State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).

Early detection of breast cancer is crucial for a better prognosis. Various studies have been conducted where tumor lesions are detected and localized on images. This is a narrative review where the studies reviewed are related to five different image modalities: histopathological, mammogram, magnetic resonance imaging (MRI), ultrasound, and computed tomography (CT) images, making it different from other review studies where fewer image modalities are reviewed. The goal is to have the necessary information, such as pre-processing techniques and CNN-based diagnosis techniques for the five modalities, readily available in one place for future studies. Each modality has pros and cons, such as mammograms might give a high false positive rate for radiographically dense breasts, while ultrasounds with low soft tissue contrast result in early-stage false detection, and MRI provides a three-dimensional volumetric image, but it is expensive and cannot be used as a routine test. Various studies were manually reviewed using particular inclusion and exclusion criteria; as a result, 91 recent studies that classify and detect tumor lesions on breast cancer images from 2017 to 2022 related to the five image modalities were included. For histopathological images, the maximum accuracy achieved was around 99 %, and the maximum sensitivity achieved was 97.29 % by using DenseNet, ResNet34, and ResNet50 architecture. For mammogram images, the maximum accuracy achieved was 96.52 % using a customized CNN architecture. For MRI, the maximum accuracy achieved was 98.33 % using customized CNN architecture. For ultrasound, the maximum accuracy achieved was around 99 % by using DarkNet-53, ResNet-50, G-CNN, and VGG. For CT, the maximum sensitivity achieved was 96 % by using Xception architecture. Histopathological and ultrasound images achieved higher accuracy of around 99 % by using ResNet34, ResNet50, DarkNet-53, G-CNN, and VGG compared to other modalities for either of the following reasons: use of pre-trained architectures with pre-processing techniques, use of modified architectures with pre-processing techniques, use of two-stage CNN, and higher number of studies available for Artificial Intelligence (AI)/machine learning (ML) researchers to reference. One of the gaps we found is that only a single image modality is used for CNN-based diagnosis; in the future, a multiple image modality approach can be used to design a CNN architecture with higher accuracy.

Colonization of the live biotherapeutic product VE303 and modulation of the microbiota and metabolites in healthy volunteers.

Manipulation of the gut microbiota via fecal microbiota transplantation (FMT) has shown clinical promise in diseases such as recurrent Clostridioides difficile infection (rCDI). However, the variable nature of this approach makes it challenging to describe the relationship between fecal strain colonization, corresponding microbiota changes, and clinical efficacy. Live biotherapeutic products (LBPs) consisting of defined consortia of clonal bacterial isolates have been proposed as an alternative therapeutic class because of their promising preclinical results and safety profile. We describe VE303, an LBP comprising 8 commensal Clostridia strains under development for rCDI, and its early clinical development in healthy volunteers (HVs). In a phase 1a/b study in HVs, VE303 is determined to be safe and well-tolerated at all doses tested. VE303 strains optimally colonize HVs if dosed over multiple days after vancomycin pretreatment. VE303 promotes the establishment of a microbiota community known to provide colonization resistance.