Background Introduction: Vitamin D plays significant role in calcium metabolism and in bone and vascular calcifications. Objective: To investigate the association between vitamin D level, arterial hypertension, arterial stiffness and coronary calcifications detected by MSCT. Method: A 2 female case report comparative to each other investigated the correlation between vitamin D serum level, blood pressure, arterial stiffness and severity of the coronary calcification using MSCT diagnostic tool estimating the calcium score. Results: The first case report showed that decreased level of vitamin D is correlated with increased blood pressure, increased arterial stiffness and with a severe coronary calcifications. The second case report showed normal blood pressure, normal vascular age and low calcium score in a no-defficient vitamin D female. Conclusion: Vitamin D has impact on blood pressure, arterial stiffness, coronary calcifications and coronary heart disease. The lower vitamin D, the higher arterial blood pressure, arterial stiffness and coronary calcium score.

The diagnosis of extensive pulmonary tuberculosis, especially in young people, should take into account the possibility of an associated systemic autoimmune disease. Infections remain an important cause of morbidity and mortalityin systemic lupus erythematosus. This case illustrates the importance of recognizing the association of systemic autoimmune diseases and infections and the need for a multidisciplinary approach.

Background Pharmacological treatment options for patients with dementia owing to Alzheimer's disease are limited to symptomatic therapy. Recently, the US Food and Drug Administration approved the monoclonal antibody lecanemab for the treatment of amyloid-positive patients with mild cognitive impairment (MCI) and early Alzheimer´s dementia. European approval is expected in 2024. Data on the applicability and eligibility for treatment with anti-amyloid monoclonal antibodies outside of a study population are lacking. Aims This study examined eligibility criteria for lecanemab in a real-world memory clinic population between 1 January 2022 and 31 July 2023. Method We conducted a retrospective, single-centre study applying the clinical trial eligibility criteria for lecanemab to out-patients of a specialised psychiatric memory clinic. Eligibility for anti-amyloid treatment was assessed following the phase 3 inclusion and exclusion criteria and the published recommendations for lecanemab. Results The study population consisted of 587 out-patients. Two-thirds were diagnosed with Alzheimer's disease (probable or possible Alzheimer's disease dementia in 43.6% of cases, n = 256) or MCI (23%, n = 135), and 33.4% (n = 196) were diagnosed with dementia or neurocognitive disorder owing to another aetiology. Applying all lecanemab eligibility criteria, 11 (4.3%) patients with dementia and two (1.5%) patients with MCI would have been eligible for treatment with this compound, whereas 13 dementia (5.1%) and 14 (10.4%) MCI patients met clinical inclusion criteria, but had no available amyloid status. Conclusions Even in a memory clinic with a good infrastructure and sufficient facilities for dementia diagnostics, most patients do not meet the eligibility criteria for treatment with lecanemab.

The Sustainable Development Goals are far off track. The convergence of global threats such as climate change, conflict and the lasting effects of the COVID-19 pandemic—among others—call for better data and research evidence that can account for the complex interactions between these threats. In the time of polycrisis, global and national-level data and research evidence must address complexity. Viewed through the lens of ‘systemic risk’, there is a need for data and research evidence that is sufficiently representative of the multiple interdependencies of global threats. Instead, current global published literature seems to be dominated by correlational, descriptive studies that are unable to account for complex interactions. The literature is geographically limited and rarely from countries facing severe polycrisis threats. As a result, country guidance fails to treat these threats interdependently. Applied systems thinking can offer more diverse research methods that are able to generate complex evidence. This is achievable through more participatory processes that will assist stakeholders in defining system boundaries and behaviours. Additionally, applied systems thinking can draw on known methods for hypothesising, modelling, visualising and testing complex system properties over time. Application is much needed for generating evidence at the global level and within national-level policy processes and structures.

African swine fever virus (ASFV) has been spreading through Europe, Asia, and the Caribbean after its introduction in Georgia in 2007 and, due to its particularly high mortality rate, poses a continuous threat to the pig industry. The golden standard to trace back the ASFV is whole genome sequencing, but it is a cost and time-intensive methodology. A more efficient way of tracing the virus is to amplify only specific genomic regions relevant for genotyping. This is mainly accomplished by amplifying single amplicons by PCR followed by Sanger sequencing. To reduce costs and processivity time, we evaluated a multiplex PCR based on the four primer sets routinely used for ASFV genotyping (B646L, E183L, B602L, and intergenic I73R-I329L), which was followed by Nanopore ligation-based amplicon sequencing. We show that with this protocol, we can genotype ASFV DNA originating from different biological matrices and correctly classify multiple genotypes and strains using a single PCR reaction. Further optimization of this method can be accomplished by adding or swapping the primer sets used for amplification based on the needs of a specific country or region, making it a versatile tool that can speed up the processing time and lower the costs of genotyping during ASFV outbreaks.

Background: Lactate dehydrogenase (LDH) isoenzyme assay was used widely in the past to diagnose myocardial infarction (MI). Recent studies show that lactate dehydrogenase seems to be a promising biomarker of adverse left ventricular remodeling. Objectives: Higher levels of these biomarkers were associated with lower odds for favorable reverse remodeling in patients with MI. Methods: The study was performed on patients with the first occurrence of acute myocardial infarction (ST-elevation myocardial infarction (STEMI) or non-ST-elevation myocardial infarction (NSTEMI)), aged 34 to 80 years who underwent catheterization at the admission or during their hospital stay depending on indications. In this study, we compared peak levels of lactate dehydrogenase (LDH) and left ventricular ejection fraction (LVEF). Peak values of LDH were used from the second to the fourth day of hospitalization. Echocardiography has been done in the first 72 hours, which represents an early phase of cardiac remodeling. The ejection fraction was evaluated using the Simpson method. Results: Spearman's rank test showed a negative, statistically significant correlation between LDH and ejection fraction ρ(80)=−0.543, p<0.001. Weighted least squares regression model included LDH concentration, age, and type of myocardial infarction (STEMI/NSTEMI), and the slope coefficient for the LDH level was −0.010 (95% confidence interval (CI): −0.013 to −0.006). With each unit of LDH increase, there was a decrease of 0.01% in left ventricular ejection fraction when age and type of myocardial infarction were held constant. Conclusion: The increased LDH level could be a new predictor for early myocardial remodeling after the first occurrence of myocardial infarction independent of age and type of myocardial infarction.

In recent advancements in robotics, Artificial Intelligence (AI) methods such as Deep Learning, Deep Reinforcement Learning (DRL), Transformers, and Large Language Models (LLMs) have significantly enhanced robotic capabilities. Key AI models driving advancements in robotic vision include Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), the DEtection Transformers (DETR), the YOLO family of algorithms, segmentation techniques, and 3D vision technologies. Deep Reinforcement Learning (DRL), an AI technique where agents learn optimal behaviors through trial and error interactions with their environment, enables robots to perform complex tasks autonomously. Transformers, originally developed for natural language processing, have been adapted to robotics for tasks involving sequence prediction and data understanding, improving perception and decision-making processes. LLMs leverage vast amounts of text data to enhance robot-human interaction, enabling robots to understand and generate human-like language, thus improving their communicative and collaborative abilities in various applications. The integration of these AI methods enhances the adaptability, efficiency, and overall performance of robotic systems, paving the way for more sophisticated and intelligent autonomous agents.

Objective The aim was to test the Belgrade age formula based on the calculation of open apices of two permanent mandibular teeth on a Bosnian children population and compare its accuracy with European formula. Material and methods We included 412 panoramic images of children (204 female and 208 male) 7 to 13 years of age. We assessed the performance of both methods (the European formula and the BAF) and compared their results in both sexes. Results The results showed a high point of average understanding between the age estimated by chronological age and the European formula (ICC=0.927, 95% CI 0.904–0.944, p<0.001)., BAF also confirmed a high point of agreement with chronological age in boys (ICC=0.941, 95% CI 0.922–0.955, p<0.001) and girls (ICC=0.913, 95% CI 0.886–0.934, p<0.001). BAF was better than the European formula in estimating age in males (0.4448±0.9135 vs. 0.9807±0.9422). Conclusion The Belgrade Age Formula (BAF) demonstrates comparable accuracy to the European formula for age determination in Bosnian children, while offering the advantage of being easier and faster to use. This makes the BAF a practical alternative in clinical and research settings where efficiency and reliability are essential.

Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional machine learning models than DL models, which are largely attributed to the size and structure of tabular datasets and the specific application contexts in which they are utilized. Thus, the primary objective of this paper is to propose a method to use the supremacy of Stacked Bidirectional LSTM (Long Short-Term Memory) deep learning algorithms in pattern discovery incorporating tabular data with customized 3D tensor modeling in feeding neural networks. Our findings are empirically validated using six diverse, publicly available datasets each varying in size and learning objectives. This paper proves that the proposed model based on time-sequence DL algorithms, which were generally described as inadequate when dealing with tabular data, yields satisfactory results and competes effectively with other algorithms specifically designed for tabular data. An additional benefit of this approach is its ability to preserve simplicity while ensuring fast model training also with large datasets. Even with extremely small datasets, models can be applied to achieve exceptional predictive results and fully utilize their capacity.

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

Abstract This paper introduces a novel method that leverages artificial neural networks to estimate magnetic flux density in the proximity of overhead transmission lines. The proposed method utilizes an artificial neural network to estimate the parameters of a mathematical model that describes the magnetic flux density distribution along the lateral profile for various configurations of overhead transmission lines. The training target data is acquired using the particle swarm optimization algorithm. A performance comparison between the proposed method and the Biot-Savart law-based method is conducted using an extensive test dataset. The resulting coefficient of determination and mean square error values demonstrate the successful application of the proposed method for a range of different spatial arrangements of phase conductors. Furthermore, the performance of the proposed method is thoroughly assessed on multiple test cases. The practical relevance of the proposed method is highlighted by contrasting its results with the field measurements obtained in the proximity of a 400 kV overhead transmission line.