Colorectal cancer is the third most common cancer in Korea and the third leading cause of death from cancer. Treatment outcomes for colon cancer are steadily improving due to national health screening programs with advances in diagnostic methods, surgical techniques, and therapeutic agents.. The Korea Colon Cancer Multidisciplinary (KCCM) Committee intends to provide professionals who treat colon cancer with the most up-to-date, evidence-based practice guidelines to improve outcomes and help them make decisions that reflect their patients’ values and preferences. These guidelines have been established by consensus reached by the KCCM Guideline Committee based on a systematic literature review and evidence synthesis and by considering the national health insurance system in real clinical practice settings. Each recommendation is presented with a recommendation strength and level of evidence based on the consensus of the committee.

Background: As the population ages, the rates of hip diseases and fragility fractures are increasing, making total hip arthroplasty (THA) one of the best methods for treating elderly patients. With the increasing number of THA surgeries and diverse surgical methods, there is a need for standard evaluation protocols. This study aimed to use deep learning algorithms to classify THA videos and evaluate the accuracy of the labelling of these videos. Methods: In our study, we manually annotated 7 phases in THA, including skin incision, broaching, exposure of acetabulum, acetabular reaming, acetabular cup positioning, femoral stem insertion, and skin closure. Within each phase, a second trained annotator marked the beginning and end of instrument usages, such as the skin blade, forceps, Bovie, suction device, suture material, retractor, rasp, femoral stem, acetabular reamer, head trial, and real head. Results: In our study, we utilized YOLOv3 to collect 540 operating images of THA procedures and create a scene annotation model.The results of our study showed relatively high accuracy in the clear classification of surgical techniques such as skin incision and closure, broaching, acetabular reaming, and femoral stem insertion, with a mean average precision (mAP) of 0.75 or higher. Most of the equipment showed good accuracy of mAP 0.7 or higher, except for the suction device, suture material, and retractor. Conclusions Scene annotation for the instrument and phases in THA using deep learning techniques may provide potentially useful tools for subsequent documentation, assessment of skills, and feedback.

Viral load and the duration of viral shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are important determinants of the transmission of coronavirus disease 2019.In this study, we examined the effects of viral doses on the lung and spleen of K18-hACE2 transgenic mice by temporal histological and transcriptional analyses. Approximately, 1×105 plaque-forming units (PFU) of SARS-CoV-2 induced strong host responses in the lungs from 2 days post inoculation (dpi) which did not recover until the mice died, whereas responses to the virus were obvious at 5 days, recovering to the basal state by 14 dpi at 1×102 PFU. Further, flow cytometry showed that number of CD8+ T cells continuously increased in 1×102 PFU-virusinfected lungs from 2 dpi, but not in 1×105 PFU-virus-infected lungs. In spleens, responses to the virus were prominent from 2 dpi, and number of B cells was significantly decreased at 1×105PFU; however, 1×102 PFU of virus induced very weak responses from 2 dpi which recovered by 10 dpi. Although the defense responses returned to normal and the mice survived, lung histology showed evidence of fibrosis, suggesting sequelae of SARS-CoV-2 infection. Our findings indicate that specific effectors of the immune response in the lung and spleen were either increased or depleted in response to doses of SARS-CoV-2. This study demonstrated that the response of local and systemic immune effectors to a viral infection varies with viral dose, which either exacerbates the severity of the infection or accelerates its elimination.

A 70-year-old man with dilated cardiomyopathy underwent left ventricular assist device (LVAD) implantation, using a HeartWare ventricular assist device, as a bridge to candidacy.After 26 months, computed tomography (CT) angiography indicated stenosis in the LVAD outflow graft; however, the patient was asymptomatic, prompting a decision to manage his condition with close monitoring. Ten months later, the patient presented with dizziness and low-flow alerts. Subsequent CT angiography revealed a critical obstruction involving the entire LVAD outflow graft. The patient underwent emergency surgery, during which an organized seroma causing the graft obstruction was found between a wrapped expanded polytetrafluoroethylene (ePTFE) graft and a Dacron outflow graft. The covering of the outflow graft was removed, along with the organized seroma. Following removal of the ePTFE wrap and decompression of the outflow graft, normal LVAD flow was reestablished.The practice of wrapping the outflow graft with synthetic material, commonly done to facilitate later redo sternotomy, may pose a risk for outflow graft obstruction.

The HeartWare Ventricular Assist Device (HVAD) was widely used for mechanical circulatory support in patients with end-stage heart failure. However, there have been reports of a critical issue with HVAD pumps failing to restart, or experiencing delays in restarting, after being stopped. This case report describes 2 instances of HVAD failure-to-restart during heart transplantation surgery and routine outpatient care. Despite multiple attempts to restart the pump using various controllers and extensions, the HVAD failed to restart, triggering a hazard alarm for pump stoppage. In one case, the patient survived after receiving a heart transplantation, while in the other, the patient died immediately following the controller exchange. These cases highlight the rare but life-threatening complication of HVAD failure-to-restart, underscoring the importance of awareness among clinicians, patients, and caregivers, and adherence to the manufacturer’s guidelines and recommendations for HVAD management.

This study aimed to assess the accuracy of tooth widths, intermolar widths, and arch lengths acquired through two intraoral scanners, including iTero Element Plus Series (Align Technology, Santa Clara, CA, USA) and Trios 4 (3Shape, Copenhagen, Denmark), specifically on mixed dentition. A total of 30 subjects were divided into 2 groups, each undergoing both alginate impressions and intraoral scanning using either the iTero or Trios scanner. The plaster models were measured with a caliper, while the digital models were measured virtually. In the iTero group, all tooth width measurements exhibited differences compared to the plaster values, except for maxillary left lateral incisors (p = 0.179), mandibular right (p = 0.285), and left (p = 0.073) central incisors. The Trios group did not display significant differences in any of the tooth width measurements. Intermolar width comparisons for both groups indicated differences, except for mandibular primary canine to primary canine values (p = 0.426) in the iTero group. Regarding arch length, the mandibular anterior, maxillary right, and left arch lengths in the iTero group demonstrated larger caliper values than those of iTero. Conversely, in the Trios group, all parameters showed smaller caliper values, especially in upper anterior, maxillary right, mandibular right, and mandibular left arch lengths with significance (p = 0.027, 0.007, 0.003, and 0.047, respectively). Despite the differences between the two groups, digital models might be clinically suitable alternatives for plaster models. Pediatric dentists should carefully assess these differences, as a comprehensive evaluation would result in precise orthodontic treatment planning and favorable outcomes for young patients with mixed dentition.

Background: Actinic keratosis (AK) is a common precancerous cutaneous lesion. There are various AK treatment methods; however, no specific method is considered superior. Most existing studies on AK treatment are Caucasian-based and include patients with multiple lesions. Objective: This study aimed to compare the treatment duration, financial costs, and success rates of topically applied 5% 5-fluorouracil (5-FU) solution, 5% imiquimod (IMIQ) cream, and cryosurgery in the treatment of histologically proven single-lesion AK in Korean patients. Methods: We retrospectively reviewed the medical records of patients with single-lesion AK at a single Korean center from January 2018 to February 2021. Each patient was treated using the three aforementioned local treatments. Results: The study included 58 patients. Nineteen of 23 (82.6%), 27 of 27 (100%), and eight of eight patients (100%) were successfully treated after topical 5-FU, topical IMIQ, and cryosurgery, respectively. Further analysis on treatment success by subgroup revealed the following: mean number of outpatient visits, 3.78 vs. 4.41 vs. 3.75;mean treatment duration, 5.79 vs. 6.00 vs. 9.38 weeks; and mean total treatment cost, 79,380 vs. 170,023 vs. 173,116 won; for the 5-FU, IMIQ, and cryosurgery subgroups, respectively. The total treatment cost was significantly different among the subgroups (p≤0.001). Conclusion Topical IMIQ and cryosurgery were superior in terms of treatment efficacy, but considering its low cost, 5-FU may also be a reasonable option. Hence, an individualized approach to treating localized AK should be taken.

Background: The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database. Methods: The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used. Results: In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender. Conclusions ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.

Background: Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology. Methods: The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including “hip fractures” AND “artificial intelligence”. Results: A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%. Conclusions We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.