Disagreements in cloning for business purposes

Nurses and nursing students increasingly confront ethical problems in clinical practice. Moral sensitivity, moral reasoning, and ethical decision-making are therefore important skills throughout the nursing profession. Innovative teaching methods as part of the ethics training of nursing students help them acquire these fundamental skills. This study investigated the effects and potential benefits of using standardized patients in ethics education on nursing baccalaureate students' moral sensitivity, moral reasoning, and ethical decision-making by comparing this method with in-class case analyses. This is a quasi-experimental study. The sample comprised 89 students in Hacettepe University's Faculty of Nursing. Following lectures describing the theoretical components of ethics, students were randomly assigned to two working groups, one using standardized patients and the other using in-class case analyses. Data were collected using the Moral Sensitivity Questionnaire, Rest's Defining Issues Test, and the Nursing Dilemma Test. All data were analysed using IBM SPSS Statistics Version 23. Ethical approval and official permission were obtained. All participating students completed informed consent forms. According to the results, the moral sensitivity of students in the standardized patient group significantly improved over time compared to those in the case analysis group, while the mean scores of students in both groups for moral reasoning and ethical decision-making were not statistically significant. Based on our results, we recommend the use of both standardized patients and case analysis as appropriate teaching methods in ethics education.

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As contemporary enterprises navigate the complex landscape of global business, the imperative to embrace and effectively manage social responsibility has become paramount. This research article delves into the multifaceted dimensions of social responsibility within modern enterprises, exploring the evolving role of businesses in addressing societal and environmental challenges. The study employs a comprehensive review of existing literature, case analyses, and empirical investigations to illuminate the strategies and frameworks employed by enterprises in managing their social responsibility. The first section analyses the theoretical foundations of social responsibility management, emphasizing the evolving perspectives and conceptual frameworks that guide corporate behavior. It critically evaluates the integration of ethical considerations, stakeholder engagement, and sustainable business practices within the broader context of social responsibility. The second section investigates the practical implementation of social responsibility initiatives across diverse industries. The article examines successful models and identifies common challenges faced by enterprises in aligning their operations with socially responsible practices. It analyzes the impact of such initiatives on corporate reputation, stakeholder relations, and overall business performance. This research article contributes to the existing body of knowledge by offering theoretical insights, practical implications, and future considerations for businesses seeking to navigate the dynamic landscape of social responsibility.

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

The ChatGPT initiative, an advanced AI application, has gained significant attention in diverse sectors, including business, technology, and research. Despite its novelty, it has been accepted across various domains, sparking discussions and shaping roadmaps. This article reviews different perspectives on ChatGPT, highlighting existing literature's prevailing lack of standardized evaluation methodologies. The study examines ChatGPT among natural language processing models, exploring its merits, drawbacks, limitations, and future expectations for its application in health quality and management. A meticulous analysis of 104 publications, covering titles, abstracts, and keywords, was independently conducted, addressing four main and seven sub-subjects. The review utilized PubMed, Scopus, Google Scholar, ScienceDirect, and preprint databases such as medRxiv, arXiv, and SSRN to select relevant articles. ChatGPT is expected to efficiently process and analyze extensive healthcare data, facilitating data-driven decision-making for healthcare organizations to enhance their services. However, it is crucial to acknowledge that ChatGPT models are incomplete substitutes for human healthcare professionals and have limitations in addressing complex medical issues. Consequently, this study underscores the imperative for full human supervision in medical services, emphasizing the essential role of humans in ensuring the quality and effective management of healthcare services.

Background: Accurately predicting difficult airways is essential to ensuring patient safety in anesthesiology and emergency medicine. However, traditional assessment tools often lack sufficient sensitivity and specificity, particularly in high-pressure or resource-limited settings. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for enhancing airway assessment. Objective: This review evaluates the performance of AI- and ML-based models for predicting difficult airways and compares them with traditional clinical methods. The review also analyzes the models’ methodological robustness, clinical applicability, and ethical considerations. Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Scopus to identify studies published between 2020 and 2025 that employed AI/ML models to predict difficult airways. Both original research and review articles were included. Key metrics, such as the area under the curve (AUC), sensitivity, and specificity, were extracted and compared. A qualitative analysis was performed to focus on dataset characteristics, validation strategies, model interpretability, and clinical relevance. Results: AI models demonstrated superior performance compared to traditional assessment tools. The MixMatch semi-supervised deep learning (DL) model achieved the highest performance (area under the curve [AUC] of 0.9435, sensitivity of 89.58%, and specificity of 90.13%). Models that used facial imaging combined with deep learning consistently outperformed those that relied solely on clinical parameters. However, methodological heterogeneity, a lack of standardized evaluation metrics, and limited population diversity impeded cross-study comparability. Few studies incorporated interpretability frameworks or addressed ethical challenges related to data privacy and algorithmic bias. Conclusions: AI and ML models have the potential to transform the assessment of difficult airways by improving diagnostic accuracy and enabling real-time clinical decision support.

The article is devoted to the application of generative artificial intelligence (AI) in the study and teaching of foreign languages. In recent years, AI has been actively introduced into the educational process, offering personalized approaches, automated knowledge assessment and interactive teaching methods. The authors analyze existing research and identify the main areas of use of generative AI: chatbots, dialog systems, platforms for teaching writing and adaptive educational technologies. In conclusion, practical recommendations are formulated for teachers, researchers and educational organizations on the integration of generative AI into language education. The work highlights the importance of further research and collaboration between scientists, educators, and technology developers for the effective use of AI in the educational process. As a problem, especially important for the national education system, the task of forming ethical principles is outlined, indicating the limits of what is acceptable in the use of generative AI by teachers and students. Methodically, the article is carried out in the genre of a systematic review. The criteria for selecting sources included the year of publication (from 2017 to 2024) and the type of publication (theoretical articles, empirical research, review articles, technology reviews, editorial opinions, and discussion articles). Although there has been a significant body of published research and literature reviews on artificial intelligence (AI) in recent years, the existing literature specifically devoted to the application of generative AI in language education, as well as generative AI in general, is still very limited. Based on the review, the article makes a number of recommendations. First, educators should consider including generative AI tools in their teaching practice, while remaining vigilant and attentive to potential risks. Continuous professional development is crucial to ensure informed decisions and effective integration of generative AI tools. Secondly, researchers need to continue empirical research on the effectiveness and impact of generative AI tools, ethical considerations, educational interventions to develop specific language skills and involve stakeholders in responsible integration. Finally, society as a whole should take into account that the impact of generative AI goes beyond language education, and it is important to consider its integration into other subject areas.

The Promise and Therapeutic Potential of Human ES and iPS Cells

Drugs are prescribed worldwide to treat diseases but with the risk of idiosyncratic drug-induced liver injury (iDILI). The most important difficulty is how best to establish causality. Based on strong evidence and principles of artificial intelligence (AI) to solve complex processes through quantitative algorithms using scored elements, progress was achieved with the Roussel Uclaf Causality Assessment Method (RUCAM) in its original and updated versions, often viewed now as the gold standard. As a highly appreciated diagnostic algorithm, the RUCAM is in global use with around 100,000 iDILI cases published worldwide using RUCAM to assess causality, largely outperforming any other specific causality assessment tool in terms of case numbers. Consequently, the RUCAM helps to establish a list of top-ranking drugs worldwide implicated in iDILI and to describe clinical and mechanistic features of iDILI caused by various drugs. In addition, the RUCAM was recently applied in iDILI cases of patients treated for coronavirus disease 2019 (COVID-19) infections or cancer patients treated with immune checkpoint inhibitors (ICIs), as well as in the search for new treatment options with conventional drugs in iDILI. Analyses of RUCAM-based iDILI cases are helpful to support pathogenetic steps like immune reactions, genetic predisposition as evidenced by human leucocyte antigens (HLA) genotypes for selected drugs, and the role of the gut microbiome. To achieve consistency in data collection, analysis, and specific clinical and pathogenetic presentation, researchers, regulatory agencies, and pharmaceutical firms should place iDILI and the updated RUCAM as the causality tool under one and the same hat in review articles and clinical guidelines for the diagnosis and treatment of iDILI.

Aim: To determine if there is enough evidence to support the use of intracameral antibiotics for the prevention of endophthalmitis post cataract surgery. Method: A systemic review of English language articles was conducted by performing a broad search using Medline/ProQuest, Science Direct, the Cochrane Library and Embase. The search was restricted to articles published between January 2005 and October 2010. The terms endophthalmitis and intracameral antibiotics were used to initiate the search but the term cataract was used for further exclusion. The population base incorporated adult patients undergoing cataract surgery. Limitations include one author as a reviewer of the titles resulting from the literature searches. A team review would give a more robust methodological quality. Results: Of the 493 results that were identified in the literature search 451 citations were excluded after screening titles and abstracts. Forty two publications were included for this review and were identified as being relevant after scrutiny. Publications showing retrospective data far exceeded the number of prospective case controlled studies negatively affecting the number of high quality articles reviewed. A randomized controlled study released by the European Society of Cataract and Refractive Surgeons (ESCRS) published results in 2006 showing a near _fivefold decrease in the rates of post-operative endophthalmitis with the use of intracameral cefuroxime. This study was discontinued due to ethical considerations. The study observed a clear benefit from the use of intracameral cefuroxime. Moxifloxacin has not been truly evaluated and has a potential for bacterial resistance. Topical antibiotics are unreliable relying on patient compliance and requiring frequent administration. From this review an effective solution for endophthalmitis prevention remains unclear. Conclusion: Intracameral cefuroxime administered at the time of surgery seems to work well for reducing the incidence of post-operative endophthalmitis as it is a broad spectrum and cost-effective antibiotic. The major barrier to its clinical use is the lack of a commercial preparation which increases the threat of dilution errors, which is known to be toxic to human endothelial cells.

Real-world evidence (RWE) is increasingly considered in regulatory decision making. When, and to which extent, RWE is considered relevant by regulators likely depends on many factors. This review aimed to identify factors that make RWE necessary or desirable to inform regulatory decision making. A scoping review was conducted using literature databases (PubMed, Embase, Emcare, Web of Science, and Cochrane Library) and websites of regulatory agencies, health technology assessment agencies, research institutes, and professional organizations involved with RWE. Articles were included if: (1) they discussed factors or contexts that impact whether RWE could be necessary or desirable in regulatory decision making; (2) focused on pharmacological or biological interventions in humans; and (3) considered decision making in Europe or North America, or without a focus on a specific region. We included 118 articles in the scoping review. Two major themes and six subthemes were identified. The first theme concerns questions addressable with RWE, with subthemes epidemiology and benefit-risk assessment. The second theme concerns contextual factors, with subthemes feasibility, ethical considerations, limitations of available evidence, and disease and treatment-specific aspects. Collectively, these themes encompassed 43 factors influencing the need for RWE in regulatory decisions. Although single factors may not make RWE fully necessary, their cumulative influence could make RWE essential and pivotal in regulatory decision making. This overview contributes to ongoing discussions emphasizing the nuanced interplay of factors influencing the necessity or desirability of RWE to inform regulatory decision making.

This article reviews, integrates, and expands upon research initiatives that explore the development and implementation of advanced artificial intelligence (AI)–driven tools and methodologies. In exploring collaborations with leading U.S. AI technology firms, including Nvidia, Dataiku, DataRobot, and C3.ai, this study is specifically aimed at identifying, preventing, and combating human trafficking. This collaborative effort seeks to create a synergistic framework that capitalizes on the unique capabilities and resources of each firm. The overarching goal is to enhance the effectiveness of AI technologies in detecting trafficking activities, analyzing diverse data sources, and providing vital support to law enforcement and NGOs in rescue and prevention efforts. The comprehensive review delves into the multifaceted applications of AI, emphasizing its role in prevention through machine learning, data analytics, and natural language processing. It navigates through collaborative initiatives, presenting partnerships with prominent U.S. tech firms, accompanied by case studies that showcase successful collaborative projects. Challenges inherent in collaborative AI efforts are addressed, and the paper proposes strategic solutions to overcome these obstacles. The ethical and legal considerations section scrutinizes the implications of deploying AI in human trafficking prevention, exploring the delicate balance between innovation and safeguarding privacy and civil liberties. Furthermore, the final section anticipates future directions and recommendations, providing insights into emerging technologies that could enhance anti-trafficking efforts. Specific recommendations are offered for fortifying collaborations between AI developers and tech firms, and the paper discusses broader implications for policy, suggesting avenues for future research in the dynamic field of AI-driven human trafficking prevention. In essence, this comprehensive review not only synthesizes existing knowledge but also integrates a forward-looking research initiative, contributing significantly to the ongoing discourse on leveraging technology to combat human trafficking on a global scale.

Procurement reforms are seen as essential for achieving a long term improvement in financial management within Africa. To gain insight into this situation an analysis of success stories and cases where challenges and difficulties have arisen were investigated. This material was content analysed using Mayring (2008) four-step process model. The findings demonstrate some initial improvement within African public sector financial management; however the complete and successful implementation of these reforms is fraught with problems. These issues that limit improved financial management through procurement reform included; inadequate education and training, legislative constraints, especially those associated with procurement technology, and a lack of political support for the reform process. In political terms the study identified that the current political position coupled with socio-cultural sensitivities of participating countries, has created a neglect of ethical and environmental considerations which in turn has slowed the pace and acceptance of reforms.

Using the CRISPR-Cas9 gene editing technique, researchers have corrected for the first time a mutation implicated in hypertrophic cardiomyopathy—a heritable heart condition that is the most common cause of sudden death in otherwise healthy young athletes—in human embryos. Successfully correcting the mutation during embryonic development would prevent the defect from being passed on to future generations. In a Nature study, a team led by scientists from Oregon Health & Science University fertilized oocytes from healthy donors with sperm from a heterozygous male carrier of the MYBPC3 mutation. Inheriting a single copy of the MYBPC3 gene mutation can lead to hypertrophic cardiomyopathy, which affects ≈1 in 500 people overall and is characterized by left ventricular hypertrophy, myofibrillar disarray, and myocardial stiffness. MYBPC3 mutations account for ≈40% of all genetic defects causing hypertrophic cardiomyopathy and can also cause other inherited cardiomyopathies, including dilated cardiomyopathy and left ventricular noncompaction. After fertilization and development of embryos, the investigators …

The COVID-19 pandemic forced governments, multilateral public health organisations and research institutions to undertake research quickly to inform their responses to the pandemic. Most COVID-19-related studies required swift approval, creating ethical and practical challenges for regulatory authorities and researchers. In this paper, we examine the landscape of ethics review processes in Africa during public health emergencies (PHEs). We searched four electronic databases (Web of Science, PUBMED, MEDLINE Complete, and CINAHL) to identify articles describing ethics review processes during public health emergencies and/or pandemics. We selected and reviewed those articles that were focused on Africa. We charted the data from the retrieved articles including the authors and year of publication, title, country and disease(s) reference, broad areas of (ethical) consideration, paper type, and approach. Of an initial 4536 records retrieved, we screened the titles and abstracts of 1491 articles, and identified 72 articles for full review. Nine articles were selected for inclusion. Of these nine articles, five referenced West African countries including Liberia, Guinea and Sierra Leone, and experiences linked to the Ebola virus disease. Two articles focused on South Africa and Kenya, while the other two articles discussed more general experiences and pitfalls of ethics review during PHEs in Africa more broadly. We found no articles published on ethics review processes in Africa before the 2014 Ebola outbreak, and only a few before the COVID-19 outbreak. Although guidelines on protocol review and approval processes for PHEs were more frequently discussed after the 2014 Ebola outbreak, these did not focus on Africa specifically. There is a gap in the literature about ethics review processes and preparedness within Africa during PHEs. This paper underscores the importance of these processes to inform practices that facilitate timely, context-relevant research that adequately recognises and reinforces human dignity within the quest to advance scientific knowledge about diseases. This is important to improve fast responses to PHEs, reduce mortality and morbidity, and enhance the quality of care before, during, and after pandemics.