Aptamer-functionalized MOFs and AI-driven strategies for early cancer diagnosis and therapeutics

Metal-Organic Frameworks (MOFs) have exceptional inherent properties that make them highly suitable for diverse applications, such as catalysis, storage, optics, chemo sensing, and biomedical science and technology. Over the past decades, researchers have utilized various techniques, including solvothermal, hydrothermal, mechanochemical, electrochemical, and ultrasonic, to synthesize MOFs with tailored properties. Post-synthetic modification of linkers, nodal components, and crystallite domain size and morphology can functionalize MOFs to improve their aptamer applications. Advancements in AI and machine learning led to the development of nonporous MOFs and nanoscale MOFs for medical purposes. MOFs have exhibited promise in cancer therapy, with the successful accumulation of a photosensitizer in cancer cells representing a significant breakthrough. This perspective is focused on MOFs' use as advanced materials and systems for cancer therapy, exploring the challenging aspects and promising features of MOF-based cancer diagnosis and treatment. The paper concludes by emphasizing the potential of MOFs as a transformative technology for cancer treatment and diagnosis.

Falling Detection Research Based on Elderly Behavior Infrared Video Image Contours Ellipse Fitting

Throughout the world, the proportion of the elders in the total population is increasing dramatically, and home-based care has become the most important form of old-age care. Falling is the most common cause of accidents among the elders at home that poses a huge threat to their health and lives. In order to protect the privacy of the elders an accidental falling detection algorithm for the elders in the home has been proposed in this paper. First, contour-based infrared motion video images are used instead of high-definition cameras to collect the elderly behaviors to protect their privacy. Second, ellipse fitting is performed on the infrared video images of the five behaviors including standing, sitting, squatting, bending and falling. The five geometric characteristic variables of the contour-fitting ellipses including the number of ellipses, centroid positions, ellipsoidal areas, horizontal inclinations and long-short axis ratios of the images, have been extracted. Next, an LSTM model is established using the above variables as inputs for feature extraction and classification. Finally, infrared video images of different types of active behaviors of the elders aged from 50 to 70 years have been selected as IFD database for classification detection. Sixty percent of the IFD images are used as training datasets, and 40% of the IFD images are used as test datasets, and compared with the classification detection of URFD datasets which contains optical RGB HD video images of the different behaviors. The experimental results show the effectiveness of the algorithm proposed in this paper which combines the contour ellipse fitting of the infrared video images and the LSTM feature extraction. The average correct classification rate of the normal and falling down behaviors of the elders is above 95%, which is comparable to the optical RGB datasets. The precision of behavior recognition can effectively protect the privacy of the elders, and provide protection for the accidental falling detection of the elders living alone.