Hyaluronic acid (HA) microspheres (MSs) crosslinked with polyethylene glycol diglycidyl ether (PEGDE) are prepared using a simple fluidic device (SFD) to investigate the optimized parameters. A solution mixture of PEGDE in 2-methyl-1-propanol was prepared as a continuous phase in SFD. HA solutions of 1 wt% concentration were introduced into SFD as a discontinuous phase. The HA solution prepared by stirring for more than 48 h exhibited spherical MSs at the needle tip inside the ring cap. As the flow rate of the continuous phase increased from 0.7 to 1.9 mL/min, the diameter of the MS decreased from 173±36 ㎛ to 129±13 ㎛. Although the PEGDE concentration in the range of 0.2 to 1.8 vol% did not affect the diameter of the MS, the microstructure of MS, consisting of inner hollow void and wall, was changed. The inner void and wall size decreased and increased from 79.5 ㎛ to 57.2 ㎛ and from 10.3 ㎛ to 21.4 ㎛, respectively, with increasing PEGDE concentration from 0.2 vol% to 1.8 vol%. FT-IR peaks located around 2867 cm^-1 and 1088 cm^-1 indicated that the HA MS prepared at different PEGDE concentrations were chemically crosslinked. The HA MSs containing different PEGDE concentrations exhibited quantitative cell viability of more than 98%. L-929 cells adhered well to the HA MSs and proliferated continuously with increasing culture time to 48 h regardless of PEGDE concentration, implying that the HA MSs are clinically safe and effective.

Recently, the market for personal health care and medical devices based on Bluetooth Low Energy(BLE) has grown rapidly. BLE is being used in various medical data communication devices based on low power consumption and universal compatibility. However, since data errors occurring in the transmission of medical data can lead to medical accidents, it is necessary to analyze the causes of errors and study methods to reduce data error. In this paper, the minimum communication speed to be used in medical devices was set to at least 800 byte/sec based on the wireless electrocardiography regulations of the Ministry of Food and Drug Safety. And the data loss rate was tested when data was transmitted at a speed higher than 800 byte/sec. The factors that cause communication data error were classified, and the relationship between each factor and the data error rate was analyzed through experiments. When there were two or more activated peripherals connected to the central, data error occurred due to channel hopping and bottleneck, and the data error rate increased in proportion to the communication distance and the number of activated peripherals. Through this experiment, when the BLE is used in a medical device that intermittently transmits biosignal data, the risk of a medical accident is predicted to be low if the number of peripherals is 3 or less. But, it was determined that BLE would not be suitable for the development of a biosignal measuring device that must be continuously transmitted in real time, such as an electrocardiogram.

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

The global market size of AI based SaMD for medical image in 2023 will be anticipated to reach around 620 billion won (518 million dollars). In order for Korean manufacturers to efficiently obtain CE marking for marketing in the EU countries, the paper is to introduce the recommendation and suggestion of how to reclassify SaMD based on classification rules of MDR because, after introducing the Regulation EU 2017/745, classification rules are quite modified and newly added compared to the Directive 93/42/EEC. In addition, the paper is to provide several rules of MDR that may be applicable to decide the classification of SaMD. Lastly, the paper is to examine and demonstrate various secondary data supported by qualitative data because the paper focuses on the suggestion and recommendation with a public trust on the basis of various secondary data conducted by the analysis of field data. In conclusion, the paper found that the previous classification of SaMD followed by the rule of MDD should be reclassified based on the Regulation EU 2017/745. Therefore, the suggestion and recommendation are useful for Korean manufacturers to comprehend the classification of SaMD for marketing in the EU countries.

Cardiotoxicity assessment of all drugs has been performed according to the ICH guidelines since 2005. Non-clinical evaluation S7B has focused on the hERG assay, which has a low specificity problem. The comprehensive in vitro proarrhythmia assay (CiPA) project was initiated to correct this problem, which presented a model for classifying the Torsade de pointes (TdP)-induced risk of drugs as biomarkers calculated through an in silico ventricular model. In this study, we propose a TdP-induced risk group classifier of artificial neural network (ANN)-based. The model was trained with 12 drugs and tested with 16 drugs. The ANN model was performed according to nine features, seven features, five features as an individual ANN model input, and the model with the highest performance was selected and compared with the classification performance of the qNet input logistic regression model. When the five features model was used, the results were AUC 0.93 in the high-risk group, AUC 0.73 in the intermediate-risk group, and 0.92 in the low-risk group. The model's performance using qNet was lower than the ANN model in the high-risk group by 17.6% and in the low-risk group by 29.5%. This study was able to express performance in the three risk groups, and it is a model that solved the problem of low specificity, which is the problem of hERG assay.

Idiopathic normal-pressure hydrocephalus (INPH) is considered a potentially treatable neurological disorder by shunt surgery and characterized by a triad of symptoms including gait disturbance, cognitive impairment and urinary dysfunction. Although disorders of white matter are generally viewed as the principal pathological features of INPH, analysis of cortical features are important since the destruction of neural tracts could be associated with cortical structural changing. The aim of the study was to determine whether there was any relationship between gait parameter and structural features of cerebral cortex in INPH patients. Gait parameters were measured as follows: step width, toe in/out angle, coefficient of variation (CV) value of stride length, CV value of stride time. After obtaining individual brain MRI of patients with INPH and hemispheric cortical surfaces were automatically extracted from each MR volume, which reconstructed the inner and outer cortical surface. Then, cortical thickness, surface area, and volume were calculated from the cortical surface. As a result, step width was positively correlated with bilateral postcentral gyrus and left precentral gyrus, and toe in/out was positively correlated with left posterior parietal cortex and left insula. Also, the CV value of stride length showed positive correlation in the right superior frontal sulcus, left insula, and the CV value of stride time showed positive correlation in the right superior frontal sulcus. Unique parameter of cerebral cortical changes, as measured using MRI, might underline impairments in distinct gait parameters in patients with INPH.