Abstract Objective The objective of this prospective study was to assess the concentration and impact of maternal 25(OH)D status on neonatal vitamin D concentrations and early neonatal outcomes in the newborns of mothers who did not take vitamin D supplements during pregnancy. Methods and participants The study is a cohort prospective study of the correlation of VD concentrations in mothers and their newborns. The study included 100 pairs. Results Only 16 mothers had a VD concentration in the reference interval (75–100 nmol/L), while 84 mothers had values less than 75 nmol/L (p<0.001). A significant difference in maternal VD concentration was determined in relation to tobacco consumption habits during pregnancy and placental condition (p<0.001). 95% of the neonates (95/100) of older, obese multigravida, who had hypovitaminosis D and inadequate exposure to sunshine, had normal VD concentrations (the mean=49.27 nmol/L) on the first day of life. The majority of the mothers gave birth to full-termnewborns with normal vitality scores and CRP and bilirubin levels in the reference interval. Conclusion The conclusion of this prospective study is that 84% of the healthy pregnant women had hypovitaminosis D. However 95% of their newborns were born full term, with normal anthropometric measurements, normal vitality scores, and normal VD concentrations. This study also confirmed that there is still no cause-and-effect association between hypovitaminosis D in pregnant women and their offspring with outcome parameters for both.

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.

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.

INTRODUCTION Hypothermia is defined as a body core temperature below 35 °C and can be caused by internal or external stress. Primary hypothermia is caused by excessive exposure to low environmental temperature without any medical conditions prior to that. Secondary hypothermia is caused by alteration in thermoregulation by disease, trauma, surgery, drugs, or infections. The aim of the research is to investigate core temperature values in rats subjected to specific water temperatures at five different time points. It focuses on distinguishing between primary and secondary hypothermia in these rats. METHODS The total 21 Wistar rats were divided into three experimental groups as: Control group rats exposed only to hypothermic condition (n = 7); Alcohol + hypothermia (n = 7); and Benzodiazepines + hypothermia (n = 7). The temperature spots analyzed in the study were: normal core temperature, core temperature during injection of 0,3 ketamine, temperature of immersion and the temperature at the onset of hypothermia and temperature at the time of death. RESULTS In our study the comparative analysis of body temperatures at various time points following submersion in water revealed significant differences among the study groups treated with either alcohol or benzodiazepines and the control group. Notable differences were observed in baseline temperature, post-anesthesia induction temperature, and immediate post-submersion temperature. Specifically, significant differences were discovered among the alcohol and benzodiazepine groups (p < 0.001) and ranging from the alcohol and control groups (p < 0.001). The analysis of survival times following induced hypothermia revealed a statistically significant difference among the three experimental groups (p = 0.04), though subsequent post-hoc comparisons did not demonstrate significant differences in mean survival times. CONCLUSION There is a difference in survival time between primary and secondary hypothermia groups, depending on consumption and intoxication with alcohol or benzodiazepines. The analysis of survival times following induced hypothermia showed a statistically significant difference among the groups.

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.

From ancient cold-blooded fishes to mammals, all vertebrates are protected by adaptive immunity, and retain immunological memory. Although immunologists can demonstrate these phenomena in all fish, the responding cells remain elusive for lack of defining markers and tools to study them. Fundamentally, we posited that it is longevity that defines a memory cell like how antibody production defines a plasma cell. We infected the common carp with Sphaerospora molnari, a cnidarian parasite which causes seasonal outbreaks to which no vaccine is available. B cells proliferated and expressed gene signatures of differentiation. Despite a half-year gap between EdU labeling and sampling, B cells retained the thymidine analogue, suggesting that these are at least six-month-old resting memory cells stemming from proliferating precursors. Additionally, we identified a lymphoid organ-resident population expressing exceptional levels of IgM as plasma cells. Thus, teleost fish produce the lymphocytes key to vaccination success and long-term disease protection, and immunological memory is universal and universally demonstrable.

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.

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.

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.

This paper is the first to analyse the role of women authors in fostering justice-relevant topics in climate adaptation research. As representation, citation and payment patterns remain gender-biased across scientific disciplines, we explore the case of climate science, particularly adaptation, as its most human-oriented facet. In climate research and policy, there has been a recent surge of interest in climate justice topics: mentions of justice have increased almost tenfold in Intergovernmental Panel on Climate Change Working Group 2 reports between the latest assessment cycles (AR5 and AR6). We conduct a systematic examination of the topic space in the adaptation policy scholarship. As it is a vast and rapidly growing field, we use topic modelling, an unsupervised machine learning method, to identify the literature on climate justice and related fields, as well as to examine the relationship between topic prevalence and the gender of the authors. We find climate change adaptation policy research to be male dominated, with women holding 38.8% of first and 28.8% of last authorships. However, we observe topic-specific variability, whereby the share of female authors is higher among publications on justice-relevant topics. Female authorship is highly linked to topics such as Community, Local Knowledge, and Governance, but less to Food Security and Climate Finance. Our findings corroborate the evidence that female authors play a significant role in advancing the research and dialogue on the relationship between climate change and areas that have meaningful impact on lives of women and other marginalised groups.