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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]

Purpose: This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool. Design/methodology/approach: This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated. Findings: The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it. Originality/value: The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS. [ABSTRACT FROM AUTHOR]

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]

Precision agriculture tools like decision support systems increasingly use machine‐learning algorithms and other types of artificial intelligence (AI) to analyze large quantities of agricultural data and provide recommendations to producers and crop advisers. However, several barriers threaten adoption of these tools. Three papers in the recent Agronomy Journal special section, "Machine Learning in Agriculture," explore this phenomenon and offer solutions and opportunities for building trust in these technologies. [ABSTRACT FROM AUTHOR]

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]