Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates are used to extract polar expression features such as the key parameters deviation (cm) and accumulation angle (rad). These parameters are quantitative manner to scale the variations of functional tremors in normal control subjects and patients with Parkinson’s disease (PD) and essential tremor (ET). However, difficulty arises in using nonlinear polar expression features in the two-dimensional feature space to separate normal control subjects from those with PD and ET. To solve the nonlinear separable classification problem, hash transformation is used to map polar expression features to a high-dimensional space using hash weighing function and modulo operation. Then, a machine learning method, such as the generalized regression neural network (GRNN), is implemented to train a decision-making classifier using the particle swarm optimization (PSO) algorithm for possible class assessment. With the enrolled data from 50 subjects, the fivefold cross validation, mean true positive, mean true negative, and mean hit rates of 98.93%, 98.96%, and 98.93%, respectively, are obtained to quantify the performance of the proposed decision-making classifier to identify normal controls and subjects with PD or ET. The experimental results indicate that the proposed screening model can improve the accuracy rate compared with the conventional machine learning classifier.

Analyzing digitalized hand-drawn patterns, such as Archimedes’ spirals, words, and sentences, is one strategy for evaluating functional tremors and upper-limb movement disorders for neurodegenerative diseases. A pattern, such as a spiral or a line, in polar coordinates is a straight line or a curve that can be easily compared to a hand-drawn pattern with the same coordinates. Hence, in this study, using polar expression features, the deviation (cm), the accumulation angle (rad), and the drawing velocity (cm/s) were extracted to scale the variability in different tremor levels associated with Parkinson’s disease (PD) or essential tremor (ET). Then, a nonlinear support vector machine (SVM)-based classifier was used to separate the normal condition from PD or ET. The classifier was trained by the wolf pack search (WPS) optimization method. Tremor level progress was evaluated by integrating an assistive method in a smart mobile device (iPad), along with the nonlinear SVM-based classifier, into a decision-making system for individualized functions. In contrast to the multilayer machine learning method, with the 8-fold cross-validation, the proposed classifier exhibits superior performance in identifying normal controls and PD or ET, with the mean true positive, mean true negative, and mean hit rates being 93.72%, 86.79%, and 90.84%, respectively. The experimental results indicated that the proposed decision-making method is effective for detecting the progression of Parkinson-related diseases. This exami- nation technique is simple, comfortable, and repeatable for helping neurologists with preliminary diagnoses, drug treatments, and at-home monitoring.

Medical gases, particularly oxygen and nitric oxide (NO), have attracted significant interest in clinical applications, notably wound healing, due to their role in enhancing cell proliferation, angiogenesis, and collagen deposition. However, the use of these gases has been limited by challenges such as inefficient gas delivery and potential toxicity to normal tissues. In this study, we elucidate a feasible approach using a porphyrin-based metal-organic framework (MOF), a unique material that shows immense potential for dual-gas-assisted wound healing. The MOF nanorods, meticulously designed, contain catalytically active manganese clusters, enabling spontaneous water decomposition and subsequent oxygen generation upon water exposure. The MOF incorporates Fe-chelated porphyrins, which not only serve as ligands connecting the manganese clusters but also exhibit a strong affinity with NO gas for the successful delivery of NO. The loaded NO, tethered to the MOF, can be released in a water environment. We employed a wound-healing assay to evaluate the efficacy of the NO-loaded MOF. After adding the MOF to fibroblast cell culture for O₂ and NO supply, significantly accelerated migration and proliferation were obtained, providing strong evidence for the potential of the MOF in water-driven dual-gas therapy for wound care.

Biogenic microvesicles (MVs) play a pivotal role in intercellular signal communication, thus initiating critical biological responses such as the proliferation of cancer cells, gene and protein transport, and chemo-drug resistance. In addition, they have been recognized as having great potential in drug delivery applications. However, the productivity of biologically produced MVs is not sufficient for clinical applications. In this study, synthetic poly(lactic-co-glycolic acid) (PLGA) MVs were prepared via a double emulsion method. The PLGA MVs had a biogenic MV-mimic vesicular structure with a hydrophilic core/surface and hydrophobic interior of the shell, showing great potential for drug delivery. We successfully embedded hydrophobic iron carbonyl (IC), a carbon monoxide (CO) donor, in the PLGA shell region, enabling the delivery of IC in an aqueous solution. Because of the intrinsic properties of PLGA, it was susceptible to temperature, and the MVs could easily collapse in a warm environment, leading to the decomposition of IC into CO. The in vitro result indicated that the cell viability of A549 lung carcinoma cells significantly decreased to 14% after treatment with IC-loaded PLGA MVs for 24 h, suggesting that these synthetic PLGA MVs constitute an excellent drug delivery platform.

The increasing incidence of end-stage renal disease (ESRD) is the major burden to health budgets and a threat to public health worldwide. For many years, Taiwan has been ranked the first in the world for the number of hemodialysis patients. For solving the above-mentioned circumstance, we demonstrate the project, here, which goal is to construct the holistic health for hemodialysis patient. The project is to design a wearable medicine-device which can simultaneously measure and monitor the vital sign, including heart rate (HR), pulse oximetry (SPO2), continuous non-invasive blood pressure (c-NIBP), and total body water (TBW), of hemodialysis patient. By aid of the device we design, hemodialysis patients will get better health care than before. This device comprises three techniques. The first is named "Using phonoangiography technique to early detect the dysfunction of arteriovenous access by arteriovenous access (AVA) stenosis detector". The stenosis detector based on autoregressive model was employed to simultaneously estimate the status of AVA life cycle and to tract changes in frequency spectra. It helps hemodialysis patients to early detect the dysfunction of AVA and alarms them to make a return visit. The second technique is named "Physiological detecting device for wearable medical device and encoding algorithm development". The feature of the second technique is to optimize the prognosis by analyzing physiological signals, including water content index, pulse oximetry, and blood pressure in the meanwhile. The third technique is named "Intelligent and smart tourniquet". This technique aims to preclude AVA dysfunction caused by inappropriate hemostasis.