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Global burden of diseases studies collected and analysed data from more than 195 countries, reflecting premature death and disability related to more than 350 diseases and injuries, by age and sex, since 1990 to the present. The national profile of each country in the studies contains comparative data for 2007 and 2019. The purpose of this paper was to analyse the differences in leading risk factors and causes of global burden of diseases between Balkan countries. The leading causes of years of life lost due to disability are mainly chronic non-communicable diseases, with the leading positions being low back pain, falls, headaches, diabetes, etc. The summary indicator for lost years of life due to premature death and disability-adjusted life years (DALYs) for 2017 again shows the leading role of ischemic heart disease and stroke. Preventable mortality in Bulgaria remains very high. Much of the overall burden of disease in Bulgaria can be attributed to behavioural risk factors, including smoking, alcohol consumption, dietary risks and low physical activity.

BACKGROUND: Scoliosis is one of the most common spinal deformations that are usually diagnosed on frontal radiographs using Cobb’s method. Automatic measurement methods based on artificial intelligence can overcome many drawbacks of the usual method and can significantly save radiologist’s time. AIM: To analyze the accuracy, advantages, and disadvantages of a newly developed artificial intelligence program for the automatic diagnosis of scoliosis and measurement of Cobb’s angle on frontal radiographs. MATERIALS AND METHODS: In total, 114 digital radiographs were used to test the agreement of Cobb’s angle measurements between the new automatic method and the radiologist using the Bland–Altman method on Microsoft Excel. A limited clinical accuracy test was also conducted using 120 radiographs. The accuracy of the system in defining the scoliosis grade was evaluated by sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. RESULTS: The agreement of Cobb’s angle measurement between the system and the radiologist’s calculation was found mostly in grade 1 and 2 scoliosis. Only 2.8% of the results showed a clinically significant angle variability of 5°. The diagnostic accuracy metrics of the limited clinical trial in City Mariinsky Hospital (Saint Petersburg, Russia) also proved the reliability of the system, with a sensitivity of 0.97, specificity of 0.88, accuracy (general validity) of 0.93, and area under the receiver operating characteristic curve of 0.93. CONCLUSION: Overall, the artificial intelligence program can automatically and accurately define the scoliosis grade and measure the angles of spinal curvatures on frontal radiographs.

Abstract Background Dilated cardiomyopathy (DCM) commonly leads to heart failure (HF) and represents the most common indication for cardiac transplantation in the pediatric population. Clinical manifestations of DCM are mainly the symptoms of heart failure; it is diagnosed by EKG, chest x-ray and echocardiography. For the idiopathic and familial diseases cases of DCM, there are no definite guidelines for treatment in children as they are treated for prognostic improvement. Case presentation We report the case of a 2-year-old girl diagnosed with dilated cardiomyopathy associated with homozygous mutation in the Myosin Light Chain 3 gene admitted for edema in lower extremities, muscle weakness, lethargy and vomiting, and she was found to be in cardiogenic shock. Chest x-ray showed cardiomegaly and EKG showed first degree atrioventricular block. Echocardiogram showed severe biventricular systolic and diastolic dysfunction. After 70 days of hospitalization, the patient went into cardiac arrest with cessation of electrical and mechanical activity of the heart, despite cardiopulmonary resuscitative efforts. Conclusion Although rare, pediatric DCM carries a high risk of morbidity and mortality and a lack of curative therapy.

Reversible Data Hiding Techniques (RDH) play an increasingly pivotal role in the field of cybersecurity. Overlooking the properties of the carrier image and neglecting the influence of texture can lead to undesirable distortions and irreversible data hiding. In this paper, a novel block-based RDH technique is proposed that harnesses the relative correlation between multidirectional prediction error histograms (MPEH) and pixel fluctuation values to mitigate undesirable distortions and enable RDH, thereby ensuring heightened security and efficiency in the distribution process and improving the robustness of the block-based RDH technique. The proposed technique uses a combination of pixel fluctuation and local complexity measures to determine the best embedding locations within smooth regions based on the cumulative peak regions of the MPEH with the lowest fluctuation values. Similarly, during the extraction process, the same optimal embedding locations are identified within smooth regions. The multidirectional prediction error histograms are then used to accurately extract the hidden data from the pixels with lower fluctuation values. Overall, the experimental results highlight the effectiveness and superiority of the proposed technique in various aspects of data embedding and extraction, and demonstrate that the proposed technique outperforms other state-of-the-art RDH techniques in terms of embedding capacity, image quality, and robustness against attacks. The average Peak Signal-to-Noise Ratio (PSNR) achieved with an embedding capacity ranging from 0.5×104 bits to 5×104 bits is 52.72 dB. Additionally, there are no errors in retrieving the carrier image and secret data.

Abstract In the last decades, global awareness toward the importance of diverse representation has been increasing. The lack of diversity and discrimination toward minorities did not skip the film industry. Here, we examine ethnic bias in the film industry through commercial posters, the industry’s primary advertisement medium for decades. Movie posters are designed to establish the viewer’s initial impression. We developed a novel approach for evaluating ethnic bias in the film industry by analyzing nearly 125,000 posters using state-of-the-art deep learning models. Our analysis shows that while ethnic biases still exist, there is a trend of reduction of bias, as seen by several parameters. Particularly in English-speaking movies, the ethnic distribution of characters on posters from the last couple of years is reaching numbers that are approaching the actual ethnic composition of the US population. An automatic approach to monitoring ethnic diversity in the film industry, potentially integrated with financial value, may be of significant use for producers and policymakers.

Agricultural residue-activated carbon and biochar, inexpensive and environmentally friendly adsorbent materials, have recently received significant research attention. This study investigated the potential use of oak cupules in activated carbon form to remove widespread heavy metals (Pb2+, Cu2+, and Ni2+) from wastewater. The oak-activated carbon was prepared from oak cupules and activated with phosphoric acid. Oak-activated carbon was characterized using FTIR, BET analysis, energy-dispersive X-ray spectrometry (EDS), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The Freundlich, Langmuir, and Temkin isotherm models were used to assess the equilibrium data. The impact of various parameters, including pH effect, temperature, adsorbent dose, and contact time, was estimated. The Freundlich model was the most agreeable with Pb2+ adsorption by oak-based activated carbon, and Langmuir was more compatible with Cu2+ and Ni2+. Under optimum conditions, the average maximum removal was 63% Pb2+, 60% Cu2+, and 54% Ni2+ when every ion was alone in the aqueous solution. The removal was enhanced to 98% Pb2+, 72% Cu2+, and 60% Ni2+ when found as a mixture. The thermodynamic model revealed that the adsorption of ions by oak-based activated carbon is endothermic. The pseudo-second-order kinetic best describes the adsorption mechanism in this study; it verifies chemical sorption as the rate-limiting step in adsorption mechanisms. The oak-activated carbon was effective in removing Pb2+, Cu2+, and Ni2+ from wastewater and aqueous solutions.

Slow gait speed is associated with poorer clinical outcomes and higher rates of functional limitation and mortality in older adults, especially when combined with overweight or obesity. Aging is also associated with nutritional deficits. The aim of our study was to assess the potential association between dietary practice and gait speed performance in community-dwelling older adults with overweight and obesity. Participants underwent body composition measurement with the Tanita MC-780MA Bioimpedance Analyzer (BIA). Dietary patterns were assessed with the Mini Nutritional Assessment (MNA) questionnaire, and a dietary adequacy (DA) score system was constructed. The four-meter gait speed test was performed in order to assess gait speed. Of 222 participants, aged 67.6 ± 6.6 years, with a body mass index (BMI) of 31.9 ± 4.5 kg/m2, 34.7% had reduced gait speed and lower DA compared to those with normal gait speed (2.99 ± 1.12 vs. 3.37 ± 1.07; p < 0.05). The DA score of participants with slower gait speed was more likely to fall below the median than that of participants with normal gait speed (70.1% vs. 51.7%; p < 0.05). Participants with slower gait speed were more likely to be nutritionally at risk of low DA (22.1% vs. 10.3%; p < 0.05). Logistic regression analysis, after adjustment for confounders, showed that the risk of having a slow gait speed was 75% lower among those with a higher DA score (OR = 0.25; 95% CI = 0.11–0.53). Older adults with overweight or obesity in community dwellings might need to be supported with nutritional interventions that can improve their gait speed.

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately.Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes.Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD.Conclusion: The retained molecules and metabolite–protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

Epithelial-to-mesenchymal transition (EMT) is a complex process regulated at multiple molecular levels. Here, the authors implement an analytic framework - PAMAF - to integrate data from twelve distinct omics modalities, which they use to understand the molecular changes and regulation during EMT in vitro.

Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.

Physical activity (PA) plays a vital role in knee osteoarthritis (KOA) management. However, engaging individuals with KOA in regular exercise is challenging, especially when they are affected by obesity. The aim of the current review is to elucidate how to increase adherence to exercise in this population. When implementing a PA program with patients with KOA and obesity, a specific multi-step approach can be adopted. In phase I (the baseline assessment), the patients’ eligibility for exercise is ascertained and a physical fitness assessment, sarcopenic obesity screening and quantification of the pain experienced are undertaken. Phase II adopts a patient-centered approach in implementing a PA program that combines an active lifestyle (>6000 steps/day) with land- or water-based exercise programs performed over eight to twelve weeks, with a frequency of three to five sessions per week, each lasting 60 min. In phase III, several strategies can be used to increase the patients’ adherence to higher levels of PA, including the following: (i) personalizing PA goal-setting and real-time monitoring; (ii) enhancing physical fitness and the management of sarcopenic obesity; (iii) building a sustainable environment and a supportive social network for an active lifestyle; and (iv) reducing pain, which can ameliorate the clinical severity of KOA and help with weight management in this population.

Co-fractionation/mass spectrometry (CF/MS) allows mapping protein interactomes but efficiency and quantitative accuracy are limited. Here, the authors develop a reproducible multiplexed CF/MS method and apply it to characterize interactome rewiring in breast cancer cells.

Nucleic acid therapeutics are rapidly expanding because of recent advancements in production and purification. This class of therapeutics may change the field of disease treatment and personalized medicine since it can cure diseases. However, drug delivery systems are crucial for these therapeutics to fulfill their potential. Liposomes have long been considered ideal platforms for systemic drugs delivery. Considering the development in cancer therapeutics, this work emphases on the current advancements in liposomes’ Nano-formulations, functionalization, and design and how it has been applied to nucleic acid therapeutics. Accordingly, this review covers the literature that deals with liposomes in nucleic acid therapy.

Little remains known regarding the impact of weight loss on sarcopenic obesity (SO), and for this reason we aimed to assess the relationship between the two during a weight management program. Body composition was measured at baseline and six-month follow-up using the Tanita BC-418, and step measurements were obtained daily over a period of six months using an Omron HJ-320 pedometer, in 41 adults of both genders with obesity. The participants were then categorized according to the presence or absence of SO. After a significant weight loss, an improvement in the appendicular skeletal mass (ASM) to weight ratio (24.5 ± 3.5 vs. 26.2 ± 3.6, p < 0.01), indicated a decrease in the prevalence of SO by 12.2%. Moreover, these findings were confirmed by logistic regression analysis revealing a significant WL% ≥ 5% combined with an active lifestyle (i.e., ≥8000 steps/day), decreased the risk of SO by 91% (OR = 0.09; 95% CI: 0.02–0.56), after adjusting for age and gender. In conclusion, in a weight management setting, a personalized program for individuals with SO that incorporates new strategies in terms of weight loss and physical activity targets may be adopted to improve the sarcopenia-related index and reduce the prevalence of SO in this population.

Majocchi’s granuloma (MG) is an uncommon form of deep fungal folliculitis that is most frequently caused by dermatophytes. Here, we present a case of facial MG.

Abstract Content addressable memory (CAM) for search and match operations demands high speed and low power for near real-time decision-making across many critical domains. Resistive RAM (RRAM)-based in-memory computing has high potential in realizing an efficient static CAM for artificial intelligence tasks, especially on resource-constrained platforms. This paper presents an XNOR-based RRAM-CAM with a time-domain analog adder for efficient winning class computation. The CAM compares two operands, one voltage and the second one resistance, and outputs a voltage proportional to the similarity between the input query and the pre-stored patterns. Processing the summation of the output similarity voltages in the time-domain helps avoid voltage saturation, variation, and noise dominating the analog voltage-based computing. After that, to determine the winning class among the multiple classes, a digital realization is utilized to consider the class with the longest pulse width as the winning class. As a demonstrator, hyperdimensional computing for efficient MNIST classification is considered. The proposed design uses 65 nm CMOS foundry technology and realistic data for RRAM with total area of 0.0077 mm2, consumes 13.6 pJ of energy per 1 k query within 10 ns clock cycle. It shows a reduction of ~ 31 × in area and ~ 3 × in energy consumption compared to fully digital ASIC implementation using 65 nm foundry technology. The proposed design exhibits a remarkable reduction in area and energy compared to two of the state-of-the-art RRAM designs.