Background: The aim of this study was to analyze the clinical results of minimal single palmar-incision carpal tunnel release without a tourniquet. Methods: We reviewed the medical records of 75 patients (90 cases of carpal tunnel syndrome) who underwent minimal single-palmar incision carpal tunnel release without a tourniquet from June 2010 to January 2018. Ten patients had a bleeding tendency. We compared the preoperative and postoperative Boston Carpal Tunnel Syndrome Questionnaire (BCTQ) scores. We also analyzed outcomes and complications according to the presence of a bleeding tendency. Results: In all cases, there was a complete disappearance or marked improvement in symptoms within 6 months, with no recurrence. The postoperative BCTQ score showed a significant improvement compared to the preoperative score, and no statistically significant difference in BCTQ scores was detected according to the presence of a bleeding tendency. Conclusions Carpal tunnel release without a tourniquet using a minimal single palmar incision is effective and reliable. This technique prevents unnecessary pain associated with the tourniquet and is especially helpful in patients with a bleeding tendency or those treated with hemodialysis.

Lymphedema is a frequent complication after the treatment of various cancers, particularly breast cancer, gynecological cancers, melanomas, and other skin and urological cancers. Lymphedema patients have chronic swelling of the affected extremity, recurrent infections, limited mobility and decreased quality of life. Once lymphedema develops, it is usually progressive. Over time, lymphedema leads to fat deposition and subsequent fibrosis of the surrounding tissues. However, there is no cure for lymphedema. Recently, the development of microsurgery has led to introduction of new surgical techniques for lymphedema, such as vascularized lymph node transfer. We report here the latest trends in the surgical treatment of lymphedema, as well as diagnosis and conventional treatments of lymphedema.
Anastomosis, Surgical ; Breast Neoplasms ; Diagnosis ; Extremities ; Fibrosis ; Humans ; Lymph Nodes ; Lymphedema* ; Melanoma ; Microsurgery ; Quality of Life ; Skin ; Urologic Neoplasms

Angelica gigas has been used as a Korean traditional medicine for pain relief and gynecological health. Although the extracts are reported to have an anti-inflammatory property, the bioactive compounds of the herbal plant and the effect on T cell responses are unclear. In this study, we identified decursinol angelate (DA) as an immunomodulatory ingredient of A. gigas and demonstrated its suppressive effect on type 17 helper T (Th17) cell responses. Helper T cell culture experiments revealed that DA impeded the differentiation of Th17 cells and IL-17 production without affecting the survival and proliferation of CD4 T cells. By using a dextran sodium sulfate (DSS)-induced colitis model, we determined the therapeutic potential of DA for the treatment of ulcerative colitis. DA treatment attenuated the severity of colitis including a reduction in weight loss, colon shortening, and protection from colonic tissue damage induced by DSS administration. Intriguingly, Th17 cells concurrently with neutrophils in the colitis tissues were significantly decreased by the DA treatment. Overall, our experimental evidence reveals for the first time that DA is an anti-inflammatory compound to modulate inflammatory T cells, and suggests DA as a potential therapeutic agent to manage inflammatory conditions associated with Th17 cell responses.
Angelica ; Cell Culture Techniques ; Colitis ; Colitis, Ulcerative ; Colon ; Dextrans ; Interleukin-17 ; Medicine, Korean Traditional ; Neutrophils ; Plants ; Sodium ; T-Lymphocytes ; Th17 Cells ; Weight Loss

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations.However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world’s population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.