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Metabolic disorders, including type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome, are systemic conditions that profoundly impact the skin microbiota, a dynamic community of bacteria, fungi, viruses, and mites essential for cutaneous health. Dysbiosis caused by metabolic dysfunction contributes to skin barrier disruption, immune dysregulation, and increased susceptibility to inflammatory skin diseases, including psoriasis, atopic dermatitis, and acne. For instance, hyperglycemia in T2DM leads to the formation of advanced glycation end products (AGEs), which bind to the receptor for AGEs (RAGE) on keratinocytes and immune cells, promoting oxidative stress and inflammation while facilitating Staphylococcus aureus colonization in atopic dermatitis. Similarly, obesity-induced dysregulation of sebaceous lipid composition increases saturated fatty acids, favoring pathogenic strains of Cutibacterium acnes, which produce inflammatory metabolites that exacerbate acne. Advances in metabolomics and microbiome sequencing have unveiled critical biomarkers, such as short-chain fatty acids and microbial signatures, predictive of therapeutic outcomes. For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. Emerging therapies, such as probiotics and postbiotics, aim to restore microbial diversity, while phage therapy selectively targets pathogenic bacteria like Staphylococcus aureus without disrupting beneficial flora. Clinical trials have demonstrated significant reductions in inflammatory lesions and improved quality-of-life metrics in patients receiving these microbiota-targeted treatments. This review synthesizes current evidence on the bidirectional interplay between metabolic disorders and skin microbiota, highlighting therapeutic implications and future directions. By addressing systemic metabolic dysfunction and microbiota-mediated pathways, precision strategies are paving the way for improved patient outcomes in dermatologic care. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is a dangerous type of skin cancer that can grow quickly and spread to other parts of the body, making early detection and diagnosis essential for saving lives. However, it can be difficult to tell the difference between melanoma and harmless skin spots, even for experts. This study explores how advanced computer technologies called convolutional neural networks (CNNs) can help detect melanoma more accurately. These systems analyze skin images and identify patterns that indicate whether a spot is likely to be cancerous. We compared four different types of CNN to find the best balance between accuracy and efficiency. Our findings show that some models are not only highly accurate but also fast and lightweight, making them suitable for use in clinics or even on mobile devices. This research highlights the potential of artificial intelligence to assist doctors and improve early melanoma detection, ultimately saving more lives. Background/Objectives: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists. This study evaluates and compares the performance of four CNN architectures—DenseNet121, ResNet50V2, NASNetMobile, and MobileNetV2—for the binary classification of dermoscopic images. Methods: A dataset of 8825 dermoscopic images from DermNet was standardized and divided into training (80%), validation (10%), and testing (10%) subsets. Image augmentation techniques were applied to enhance model generalizability. The CNN architectures were pre-trained on ImageNet and customized for binary classification. Models were trained using the Adam optimizer and evaluated based on accuracy, area under the receiver operating characteristic curve (AUC-ROC), inference time, and model size. The statistical significance of the differences was assessed using McNemar's test. Results: DenseNet121 achieved the highest accuracy (92.30%) and an AUC of 0.951, while ResNet50V2 recorded the highest AUC (0.957). MobileNetV2 combined efficiency with competitive performance, achieving a 92.19% accuracy, the smallest model size (9.89 MB), and the fastest inference time (23.46 ms). NASNetMobile, despite its compact size, had a slower inference time (108.67 ms), and slightly lower accuracy (90.94%). Performance differences among the models were statistically significant (p < 0.0001). Conclusions: DenseNet121 demonstrated a superior diagnostic performance, while MobileNetV2 provided the most efficient solution for deployment in resource-constrained settings. The CNNs show substantial potential for improving melanoma detection in clinical and mobile applications. [ABSTRACT FROM AUTHOR]

Integrating artificial intelligence into border control systems may help to strengthen security and make operations more efficient. For example, the emerging application of artificial intelligence for lie detection when inspecting passengers presents significant opportunities for future implementation. However, as it makes use of technology that is associated with artificial intelligence, the system is classified as high risk, in accordance with the EU AI Act and, therefore, must adhere to rigorous regulatory requirements to mitigate potential risks. This manuscript distinctly amalgamates the technical, ethical, and legal aspects, thereby offering an extensive examination of the AI-based lie detection systems utilized in border security. This academic paper is uniquely set apart from others because it undertakes a thorough investigation into the categorization of these emerging technologies in terms of the regulatory framework established by the EU AI Act, which classifies them as high risk. It further makes an assessment of practical case studies, including notable examples such as iBorderCtrl and AVATAR. This in-depth analysis seeks to emphasize not only the enormous challenges ahead for practitioners but also the progress made in this emerging field of study. Furthermore, it seeks to investigate threats, vulnerabilities, and privacy concerns associated with AI, while providing security controls to address difficulties related to lie detection. Finally, we propose a framework that encompasses the EU AI Act's principles and serves as a foundation for future approaches and research projects. By analyzing current methodologies and considering future directions, the paper aims to provide a comprehensive understanding of the viability and consequences of deploying AI lie detection capabilities in border control. [ABSTRACT FROM AUTHOR]

The log files of Massive Open Online Courses (MOOCs) reveal useful information that can help interpret student behavior. In this study, we focus on student performance based on their access to course resources and the grades they achieve. We define states as the Moodle resources and quiz grades for each student ID, considering participation in resources such as wikis and forums. We use efficient Hidden Markov Models to interpret the abundance of information provided in the Moodle log files. The transitions among certain resources for each student or groups of students are determined as behaviors. Other studies employ Machine Learning and Pattern Classification algorithms to recognize these behaviors. As an example, we visualize these transitions for individual learners. Additionally, we have created row and column charts to present our findings in a comprehensible manner. For implementing the proposed methodology, we use the R programming language. The dataset that we use was obtained from Kaggle and pertains to a MOOC of 4037 students. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have transformed Natural Language Processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user's trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency. [ABSTRACT FROM AUTHOR]

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards. [ABSTRACT FROM AUTHOR]

Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of effective interventions. This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. It aims to determine the patterns from AMR gene data that are clinically relevant and, in public health, capable of informing strategies. Methods: We analyzed AMR gene data in the PanRes dataset by applying unsupervised learning techniques, namely K-means clustering and Principal Component Analysis (PCA). These techniques were applied to identify clusters based on gene length and distribution according to resistance class, offering insights into the resistance genes' structural and functional properties. Data preprocessing, such as filtering and normalization, was conducted prior to applying machine learning methods to ensure consistency and accuracy. Our methodology included the preprocessing of data and reduction of dimensionality to ensure that our models were both accurate and interpretable. Results: The unsupervised learning models highlighted distinct clusters of AMR genes, with significant patterns in gene length, including their associated resistance classes. Further dimensionality reduction by PCA allows for clearer visualizations of relationships among gene groupings. These patterns provide novel insights into the potential mechanisms of resistance, particularly the role of gene length in different resistance pathways. Conclusions: This study demonstrates the potential of ML, specifically unsupervised approaches, to enhance the understanding of AMR. The identified patterns in resistance genes could support clinical decision-making and inform public health interventions. However, challenges remain, particularly in integrating genomic data and ensuring model interpretability. Further research is needed to advance ML applications in AMR prediction and management. [ABSTRACT FROM AUTHOR]

Background/Objectives: Carbapenem resistance poses a significant threat to public health by undermining the efficacy of one of the last lines of antibiotic defense. Addressing this challenge requires innovative approaches that can enhance our understanding and ability to combat resistant pathogens. This review aims to explore the integration of machine learning (ML) and epidemiological approaches to understand, predict, and combat carbapenem-resistant pathogens. It examines how leveraging large datasets and advanced computational techniques can identify patterns, predict outbreaks, and inform targeted intervention strategies. Methods: The review synthesizes current knowledge on the mechanisms of carbapenem resistance, highlights the strengths and limitations of traditional epidemiological methods, and evaluates the transformative potential of ML. Real-world applications and case studies are used to demonstrate the practical benefits of combining ML and epidemiology. Technical and ethical challenges, such as data quality, model interpretability, and biases, are also addressed, with recommendations provided for overcoming these obstacles. Results: By integrating ML with epidemiological analysis, significant improvements can be made in predictive accuracy, identifying novel patterns in disease transmission, and designing effective public health interventions. Case studies illustrate the benefits of interdisciplinary collaboration in tackling carbapenem resistance, though challenges such as model interpretability and data biases must be managed. Conclusions: The combination of ML and epidemiology holds great promise for enhancing our capacity to predict and prevent carbapenem-resistant infections. Future research should focus on overcoming technical and ethical challenges to fully realize the potential of these approaches. Interdisciplinary collaboration is key to developing sustainable strategies to combat antimicrobial resistance (AMR), ultimately improving patient outcomes and safeguarding public health. [ABSTRACT FROM AUTHOR]

The process of aging leads to a progressive decline in the immune system function, known as immunosenescence, which compromises both innate and adaptive responses. This includes impairments in phagocytosis and decreased production, activation, and function of T- and B-lymphocytes, among other effects. Bacteria exploit immunosenescence by using various virulence factors to evade the host's defenses, leading to severe and often life-threatening infections. This manuscript explores the complex relationship between immunosenescence and bacterial virulence, focusing on the underlying mechanisms that increase vulnerability to bacterial infections in the elderly. Additionally, it discusses how machine learning methods can provide accurate modeling of interactions between the weakened immune system and bacterial virulence mechanisms, guiding the development of personalized interventions. The development of vaccines, novel antibiotics, and antivirulence therapies for multidrug-resistant bacteria, as well as the investigation of potential immune-boosting therapies, are promising strategies in this field. Future research should focus on how machine learning approaches can be integrated with immunological, microbiological, and clinical data to develop personalized interventions that improve outcomes for bacterial infections in the growing elderly population. [ABSTRACT FROM AUTHOR]

Antibiotic resistance presents a critical challenge in healthcare, particularly among the elderly, where multidrug-resistant organisms (MDROs) contribute to increased morbidity, mortality, and healthcare costs. This review focuses on the mechanisms underlying resistance in key bacterial pathogens and highlights how aging-related factors like immunosenescence, frailty, and multimorbidity increase the burden of infections from MDROs in this population. Novel strategies to mitigate resistance include the development of next-generation antibiotics like teixobactin and cefiderocol, innovative therapies such as bacteriophage therapy and antivirulence treatments, and the implementation of antimicrobial stewardship programs to optimize antibiotic use. Furthermore, advanced molecular diagnostic techniques, including nucleic acid amplification tests and next-generation sequencing, allow for faster and more precise identification of resistant pathogens. Vaccine development, particularly through innovative approaches like multi-epitope vaccines and nanoparticle-based platforms, holds promise in preventing MDRO infections among the elderly. The role of machine learning (ML) in predicting resistance patterns and aiding in vaccine and antibiotic development is also explored, offering promising solutions for personalized treatment and prevention strategies in the elderly. By integrating cutting-edge diagnostics, therapeutic innovations, and ML-based approaches, this review underscores the importance of multidisciplinary efforts to address the global challenge of antibiotic resistance in aging populations. [ABSTRACT FROM AUTHOR]

Background: The ASCAPE project aims to improve the health-related quality of life of cancer patients using artificial intelligence (AI)-driven solutions. The current study employs a comprehensive dataset to evaluate sleep and urinary incontinence, thus enabling the development of personalized interventions. Methods: This study focuses on prostate cancer patients eligible for curative treatment with surgery. Forty-two participants were enrolled following their diagnosis and were followed up at baseline and 3, 6, 9, and 12 months after surgical treatment. The data collection process involved a combination of standardized questionnaires and wearable devices, providing a holistic view of patients' QoL and health outcomes. The dataset is systematically organized and stored in a centralized database, with advanced statistical and AI techniques being employed to reveal correlations, patterns, and predictive markers that can ultimately lead to implementing personalized intervention strategies, ultimately enhancing patient QoL outcomes. Results: The correlation analysis between sleep quality and urinary symptoms post-surgery revealed a moderate positive correlation between baseline insomnia and baseline urinary symptoms (r = 0.407, p = 0.011), a positive correlation between baseline insomnia and urinary symptoms at 3 months (r = 0.321, p = 0.049), and significant correlations between insomnia at 12 months and urinary symptoms at 3 months (r = 0.396, p = 0.014) and at 6 months (r = 0.384, p = 0.017). Furthermore, modeling the relationship between baseline insomnia and baseline urinary symptoms showed that baseline insomnia is significantly associated with baseline urinary symptoms (coef = 0.222, p = 0.036). Conclusions: The investigation of sleep quality and urinary incontinence via data analysis through the ASCAPE project suggests that better sleep quality could improve urinary disorders. [ABSTRACT FROM AUTHOR]

Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies—including genomics, transcriptomics, epigenomics, proteomics, and metabolomics—in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy. [ABSTRACT FROM AUTHOR]

Currently, several techniques based on probabilities and statistics, along with the rapid advancements in computational power, have deepened our understanding of a football match result, giving us the capability to estimate future matches' results based on past performances. The ability to estimate the number of goals scored by each team in a football match has revolutionized the perspective of a match result for both betting market professionals and fans alike. The Poisson distribution has been widely used in a number of studies to model the number of goals a team is likely to score in a football match. Therefore, the match result can be estimated using a double Poisson regression model—one for each participating team. In this study, we propose an algorithm, which, by using Poisson distributions along with football teams' historical performance, is able to predict future football matches' results. This algorithm has been developed based on the Premier League's—England's top-flight football championship—results from the 2022–2023 season. [ABSTRACT FROM AUTHOR]

Featured Application: Machine-Learning Support for Hospital Admission Decisions. The study explores the application of automated machine learning (AutoML) using the MIMIC-IV-ED database to enhance decision-making in emergency department (ED) triage. We developed a predictive model that utilizes triage data to forecast hospital admissions, aiming to support medical staff by providing an advanced decision-support system. The model, powered by H2O.ai's AutoML platform, was trained on approximately 280,000 preprocessed records from the Beth Israel Deaconess Medical Center collected between 2011 and 2019. The selected Gradient Boosting Machine (GBM) model demonstrated an AUC ROC of 0.8256, indicating its efficacy in predicting patient dispositions. Key variables such as acuity and waiting hours were identified as significant predictors, emphasizing the model's capability to integrate critical triage metrics into its predictions. However, challenges related to the complexity and heterogeneity of medical data, privacy concerns, and the need for model interpretability were addressed through the incorporation of Explainable AI (XAI) techniques. These techniques ensure the transparency of the predictive processes, fostering trust and facilitating ethical AI use in clinical settings. Future work will focus on external validation and expanding the model to include a broader array of variables from diverse healthcare environments, enhancing the model's utility and applicability in global emergency care contexts. [ABSTRACT FROM AUTHOR]

Prostate cancer is the second most common cancer among men, with many treatment modalities available for patients, such as radical prostatectomy, external beam radiotherapy, brachytherapy, high-intensity focused ultrasound, cryotherapy, electroporation and other whole-gland or focal ablative novel techniques. Unfortunately, up to 60% of men with prostate cancer experience recurrence at 5 to 10 years. Salvage radical prostatectomy can be offered as an option in the setting of recurrence after a primary non-surgical treatment. However, the complexity of salvage radical prostatectomy is considered to be greater than that of primary surgery, making it the least popular treatment of choice. With the wide use of robotic platforms in urologic oncologic surgery, salvage radical prostatectomy has attracted attention again because, compared to past data, modern series involving salvage Robot-Assisted Radical Prostatectomy have shown promising results. In this narrative literature review, we comprehensively examined data on salvage radical prostatectomy. We investigated the correlation between the different types of primary prostate cancer therapy and the following salvage radical prostatectomy. Furthermore, we explored the concept of a robotic approach and its beneficial effect in salvage surgery. Lastly, we emphasized several promising avenues for future research in this field. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed databases. After removing duplicates and reviews, a total of 2061 articles were assessed using VOSviewer software (version 1.6.20). The results were organized into two main sections: influential factors and thematic directions of AI integration in healthcare. The first section highlights the most productive countries, authors, and institutions in terms of publications. The second section explores the keywords used in the relevant literature, and identifies the main thematic areas where AI has a significant impact in medical sector. The findings of this study aimed not only to assess AI's current contributions to medicine in general but also to highlight specific technological advancements across medical departments, offering a comprehensive overview. [ABSTRACT FROM AUTHOR]

In distance learning educational environments like Moodle, students interact with their tutors, their peers, and the provided educational material through various means. Due to advancements in learning analytics, students' transitions within Moodle generate digital trace data that outline learners' self-directed learning paths and reveal information about their academic behavior within a course. These learning paths can be depicted as sequences of transitions between various states, such as completing quizzes, submitting assignments, downloading files, and participating in forum discussions, among others. Considering that a specific learning path summarizes the students' trajectory in a course during an academic year, we analyzed data on students' actions extracted from Moodle logs to investigate how the distribution of user actions within different Moodle resources can impact academic achievements. Our analysis was conducted using a Markov Chain Model, whereby transition matrices were constructed to identify steady states, and eigenvectors were calculated. Correlations were explored between specific states in users' eigenvectors and their final grades, which were used as a proxy of academic performance. Our findings offer valuable insights into the relationship between student actions, link weight vectors, and academic performance, in an attempt to optimize students' learning paths, tutors' guidance, and course structures in the Moodle environment. [ABSTRACT FROM AUTHOR]

Simple Summary: Robot-assisted surgery facilitates the examination and improvement of artificial intelligence (AI) integration in surgical processes through the provision of comprehensive telemetry data and an advanced viewing interface. Machine learning (ML) techniques enhance the feedback on the development of surgical abilities, the efficacy of the surgical operation, surgical guiding, and predicted results. By incorporating tension sensors on the robotic arms and employing augmented reality techniques, the surgical experience can be greatly improved. This enables the continuous monitoring of organ movements in real time, resulting in enhanced precision and accuracy. The integration of artificial intelligence (AI) into robotic surgery is anticipated to have a substantial influence on the education of upcoming surgeons and improve the entire surgical process. Both endeavours strive for ultimate accuracy in order to enhance the quality of surgical care. With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master–slave" robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments. [ABSTRACT FROM AUTHOR]

This research ventures into the critical public health challenge of childhood obesity by exploring the dynamic interplay between psychological well-being and Body Mass Index (BMI) throughout various developmental stages of childhood. It delves into how emotional regulation, attachment dynamics, and social relationships correlate with obesity from early childhood to adolescence. Highlighting key findings, such as the negative correlation between psychological resilience and higher BMI in young children, the impact of social relationships on obesity risk during pre-adolescence, and the link between adaptive emotional strategies and higher BMI in adolescents, this study brings to the fore the nuanced relationship between psychological factors and obesity. Psychological metrics in this study were obtained via referenced questionnaires, leading up to the utilization of the interdisciplinary process of bioinformatics. Utilizing the interdisciplinary process of bioinformatics, this research synergizes psychometric and biomedical data to unearth psychological markers critical for crafting targeted, age-appropriate interventions. This study advocates for a holistic healthcare approach, emphasizing the integration of psychological support within obesity prevention and management strategies, thereby underscoring the indispensable role of psychological factors in the fight against childhood obesity. The application of bioinformatics methods to analyze complex datasets demonstrates how collaboration across medical specialties can enrich our understanding and response to childhood obesity, contributing significantly to the development of comprehensive, bioinformatics-enhanced healthcare solutions. [ABSTRACT FROM AUTHOR]

In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the seasonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projections for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges. [ABSTRACT FROM AUTHOR]

Simple Summary: In an age where technology is deeply intertwined with healthcare, this review focuses on the synergistic role of artificial intelligence (AI) and radiomics in the management of urological cancers, particularly bladder, kidney, and prostate cancers. Our comprehensive review explores how AI's rapid data-processing capabilities, combined with the intricate image analysis offered by radiomics, are reshaping cancer diagnosis and treatment. We delve into current research findings to illustrate how these innovative technologies are steering oncology toward more accurate, personalized care. This summary is crafted to be accessible, avoiding complex medical jargon and extensive academic references, aiming to highlight the essence and potential impact of these advancements. Our objective is to showcase how AI and radiomics are instrumental in early cancer detection, informed therapeutic decisions, and potentially improved patient outcomes. The research compiled in this paper not only charts a course for the future integration of these technologies in cancer care but also underscores the emerging trend towards patient-centric strategies in the medical community, offering renewed hope and direction in the fight against these cancers. This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the "black box" nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology. [ABSTRACT FROM AUTHOR]

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city's coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life. [ABSTRACT FROM AUTHOR]

Learning mostly involves communication and interaction that leads to new information being processed, which eventually turns into knowledge. In the digital era, these actions pass through online technologies. Formal education uses LMSs that support these actions and, at the same time, produce massive amounts of data. In a distance learning model, the assignments have an important role besides assessing the learning outcome; they also help students become actively engaged with the course and regulate their learning behavior. In this work, we leverage data retrieved from students' online interactions to improve our understanding of the learning process. Focusing on log data, we investigate the students' activity that occur close to and during assignment submission due dates. Additionally, their activity in relation to their academic achievements is examined and the response time in the forum communication is computed both for students and their tutors. The main findings include that students tend to procrastinate in the submission of their assignments mostly at the beginning of the course. Furthermore, the last-minute submissions are usually made late at night, which probably indicates poor management or lack of available time. Regarding forum interactions, our findings highlight that tutors tend to respond faster than students in the corresponding posts. [ABSTRACT FROM AUTHOR]

Purpose: The high incidence of urinary tract infections (UTIs), often in nosocomial environments, is a major cause of antimicrobial resistance (AMR). The dissemination of antibiotic-resistant infections results in very high health and economic burdens for patients and healthcare systems, respectively. This study aims to determine and present the antibiotic resistance profiles of the most common pathogens in a urology department in Greece. Methods: During the period 2019–2020, we included 12,215 clinical samples of blood and urine specimens that tested positive for the following pathogens: Escherichia coli, Enterococcus faecium, Enterococcus faecalis, Proteus mirabilis, Klebsiella pneumoniae, or Pseudomonas aeruginosa, as these are the most commonly encountered microbes in a urology department. Results: The analysis revealed a 22.30% mean resistance rate of E. coli strains with a 76.42% resistance to ampicillin and a 54.76% resistance rate to ciprofloxacin in the two-year period. It also showed an approximately 19% resistance rate of P. mirabilis strains and a mean resistance rate of 46.205% of K. pneumoniae strains, with a decreasing trend during the four semesters (p-value < 0.001), which presented an 80% resistance rate to ampicillin/sulbactam and 73.33% to ciprofloxacin. The resistance to carbapenems was reported to be 39.82%. The analysis revealed a 24.17% mean resistance rate of P. aeruginosa with a declining rate over the two-year period (p-value < 0.001). The P. aeruginosa strains were 38% resistant to fluoroquinolones and presented varying resistance against carbapenems (31.58% against doripenem and 19.79% against meropenem). Regarding the Enteroccocal strains, a 46.91% mean resistance was noted for E. faecium with 100% resistance to ampicillin, and a 24.247% mean resistance rate for E. faecalis strains that were 41% resistant to ciprofloxacin. Both types showed 100% sensitivity to linezolid. Conclusions: The dissemination of antibiotic-resistant pathogens poses the need to implement surveillance programs and, consequently, to develop strategies to prevent the emergence of such pathogens in order to optimize patient outcomes. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view. [ABSTRACT FROM AUTHOR]

Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample's Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient's clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden. [ABSTRACT FROM AUTHOR]

Hospital-acquired infections, particularly in the critical care setting, are becoming increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality, with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. As treatment options become limited, antimicrobial stewardship programs aim to optimize the appropriate use of currently available antimicrobial agents and decrease hospital costs. Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae are the most common resistant bacteria encountered in intensive care units (ICUs) and other wards. To establish preventive measures, it is important to know the prevalence of Gram-negative isolated bacteria and antibiotic resistance profiles in each ward separately, compared with ICUs. In our single centre study, we compared the resistance levels per antibiotic of P. aeruginosa, A. baumannii and K.pneumoniae clinical strains between the ICU and other facilities during a 2-year period in one of the largest public tertiary hospitals in Greece. The analysis revealed a statistically significant higher antibiotic resistance of the three bacteria in the ICU isolates compared with those from other wards. ICU strains of P. aeruginosa presented the highest resistance rates to gentamycin (57.97%) and cefepime (56.67%), followed by fluoroquinolones (55.11%) and carbapenems (55.02%), while a sensitivity rate of 97.41% was reported to colistin. A high resistance rate of over 80% of A. baumannii isolates to most classes of antibiotics was identified in both the ICU environment and regular wards, with the lowest resistance rates reported to colistin (53.37% in ICU versus an average value of 31.40% in the wards). Statistically significant higher levels of resistance to most antibiotics were noted in ICU isolates of K. pneumoniae compared with non-ICU isolates, with the highest difference—up to 48.86%—reported to carbapenems. The maximum overall antibiotic resistance in our ICU was reported for Acinetobacter spp. (93.00%), followed by Klebsiella spp. (72.30%) and Pseudomonas spp. (49.03%). [ABSTRACT FROM AUTHOR]

The sharing of data among organizations has become an increasingly common procedure in several areas like banking, electronic commerce, advertising, marketing, health, and insurance sectors. However, any organization will most likely try to keep some patterns hidden once it shares its datasets with others. This article focuses on preserving the privacy of sensitive patterns when inducing decision trees. We propose a heuristic approach that can be used to hide a certain rule which can be inferred from the derivation of a binary decision tree. This hiding method is preferred over other heuristic solutions like output perturbation or cryptographic techniques—which limit the usability of the data—since the raw data itself is readily available for public use. This method can be used to hide decision tree rules with a minimum impact on all other rules derived. [ABSTRACT FROM AUTHOR]

Data sharing among organizations has become an increasingly common procedure in several areas such as advertising, marketing, electronic commerce, banking, and insurance sectors. However, any organization will most likely try to keep some patterns as hidden as possible once it shares its datasets with others. This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach to hide critical classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques, which limit the usability of the data, since the raw data itself is readily available for public use. We propose a look ahead technique using linear Diophantine equations to add the appropriate number of instances while maintaining the initial entropy of the nodes. This method can be used to hide one or more decision tree rules optimally. [ABSTRACT FROM AUTHOR]