Gender aspects of urolithiasis development in patients with metabolic syndrome

The review summarizes and analyzes the results of domestic and major foreign studies of recent years concerning gender characteristics of the epidemiology and development mechanisms of metabolic syndrome and urolithiasis as an associated disease. A deep understanding of gender aspects in the pathogenesis of these pathologies can form the basis for development of high-quality diagnostic algorithms and pathogenetically grounded approaches to treatment.

An expert neural system for diagnostics of motor-driven valves

Trouble-free operation of motor-driven valves (MDV) is one of the key factors behind the operating safety of NPPs. As critical components, MDVs are a part of a safety system and a safety-related system. This imposes the highest possible requirements on the MDV reliability. MDVs are the most numerous category of the NPP components. Depending on design, one power unit contains 1500 to 3000 motor-driven valves alone. It follows from an analysis of the NPP failures that many of these are caused by failed motor-driven valves of safety and safety-related systems. The paper presents a description of an automated system for diagnostics of shutoff and control MDVs used in the NPP pipelines. The developed diagnostic algorithms make it possible to take into account the variability of the MDV technical parameters, while taking into account, at the same time, rated restrictions on diagnostic parameters, if any.

Heparin-induced Thrombocytopenia: Perioperative Diagnosis and Management

Heparin-induced thrombocytopenia is a severe prothrombotic disease. Timely diagnosis and treatment are essential. Application of diagnostic algorithms based on validated clinical scoring tools and rapid, specific laboratory assays may improve outcomes.

White paper peanut allergy—part 2: Diagnosis of peanut allergy with special emphasis on molecular component diagnostics

Summary Background Peanut allergy is an immunoglobulin E (IgE)-mediated immune response that usually manifests in childhood and can range from mild skin reactions to anaphylaxis. Since quality of life maybe greatly reduced by the diagnosis of peanut allergy, an accurate diagnosis should always be made. Methods A selective literature search was performed in PubMed and consensus diagnostic algorithms are presented. Results Important diagnostic elements include a detailed clinical history, detection of peanut-specific sensitization by skin prick testing and/or in vitro measurement of peanut (extract)-specific IgE and/or molecular components, and double-blind, placebo-controlled food challenge as the gold standard. Using these tools, including published cut-off values, diagnostic algorithms were established for the following constellations: 1) Suspicion of primary peanut allergy with a history of immediate systemic reaction, 2) Suspicion of primary peanut allergy with questionable symptoms, 3) Incidental findings on sensitization testing and peanut ingestion so far or 4) Suspicion of pollen-associated peanut allergy with solely oropharyngeal symptoms. Conclusion The most important diagnostic measures in determining the diagnosis of peanut allergy are clinical history and detection of sensitizations, also via component-based diagnostics. However, in case of unclear results, the gold standard—an oral food challenge—should always be used.

Applying the ESC 2016, the H2FPEF, and the HFA-PEFF diagnostic algorithms for heart failure with preserved ejection fraction to the general population – a comparative approach

Background Heart failure with preserved ejection fraction (HFpEF) is common in patients presenting with dyspnoea. Nevertheless, diagnosing HFpEF remains challenging. Recently, different algorithms were developed to predict the likelihood of HFpEF. Purpose Our objective was to provide an in-depth comparison of the ESC 2016 algorithm, the H2FPEF- and the HFA-PEFF algorithm for diagnosing and characterising HFpEF in the general population. Methods The study included 5,613 participants of the population-based H. City Health Study (HCHS), aged 62±8.7 years (51.1% women), that were enrolled between 2016 and 2019. Exclusion criteria were other common causes of dyspnea. We calculated the prevalence and compared characteristics of HFpEF according to the different diagnostic algorithms applying the ESC 2016 heart failure guidelines and the cut-off values suggested by the authors of the HFA-PEFF and H2FPEF score for defining HFpEF. Results Unexplained dyspnea was present in 407 (7.3%) subjects. In those, the estimated prevalence of HFpEF was 20.4% (ESC 2016), 12.3% (H2FPEF), and 7.6% (HFA-PEFF). The majority of subjects was classified as HFpEF not excludable according to the HFA-PEFF (57.7%) and the H2FPEF (59.2%) score. For all algorithms, subjects diagnosed with HFpEF showed elevated age and body mass index as well as a higher prevalence of atrial fibrillation, diabetes, and arterial hypertension compared to those without HFpEF or HFpEF not excludable. The distribution of those comorbidities and risk factors varied between the differently diagnosed HFpEF groups with the highest burden in the HFpEF group defined by the H2FPEF score. The overlap of subjects diagnosed with HFpEF according to the different algorithms was very limited. Conclusion Unexplained dyspnoea is common in the middle-aged general population. The ESC 2016 algorithm, the H2FPEF-, and the HFA-PEFF score detect different, discordant sub-populations of probands with breathlessness. Further classification of the HFpEF syndrome is desirable. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Innovative medicine initiative Figure 1. Prevalence and concordance of the three HFpEF algorithms in subjects with unexplained dyspnea. Of the 407 subjects with unexplained dyspnea, the prevalence ranged from 20.4% (n=83, ESC 2016 guideline) to 12.3% (n=50, H2FPEF score) and 7.6% (n=31, HFA-PEFF score). The concordance was highest between the ESC 2016 guidelines and the HFA-PEFF score reflected by a kappa coefficient of 0.38 and a reclassification rate of 16%. RecR = reclassification rate.

Evaluation of CSTB and DMBT1 Expression in Saliva of Gastric Cancer Patients and Controls

Background: Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally with late diagnosis, low survival rate and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods.Methods: Demographic data, clinical characteristics and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins were evaluated. To construct diagnostic algorithms, we used the machine learning method.Results: The mean salivary expression of CSTB in GC patients was significantly lower (115.55±7.06, p=0.001) and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88±39.67, p=0.002) than the control. Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R2=0.20, p<0.001). Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC was 83.87% and 70.97%, respectively The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity=80.65% and specificity=64.52%). The area under the ROC curve was up to 0.741. As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics and food intake habits was 0.95. The machine learning model's sensitivity, specificity, and accuracy were 100%, 70.8%, and 80.5%, respectively. Conclusion: Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.