Local Defect Detection in Bearings in the Presence of Heavy-Tailed Noise and Spectral Overlapping of Informative and Non-Informative Impulses.

The problem of the informative frequency band (IFB) selection for local fault detection is considered in the paper. There are various approaches that are very effective in this issue. Most of the techniques are vibration-based and they are related to the cyclic impulses detection (associated with the local fault) in the background noise. However, when the background noise in the vibration signal has non-Gaussian impulsive behavior, the classical methods seem to be insufficient. Recently, new techniques were proposed by several authors and interesting approaches were tested for different non-Gaussian signals. We demonstrate the comparative analysis related to the results for three selected techniques proposed in recent years, namely the Alpha selector, Conditional Variance-based selector, and Spearman selector. The techniques seem to be effective for the IFB selection for the non-Gaussian distributed vibration signals. The main purpose of this article is to investigate how spectral overlapping of informative and non-informative impulsive components will affect diagnostic procedures. According to our knowledge, this problem was not considered in the literature for the non-Gaussian signals. Nevertheless, as we demonstrated by the simulations, the level of overlapping and the location of a center frequency of the mentioned frequency bands have a significant influence on the behavior of the considered selectors. The discussion about the effectiveness of each analyzed method is conducted. The considered problem is supported by real-world examples.

Osteocalcin: Beyond Bones.

Apart from basic roles such as supporting the body, protecting internal organs, and storing calcium, the skeletal system also performs hormonal functions. In recent years, several reports have been published on proteins secreted by bones and their impact on the homeostasis of the entire body. These proteins include fibroblast growth factor 23, sclerostin, lipocalin 2, and osteocalcin. Osteocalcin, the most abundant non-collagenous protein in bone tissue, is routinely measured as a clinical marker for diagnosing bone metabolism disorders. Its molecule undergoes numerous transformations, with decarboxylation being the critical process. Decarboxylation occurs in the acidic environment typical of bone resorption, facilitating the release of the molecule into the bloodstream and enabling its hormonal action. Decarboxylated osteocalcin promotes insulin secretion and stimulates the proliferation of pancreatic islet ß-cells. It also plays a role in reducing the accumulation of visceral fat and decreasing fat storage in the liver. Furthermore, decarboxylated osteocalcin levels are inversely correlated with fasting serum glucose levels, total body fat, visceral fat area, and body mass index. Apart from its role in energy metabolism, osteocalcin affects testosterone production and the synthesis of glucagon-like peptide-1. It is also actively involved in muscle-bone crosstalk and influences cognitive function.

Uncontrolled hypertension and hypertensive urgency: One-year single-center emergency department experience.

BACKGROUND: The prevalence of uncontrolled hypertension remains a significant concern in public healthcare systems, including daily practices of emergency departments (ED). AIM: We aimed to characterize patients admitted to an ED for elevated blood pressure (BP) and to identify factors leading to hospitalization. METHODS: This retrospective analysis included all patients admitted to an ED in a tertiary hospital in 2022 due to an acute BP rise without hypertensive emergencies. RESULTS: The studied group (n = 570) constituted 1.5% of all ED admissions in 2022. The median age was 67 years (Q1-Q3) (52-75), 68.9% were females. Systolic BP (200 mm Hg [180-212]) and diastolic BP (105 mm Hg [100-115]) at home were higher than during triage (173 mm Hg [160-190] and 95 mm Hg [84-103], respectively [P <0.0001]). Thirty-nine percent of the studied population had taken BP-lowering agents before ED admission (captopril in 91.8% of cases). In the ED, nitrendipine (54.2%), captopril (38.1%), furosemide (16.3%), urapidil (10.0%), and nitroglycerine (1.9%) were administered. Eventually, a median of 140/82 mm Hg BP was reached in the median time of 288 minutes (202-400). Hospitalization was necessary in 5.4% of patients. The need for furosemide or urapidil administration in the ED doubled the risk of hospitalization (OR, 2.0; P <0.01). Before ED admission, only 17.0% of patients received guidelines-recommended single-pill combination therapy, and 17.6% had already visited ED for uncontrolled hypertension (median of 388 days earlier). CONCLUSIONS: Elevated BP is a common reason for admission to the ED. Crucially, improvements in long-term hypertension treatment and education are needed to reduce the number of patients seeking ED care for elevated BP.

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study.

INTRODUCTION: In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. METHODS: This comparative study was performed in 515 patients in the Maria Sklodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. RESULTS: We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. CONCLUSIONS: Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.