Matrix Metalloproteinases as Markers of Acute Inflammation Process in the Pulmonary Tuberculosis

The main factors of pathogenesis in the pulmonary tuberculosis are not only the bacterial virulence and sensitivity of the host immune system to the pathogen, but also the degree of destruction of the lung tissue. Such destruction processes lead to the development of caverns, in most cases requiring surgical interventions besides the drug therapy. Identification of special biochemical markers allowing to assess the necessity of surgery or therapy prolongation remains a challenge. We consider promising markers—metalloproteinases—analyzing the data obtained from patients with pulmonary tuberculosis infected by different strains of Mycobacterium tuberculosis. We argue that the presence of drug-resistant strains in lungs leading to complicated clinical prognosis could be justified not only by the difference in medians of biomarkers concentration (as determined by the Mann–Whitney test for small samples), but also by the qualitative difference in their probability distributions (as detected by the Kolmogorov–Smirnov test). Our results and the provided raw data could be used for further development of precise biochemical data-based diagnostic and prognostic tools for pulmonary tuberculosis

Protein-ligand dissociation rate constant from all-atom simulation

Dissociation of a ligand isoniazid from a protein catalase was investigated using all-atom Molecular Dynamics (MD) simulations. Random Acceleration MD (τ-RAMD) was used where a random artificial force applied to the ligand facilitates its dissociation. We have suggested an approach to extrapolate such obtained dissociation times to the zero-force limit that was never attempted before, thus allowing direct comparison with experimentally measured values. We have found that our calculated dissociation time was equal to 36.1 seconds with statistically significant values distributed in the interval 0.2-72.0 s, that quantitatively matches the experimental value of 50 ± 8 seconds despite the extrapolation over nine orders of magnitude in time

Leveraging Accelerometer Data for Lameness Detection in Dairy Cows: A Longitudinal Study of Six Farms in Germany

Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015–2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock

An Improved Diagnostic of the Mycobacterium tuberculosis Drug Resistance Status by Applying a Decision Tree to Probabilities Assigned by the CatBoost Multiclassifier of Matrix Metalloproteinases Biomarkers

In this work, we discuss an opportunity to use a set of the matrix metalloproteinases MMP-1, MMP-8, and MMP-9 and the tissue inhibitor TIMP, the concentrations of which can be easily obtained via a blood test from patients suffering from tuberculosis, as the biomarker for a fast diagnosis of the drug resistance status of Mycobacterium tuberculosis. The diagnostic approach is based on machine learning with the CatBoost system, which has been supplied with additional postprocessing. The latter refers not only to the simple probabilities of ML-predicted outcomes but also to the decision tree-like procedure, which takes into account the presence of strict zeros in the primary set of probabilities. It is demonstrated that this procedure significantly elevates the accuracy of distinguishing between sensitive, multi-, and extremely drug-resistant strains

Matrix Metalloproteinases as Markers of Acute Inflammation Process in the Pulmonary Tuberculosis

The main factors of pathogenesis in the pulmonary tuberculosis are not only the bacterial virulence and sensitivity of the host immune system to the pathogen, but also the degree of destruction of the lung tissue. Such destruction processes lead to the development of caverns, in most cases requiring surgical interventions besides the drug therapy. Identification of special biochemical markers allowing to assess the necessity of surgery or therapy prolongation remains a challenge. We consider promising markers—metalloproteinases—analyzing the data obtained from patients with pulmonary tuberculosis infected by different strains of Mycobacterium tuberculosis. We argue that the presence of drug-resistant strains in lungs leading to complicated clinical prognosis could be justified not only by the difference in medians of biomarkers concentration (as determined by the Mann–Whitney test for small samples), but also by the qualitative difference in their probability distributions (as detected by the Kolmogorov–Smirnov test). Our results and the provided raw data could be used for further development of precise biochemical data-based diagnostic and prognostic tools for pulmonary tuberculosis

Shaping spiking patterns through synaptic parameters as revealed by conventional and wavelet-based bifurcation analysis

We investigate different dynamical regimes of a small neuronal circuit. This circuit includes two cells that are interconnected by means of dynamical synapses (excitatory and inhibitory). On the individual level, each neuron is modelled by FitzHugh–Nagumo equations. To analyze complex patterns and transitions between them in this small circuit, we apply wavelet analysis. We analyse the influence of synaptic kinetics on the synchronization in this circuit. We also show that the wavelet analysis could be applicable to systems that are difficult to investigate using methods of classical bifurcation analysis

Dataset of single Mycobacterium tuberculosis bacteria cells with different antibiotic susceptibility obtained by Raman spectroscopy

This data article contains Raman experimental data, obtained with Horiba Jobin-Yvon LabRam HR800 spectrometer (Japan), which can be used for rapid identification of Mycobacterium tuberculosis (MbT) bacteria (Beijing clade) in vitro. Data present analyzed Raman spectra of bacterial cells with various drug resistances obtained from pulmonary and extra pulmonary samples. Data can provide information about characteristic maxima of different structures in biological cell. Keywords: Raman spectroscopy, Mycobacterium tuberculosis, multidrug resistance (MDR) strains, sensitive drug (SD) strains, spectral dat