE: This article aims to provide a better understanding of ultrasonography and immunohistochemistry of secondary nonhematological tumors of breast. METHODS: The study reviewed the ultrasound findings and immunohistochemical features of nonhematological metastatic breast tumor cases found in patients of West China Hospital, Sichuan University from 2007 to 2019. Each case was categorized as secondary breast malignancy using histopathological results. RESULTS: Fourteen cases were identified from West China Hospital database. Ten cases originated in the lung, 2 cases in the stomach, 1 case in the ovary and 1 case of neuroendocrine carcinomas. Fourteen masses were evaluated. Ultrasound findings showed that tumors were hypoechoic (14/14), irregular (13/14), indistinct margin (13/14), along a long axis parallel to the skin (11/14), lacked vascularity via color doppler flow imaging (9/14). Eight cases showed no posterior features. Calcification was found in 1 case of lung adenocarcinoma that had metastasized to the breast. Abnormal axillary lymph nodes were detected in 5 cases. Immunohistochemical analysis showed that estrogen receptor (ER) and progesterone receptor (PR) were both negative in 11 cases, including gastric and lung cancer metastasis. One case of ovarian metastasis was positive for ER and negative for PR. Six patients were positive for cytokeratin 7 (CK7) and negative for cytokeratin 20 (CK20), including lung and ovarian carcinoma metastasis. Thyroid transcription factor-1 (TTF-1) was positive in 9 of 10 pulmonary carcinoma metastases. The patient of ovarian metastasis was positive for Wilms' tumour 1 (WT-1) and carbohydrate antigen 125 (CA125). Two cases from gastric metastasis were positive for caudal-type homeobox 2 (CDX2). CONCLUSION: Although breast ultrasound is not useful in distinguishing metastases from primary breast cancer, it is helpful in diagnosing breast lesions as oncological diseases and provide evidence for further examination of patients. Immunohistochemistry plays an important role in distinguishing secondary breast cancer from primary, especially in patients without tumor history.

CoP QD anchored carbon skeleton modified CdS nanorods as a co-catalyst for photocatalytic hydrogen production.

An important strategy to improve the performance of catalysts is loading nanoparticle co-catalysts of better dispersion and conductivity. In this work, the ZIF-67-derived CoP quantum dot (QD) anchored graphitized carbon skeleton as a co-catalyst is loaded on CdS nanorods (NRs), while the CoP QDs derived from ZIF-67 are anchored to the carbon skeleton under phosphation and carbonization simultaneously. The porous, graphitized carbon skeleton can not only disperse CoP QDs, increasing active sites for the hydrogen reduction reaction, but also provide electron transfer channels, promoting electron transfer and increasing conductivity. In addition, the metallicity of CoP QDs makes it possible to form Schottky junctions, which is beneficial to the electron transfer at the interface. The results show that the composite photocatalyst can extensively improve the photocatalytic activity and stability, the H2 production rate is 104 947 µmol h-1 g-1 under visible light irradiation (λ ≥ 400 nm), up to 55.2 times that of bare CdS NRs, the apparent quantum yield (AQY) reaches a high value of 32.16% at 420 nm, and the structure of the photocatalyst did not change after the reaction. This work provides an innovative method for the preparation of highly efficient noble metal-free photocatalysts for the conversion of solar energy into hydrogen energy, which has bright prospects in industrial application.

[Removal Performance of Antibiotic Resistance Genes and Heavy Metal Resistance Genes in Municipal Wastewater by Magnetic-Coagulation Process].

Antibiotic resistance genes (ARGs) in municipal wastewater pose a potential threat to the environment. In this study, the change in absolute and relative abundance of ARGs, metal resistance genes (MRGs), and mobile genetic elements (MGTs) were investigated during an emergent municipal wastewater treatment by the magnetic separation process. Results indicate that all the concentrations of targeted ARGs, MRGs, and MGTs decreased significantly in the primary and secondary stirring tank. However, the absolute abundance of some ARGs and MRGs increased in the effluent, which is likely caused by the presence of ample MGTs, in the order of int1 (2.00×1010 copies·mL-1) > int2 (1.91×108 copies·mL-1) > Tn 916/1545e(5.38×108 copies·mL-1). The results obtained from network and PCA analysis showed that the removal of ARGs and MRGs were significantly associated with variations in the microbial community and common pollutants in urban wastewater, such as suspended solids, phosphorus, and COD, which are important factors for affecting the removal efficiency of antibiotic resistance genes and metal heavy resistance genes. These results show that magnetic separation can effectively reduce common pollutants in urban wastewater and might further restrict the transmission and transfer of ARGs. Moreover, it is necessary to strengthen the subsequent management of magnetic separation effluent and dehydrated sludge by disinfection technologies to lessen the risk of antimicrobial contamination.

Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer.

This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corresponding areas under the curve (AUCs) were obtained. The model with the highest AUC was selected, and its diagnostic accuracy was compared with that obtained by sonographers who performed and interpreted ultrasonographic examinations using 683 images of the comparison group (benign: 493; malignant: 190). In the model test with the test group images, the AUCs of the constructed InceptionV3, VGG16, ResNet50, and VGG19 models were 0.905, 0.866, 0.851, and 0.847, respectively. The InceptionV3 model showed the largest AUC, with statistically significant differences compared with the other models (P < 0.05). In the classification of the comparison group images, the AUC (0.913) of the InceptionV3 model was larger than that (0.846) obtained by sonographers, showing a statistically significant difference (P < 0.05). The breast ultrasound computer-aided prediction model based on CNN showed high accuracy in the prediction of breast cancer.