A comprehensive review of mycotoxins: Toxicology, detection, and effective mitigation approaches.

Mycotoxins, harmful compounds produced by fungal pathogens, pose a severe threat to food safety and consumer health. Some commonly produced mycotoxins such as aflatoxins, ochratoxin A, fumonisins, trichothecenes, zearalenone, and patulin have serious health implications in humans and animals. Mycotoxin contamination is particularly concerning in regions heavily reliant on staple foods like grains, cereals, and nuts. Preventing mycotoxin contamination is crucial for a sustainable food supply. Chromatographic methods like thin layer chromatography (TLC), gas chromatography (GC), high-performance liquid chromatography (HPLC), and liquid chromatography coupled with a mass spectrometer (LC/MS), are commonly used to detect mycotoxins; however, there is a need for on-site, rapid, and cost-effective detection methods. Currently, enzyme-linked immunosorbent assays (ELISA), lateral flow assays (LFAs), and biosensors are becoming popular analytical tools for rapid detection. Meanwhile, preventing mycotoxin contamination is crucial for food safety and a sustainable food supply. Physical, chemical, and biological approaches have been used to inhibit fungal growth and mycotoxin production. However, new strains resistant to conventional methods have led to the exploration of novel strategies like cold atmospheric plasma (CAP) technology, polyphenols and flavonoids, magnetic materials and nanoparticles, and natural essential oils (NEOs). This paper reviews recent scientific research on mycotoxin toxicity, explores advancements in detecting mycotoxins in various foods, and evaluates the effectiveness of innovative mitigation strategies for controlling and detoxifying mycotoxins.

Immersive Innovations: Exploring the Diverse Applications of Virtual Reality (VR) in Healthcare.

Virtual reality (VR) has experienced a remarkable evolution over recent decades, evolving from its initial applications in specific military domains to becoming a ubiquitous and easily accessible technology. This thorough review delves into the intricate domain of VR within healthcare, seeking to offer a comprehensive understanding of its historical evolution, theoretical foundations, and current adoption status. The examination explores the advantages of VR in enhancing the educational experience for medical students, with a particular focus on skill acquisition and retention. Within this exploration, the review dissects the applications of VR across diverse medical disciplines, highlighting its role in surgical training and anatomy/physiology education. While navigating the expansive landscape of VR, the review addresses challenges related to technology and pedagogy, providing insights into overcoming technical hurdles and seamlessly integrating VR into healthcare practices. Additionally, the review looks ahead to future directions and emerging trends, examining the potential impact of technological advancements and innovative applications in healthcare. This review illuminates the transformative potential of VR as a tool poised to revolutionize healthcare practices.

Machine Learning and Gene Editing at the Helm of a Societal Evolution.

The integration of artificial intelligence (AI) and biotechnology, whilst in its infancy, presents significant opportunities and risks, and proactive policy is needed to manage these emerging technologies. Whilst AI continues to have significant and broad impact, its relevance and complexity magnify when integrated with other emerging technologies. The confluence of Machine Learning (ML), a subset of AI, with gene editing (GE) in particular can foster substantial benefits as well as daunting risks that range from ethics to national security. These complex technologies have implications for multiple sectors, ranging from agriculture and medicine to economic competition and national security. Consideration of technology advancements and policies in different geographic regions, and involvement of multiple organisations further confound this complexity. As the impact of ML and GE expands, forward looking policy is needed to mitigate risks and leverage opportunities. Thus, this study explores the technological and policy implications of the intersection of ML and GE, with a focus on the United States (US), the United Kingdom (UK), China, and the European Union (EU). Analysis of technical and policy developments over time and an assessment of their current state have informed policy recommendations that can help manage beneficial use of technology advancements and their convergence, which can be applied to other sectors. This study is intended for policymakers to prompt reflection on how to best approach the convergence of the two technologies. Technical practitioners may also find it valuable as a resource to consider the type of information and policy stakeholders engage with.

Conventional vs PEGylated loaded liposomal formulations by microfluidics for delivering hydrophilic chemotherapy.

Developing drug delivery systems (DDSs) is one of the approaches used to improve cancer treatment, with the main goal of loading cancer drugs into a carrier targeting a specific organ and avoiding the distribution to healthy tissues. Nanoparticles (NPs) have been shown to be one of the optimum carriers that can be used as DDSs. Lipid-based NPs, such as liposomes, have been investigated in the current study due to their low toxicity and ability to carry hydrophilic and hydrophobic molecules. In the current studies, conventional liposomes composed of DPPC, and cholesterol and PEGylated liposomes composed of DPPC, cholesterol, and DSPE-PEG2000 are manufactured and loaded with Carboplatin. The study focused on investigating and comparing the impact of modifying the carboplatin-loaded liposomes with different concentrations of DSPE-PEG2000 on the NP diameter, polydispersity, ζ-potential, encapsulation efficiency (EE%), and drug release. The hydrodynamic microfluidic system was used to investigate any possible improvement in the EE% over other conventional methods. The results showed the microfluidic system's promising effect in enhancing the EE% of the Carboplatin. Moreover, the results showed a smaller diameter and higher stability of the PEGylated liposome. However, conventional liposomes represent better homogeneity and higher encapsulation efficiency for hydrophilic molecules.

Understanding PFAS toxicity through cell culture metabolomics: Current applications and future perspectives.

Per- and polyfluoroalkyl substances (PFAS), ubiquitous environmental contaminants, pose significant challenges to ecosystems and human health. While cell cultures have emerged as new approach methodologies (NAMs) in ecotoxicity research, metabolomics is an emerging technique used to characterize the small-molecule metabolites present in cells and to understand their role in various biological processes. Integration of metabolomics with cell cultures, known as cell culture metabolomics, provides a novel and robust tool to unravel the complex molecular responses induced by PFAS exposure. In vitro testing also reduces reliance on animal testing, aligning with ethical and regulatory imperatives. The current review summarizes key findings from recent studies utilizing cell culture metabolomics to investigate PFAS toxicity, highlighting alterations in metabolic pathways, biomarker identification, and the potential linkages between metabolic perturbations. Additionally, the paper discusses different types of cell cultures and metabolomics methods used for studies of environmental contaminants and particularly PFAS. Future perspectives on the combination of metabolomics with other advanced technologies, such as single-cell metabolomics (SCM), imaging mass spectrometry (IMS), extracellular flux analysis (EFA), and multi-omics are also explored, which offers a holistic understanding of environmental contaminants. The synthesis of current knowledge and identification of research gaps provide a foundation for future investigations that aim to elucidate the complexities of PFAS-induced cellular responses and contribute to the development of effective strategies for mitigating their adverse effects on human health.

Evaluating generative AI integration in Saudi Arabian education: a mixed-methods study.

Incorporating generative artificial intelligence (GAI) in education has become crucial in contemporary educational environments. This research article thoroughly investigates the ramifications of implementing GAI in the higher education context of Saudi Arabia, employing a blend of quantitative and qualitative research approaches. Survey-based quantitative data reveals a noteworthy correlation between educators' awareness of GAI and the frequency of its application. Notably, around half of the surveyed educators are at stages characterized by understanding and familiarity with GAI integration, indicating a tangible readiness for its adoption. Moreover, the study's quantitative findings underscore the perceived value and ease associated with integrating GAI, thus reinforcing the assumption that educators are motivated and inclined to integrate GAI tools like ChatGPT into their teaching methodologies. In addition to the quantitative analysis, qualitative insights from in-depth interviews with educators unveil a rich tapestry of perspectives. The qualitative data emphasizes GAI's role as a catalyst for collaborative learning, contributing to professional development, and fostering innovative teaching practices.

Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance.

Background: Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal. Methods: In this work, a novel optimization algorithm inspired by cuckoo birds' behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model's classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features. Results: The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.

Regulating bacterial biofilms in food and biomedicine: unraveling mechanisms and Innovating strategies.

Bacterial biofilm has brought a lot of intractable problems in food and biomedicine areas. Conventional biofilm control mainly focuses on inactivation and removal of biofilm. However, with robust construction and enhanced resistance, the established biofilm is extremely difficult to eradicate. According to the mechanism of biofilm development, biofilm formation can be modulated by intervening in the key factors and regulatory systems. Therefore, regulation of biofilm formation has been proposed as an alternative way for effective biofilm control. This review aims to provide insights into the regulation of biofilm formation in food and biomedicine. The underlying mechanisms for early-stage biofilm establishment are summarized based on the key factors and correlated regulatory networks. Recent developments and applications of novel regulatory strategies such as anti/pro-biofilm agents, nanomaterials, functionalized surface materials and physical strategies are also discussed. The current review indicates that these innovative methods have contributed to effective biofilm control in a smart, safe and eco-friendly way. However, standard methodology for regulating biofilm formation in practical use is still missing. As biofilm formation in real-world systems could be far more complicated, further studies and interdisciplinary collaboration are still needed for simulation and experiments in the industry and other open systems.

Comparative study of ultrasound application versus mechanical agitation on pork belly brining for bacon production.

Pork belly brining is a time-consuming step of bacon production that needs to be studied and enhanced through suitable technologies. In this sense, this study aimed at evaluating the impact of ultrasound (US), mechanical agitation (AG), and static brine (SB) on the kinetics of water loss (WL), solids gain (SG), and salt content (SC) of pork belly during brining under different temperatures. Mathematical models were used to estimate mass transfer rates, equilibrium parameters, and thermodynamic properties. Peleg model was chosen as the most suitable model to predict the kinetics experimental data (Radj2 ≥ 0.979 and RMSE ≤ 0.014). The increase in the brine temperature increased WL, SG, and SC for all treatments. Nonlinear effects of temperature were observed for WL, SG, and SC, following an Arrhenius-type behavior. The assistance of ultrasound significantly enhanced the velocity of WL, SG, and SC by 32-56%, while AG improved by 18-39% both compared to SB. Brining was considered an endothermic and non-spontaneous process through the thermodynamic assessment. The increase in temperature and the AG and US processes accelerated the formation of the activated complex. The application of ultrasound was considered the most suitable technology to reduce the brining time. However, significant improvements can be obtained by mechanical agitation. Therefore, both methods can be used to reduce the time processing of pork belly aiming at accelerating the bacon production process.

Bacteriophages: a potential game changer in food processing industry.

In the food industry, despite the widespread use of interventions such as preservatives and thermal and non-thermal processing technologies to improve food safety, incidences of foodborne disease continue to happen worldwide, prompting the search for alternative strategies. Bacteriophages, commonly known as phages, have emerged as a promising alternative for controlling pathogenic bacteria in food. This review emphasizes the potential applications of phages in biological sciences, food processing, and preservation, with a particular focus on their role as biocontrol agents for improving food quality and preservation. By shedding light on recent developments and future possibilities, this review highlights the significance of phages in the food industry. Additionally, it addresses crucial aspects such as regulatory status and safety concerns surrounding the use of bacteriophages. The inclusion of up-to-date literature further underscores the relevance of phage-based strategies in reducing foodborne pathogenic bacteria's presence in both food and the production environment. As we look ahead, new phage products are likely to be targeted against emerging foodborne pathogens. This will further advance the efficacy of approaches that are based on phages in maintaining the safety and security of food.

From static web to metaverse: reinventing medical education in the post-pandemic era.

The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes.

The evolution of medical education from the Flexnerian era to the digital age provides valuable insight into how medical education has evolved and adapted to changing social needs, technological advancements, and educational paradigm shifts (Education 1.0, Education 2.0, Education 3.0, Education 4.0).The transition from static web to metaverse applications (Virtual Reality, Augmented Reality, and Mixed Reality) has brought about transformative changes in medical education merging physical and virtual reality, where users interact in real-time to have immersive and interactive learning experiences.Metaverse platforms enable external collaboration among medical learners, faculty educators, and other healthcare professionals from various parts of the world without the limitations of physical distance allowing them to exchange insights, discuss cases, and learn from diverse perspectives, including opportunities for collaborative research.Developing strategic implementation plans can help ensure that institutions and faculty educators can harness the benefits of emerging technologies while proactively addressing potential challenges, ensuring a well-rounded and effective learning experience for future medical professionals.

Swarm intelligence-based packet scheduling for future intelligent networks.

Network operations involve several decision-making tasks. Some of these tasks are related to operators, such as extending the footprint or upgrading the network capacity. Other decision tasks are related to network functions, such as traffic classifications, scheduling, capacity, coverage trade-offs, and policy enforcement. These decisions are often decentralized, and each network node makes its own decisions based on the preconfigured rules or policies. To ensure effectiveness, it is essential that planning and functional decisions are in harmony. However, human intervention-based decisions are subject to high costs, delays, and mistakes. On the other hand, machine learning has been used in different fields of life to automate decision processes intelligently. Similarly, future intelligent networks are also expected to see an intense use of machine learning and artificial intelligence techniques for functional and operational automation. This article investigates the current state-of-the-art methods for packet scheduling and related decision processes. Furthermore, it proposes a machine learning-based approach for packet scheduling for agile and cost-effective networks to address various issues and challenges. The analysis of the experimental results shows that the proposed deep learning-based approach can successfully address the challenges without compromising the network performance. For example, it has been seen that with mean absolute error from 6.38 to 8.41 using the proposed deep learning model, the packet scheduling can maintain 99.95% throughput, 99.97% delay, and 99.94% jitter, which are much better as compared to the statically configured traffic profiles.

Scientists' Views on the Ethics, Promises and Practices of Synthetic Biology: A Qualitative Study of Australian Scientific Practice.

Synthetic biology is a broad term covering multiple scientific methodologies, technologies, and practices. Pairing biology with engineering, synbio seeks to design and build biological systems, either through improving living cells by adding in new functions, or creating new structures by combining natural and synthetic components. As with all new technologies, synthetic biology raises a number of ethical considerations. In order to understand what these issues might be, and how they relate to those covered in ethics literature on synbio, we conducted an interview study with practicing synthetic biologists affiliated with a synthetic biology centre in Australia. Scientists identified a range of ethical challenges germane to the field, including precarious employment, pressures from industry, gender inequity, and the negative effects of the hyping of synbio. These challenges differed markedly from those identified in the ethics literature, whose treatment of the harms and benefits of synbio remains largely speculative and abstract. In our discussion of the pragmatic, every day ethical issues synthetic biologists face, we illustrate how issues of waste or research integrity play pivotal roles in everything from lived experiences in the laboratory, to long-term research trajectories guiding the field. In a confirmation of the ethical relevance of our participant's views on the field, we argue that the subjects they raise must be included in any ethical analysis of synbio as a field.

A Comprehensive Review of Screening Methods for Ovarian Masses: Towards Earlier Detection.

Ovarian masses, ranging from benign cysts to malignant tumors, present complex diagnostic challenges in women's healthcare. Early detection of ovarian masses is paramount for improving patient outcomes, as delayed diagnoses often lead to advanced-stage disease with limited treatment options. This comprehensive review explores screening methods' current state, limitations, and emerging technologies to facilitate earlier detection. The limitations of existing screening methods, such as low sensitivity and specificity, underscore the need for improved early detection strategies. Imaging-based techniques, including transvaginal ultrasound, magnetic resonance imaging, and computed tomography, are vital in evaluating ovarian masses. However, the emergence of artificial intelligence (AI) and machine learning (ML) applications enhances the accuracy of image interpretation. Blood-based biomarkers, such as CA-125, have been the focus of research for ovarian mass detection. While CA-125 remains widely used, its limitations have prompted investigations into alternative serum biomarkers, including HE4 and miRNA, along with liquid biopsy and circulating tumor DNA. Ultrasound-based scoring systems, such as the risk of malignancy index (RMI), Ovarian-Adnexal Reporting and Data System (O-RADS), and guidelines from the International Ovarian Tumor Analysis (IOTA) group, provide structured approaches for classifying ovarian masses. These systems aid healthcare providers in clinical decision-making. Emerging technologies, such as liquid biopsy, AI, and proteomic/metabolomic approaches, offer promising avenues for enhancing early detection and risk assessment. Liquid biopsy provides noninvasive, real-time monitoring of ovarian masses, while AI and ML applications improve the accuracy of image interpretation. Proteomic and metabolomic studies reveal novel biomarkers and molecular insights. High-risk populations, often associated with genetic mutations such as BRCA1 and BRCA2, require specialized screening strategies. Current guidelines recommend screening modalities, risk-reduction strategies, and shared decision-making. Ongoing research focuses on refining risk assessment and personalized screening for high-risk individuals. This review underscores the importance of early detection in managing ovarian masses, emphasizing the need for improved screening methods, tailored approaches for high-risk populations, and ongoing research to further enhance diagnostic accuracy and patient outcomes.

Multimedia IoT-surveillance optimization model using mobile-edge authentic computing.

Smart technologies are advancing the development of cutting-edge systems by exploring the future network. The Internet of Things (IoT) and many multimedia sensors interact with each other for collecting and transmitting visual data. However, managing enormous amounts of data from numerous network devices is one of the main research challenges. In this context, various IoT systems have been investigated and have provided efficient data retrieval and processing solutions. For multimedia systems, however, controlling inefficient bandwidth utilization and ensuring timely transmission of vital information are key research concerns. Moreover, to transfer multimedia traffic while balancing communication costs for the IoT system, a sustainable solution with intelligence in real-life applications is demanded. Furthermore, trust must be formed for technological advancement to occur; such an approach provides the smart communication paradigm with the incorporation of edge computing. This study proposed a model for optimizing multimedia using a combination of edge computing intelligence and authentic strategies. Mobile edges analyze network states to discover the system's status and minimize communication disruptions. Moreover, direct and indirect authentication determines the reliability of data forwarders and network stability. The proposed authentication approach minimizes the possibility of data compromise and increases trust in multimedia surveillance systems. Using simulation testing, the proposed model outperformed other comparable work in terms of byte delivery, packet overhead, packet delay, and data loss metrics.

The Application of Artificial Intelligence and Big Data in the Food Industry.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

Improving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review.

This comprehensive review delves into the critical role of effective hospital administration in shaping patient outcomes within the healthcare ecosystem. Exploration of key components, strategies, measurement methodologies, and future trends elucidates the multifaceted nature of hospital administration. Key findings underscore the profound impact of administrative decisions and practices on patient safety, satisfaction, and overall well-being. The review highlights the importance of patient-centred care and interdisciplinary collaboration for enhancing patient outcomes. It emphasises the significance of data-driven measurement and benchmarking, which are instrumental in assessing hospital performance and fostering continuous improvement. Looking ahead, emerging technologies, evolving healthcare policies, and persistent challenges are drivers of change in healthcare administration. However, amidst these transformations, the overarching message remains consistent: effective hospital administration is integral to improving patient outcomes. The conclusion calls for a collective commitment from healthcare leaders and policymakers to prioritise the development of capable administrators, invest in technology, promote value-based care, and address healthcare disparities. This collaborative effort ensures that the pursuit of better patient outcomes remains at the forefront of healthcare administration, ultimately shaping the future of healthcare for generations to come.