No abstract available.
Hypothyroidism* ; Myasthenia Gravis*

Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.

“Chronic pain after spinal surgery” (CPSS) is a nonspecific term for cases in which the end result of surgery generally does not meet the preoperative expectations of the patient and surgeon. This term has replaced the previous term i.e., failed back surgery syndrome. CPSS is challenging for both patients and doctors. Despite advancements in surgical techniques and technologies, a subset of patients continue to experience persistent or recurrent pain postoperatively. This review provides an overview of the multimodal management for CPSS, ranging from conservative management to revision surgery. Drawing on recent research and clinical experience, we aimed to offer insights into the diverse strategies available to improve the quality of life of CPSS patients.

Background/Aims: Data on the immunoprotective status against measles, mumps, rubella, varicella zoster virus (VZV), hepatitis A virus (HAV), and Epstein-Barr virus (EBV) infection in patients with inflammatory bowel disease (IBD) are still lacking. Therefore, we investigated the seropositivity rates for viral infectious diseases and the associated factors in Korean patients with IBD. Methods: In this retrospective cohort study, serum immunoglobulin G antibody positivity rates against measles virus, mumps virus, rubella virus, VZV, HAV, and EBV viral capsid antigen (VCA) were measured in patients with Crohn’s disease or ulcerative colitis (UC) who first visited the IBD clinic. Seropositivity rates and their associated factors were analyzed. Results: Between January 2016 and December 2018, 263 patients were enrolled (male, 167 [67.3%]; UC, 134 [50.9%]). The median age at serological test was 30 years (interquartile range, 22 to 46). The seropositivity rates were 84.0%, 85.2%, 66.5%, 87.4%, 50.0%, and 93.7% for measles, mumps, rubella, VZV, HAV, and EBV, respectively. Younger age at serological test was associated with seronegative status for measles (adjusted odds ratio [aOR], 0.92; 95% confidence interval [CI], 0.88 to 0.96), VZV (aOR, 0.83; 95% CI, 0.74 to 0.93), and HAV (aOR, 0.93; 95% CI, 0.91 to 0.95). Furthermore, IBD type-UC was associated with seronegative status against VZV (aOR, 0.33; 95% CI, 0.11 to 0.99). Conclusions Seropositivity rates for common viral infectious diseases in Korean patients with IBD were similar to those of the general population. In the younger age group, protective immunity against measles, VZV, and HAV is required, with proper vaccination, as necessary.

Purpose: Although numerous studies have identified potential risk factors for ipsilateral lymphedema development in patients with breast cancer following axillary node dissection, the risk factors for lymphedema in patients undergoing sentinel node biopsy without axillary dissection remain unclear. In this study, we aimed to determine the real-world incidence and risk factors for lymphedema in such patients. Methods: We conducted a single-center, retrospective review of medical records of patients with breast cancer who underwent sentinel node biopsy alone. The development cohort (5,051 patients, January 2017–December 2020) was analyzed to identify predictors of lymphedema, and a predictive model was subsequently created. A validation cohort (1,627 patients, January 2014–December 2016) was used to validate the model. Results: In the development cohort, 49 patients (0.9%) developed lymphedema over a median follow-up of 56 months, with most cases occurring within the first three years post-operation.Multivariate analysis revealed that a body mass index (BMI) of 30 kg/m2 or above, radiation therapy (RTx), chemotherapy, and more than three harvested lymph nodes significantly predicted lymphedema. The predictive model showed an area under the curve of 0.824 for systemic chemotherapy, with the number of harvested lymph nodes being the most significant factor. Patients were stratified into four risk groups, showing lymphedema incidences of 3.3% in the highest-risk group and 0.1% in the lowest-risk group. In the validation cohort, the incidences were 1.7% and 0.2% for the highest and lowest risk groups, respectively. Conclusion The lymphedema prediction model identifies RTx, chemotherapy, BMI ≥ 30 kg/m2 , and more than three harvested lymph nodes as significant risk factors. Although the overall incidence is low, the risk is notably influenced by the extent of lymph node removal and systemic therapies. The model’s high negative predictive value supports its application in designing tailored lymphedema surveillance programs for early intervention.

Purpose: Although numerous studies have identified potential risk factors for ipsilateral lymphedema development in patients with breast cancer following axillary node dissection, the risk factors for lymphedema in patients undergoing sentinel node biopsy without axillary dissection remain unclear. In this study, we aimed to determine the real-world incidence and risk factors for lymphedema in such patients. Methods: We conducted a single-center, retrospective review of medical records of patients with breast cancer who underwent sentinel node biopsy alone. The development cohort (5,051 patients, January 2017–December 2020) was analyzed to identify predictors of lymphedema, and a predictive model was subsequently created. A validation cohort (1,627 patients, January 2014–December 2016) was used to validate the model. Results: In the development cohort, 49 patients (0.9%) developed lymphedema over a median follow-up of 56 months, with most cases occurring within the first three years post-operation.Multivariate analysis revealed that a body mass index (BMI) of 30 kg/m2 or above, radiation therapy (RTx), chemotherapy, and more than three harvested lymph nodes significantly predicted lymphedema. The predictive model showed an area under the curve of 0.824 for systemic chemotherapy, with the number of harvested lymph nodes being the most significant factor. Patients were stratified into four risk groups, showing lymphedema incidences of 3.3% in the highest-risk group and 0.1% in the lowest-risk group. In the validation cohort, the incidences were 1.7% and 0.2% for the highest and lowest risk groups, respectively. Conclusion The lymphedema prediction model identifies RTx, chemotherapy, BMI ≥ 30 kg/m2 , and more than three harvested lymph nodes as significant risk factors. Although the overall incidence is low, the risk is notably influenced by the extent of lymph node removal and systemic therapies. The model’s high negative predictive value supports its application in designing tailored lymphedema surveillance programs for early intervention.

Purpose: Although numerous studies have identified potential risk factors for ipsilateral lymphedema development in patients with breast cancer following axillary node dissection, the risk factors for lymphedema in patients undergoing sentinel node biopsy without axillary dissection remain unclear. In this study, we aimed to determine the real-world incidence and risk factors for lymphedema in such patients. Methods: We conducted a single-center, retrospective review of medical records of patients with breast cancer who underwent sentinel node biopsy alone. The development cohort (5,051 patients, January 2017–December 2020) was analyzed to identify predictors of lymphedema, and a predictive model was subsequently created. A validation cohort (1,627 patients, January 2014–December 2016) was used to validate the model. Results: In the development cohort, 49 patients (0.9%) developed lymphedema over a median follow-up of 56 months, with most cases occurring within the first three years post-operation.Multivariate analysis revealed that a body mass index (BMI) of 30 kg/m2 or above, radiation therapy (RTx), chemotherapy, and more than three harvested lymph nodes significantly predicted lymphedema. The predictive model showed an area under the curve of 0.824 for systemic chemotherapy, with the number of harvested lymph nodes being the most significant factor. Patients were stratified into four risk groups, showing lymphedema incidences of 3.3% in the highest-risk group and 0.1% in the lowest-risk group. In the validation cohort, the incidences were 1.7% and 0.2% for the highest and lowest risk groups, respectively. Conclusion The lymphedema prediction model identifies RTx, chemotherapy, BMI ≥ 30 kg/m2 , and more than three harvested lymph nodes as significant risk factors. Although the overall incidence is low, the risk is notably influenced by the extent of lymph node removal and systemic therapies. The model’s high negative predictive value supports its application in designing tailored lymphedema surveillance programs for early intervention.