In Vivo Stem Cell Imaging Principles and Applications.

Stem cells are the foundational cells for every organ and tissue in our body. Cell-based therapeutics using stem cells in regenerative medicine have received attracting attention as a possible treatment for various diseases caused by congenital defects. Stem cells such as induced pluripotent stem cells (iPSCs) as well as embryonic stem cells (ESCs), mesenchymal stem cells (MSCs), and neuroprogenitors stem cells (NSCs) have recently been studied in various ways as a cell-based therapeutic agent. When various stem cells are transplanted into a living body, they can differentiate and perform complex functions. For stem cell transplantation, it is essential to determine the suitability of the stem cell-based treatment by evaluating the origin of stem, the route of administration, in vivo bio-distribution, transplanted cell survival, function, and mobility. Currently, these various stem cells are being imaged in vivo through various molecular imaging methods. Various imaging modalities such as optical imaging, magnetic resonance imaging (MRI), ultrasound (US), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) have been introduced for the application of various stem cell imaging. In this review, we discuss the principles and recent advances of in vivo molecular imaging for application of stem cell research.

Peripheral Microneedle Patch for First-Aid Hemostasis.

Despite the minimized puncture sizes and high efficiency, microneedle (MN) patches have not been used to inject hemostatic drugs into bleeding wounds because they easily destroy capillaries when a tissue is pierced. In this study, a shelf-stable dissolving MN patch is developed to prevent rebleeding during an emergency treatment. A minimally and site-selectively invasive hemostatic drug delivery system is established by using a peripheral MN (p-MN) patch that does not directly intrude the wound site but enables topical drug absorption in the damaged capillaries. The invasiveness of MNs is histologically examined by using a bleeding liver of a Sprague-Dawley (SD) rat as an extreme wound model in vivo. The skin penetration force is quantified to demonstrate that the administration of the p-MN patch is milder than that of the conventional MN patch. Hemostatic performance is systematically studied by analyzing bleeding weight and time and comparing them with that of conventional hemostasis methods. The superior performance of a p-MN for the heparin-pretreated SD rat model is demonstrated by intravenous injection in vivo.

Convolutional Neural Network Classifies Pathological Voice Change in Laryngeal Cancer with High Accuracy.

Voice changes may be the earliest signs in laryngeal cancer. We investigated whether automated voice signal analysis can be used to distinguish patients with laryngeal cancer from healthy subjects. We extracted features using the software package for speech analysis in phonetics (PRAAT) and calculated the Mel-frequency cepstral coefficients (MFCCs) from voice samples of a vowel sound of /a:/. The proposed method was tested with six algorithms: support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosted machine (LGBM), artificial neural network (ANN), one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN). Their performances were evaluated in terms of accuracy, sensitivity, and specificity. The result was compared with human performance. A total of four volunteers, two of whom were trained laryngologists, rated the same files. The 1D-CNN showed the highest accuracy of 85% and sensitivity and sensitivity and specificity levels of 78% and 93%. The two laryngologists achieved accuracy of 69.9% but sensitivity levels of 44%. Automated analysis of voice signals could differentiate subjects with laryngeal cancer from those of healthy subjects with higher diagnostic properties than those performed by the four volunteers.