Hepatitis C virus (HCV) is a significant cause of liver-related diseases including acute and chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Despite the availability of advanced treatments, underdiagnosis remains a critical challenge, particularly in resource-limited settings. This study explores the application of machine learning algorithms, specifically the K-Nearest Neighbors (KNN) method, to enhance the diagnosis of HCV by classifying patients into healthy, potentially diseased, and diseased categories based on liver function test results. Using a biomedical dataset of 615 patients, the model achieved high accuracy (99%), precision (98%), and sensitivity (99%), indicating its potential effectiveness in identifying HCV-infected individuals. The study highlights the importance of feature selection in improving model performance and discusses the implications of the findings for enhancing HCV diagnosis and management

Key Points Question What is the current red blood cell (RBC) transfusion practice for preterm infants born before 32 weeks’ gestation in Europe? Findings This cohort study included 1143 infants from 64 neonatal intensive care units across 22 European countries. By day 28 of life, 36.5% of infants had received an RBC transfusion, and most transfusions based on hemoglobin threshold were given above restrictive thresholds tested in recent trials. Meaning These findings suggest that there is a need to address the gap between evidence and practice and to understand factors influencing ongoing variable practices of RBC transfusions among preterm infants.

Logistic, transport and mobility is an important human need. To meet these mobility needs, our driving and movements must be safe, i.e. without consequences and injuries. Traffic safety can result in significant human and economic losses. As road traffic fatalities remain a global societal problem, finding effective solutions has become a top priority. Leveraging artificial intelligence (AI) presents a promising avenue for improving traffic safety through innovative approaches and applications. Improving traffic safety using artificial intelligence involves various applications and strategies aimed at enhancing road safety through advanced technological means. Improving traffic safety using artificial intelligence can be through next approaches: Intelligent traffic management systems; Predictive maintenance; Autonomous vehicles; Accident prediction and prevention; Driver assistance systems. By integrating artificial intelligence into traffic safety measures,we make transportation safer for everyone.

Područje rijeke Blihe na lokalitetu vodopada Blihin skok, uslijed značajnih hidroekoloških karakteristika, predstavlja poseban prirodni fenomen. Fenomen prirode je naglasak na vodopadu koji tvori malo jezerce (nakon pada od 50–60 m), a nizvodno nastavlja brzim tokom tekućice okružene obroncima (desna obala korita) gusto obraslim stablima hrasta, bukve, javora, graba i dr. Kao posljedica sve intenzivnijeg korištenja prirode i njenih resursa, ovaj dio toka nije ostao netaknut. Neplanska sječa šuma je sveprisutniji vremenski uticaj na hidro-ekološke karakteristike samog vodotoka rijeke Blihe. Jedan od intenzivnijih pritisaka na datom prostoru (koji nema nikakav aspekt zaštite) predstavlja uzvodni površinski kop uglja na području Kamengrada. Cilj rada je ukazati na prirodne vrijednosti rijeke Blihe na području vodopada, koje su pod direktnim pritiscima, koji će rezultirati nestajanjem ili iščezavanjem biljnih i životinjskih organizama. Terenska istraživanja za potrebe ovog rada realizovana su u periodu od 2020. do 2021. godine. Na terenu su registrovane vegetacijske zajednice, izvršen je popis vrsta, procijenjena je pokrovnost, a za potrebe upoznavanja akvatičnih zajednica organizama izvršeno je uzorkovanje bentosa. Na obroncima istraživanog dijela rijeke Blihe razvijene su zajednice klasa: Betula-Adenostyletea Br.-Bl. 1946, Alneta glutinosa Br.-Bl. et Tx 1943, Molinio-Juncetea Br.-Bl. Ex O.Bolós 1950 i Querco-Fagetaea Br.-Bl. et Vliegere in Vlieger 1937. Iako je konstatovan značajan uticaj faktora degradacije (antropogeni), istraživani prostor karakteriše visok specijski diverzitet biljaka koji upućuje na visok ekosistemski i pejzažni diverzitet. Akvatični diverzitet karakteriše prisustvo predstavnika Amphibia, Pisces i bogatstvo oblika makrozoobentosa. Na temelju kvalitativno-kvantitativnog sastava uzoraka makrozoobentosa utvrđeno je prisustvo 18 taksa invertebrata sa dominacijom preimaginalnih stadija senzitivnih skupina insekata redova: Ephemeroptera, Plecoptera i Diptera. Ovaj dio vodotoka stanište je i zaštićene vrste Austropotamobius torrentium (ovo je prvi nalaz vrste na datom prostoru). Pritisci na ekosisteme kopna i vode se intenziviraju, te se u budućnosti očekuju daleko vidljiviji degradacijski stadiji.

Simple Summary This study explored the differences in skull and mandible shapes between male and female brown bears using advanced 3D imaging techniques. The main goal was to understand how and why these shapes vary, focusing on whether these differences are due to size or sex. The research found that male brown bears have longer and thinner skulls, with notable changes in specific areas like the back of the head and jaw muscles. These differences are likely linked to the larger body size of male bears, which may require stronger muscles and a different skull structure to support their greater weight and chewing needs. The study highlights the importance of these shape variations for the bears’ adaptation to their environment. Understanding these differences can help conserve and manage brown bear populations by providing insights into their physical development and needs. This knowledge is valuable for wildlife biologists, conservationists, and anyone interested in the natural world, offering a clearer picture of how animals adapt over time. Abstract The present study aimed to describe the skull and mandibles of the brown bear (U. arctos) from the Dancing Bear Belitsa Park using advanced 3D morpho-geometric techniques. The objective was to explore how sexual dimorphism and size influence cranial structures using advanced 3D geometric morphometric methods. Three-dimensional models of the skulls and mandibles of 12 brown bears were used. Differences in skull morphology between male and female brown bears were observed in this study. The male brown bear skull, larger than the female, exhibited a more pronounced extension of the upper part of the nuchal region towards the posterior. Additionally, the posterior part of the frontal region appeared notably thinner in male brown bears compared to females. Analysis of the mandible revealed that the masseteric fossa was more developed in males than females. These shape differences between males and females were found to be influenced by body size. Statistical analyses indicated a significant allometric effect of body size on skull PC1 values, suggesting that giant bears tend to have more elongated skulls. This implies a relationship in which larger individuals exhibit greater cranial elongation. In contrast, mandible PC1 values showed no size-related variation, indicating that mandibular shape is less influenced by overall body size. However, PC2 values in the mandible increased significantly with larger specimens, indicating a larger masseteric fossa in larger bears. This morphological adaptation potentially enhances feeding efficiency and bite strength in larger individuals, reflecting functional adaptations in brown-bear mandibular morphology. These findings contribute to our understanding of sexual dimorphism and size-related morphological adaptations in brown bears, providing insights into their adaptation biology and ecological roles within their habitats.

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.

Diabetes mellitus and inflammatory bowel disease are chronic conditions with significant overlap in their pathophysiology, primarily driven by chronic inflammation. Both diseases are characterized by an aberrant immune response and disrupted homeostasis in various tissues. However, it remains unclear which disease develops first, and which one contributes to the other. Diabetes mellitus increases the risk of inflammatory bowel disease and inflammatory bowel disease may increase the risk of developing diabetes. This review focuses on comprehensively discussing the factors commonly contributing to the pathogenesis of diabetes mellitus and inflammatory bowel disease to draw a relationship between them and the possibility of targeting common factors to attenuate the incidence of one if the other is present. A key player in the intersection of diabetes mellitus and inflammatory bowel disease is the NLRP3 inflammasome, which regulates the production of pro-inflammatory cytokines leading to prolonged inflammation and tissue damage. Additionally, toll-like receptors via sensing microbial components contribute to diabetes mellitus and inflammatory bowel disease by initiating inflammatory responses. Gut dysbiosis, a common link in both diseases, further intensifies inflammation and metabolic dysfunction. Alterations in gut microbiota composition affect intestinal permeability and immune modulation, perpetuating a vicious cycle of inflammation and disease progression by changing protein expression. The overlap in the underlying inflammatory mechanisms has led to the potential of targeting mediators of chronic inflammation using anti-inflammatory drugs and biologics that benefit both conditions or attenuate the incidence of one in the presence of the other.

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.

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.

Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways, and examine human-AI interaction effects to explore automation bias. Operating an AI reader as a second reader or as a high confidence filter improves current screening outcomes by 1.9–2.5% in sensitivity and up to 0.6% in specificity, achieving 4.6–10.9% reduction in assessments and 48–80.7% reduction in human reads. Automation bias degrades performance in multi-reader settings but improves it for single-readers. This study provides insight into feasible approaches for AI-integrated screening pathways and prospective studies necessary prior to clinical adoption. Successful human-AI collaboration could greatly contribute to breast cancer mammographic screening. Here, the authors use a large-scale retrospective mammography dataset to simulate and compare five plausible AI-integrated screening pathways, finding optimal ways in which human-AI collaboration could be implemented in real-world settings.

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.