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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.

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.

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.

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.

The cultivated apple (Malus domestica Borkh.) is an economically important fruit crop in countries worldwide, including Bosnia and Herzegovina (BIH).The gene bank activities in BIH were initiated in the 1930s and continued until the war in the 1990s, when much of the documentation was lost. Since then, uncoordinated efforts were made to establish apple collections in different regions, but a comprehensive analysis of genetic resources was lacking. This prompted the current study where we present the first thorough overview of the national genetic resources of BIH apples. Thus, we analyzed 165 accessions in the apple gene bank at the Institute for Genetic Resources (IGR) established at Banja Luka using the 20 K apple Infinium® single nucleotide polymorphism (SNP) array. We combined the results with previously published data on the germplasm collections at Srebrenik and Goražde, genotyped using the Axiom® Apple 480 K SNP array. In total, 234 accessions were included in the study of which 220 were presumed to be local cultivars and 14 were known international reference cultivars. We identified numerous genotypic duplicates within and between collections and suggested preferred names to be used in the future. We found the BIH germplasm to have relatively few parent-offspring relationships, particularly among local cultivars, which might reflect the country’s history and patterns of apple cultivar introduction. A number of cultivars unique to BIH and a weakly defined genetic group were identified via STRUCTURE analysis, representing interesting targets for future research and preservation efforts.

Melanoma is the most severe type of skin cancer and among the most malignant neoplasms in humans. With the growing incidence of melanoma, increased numbers of therapeutic options, and the potential to target specific proteins, understanding the basic mechanisms underlying the disease’s progression and resistance to treatment has never been more important. LOXL3, SNAI1, and NES are key factors in melanoma genesis, regulating tumor growth, metastasis, and cellular differentiation. In our study, we explored the potential role of LOXL3, SNAI1, and NES in melanoma progression and metastasis among patients with dysplastic nevi, melanoma in situ, and BRAF+ and BRAF− metastatic melanoma, using immunofluorescence and qPCR analysis. Our results reveal a significant increase in LOXL3 expression and the highest NES expression in BRAF+ melanoma compared to BRAF−, dysplastic nevi, and melanoma in situ. As for SNAI1, the highest expression was observed in the metastatic melanoma group, without significant differences among groups. We found co-expression of LOXL3 and SNAI1 in the perinuclear area of all investigated subgroups and NES and SNAI1 co-expression in melanoma cells. These findings suggest a codependence or collaboration between these markers in melanoma EMT, suggesting new potential therapeutic interventions to block the EMT cascade that could significantly affect survival in many melanoma patients.

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.

With the dynamic nature of modern software development and operations environments and the increasing complexity of cloud-based software systems, traditional monitoring practices are often insufficient to timely identify and handle unexpected operational failures. To address these challenges, this paper presents the findings from a quantitative industry survey focused on the application of Machine Learning (ML) to enhance software monitoring and alert management strategies. The survey targets industry professionals, aiming to understand the current challenges and future trends in ML-driven software monitoring. We analyze 25 responses from 11 different software companies to conclude if and how ML is being integrated into their monitoring systems. Key findings revealed a growing but still limited reliance on ML to intelligently filter raw monitoring data, prioritize issues, and respond to system alerts, thereby improving operational efficiency and system reliability. The paper also discusses the barriers to adopting ML-based solutions and provides insights into the future direction of software monitoring.

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations.

The Rasch‐Built Pompe‐Specific Activity (R‐PAct) scale is a patient‐reported outcome measure specifically designed to quantify the effects of Pompe disease on daily life activities, developed for use in Dutch‐ and English‐speaking countries. This study aimed to validate the R‐PAct for use in other countries.

Organic materials have found widespread applications but require doping to overcome their intrinsically low carrier concentration. Doping injects free carriers into the polymer, moving the position of the Fermi level, and creates coulombic traps, changing the shape of the electronic density of states (DOS). We develop equations to explicitly map the DOS parameters to the Seebeck vs conductivity relationship. At low carrier concentrations, this relationship is a universal slope -kB/q\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-{k}_{B}/q$$\end{document}, while at higher carrier concentrations, the slope becomes dependent on the shape of the DOS. We conclude that, at high doping, a heavy-tailed DOS leads to higher thermoelectric power factors.