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Background/Aims Sofosbuvir plus ribavirin is a standard treatment for patients infected with chronic hepatitis C virus (HCV) genotype 2 in Korea. The purpose of this study was to examine the efficacy and safety of this treatment in Korean patients with chronic HCV genotype 2 infection. Methods We retrospectively analyzed clinical data of patients treated with sofosbuvir plus ribavirin for chronic HCV genotype 2 from May 2016 to December 2017 at eight hospitals located in the Daejeon-Chungcheong area. Results A total of 172 patients were treated with sofosbuvir plus ribavirin. Of them, 163 patients completed the treatment, and 162 patients were tested for sustained virologic response 12 weeks after treatment discontinuation (SVR12). Mean age was 59.6±12.3 years (27–96), and 105 (64.4%) patients were female. Of the total patients, 49 (30.1%) were diagnosed with cirrhosis, and 31 of them were treated for 16 weeks. Sofosbuvir plus ribavirin was the first-line treatment for 144 (88.3%) patients. Eleven (6.7%) patients were intolerant to previous interferon-based treatment. Eight (5.0%) patients relapsed after interferon-based treatment. HCV RNA non-detection rate at 4, 8, and 12 weeks was 97.5%, 99.1%, and 99.3%, respectively, and SVR12 was 98.8% (161/163). During treatment, 18 (11.0%) patients had to reduce their administrated dose of ribavirin because of anemia. One patient stopped the treatment because of severe anemia. Other adverse events, including dizziness, indigestion, and headache, were found in 26 (16.0%) patients. Conclusions A 12-16 week treatment with sofosbuvir plus ribavirin is remarkably effective and well tolerated in Korean patients with chronic HCV genotype 2 infection.

Abstract Background Hesperidin is a flavonoid with antioxidant, anti-inflammatory, and immune modulatory activities. Photoaging is a consequence of chronic exposure to the sun and ultraviolet (UV) radiation. This study was designed to evaluate the efficacy of hesperidin against photoaging of dorsal skin in hairless mice. Methods Hairless male mice (6-week-old) were divided into three groups (n = 7): control, UVB-treated vehicle, and UVB-treated hesperidin groups. UVB-irradiated mice from hesperidin group were orally administered 0.1 mL of water containing 100 mg/kg body weight per day hesperidin. Results The mean length and depth of wrinkles in the UVB-treated hesperidin group significantly improved after the oral administration of hesperidin, which significantly inhibited the increase in epidermal thickness and epidermal hypertrophy (P < 0.05). UVB irradiation of mice induced epidermal barrier dysfunction including an increase in the transepidermal water loss (TEWL); however, hesperidin decreased the TEWL. UVB irradiation increased the expression of MMP-9 and pro-inflammatory cytokines whereas UVB-treated hesperidin group showed reduced expression. These results indicate that hesperidin showed anti-photoaging activity in the UVB-irradiated hairless mice. In conclusion, hesperidin inhibited the UVB-induced increase in skin thickness, wrinkle formation, and collagen fiber loss in male hairless mice. Conclusions These results suggest that hesperidin shows potent anti-photoaging activity by regulating MMP-9 expression through the suppression of MAPK-dependent signaling pathways.

Context: Ampelopsis brevipedunculata Maxim (Vitaceae) is a traditional medicinal herb used for treating liver disorders. Objective: The hepatoprotective effects of A. brevipedunculata ethanol extract (ABE) was investigated in experimental models of fibrosis. Materials and methods: Hepatic stellate cells (HSCs) system in vitro and thioacetamide (TAA)-induced liver fibrosis rat model in vivo were used. Sprague–Dawley rats were divided into five groups of eight each (control, TAA, TAA with ABE 10 mg/kg, ABE 100 mg/kg and silymarin 50 mg/kg groups, respectively). Fibrosis was induced except to the control group by TAA (200 mg/kg, i.p.) twice per week for 13 weeks. ABE and silymarin was administered orally six times per week from the 7th week to the 13th week. Results: In HSC-T6 cells, ABE (0.1 mg/mL) and silymarin (0.05 mg/mL) significantly (p

Context: Cuscuta chinensis Lam. (Convolvulaceae) has been used as a traditional herbal remedy for treating liver and kidney disorders. Objective: Anti-fibrotic effects of C. chinensis extract (CCE) in cellular and experimental animal models were investigated. Materials and methods: HSC-T6 cell viability, cell cycle and apoptosis were analysed using MTT assay, flow cytometry and Annexin V-FITC/PI staining techniques. Thioacetamide (TAA)-induced fibrosis model was established using Sprague Dawley rats (n = 10). Control, TAA, CCE 10 (TAA with CCE 10 mg/kg), CCE 100 (TAA with CCE 100 mg/kg) and silymarin (TAA with silymarin 50 mg/kg). Fibrosis was induced by TAA (200 mg/kg, i.p.) twice per week for 13 weeks. CCE and silymarin were administered orally two times per week from the 7th to 13th week. Fibrotic related gene expression (α-SMA, Col1α1 and TGF-β1) was measured by RT-PCR. Serum biomarkers, glutathione (GSH) and hydroxyproline were estimated by spectrophotometer using commercial kits. Results: CCE (0.05 and 0.1 mg/mL) and silymarin (0.05 mg/mL) treatment significantly (p

ObjectivesRemote medical services have been expanding globally, and this is expansion is steadily increasing. It has had many positive effects, including medical access convenience, timeliness of service, and cost reduction. The speed of research and development in remote medical technology has been gradually accelerating. Therefore, it is expected to expand to enable various high-tech information and communications technology (ICT)-based remote medical services. However, the current state lacks an appropriate security framework that can resolve security issues centered on the Internet of things (IoT) environment that will be utilized significantly in telemedicine.MethodsThis study developed a medical service-oriented frame work for secure remote medical services, possessing flexibility regarding new service and security elements through its service-oriented structure. First, the common architecture of remote medical services is defined. Next medical-oriented secu rity threats and requirements within the IoT environment are identified. Finally, we propose a "service-oriented security frame work for remote medical services" based on previous work and requirements for secure remote medical services in the IoT.ResultsThe proposed framework is a secure framework based on service-oriented cases in the medical environment. A com parative analysis focusing on the security elements (confidentiality, integrity, availability, privacy) was conducted, and the analysis results demonstrate the security of the proposed framework for remote medical services with IoT.ConclusionsThe proposed framework is service-oriented structure. It can support dynamic security elements in accordance with demands related to new remote medical services which will be diversely generated in the IoT environment. We anticipate that it will enable secure services to be provided that can guarantee confidentiality, integrity, and availability for all, including patients, non-patients, and medical staff.

ObjectivesNew methods for obtaining appropriate information for users have been attempted with the development of information technology and the Internet. Among such methods, the demand for systems and services that can improve patient satisfaction has increased in hospital care environments.MethodsIn this paper, we proposed the Hospital Exam Reservation System (HERS), which uses the data mining method. First, we focused on carrying clinical exam data and finding the optimal schedule for generating rules using the multi-examination pattern-mining algorithm. Then, HERS was applied by a rule master and recommending system with an exam log. Finally, HERS was designed as a user-friendly interface.ResultsHERS has been applied at the National Cancer Center in Korea since June 2014. As the number of scheduled exams increased, the time required to schedule more than a single condition decreased (from 398.67% to 168.67% and from 448.49% to 188.49%; p < 0.0001). As the number of tests increased, the difference between HERS and non-HERS increased (from 0.18 days to 0.81 days).ConclusionsIt was possible to expand the efficiency of HERS studies using mining technology in not only exam reservations, but also the medical environment. The proposed system based on doctor prescription removes exams that were not executed in order to improve recommendation accuracy. In addition, we expect HERS to become an effective system in various medical environments.

Anorectal melanoma is a rare neoplasm that accounts for less than 1-4% of anorectal malignant tumors. The main therapeutic modality for anorectal melanoma is surgical treatment, with abdominoperineal resection or wide local excision being the most common approaches. A 77-year-old male with a history of cerebral infarction and hypertension presented with anal bleeding. Here, we report a case of anorectal melanoma treated by endoscopic mucosal resection with adjuvant interferon therapy rather than surgical resection. The patient has been disease-free for 5 years after endoscopic treatment.

Here we report on the issue of Advanced Persistent Threats (APT), which use malware for the purpose of leaking the data of large corporations and government agencies. APT attacks target systems continuously by utilizing intelligent and complex technologies. To overthrow the elaborate security network of target systems, it conducts an attack after undergoing a pre-reconnaissance phase. An APT attack causes financial loss, information leakage, etc. They can easily bypass the antivirus system of a target system. In this paper, we propose a Multi-Layer Defense System (MLDS) that can defend against APT. This system applies a reinforced defense system by collecting and analyzing log information and various information from devices, by installing the agent on the network appliance, server and end-user. It also discusses how to detect an APT attack when one cannot block the initial intrusion while continuing to conduct other activities. Thus, this system is able to minimize the possibility of initial intrusion and damages of the system by promptly responding through rapid detection of an attack when the target system is attacked.

Recently, intelligent transport systems have been applied to vehicle cloud environments. Such technology is especially useful for the systematic management of road traffic. Moreover, automobiles are increasingly equipped with a black box for accident prevention and preservation of evidence. Vehicle black boxes have become mandatory because black box images and voice data have served as forensic evidence in courts. However, the data from black boxes can be forged or modified by man-in-the-middle (MITM) attacks and message hijacking. In this paper, we propose a vehicle cloud computing-based black box service model that can provide integrity for black box data through digital signatures in vehicle cloud computing (VCC) environments. Our proposed model protects against MITM attacks and message hijacking using only a hash value and digital signature. Moreover, a mirroring technique (RAID 1) provides backup and recovery to protect the data from a traffic accident.

Abstract Background Acupuncture is known for a harmless treatment when administered by well-trained clinicians. However, multiple case reports of traumatic adverse events (AEs) related to acupuncture treatments continue to be published in literature. In this review, we evaluated the reporting quality and conducted causality assessments of case studies that have reported acupuncture-related traumatic AEs in Korea. Methods Eight databases were searched from their inception to January 2024. Only Korean case studies that reported traumatic AEs following acupuncture procedures were included without any language restrictions. Reporting quality was evaluated based on patient characteristics, AEs, and acupuncture practice. Causality was assessed using the modified WHO-UMC causality criteria. Results Twenty-eight studies were included from a total of 1,154 identified studies. The quality of reporting in the included studies was low overall. While the descriptions of patient characteristics and AEs were relatively well detailed, most information on acupuncture practice was not reported at all. During the causality assessment, only three (10.7%) studies were judged to be “certain”. Twelve (42.9%) studies were “unassessable” because they inadequately described the information necessary for decision-making. It was practically difficult to establish the causality between acupuncture and AEs, as well as the appropriateness of acupuncture practice. Conclusions Insufficient and inappropriate reporting was observed in most case studies reporting acupuncture-related traumatic AEs in Korea. To overcome these limitations, we have suggested tentative guidelines in the form of a set of items that should be reported by future authors who plan to publish case studies on acupuncture-related traumatic AEs in a clinical setting.

Background: The utilization of herbal medicine has been noteworthy for treating cancer; however, there is not enough information regarding the characteristics of clinical trials of herbal medicine interventions. This study aimed to evaluate the characteristic of registered trials using herbal medicine interventions for cancer. Methods: A cross-sectional study was performed via the website ClinicalTrials.gov, ISRCTN registry, Chinese clinical trial registry, and international clinical trials registry platform to gather associated registered clinical trials using an advanced search with the developed keyword strategy as of March 26, 2023. All obtainable information from the trials was collected without any restrictions to conduct a comprehensive review. Results: A total of 169 registered trials were included for evaluation. Of all trials, 102 trials were eligible for this study. Countries from Asia registered the most trials (62.75%), and hospitals sponsored most of the trials (42.16%). Randomized, Phase 2, interventional trials were dominant, and approximately 64.71% of the trials anticipated recruiting less than 100 participants. More than half of the trials were from 2016 to 2023 (53.92%). While 45 trials were completed, only 16 trials had results for further analysis. According to the completed results, the types of herbal medicines from the trials mainly focused on lung, breast, and colorectal cancer. Conclusion: This study is the first to explore the characteristics of clinical trials of herbal medicine for cancer registered in large clinical databases. The acquired trials had relatively informative data; however, better-designed trials may be needed for health professionals to consider herbal medicine as an option when treating cancer patients.

This paper proposes a hybrid deep learning emotion classification system (HDECS), a hybrid multimodal deep learning system designed for emotion classification in a specific national language. Emotion classification is important in diverse fields, including tailored corporate services, AI advancement, and more. Additionally, most sentiment classification techniques in speaking situations are based on a single modality: voice, conversational text, vital signs, etc. However, analyzing these data presents challenges because of the variations in vocal intonation, text structures, and the impact of external stimuli on physiological signals. Korean poses challenges in natural language processing, including subject omission and spacing issues. To overcome these challenges and enhance emotion classification performance, this paper presents a case study using Korean multimodal data. The case study model involves retraining two pretrained models, LSTM and CNN, until their predictions on the entire dataset reach an agreement rate exceeding 0.75. Predictions are used to generate emotional sentences appended to script data, which are further processed using BERT for final emotion prediction. The research result is evaluated by using categorical cross-entropy (CCE) to measure the difference between the model’s predictions and actual labels, F1 score, and accuracy. According to the evaluation, the case model outperforms the existing KLUE/roBERTa model with improvements of 0.5 in CCE, 0.09 in accuracy, and 0.11 in F1 score. As a result, the HDECS is expected to perform well not only on Korean multimodal datasets but also on sentiment classification considering the speech characteristics of various languages and regions.

This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user’s evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.