Attenuated Strain of Mycobacterium tuberculosis BN: Characteristics.

We evaluated the vaccine properties of a novel attenuated strain of M. tuberculosis BN (Mtb BN) and its impact on the gut microbiota in inbred female mice in comparison with a virulent strain Mtb H37Rv and a vaccine strain BCG. The Mtb BN strain demonstrated the highest anti-tuberculosis vaccine effect in I/St mice highly susceptible to tuberculosis infection and the same effect as BCG in mice of the recombinant strain B6.I-100 and in ß2 microglobulin gene knockout mice. No adverse effects of the new Mtb BN strain on the gut microbiota of BALB/c mice were revealed. The virulent strain Mtb H37Rv and the vaccine strain BCG decreased the main indicators of normocenosis (Bifidobacterium spp., Bifidobacterium animalis subsp. lactis, Akkermansia, and Erysipelotrichaceae) and led to disappearance of Clostridium perfingens, E. coli, Pseudomonas spp., which contributed to reduction of species diversity and the development of dysbiosis.

Technology of Two-dimensional Bioimpedance Analysis of the Human Body Composition.

The BIA primary result sheets as a rule contain one-dimensional graphical scales with a selected area of normal values. In 1994, Piccoli et al. proposed BIVA, an alternative form of BIA data presentation, where two bioimpedance parameters are considered simultaneously as tolerance ellipses: resistance and reactance normalized to height. The purpose of this study is to develop an approach to data analysis in body composition bioimpedance research in two-dimensional representations. The data of 1.124.668 patients aged 5 to 85 years who underwent a bioimpedance study in Russian Health Centers from 2009 to 2015 were used. Statistical programming in the R Studio environment was carried out to estimate two-dimensional distribution densities of pairs of body composition parameters for each year of life. The non-Gaussian distribution is found in most parameters of bioimpedance analysis of body composition for most ages (Lilliefors test, p-value << 0.0001). The slices of the actual two-dimensional distribution pairs of body composition parameters had an irregular shape. The authors of the article propose using the actually observed distribution for populations where numerous bioimpedance studies have already been carried out. Such technology can be called two-dimensional bioimpedance analysis of human body composition (2DBIA). The 2DBIA approach is clearer for practitioners and their patients due to the use of body composition parameters in addition to electrical impedance parameters.

[Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks].

BACKGROUND: Pathological low-energy (LE) vertebral compression fractures (VFs) are common complications of osteoporosis and predictors of subsequent LE fractures. In 84% of cases, VFs are not reported on chest CT (CCT), which calls for the development of an artificial intelligence-based (AI) assistant that would help radiology specialists to improve the diagnosis of osteoporosis complications and prevent new LE fractures. AIMS: To develop an AI model for automated diagnosis of compression fractures of the thoracic spine based on chest CT images. MATERIALS AND METHODS: Between September 2019 and May 2020 the authors performed a retrospective sampling study of ССТ images. The 160 of results were selected and anonymized. The data was labeled by seven readers. Using the morphometric analysis, the investigators received the following metric data: ventral, medial and dorsal dimensions. This was followed by a semiquantitative assessment of VFs degree. The data was used to develop the Comprise-G AI mode based on CNN, which subsequently measured the size of the vertebral bodies and then calculates the compression degree. The model was evaluated with the ROC curve analysis and by calculating sensitivity and specificity values. RESULTS: Formed data consist of 160 patients (a training group - 100 patients; a test group - 60 patients). The total of 2,066 vertebrae was annotated. When detecting Grade 2 and 3 maximum VFs in patients the Comprise-G model demonstrated sensitivity - 90,7%, specificity - 90,7%, AUC ROC - 0.974 on the 5-FOLD cross-validation data of the training dataset; on the test data - sensitivity - 83,2%, specificity - 90,0%, AUC ROC - 0.956; in vertebrae demonstrated sensitivity - 91,5%, specificity - 95,2%, AUC ROC - 0.981 on the cross-validation data; for the test data sensitivity - 79,3%, specificity - 98,7%, AUC ROC - 0.978. CONCLUSIONS: The Comprise-G model demonstrated high diagnostic capabilities in detecting the VFs on CCT images and can be recommended for further validation.