Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method.

Purpose: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.

Methods: In this study, we retrospectively analyzed clinical data and five types of MRI images (FS-T1WI, T2WI, Contrast-enhanced T1-weighted imaging (T1-C), DWI, and ADC) from 325 patients with pathologically confirmed breast cancer. Using the five types of MRI images as inputs to the ResNeXt50 model respectively, five base models were constructed, and then the outputs of the five base models were fused using an ensemble learning approach to develop a multi-parametric MRI model.

Identification of Luminal A breast cancer by using deep learning analysis based on multi-modal images.

Purpose: To evaluate the diagnostic performance of a deep learning model based on multi-modal images in identifying molecular subtype of breast cancer.

Materials And Methods: A total of 158 breast cancer patients (170 lesions, median age, 50.8 ± 11.

Biomineralization behavior and mechanism of microbial-mediated removal of arsenate from water.

The microbial-mediated removal of arsenate by biomineralization received much attention, but the molecular mechanism of Arsenic (As) removal by mixed microbial populations remains to be elucidated. In this study, a process for the arsenate treatment using sulfate-reducing bacteria (SRB) containing sludge was constructed, and the performance of As removal was investigated at different molar ratios of AsO to SO. It was found that biomineralization mediated by SRB could achieve the simultaneous removal of arsenate and sulfate from wastewater but only occurred when microbial metabolic processes were involved.

Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model.

Purpose: To develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer.

Methods: Clinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks.

Functional CdS nanocomposites recovered from biomineralization treatment of sulfate wastewater and its applications in the perspective of photocatalysis and electrochemistry.

The sulfur ions generated during the microbial treatment of sulfate wastewater could cause secondary pollution problem, however, the application of the biomineralization technique could convert sulfur ions into sulfide nanocomposites with diverse properties. This study constructed a multi-stage process for sulfate wastewater treatment and CdS nanocomposites (CdS-NCs) recovery by using biomineralization, which simultaneously achieved the removal of pollutants and recovery of functional nanocomposites. In this process, about 97% of the sulfate could be removed, and the CdS-NCs with a diameter of 16.