Biomarkers of Community-Acquired Pneumonia: A Key to Disease Diagnosis and Management

Community-acquired pneumonia (CAP) is a dangerous disease caused by a spectrum of bacterial and viral pathogens. The choice of specific therapy and the need for hospitalization or transfer to the intensive care unit are determined by the causative agent and disease severity. The microbiological analysis of sputum largely depends on the quality of the material obtained. The prediction of severity and the duration of therapy are determined individually, and existing prognostic scales are used generally. This review examines the possibilities of using specific serological biomarkers to detect the bacterial or viral aetiology of CAP and to assess disease severity. Particular emphasis is placed on the use of biomarker signatures and the discovery of biomarker candidates for a single multiplex analysis

Multiplex Autoantibody Detection in Patients with Autoimmune Polyglandular Syndromes

The diagnosis of autoimmune polyglandular syndrome (APS) types 1/2 is difficult due to their rarity and nonspecific clinical manifestations. APS-1 development can be identified with assays for autoantibodies against cytokines, and APS-2 development with organ-specific antibodies. In this study, a microarray-based multiplex assay was proposed for simultaneous detection of both organ-specific (anti-21-OH, anti-GAD-65, anti-IA2, anti-ICA, anti-TG, and anti-TPO) and APS-1-specific (anti-IFN-ω, anti-IFN-α-2a, and anti-IL-22) autoantibodies. Herein, 206 serum samples from adult patients with APS-1, APS-2, isolated autoimmune endocrine pathologies or non-autoimmune endocrine pathologies and from healthy donors were analyzed. The prevalence of autoantibodies differed among the groups of healthy donors and patients with non-, mono- and multi-endocrine diseases. APS-1 patients were characterized by the presence of at least two specific autoantibodies (specificity 99.5%, sensitivity 100%). Furthermore, in 16 of the 18 patients, the APS-1 assay revealed triple positivity for autoantibodies against IFN-ω, IFN-α-2a and IL-22 (specificity 100%, sensitivity 88.9%). No anti-cytokine autoantibodies were found in the group of patients with non-APS-1 polyendocrine autoimmunity. The accuracy of the microarray-based assay compared to ELISA for organ-specific autoantibodies was 88.8–97.6%. This multiplex assay can be part of the strategy for diagnosing and predicting the development of APS

Prognosis of Alzheimer’s Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

Early recognition of the risk of Alzheimer’s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA–plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels

A Comparison of the Sensititre MycoTB Plate, the Bactec MGIT 960, and a Microarray-Based Molecular Assay for the Detection of Drug Resistance in Clinical <i>Mycobacterium tuberculosis</i> Isolates in Moscow, Russia

<div><p>Background</p><p>The goal of this study was to compare the consistency of three assays for the determination of the drug resistance of <i>Mycobacterium tuberculosis</i> (MTB) strains with various resistance profiles isolated from the Moscow region.</p><p>Methods</p><p>A total of 144 MTB clinical isolates with a strong bias toward drug resistance were examined using Bactec MGIT 960, Sensititre MycoTB, and a microarray-based molecular assay TB-TEST to detect substitutions in the <i>rpoB</i>, <i>katG</i>, <i>inhA</i>, <i>ahpC</i>, <i>gyrA</i>, <i>gyrB</i>, <i>rrs</i>, <i>eis</i>, and <i>embB</i> genes that are associated with resistance to rifampin, isoniazid, fluoroquinolones, second-line injectable drugs and ethambutol.</p><p>Results</p><p>The average correlation for the identification of resistant and susceptible isolates using the three methods was approximately 94%. An association of mutations detected with variable resistance levels was shown. We propose a change in the breakpoint minimal inhibitory concentration for kanamycin to less than 5 μg/ml in the Sensititre MycoTB system. A pairwise comparison of the minimal inhibitory concentrations (MICs) of two different drugs revealed an increased correlation in the first-line drug group and a partial correlation in the second-line drug group, reflecting the history of the preferential simultaneous use of drugs from these groups. An increased correlation with the MICs was also observed for drugs sharing common resistance mechanisms.</p><p>Conclusions</p><p>The quantitative measures of phenotypic drug resistance produced by the Sensititre MycoTB and the timely detection of mutations using the TB-TEST assay provide guidance for clinicians for the choice of the appropriate drug regimen.</p></div

MICs based on the comparison of the MycoTB plate and MGIT 960 results.

<p>MICs based on the comparison of the MycoTB plate and MGIT 960 results.</p

MIC distributions of the clinical isolates characterized using the MGIT and TB-TEST assays.

<p>Resistant and susceptible isolates based on the MGIT results are indicated by the red and green lines, respectively. The light-red and light-green bars represent the numbers of resistant and susceptible isolates with mutations detected by the TB-TEST. The MGIT was not performed for rifabutin (RFB); therefore, only the distributions of all isolates and the isolates with mutations are shown.</p