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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 recent article in Blood Weekly highlights the growing importance of advanced robotics and tissue engineering in the field of biomedical research. Robotic systems have traditionally played a role in nurturing tissue growth in controlled environments, but recent advancements in soft robots and biomimetic robots offer new possibilities for enhancing tissue engineering. These robots can provide realistic mechanical stimulation to grafts and implants, potentially accelerating their clinical implementation and deepening our understanding of biological tissues. The article calls for researchers to share their latest breakthroughs in this field, with topics ranging from smart scaffolds to biohybrid and biomimetic robots. [Extracted from the article]

A report from Diponegoro University discusses the use of artificial intelligence (AI) and deep learning in medical imaging to identify kidney stones. The research highlights that deep learning is more commonly used than traditional machine learning in this field. The aim of the study was to review existing literature on deep learning in medical imaging for detecting kidney stones. The research concludes that using the appropriate AI model with high accuracy can assist radiologists in accurately detecting kidney stones. This information may be useful for those interested in AI applications in medical diagnostics. [Extracted from the article]

A patent has been issued for a device and method to assess hand flexion tone. The device, called the Isometric Force Pillow (IFP), measures static flexion force from the fingers to provide a quantitative measure of involuntary finger grip due to spastic hypertonia. The IFP can be used to evaluate hand flexion caused by muscle tone, hypertonia, spastic hypertonia, the flexion synergy, or contractures. It consists of an inflatable pillow that is wrapped around the hand and connected to an air pressure sensor, which converts the air pressure changes into therapeutic meaningful values. The device could be used to assess the effectiveness of interventions for relieving hypertonia. [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]

A report from the University of Newcastle in Australia discusses the importance of scope of practice regulation in medicine for patient safety, access to care, and professional autonomy. The paper explores the impact of these regulations on healthcare delivery, professional responsibilities, and patient outcomes. It highlights the benefits and drawbacks of rigorous scope of practice regulations, including their impact on clinical innovation and access to care. The author proposes implementing a national, artificial intelligence-powered, real-time outcome monitoring system to address these challenges. The paper emphasizes the need for a balanced approach to regulation to avoid stifling clinical innovation while ensuring patient safety and professional accountability. [Extracted from the article]

Researchers from the University of Calabria in Italy have utilized a machine learning platform called MySLR to identify case-control studies on mutations of the apolipoprotein E (ApoE) gene in Italian Alzheimer's Disease patients. The platform analyzed 164 scientific publications and identified 92 relevant papers, with 65.22% focusing on Italian Alzheimer's Disease patients and 5.4% discussing mutations in the ApoE gene. The study concluded that the machine learning platform was effective in identifying case-control studies on ApoE gene mutations in Italy. [Extracted from the article]