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Researchers from COMSATS University in Lahore, Pakistan have developed a cellulose paper-based aptasensing protocol for the detection of the cancer biomarker mucin 1 (MUC1). The paper surface was modified using polydopamine modified carbon nanotubes (PDA@CNTS) to create a strong adhesion and binding interface. The researchers used dynamic laser speckle (DLS) analysis to analyze the speckle patterns generated from the cellulose-based paper surface, providing sensitive and accurate analysis of the target biomarker. The paper-based aptasensor demonstrated sensitive and selective detection of MUC1, making it a promising tool for low-cost and portable biological applications. [Extracted from the article]

A report from the Thapar Institute of Engineering & Technology in Punjab, India discusses the use of machine learning and deep learning techniques for the detection of epileptic seizures. The paper compares different algorithms and models, such as support vector machine classifiers, artificial neural network classifiers, convolutional neural network classifiers, and long-short term memory networks. The effectiveness of different physiological signals, including electroencephalogram (EEG) and electrocardiogram (ECG), is also explored. The paper concludes that sentiment analysis could be another dimension of seizure detection. This research provides a summary of previous techniques and offers direction for further research in the field. [Extracted from the article]

Researchers from Abu Dhabi University have conducted a study on stress corrosion cracking (SCC) under harsh environmental conditions. The study highlights the challenges in accurately predicting material degradation and remaining service life due to the complex interplay of mechanical, chemical, and electrochemical factors. The researchers emphasize the need for comprehensive review papers that consolidate current knowledge and advancements in prognosis methods. The paper covers various prognosis methods, including non-destructive testing, electrochemical techniques, numerical modeling, and machine learning. The study aims to serve as a valuable resource for researchers and practitioners working in the field to develop effective strategies for mitigating SCC and ensuring material integrity in challenging environments. [Extracted from the article]

A report from Universita Campus Bio-Medico di Roma discusses the use of Artificial Intelligence (AI) in the rehabilitation of individuals with lower-limb impairments using robotic exoskeletons. The report highlights the potential benefits of incorporating AI into rehabilitation therapies, such as personalized feedback and guidance. The researchers conducted a review of 37 peer-reviewed papers, categorizing them based on robotic application scenarios or AI methodologies. The report aims to provide a clear understanding of the suitability of different algorithms for specific tasks, guiding future developments in the field of lower-limb exoskeleton and AI applications. [Extracted from the article]

Researchers at Mass General Brigham have developed two large-scale foundation models, UNI and CONCH, in the field of computational pathology (CPath). These models have the potential to advance diagnostic accuracy, prognostic insights, and therapeutic response predictions. UNI is a model for understanding pathology images and outperformed established pathology models in 34 tasks, while CONCH is a model for understanding pathology images and language and excelled in tasks like identifying rare diseases and tumor segmentation. The research team has made the code publicly available for other academic groups to use. [Extracted from the article]

A recent report from the University of Nevada discusses the challenges and potential solutions of using machine learning in digital health. The researchers highlight issues such as incomplete and fragmented data, as well as concerns about data privacy and security. They propose transfer learning as a promising approach to address these challenges by transferring knowledge from previously trained tasks to develop accurate prediction models for new tasks. The report provides a comprehensive study of transfer learning methods and strategies for digital health applications, aiming to inspire researchers to enhance current approaches and develop new strategies to overcome limitations in the field. [Extracted from the article]

Researchers from the University of Liverpool have developed a framework for using artificial intelligence (AI) to address the global challenge of antimicrobial resistance (AMR). The researchers outline an adaptive implementation and maintenance framework for AI to improve antimicrobial use and infection care. They emphasize the need for collaboration between the fields of AMR and AI to design, maintain, and globalize AI systems that support clinician-driven strategies to minimize AMR. The full research paper is available for further reading. [Extracted from the article]