Development of a multiple convolutional neural network-facilitated diagnostic screening program for immunofluorescence images of IgA nephropathy and idiopathic membranous nephropathy.

Background: Immunoglobulin A nephropathy (IgAN) and idiopathic membranous nephropathy (IMN) are the most common glomerular diseases. Immunofluorescence (IF) tests of renal tissues are crucial for the diagnosis. We developed a multiple convolutional neural network (CNN)-facilitated diagnostic program to assist the IF diagnosis of IgAN and IMN. Methods: The diagnostic program consisted of four parts: a CNN trained as a glomeruli detection module, an IF intensity comparator, dual-CNN (D-CNN) trained as a deposition appearance and location classifier and a post-processing module. A total of 1573 glomerular IF images from 1009 patients with glomerular diseases were used for the training and validation of the diagnostic program. A total of 1610 images of 426 patients from different hospitals were used as test datasets. The performance of the diagnostic program was compared with nephropathologists. Results: In >90% of the tested images, the glomerulus location module achieved an intersection over union >0.8. The accuracy of the D-CNN in recognizing irregular granular mesangial deposition and fine granular deposition along the glomerular basement membrane was 96.1% and 93.3%, respectively. As for the diagnostic program, the accuracy, sensitivity and specificity of diagnosing suspected IgAN were 97.6%, 94.4% and 96.0%, respectively. The accuracy, sensitivity and specificity of diagnosing suspected IMN were 91.7%, 88.9% and 95.8%, respectively. The corresponding areas under the curve (AUCs) were 0.983 and 0.935. When tested with images from the outside hospital, the diagnostic program showed stable performance. The AUCs for diagnosing suspected IgAN and IMN were 0.972 and 0.948, respectively. Compared with inexperienced nephropathologists, the program showed better performance. Conclusion: The proposed diagnostic program could assist the IF diagnosis of IgAN and IMN.

Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning.

BACKGROUND: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. METHODS: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. RESULTS: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. CONCLUSION: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.

A coupling simulation and optimization method developed for environmental-economic management of Lake watershed.

Many lake basins are facing the challenge of mitigating water shortage and water pollution while maintaining economic growth. Existing planning method for water pollution control often focus on how to alleviate water pollution effectively at the lowest cost, but rarely pay attention to the dynamic feedback and synergy effects between water pollution abatement, water conservation and social economy. This article proposes a method which consists of system dynamics model (SDM), Soil and Water Assessment Tool (SWAT) and objective programming model (OPM). It could be used to create insights on basin-wide water problems from a systematic perspective. The case study on Yilong Lake Watershed evaluates the proposed measures in existing local planning, calculates the optimal scheme, and discusses issues including the uncertainty of effectiveness, choice between recycling sewage and transferring outside, and the necessity of restricting food processing industry. This method could be improved on the simulation of social and industrial economy, the simulation of water cycle, and the spatial planning.

Dynamic metabolism network simulation for energy-water nexus analysis: A case study of Liaoning Province, China.

Faced with gradually serious energy and water crisis, the exploration of energy-water nexus metabolism relationships could provide a new direction for achieving the resources conservation and consumption reduction. In this paper, a comprehensive assessment for energy-water metabolic network is proposed to search the dynamic evolution and quantify the sectoral metabolic processes in Liaoning Province, China. Input-output analysis (IOA) and ecological network analysis (ENA) are integrated to clarify the embodied linkages and the complicated system interactions in the energy-water nexus network. The coefficients of "water-related energy" and "energy-related water" are explored through the system dynamics model (SDM) based on the consecutive input-output tables from 2012 to 2018. In addition, energy-water nexus efficiency index (EWEI) is constructed to evaluate the metabolism circulation efficiency of energy-water nexus network. The results indicate that (a) the energy and water outflow sectors mainly concentrate in primary industries and tertiary industries, while inflow sectors mainly concentrate in secondary industries; (b) the sectoral relationship provides effective pathways to implement collaborative resource saving and efficiency improvement measures, and the key sectors with the highest correlation with other sectors in energy-water nexus are AGR (agriculture), MIN (mining) and WRC (wholesale, retail and catering services). (c) the EWEI shows a gradually downward trend and the state of the energy system would determine the health of the energy-water nexus metabolism more strongly than water system.

The effect of FBXW7 gene point mutation to colorectal cancer HCT-116 cells’proliferation, migration, invasion, and its apoptosis resistance / 中国肿瘤生物治疗杂志

@#[Abstract] Objective: To investigate the impact of FBXW7 gene mutation on cell proliferation, apoptosis, migration, and invasion processes of colorectal cancer HCT-116 cell line. Methods: Recombinant plasmids carrying wild-type and mutant-type FBXW7 SNP were constructed and transfected into HCT-116 cell line; the FBXW7 protein expression level in HCT-116 strains after transfection was detected by Western blotting. Subsequently, cell proliferation capacity was tested by CCK-8 assay; tumor cell colony formation ability was tested by HTCA; cell apoptosis function was tested by FCM; cell migration and invasion were tested by scratch assay and Transwell assay, respectively. Results: Higher HBXW7 protein expression level was detected in HCT-116 strain transfected with wild-type HBXW7 in comparison to the control group (strains transfected with mutant-type HBXW7), negative-control (strains transfected with empty plasmids), and blank-control (strains untransfected) (all P<0.05). Compared with the other groups, strains transfected with wildtype HBXW7 exhibited significantly reduced proliferation, colony formation, migration and invasion ability (all P<0.05), but obviously increased apoptosis rate (P<0.05). Conclusion： ：FBXW7 gene mutation can down-regulate its protein expression, and further promote the proliferation, migration and invasion as well as inhibit the apoptosis of HCT-116 cells.