Review
Health Care Sciences & Services
Diana Gina Poalelungi, Carmina Liana Musat, Ana Fulga, Marius Neagu, Anca Iulia Neagu, Alin Ionut Piraianu, Iuliu Fulga
Summary: Artificial Intelligence (AI) has the potential to transform medicine through machine learning and deep learning, assisting in diagnosis, treatment selection, and patient monitoring. Its widespread implementation can revolutionize patient outcomes and the practice of healthcare, improving accessibility, affordability, and quality of care. This article explores AI's diverse applications in healthcare and reviews its current state of adoption, emphasizing the importance of collaboration between physicians and technology experts to fully harness its potential.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Pharmacology & Pharmacy
Grandhi Sandeep Ganesh, Aravinda Sai Kolusu, Konduri Prasad, Pavan Kumar Samudrala, Kumar V. S. Nemmani
Summary: The application of artificial intelligence plays a crucial role in pharmaceutical research and clinical practice, accelerating the discovery and development of new drugs and providing better treatment options. Additionally, AI can be utilized in various areas such as diagnosis, drug delivery, and patient monitoring, contributing to improved healthcare outcomes and safety.
EUROPEAN JOURNAL OF PHARMACOLOGY
(2022)
Editorial Material
Anesthesiology
Susana Vacas
Summary: The use of large amounts of uniform electronic data over long periods helps understand and shape the cognitive trajectory of older patients during the perioperative period. Advances in electronic health record, machine learning, and big data analysis will contribute to enhanced studies on perioperative brain health, including the risk of dementia.
BRITISH JOURNAL OF ANAESTHESIA
(2023)
Article
Gastroenterology & Hepatology
Dennis L. Shung
Summary: The future of gastrointestinal bleeding treatment will integrate machine learning algorithms to improve clinician risk assessment and decision making. These algorithms can identify patients with acute gastrointestinal bleeding using data from electronic health records, predict risks, and guide treatment decisions.
JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY
(2021)
Article
Quantum Science & Technology
Hiroshi Ohno
Summary: This paper investigates the possibility of using quantum computation to speed up the K-means algorithm for large data sizes. By removing centroid calculations and introducing a quantum subroutine based on quantum entanglement, the proposed algorithm achieves clustering performance comparable to that of the classical K-means algorithm on three datasets.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Materials Science, Multidisciplinary
Mohd Zaki, Amreen Jan, N. M. Anoop Krishnan, John C. Mauro
Summary: Glass science has made rapid progress in recent decades, thanks to advanced experimental techniques, simulation methods, and computing capabilities. Glassomics, inspired by the omics approach in biological science, provides a holistic way to study glasses. By utilizing artificial intelligence, experiments, and simulations, glassomics allows high-throughput screening of glasses based on the entire periodic table. This approach offers a comprehensive understanding of the composition, structure, process, and properties of glasses through simulations, machine learning, and natural language processing.
Article
Biochemistry & Molecular Biology
Mauro Giuffre, Rita Moretti, Claudio Tiribelli
Summary: The human gut microbiome is important for human health and is the subject of increasing research. Omics-based methods, such as metagenomics and metabolomics, are commonly used for studying the gut microbiome due to their ability to provide high-throughput and high-resolution data. However, there are challenges in using machine learning-based approaches to analyze the relationship between microbiota and disease, such as small sample sizes and inconsistent experimental protocols. Efforts are being made to address these challenges, including the construction of data repositories and improved data transparency guidelines.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Food Science & Technology
Md Wadud Ahmed, Sahir Junaid Hossainy, Alin Khaliduzzaman, Jason Lee Emmert, Mohammed Kamruzzaman
Summary: The integration of innovative technologies such as IoT, optical sensors, robotics, AI, big data, and cloud computing has transformed the egg industry into a smart and sustainable industry known as Egg Industry 4.0. This transformation has the potential to improve automation, enhance biosecurity, promote animal welfare, increase intelligent grading and quality inspection, and improve efficiency. This review critically analyzes the existing non-destructive optical sensing technologies for the egg industry and discusses the potential of EI 4.0, its limitations, and future trends.
COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY
(2023)
Review
Food Science & Technology
Ting Liu, Yuting Zhai, Kwangcheol Casey Jeong
Summary: Microbial biofilms are common in various environments and present significant challenges to food safety and public health. These biofilms formed by pathogens can lead to food spoilage, foodborne illnesses, and infectious diseases, which are challenging to treat due to increased antimicrobial resistance. While there has been extensive research on the composition and development of biofilms, their profound impact on food, the food industry, and public health has not been adequately summarized. This review provides a comprehensive overview of microbial biofilms in the food industry and their implications for public health, highlighting their presence throughout the food production chains and the underlying mechanisms of biofilm-associated diseases. Additionally, the review thoroughly summarizes the enhanced understanding of microbial biofilms achieved through machine learning approaches in biofilm research. By consolidating existing knowledge, this review aims to facilitate the development of effective strategies to combat biofilm-associated infections in both the food industry and public health.
FOOD SCIENCE AND BIOTECHNOLOGY
(2023)
Review
Food Science & Technology
Ting Liu, Yuting Zhai, Kwangcheol Casey Jeong
Summary: This review provides a comprehensive overview of microbial biofilms in the food industry and their implications on public health. It highlights the existence of biofilms along the food-producing chains and the underlying mechanisms of biofilm-associated diseases. The review also summarizes the enhanced understanding of microbial biofilms achieved through machine learning approaches.
FOOD SCIENCE AND BIOTECHNOLOGY
(2023)
Review
Multidisciplinary Sciences
Marta Bordonhos, Tiago L. P. Galvao, Jose R. B. Gomes, Jose D. Gouveia, Miguel Jorge, Mirtha A. O. Lourenco, Jose M. Pereira, German Perez-Sanchez, Moises L. Pinto, Carlos M. Silva, Joao Tedim, Bruno Zezere
Summary: This article presents an overview of a collaborative research effort on computational approaches to understand materials and tools at different length and time scales. It introduces various techniques and models used in the study, as well as recent computational case studies focusing on material synthesis, interpretation of experimental results, prediction of material properties, and material selection for specific applications.
ADVANCED THEORY AND SIMULATIONS
(2023)
Article
Critical Care Medicine
Michiel Schinkel, Ketan Paranjape, Justin Kundert, Rishi S. Nannan Panday, Nadia Alam, Prabath W. B. Nanayakkara
Summary: The study investigated whether subgroups of sepsis patients benefit from early administration of antibiotics in a prehospital setting and which key traits drive these benefits. An interaction between age and benefits of early antibiotics for sepsis was found, which has not been reported before. This new insight may have major implications for clinical practice in providing more effective care for younger sepsis patients.
Article
Computer Science, Artificial Intelligence
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost
Summary: Computational biology and bioinformatics provide valuable data for the development of language models in natural language processing. In this study, six different models were trained on protein sequence data and the resulting embeddings were used for various protein structure prediction tasks, demonstrating their advantages over traditional methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Education & Educational Research
Xiaoming Zhai
Summary: The article responds to a review on different types of guidance for supporting student inquiry in virtual and remote labs, emphasizing the importance of personalized guidance in computer-supported inquiry learning. It suggests that machine learning could enhance personalization without burdening teachers.
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT
(2021)
Editorial Material
Biochemistry & Molecular Biology
Predrag Radivojac
Summary: Identifying homologous proteins with divergent amino acid sequences can enhance our understanding of protein evolution, structure, and function. A new study has developed a deep-network-based method to identify 6.8 million new Pfam members, a remarkable increase that surpasses a decade of accumulation using traditional approaches.
Editorial Material
Medicine, General & Internal
Bjorn Jobke, Thomas McBride, Linda Nevin, Larry Peiperl, Amy Ross, Clare Stone, Richard Turner
Review
Biology
Linda M. Nevin, Estuardo Robles, Herwig Baier, Ethan K. Scott
Article
Developmental Biology
Linda M. Nevin, Tong Xiao, Wendy Staub, Herwig Baier
Article
Multidisciplinary Sciences
Nathan J. Gosse, Linda M. Nevin, Herwig Baier
Article
Developmental Biology
Linda M. Nevin, Michael R. Taylor, Herwig Baier
NEURAL DEVELOPMENT
(2008)
Article
Developmental Biology
Deborah M. Kurrasch, Linda M. Nevin, Jinny S. Wong, Herwig Baier, Holly A. Ingraham
NEURAL DEVELOPMENT
(2009)
Article
Genetics & Heredity
A Muto, MB Orger, AM Wehman, MC Smear, JN Kay, PS Page-McCaw, E Gahtan, T Xiao, LM Nevin, NJ Gosse, W Staub, K Finger-Baier, H Baier
