AIM To investigate the risk for falls in elderly patients treated in the Primary Health Care Centre Gradiška, Bosnia and Herzegovina. METHODS This study included 500 patients aged 65 and older. They were chosen randomly by 10 family physicians. Data collection took place every Wednesday and Friday, between January 2022 and July 2022. The patients' gait and balance assessment were performed using the Tinetti Gait and Balance Tool to assess the risk of falls. A supplementary questionnaire was created to record data about the patients' age, sex, chronic diseases, and drugs they take. RESULTS Among the included patients there were 266 females (53.2%) and 234 (46.8%) males, with the mean age of 75.25 years. The Tinetti test showed that the risk of falls was high for patients older than 75 years, 111 patients (69.8%), and 48 patients (30.2%) aged 65 to 74 (p=0.000). The risk of falling was higher for female, 93 (35%), than male patients, 66 (28.2%) (p=0.018). Considering chronic diseases, a high risk of falls was found in 32 (2.1%) patients with heart failure (p=0.029) and 19 (11.9%) patients with osteoporosis (p=0.000). Patients who used antihypertensive drugs had the highest risk for falls, 124 (78.0%) (p=0.757). CONCLUSION About two-thirds of the examinees over the age of 75 had a high risk of falls, which indicates that family doctors should be more involved in fall prevention of elderly patients and constantly educate older patients and their families about it.

Using AI through industries and business processes is increasingly becoming the subject of theorists and practitioners. In the HRM process, the use of AI gives companies numerous advantages in employee performance, and processes, but also presents them with organizational, financial, technical, legal, and personnel challenges. This paper explores the application of AI systems in recruitment and selection through gamification strategies, people analytics, talent intelligence, AI platforms, video interviews, and conversational AI. It provides an overview of the benefits and challenges associated with their implementation. Additionally, the paper delves into ethical considerations and legislation, focusing on the EU Act, domestic laws, and ISO AI standards. The primary goal of this paper is to provide a comprehensive understanding of AI's role in HR processes and the complexities of implementing AI solutions in recruitment and selection.

In this study, we develop an in silico model of a neuron’s behaviour under demyelination caused by a cytokine storm to investigate the effects of viral infections in the brain. We use a comprehensive model to measure how cytokine-induced demyelination affects the propagation of action potential (AP) signals within a neuron. We analysed the effects of neuron-neuron communications by applying information and communication theory at different levels of demyelination. Our simulations demonstrate that virus-induced degeneration can play a role in the signal power and spiking rate, which compromise the propagation and processing of information between neurons. We propose a transfer function to model the weakening effects on the AP. Our results show that demyelination induced by a cytokine storm not only degrades the signal but also impairs its propagation within the axon. Our proposed in silico model can analyse virus-induced neurodegeneration and enhance our understanding of virus-induced demyelination.

Background and Objectives: The saphenous vein graft (SVG) remains the most frequently used conduit worldwide, despite its common disadvantage of early graft failure. To solve the problem and reduce the SVG damage, Souza implemented a new technique where a vein is harvested with surrounding fascia and fat tissue (the so-called no-touch technique). Materials and Methods. A prospective study conducted from February 2019 to June 2024 included 23 patients who underwent myocardial revascularization using a no-touch vein, with follow-up control examinations using computed tomographic angiography to detect graft stenosis or occlusion. Results. Of the entire patient group, 17 (73.9%) were male, with a mean age of 67.39 ± 7.71 years. The mean follow-up period was 25 months. There were no major adverse cardiovascular or cerebrovascular events (MACCEs) during hospitalization, although one patient died in the hospital. Another patient died due to malignancy, but no MACCEs occurred during the follow-up period. According to multi-slice CT coronary angiography, the results were impeccable, with an astonishing 100% patency observed in all 20 IMA grafts and 58 no-touch SVGs examined. Conclusions. The excellent patency rate during the early follow-up period confirmed that the no-touch technique is a good option for surgical revascularization.

Purpose: Previous studies of the Public Opinion Survey of Human Attributes-Stuttering (POSHA-S), using test and retest designs in modest-sized samples, have reported satisfactory test-retest reliability, i.e., correlations of about 0.80. Simultaneously, lower but moderate correlations between different first and second test respondents were observed and hypothesized to represent unspecified “societal” influences on stuttering attitudes. This study sought to clarify this and other potential relationships between first and second tests with the POSHA-S in a large, geographically and linguistically diverse sample.Methods: POSHA-S Overall Stuttering Scores (OSSs) of 345 respondents from 12 test-retest samples from four countries and languages, with no intervening interventions, were analyzed with correlations and by grouping respondents according to whose stuttering attitudes improved, remained the same, or worsened from test to retest.Results: Test and retest OSSs generally conformed to normal distributions and were not significantly different. Correlations between first versus second tests replicated earlier research. However, when the degree and direction of change from test to retest was considered, both in other correlations and in sorts of respondents, unexpected results emerged. Respondents with intermediate attitudes changed minimally, while those with most and least positive attitudes at the first test changed in opposite directions past the overall mean at second test.Conclusions: While demonstrating adequate test-retest reliability correlations on the POSHA-S, public attitudes were found to be less stable than previously assumed.

Background and Objectives Myelin and iron play essential roles in remyelination processes of multiple sclerosis (MS) lesions. χ-separation, a novel biophysical model applied to multiecho T2*-data and T2-data, estimates the contribution of myelin and iron to the obtained susceptibility signal. We used this method to investigate myelin and iron levels in lesion and nonlesion brain areas in patients with MS and healthy individuals. Methods This prospective MS cohort study included patients with MS fulfilling the McDonald Criteria 2017 and healthy individuals, aged 18 years or older, with no other neurologic comorbidities. Participants underwent MRI at baseline and after 2 years, including multiecho GRE-(T2*) and FAST-(T2) sequences. Using χ-separation, we generated myelin-sensitive and iron-sensitive susceptibility maps. White matter lesions (WMLs), cortical lesions (CLs), surrounding normal-appearing white matter (NAWM), and normal-appearing gray matter were segmented on fluid-attenuated inversion recovery and magnetization-prepared 2 rapid gradient echo images, respectively. Cross-sectional group comparisons used Wilcoxon rank-sum tests, longitudinal analyses applied Wilcoxon signed-rank tests. Associations with clinical outcomes (disease phenotype, age, sex, disease duration, disability measured by Expanded Disability Status Scale [EDSS], neurofilament light chain levels, and T2-lesion number and volume) were assessed using linear regression models. Results Of 168 patients with MS (median [interquartile range (IQR)] age 47.0 [21.7] years; 101 women; 6,898 WMLs, 775 CLs) and 103 healthy individuals (age 33.0 [10.5] years, 57 women), 108 and 62 were followed for a median of 2 years, respectively (IQR 0.1; 5,030 WMLs, 485 CLs). At baseline, WMLs had lower myelin (median 0.025 [IQR 0.015] parts per million [ppm]) and iron (0.017 [0.015] ppm) than the corresponding NAWM (myelin 0.030 [0.012]; iron 0.019 [0.011] ppm; both p < 0.001). After 2 years, both myelin (0.027 [0.014] ppm) and iron had increased (0.018 [0.015] ppm; both p < 0.001). Younger age (p < 0.001, b = −5.111 × 10−5), lower disability (p = 0.04, b = −2.352 × 10−5), and relapsing-remitting phenotype (RRMS, 0.003 [0.01] vs primary progressive 0.002 [IQR 0.01], p < 0.001; vs secondary progressive 0.0004 [IQR 0.01], p < 0.001) at baseline were associated with remyelination. Increment of myelin correlated with clinical improvement measured by EDSS (p = 0.015, b = −6.686 × 10−4). Discussion χ-separation, a novel mathematical model applied to multiecho T2*-images and T2-images shows that young RRMS patients with low disability exhibit higher remyelination capacity, which correlated with clinical disability over a 2-year follow-up.

In the digital era of e-commerce, effective content management is crucial for engaging and retaining online consumers. Traditional manual approaches to content creation often fall short in terms of speed, scalability, and adaptability. With over 26.5 million e-commerce stores worldwide, staying competitive requires leveraging all available tools. This research paper investigates the efficiency and effectiveness of AI-driven content generation compared to traditional methods. We examine AI technologies for creating titles, subtitles, and SEO optimization against content writers. The study involves five authors and an AI tool generating content for five products, with time taken for content creation measured and compared. Additionally, a group of 15 participants will evaluate the professional quality and click ability of the generated content. Using Python, we will analyze the potential time savings for generating 100 titles and assess the overall quality improvement. The results aim to provide empirical evidence on the benefits of AI in content creation for e-commerce. Our findings reveal that AI significantly reduces the time required for content creation. Specifically, AI-generated titles are 84.17% faster and AI-generated subtitles are 77.31% faster compared to those created by content writers. The content writers worked without the aid of any tools, relying solely on provided specifications. Additionally, 81.33% of participants preferred the titles generated by AI, while 88% favoured the AI-generated subtitles. These results underscore the potential of AI to enhance efficiency and effectiveness in e-commerce content management.

Extensive research into platinum-based chemotherapeutics has been underway for decades with ruthenium-based complexes emerging as interesting and potent candidates. Even still, there is no evidence of a single mechanism of action across all synthesized and tested Ru-based complexes, prompting the continuance of research in this field. In addition, the mechanism of action varies according to cell line and/or animal model and is seemingly highly individualized and personalized. In accordance with this, the ruthenium complexes are able to activate specific molecular pathways and interact with certain targets within the cell, sometimes reported simultaneously. In this review, we attempt to give a new perspective on ruthenium complexes’ anti-cancer properties and organize selected results from the past 15 years of research connecting their structure with the reported mechanism of action. These results corroborate the previously reported great potential that ruthenium complexes have on cancer in vitro. In addition, the review provides insight into Ru drugs in their clinical trials and their efficacy against cancer including a historical context on metallodrugs, particularly platinum-based complexes, and their antitumor capability.

The pyrimidine heterocycle plays an important role in anticancer research. In particular, the pyrimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning quantitative structure-activity relationships (QSARs) that allow for generalization and predictive capabilities in the search for new active compounds. To achieve this, a dataset of pyrimidine and uracil compounds from ChEMBL were collected and curated. A workflow was developed for data-driven machine learning QSAR using an intuitive dataset design and forwards selection of molecular descriptors. The model was thoroughly externally validated against available data. Blind validation was also performed by synthesis and antiproliferative evaluation of new synthesized uracil-based and pyrimidine derivatives. The most active compound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with the QSAR model with differences of 0.02 compared to experimentally tested activity.