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One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare.

ABSTRACT Objective Methods Results Conclusions Ghrelin is emerging as a promising therapeutic option for heart failure (HF) due to its potent inotropic, anabolic, and cardioprotective properties. This review aims to critically examine the available clinical evidence on ghrelin therapy in HF, while also incorporating key findings from preclinical studies that support its therapeutic potential.A comprehensive search was conducted in PubMed and the Cochrane Library up to September 15, 2024, using the keywords “heart failure” and “ghrelin.” From 247 identified records, four randomized controlled trials, one open‐label trial, one observational study, and key preclinical studies were included. Two independent authors performed the screening and quality assessment, with any discrepancies resolved through consensus.Clinical trials investigating ghrelin's acute effects in HF patients have demonstrated significant improvements in cardiac output, ranging from 15% to 30%. Moreover, one study showed that a 3‐week course of ghrelin therapy significantly increased maximal oxygen consumption, lean body mass, and grip strength in HF patients. Preclinical studies further support these clinical findings, highlighting additional benefits of ghrelin, including modulation of the autonomic nervous system, promotion of vasodilation, enhancement of endothelial function, prevention of myocardial remodeling, reduction of arrhythmogenic risk, and increased muscle mass in HF models.Ghrelin is a promising therapeutic option for HF, particularly as an inotropic agent with multifaceted benefits, including autonomic nervous system modulation, anabolic effects, and metabolic regulation. However, further trials are required to confirm its long‐term efficacy and safety and assess whether its benefits can translate into reductions in hard clinical endpoints. [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]

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]

Introduction: Tinnitus, the perception of sound without an external auditory stimulus, affects approximately 10–15% of the population and is often associated with significant comorbidities such as headaches. These conditions can severely impact the quality of life. The aim of this study was to evaluate the efficacy of a food supplement in reducing the symptoms of both tinnitus and headache in patients experiencing these conditions concurrently. Methods: This prospective study included 32 patients (21 males and 11 females) aged between 23 and 68 years (mean age 49.38 years) who were experiencing both tinnitus and headache. The study assessed the impact of a food supplement on tinnitus and headache over a 90-day treatment period using three main instruments: the Tinnitus Handicap Inventory (THI), the Headache Impact Test (HIT-6), and a Visual Analog Scale (VAS) for discomfort. Statistical analyses, including paired t tests, were conducted to compare pre- and posttreatment scores. In the same dataset, Ridge Regression, a linear regression model with L2 regularization, was used to predict posttreatment scores (THI90, HIT90, VAS90). Results: The results indicated a statistically significant reduction in all three measures after 90 days of treatment. The mean THI score decreased from 29.81 to 27.06 (p = 0.011), the mean HIT-6 score decreased from 50.41 to 48.75 (p = 0.019), and the mean VAS score for discomfort decreased from 7.63 to 7.13 (p = 0.033). The optimal Ridge Regression model was found with an “alpha” value of approximately 3.73. The performance metrics on the test set were as follows: Mean Squared Error (MSE) of 13.91 and an R-squared score of 0.61, indicating that the model explains approximately 61% of the variance in the posttreatment scores. These results indicate that pretreatment scores are significant predictors of posttreatment outcomes, and gender plays a notable role in predicting HIT and VAS scores posttreatment. Conclusion: This study demonstrates that a food supplement is effective in reducing the symptoms of tinnitus and headache in patients suffering from both conditions. The significant improvements in THI, HIT-6, and VAS scores indicate a positive impact on patient quality of life. Further research with larger sample sizes and more detailed subgroup analyses is recommended to fully understand the differential impacts of treatment across various demographics. [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]

In Generative Artificial Intelligence (AI), Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama, significantly impact healthcare by aiding in patient care, medical research, and administrative tasks. AI-powered chatbots offer real-time responses and manage chronic diseases, improving patient outcomes and operational efficiency. However, these models pose security and ethical challenges, necessitating robust data privacy, adversarial training, and ethical guidelines. This paper proposes a secure, ethical pipeline for deploying AI healthcare chatbots, integrating advanced privacy-preserving techniques and continuous security assessments to enhance data privacy, resilience, and user trust. [ABSTRACT FROM AUTHOR]

This study leverages the DCTPep database, a comprehensive repository of cancer therapy peptides, to explore the application of machine learning in accelerating cancer research. We applied Principal Component Analysis (PCA) and K-means clustering to categorize cancer therapy peptides based on their physicochemical properties. Our analysis identified three distinct clusters, each characterized by unique features such as sequence length, isoelectric point (pI), net charge, and mass. These findings provide valuable insights into the key properties that influence peptide efficacy, offering a foundation for the design of new therapeutic peptides. Future work will focus on experimental validation and the integration of additional data sources to refine the clustering and enhance the predictive power of the model, ultimately contributing to the development of more effective peptide-based cancer treatments. [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]