The aim of this video is to present the procedural details of laparoscopic right hemicolectomy with aortocaval (infrarenal aortic bifurcation) lymphadenectomy, partial resection of the pelvic peritoneum (peritoneal carcinomatosis index, 3), and hyperthermic intraperitoneal chemotherapy in a patient who received neoadjuvant chemotherapy for stage IVc colorectal cancer. The total operation time was 290 minutes, and the patient was discharged on a postoperative day 13 without any complications. No postoperative complications occurred until postoperative day 60. The pathological stage of the tumor was determined to be T3N2bM1c. The pelvic peritoneal nodule was pathologically confirmed as a metastatic lesion. Among the 12 harvested aortocaval lymph nodes, 6 were metastatic lymph nodes. The minimally invasive approach was safe and feasible in this highly selected patient with colon cancer, aortocaval lymph nodes, and peritoneal metastases.

OBJECTIVES: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. METHODS: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. RESULTS: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. CONCLUSIONS: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.
Cell Phones ; Communicable Diseases ; Data Collection ; Dataset ; Epidemiologic Studies* ; Epidemiological Monitoring ; Epidemiology* ; Geographic Information Systems ; Humans ; Information Storage and Retrieval ; Internet ; Life Style ; Machine Learning ; Mental Health ; Methods ; Privacy ; Public Health Surveillance ; Social Media

Seizure is the second most common neurologic complication after liver transplantation and may be caused by metabolic abnormalities, electrolyte imbalance, infection, and immunosuppressant toxicity. A 61-year-old male patient underwent liver transplantation due to hepatitis B virus-related liver cirrhosis with portal systemic encephalopathy. The immediate postoperative course of the patient was uncomplicated. However, on postoperative day (POD) 6, weakness developed in both lower extremities. No abnormal findings were detected on a brain computed tomography (CT) scan on POD 8, but a generalized tonic clonic seizure developed which was difficult to control even with multiple antiepileptic drugs. A follow-up brain CT scan on POD 15 showed a 2.7 cm sized acute intracranial hemorrhage (ICH) in the left parietal lobe. The patient's mental status improved after 2 months and he was able to communicate through eye blinking or head shaking. Our case reports an acute ICH that manifested into a refractory seizure in a patient who underwent a liver transplant.
Anticonvulsants ; Blinking ; Brain ; Brain Diseases ; Follow-Up Studies ; Head ; Hepatic Encephalopathy ; Hepatitis B ; Humans ; Intracranial Hemorrhages* ; Liver Cirrhosis ; Liver Transplantation ; Liver* ; Lower Extremity ; Male ; Middle Aged ; Parietal Lobe ; Seizures* ; Tissue Donors* ; Tomography, X-Ray Computed

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.

Due to an increasing proportion of older individuals and the adoption of a westernized lifestyle, the incidence rate of breast cancer is expected to rapidly increase within the next 10 years in Korea. The National Cancer Screening Program (NCSP) of Korea recommends biennial breast cancer screening through mammography for women aged 40–69 years old and according to individual risk and preference for women above 70 years old. There is an ongoing debate on how to most effectively screen for breast cancer, with many proponents of personalized screening, or screening according to individual risk, for women under 70 years old as well. However, to accurately stratify women into risk categories, further study using more refined personalized characteristics, including potentially incorporating a polygenic risk score (PRS), may be needed. While most breast cancer risk prediction models were developed in Western countries, the Korean Breast Cancer Risk Assessment Tool (KoBCRAT) was developed in 2013, and several other risk models have been developed for Asian women specifically. This paper reviews these models compared to commonly used models developed using primarily Caucasian women, namely, the modified Gail, Breast Cancer Surveillance Consortium, Rosner and Colditz, and Tyrer-Cuzick models. In addition, this paper reviews studies in which PRS is included in risk prediction in Asian women. Finally, this paper discusses and explores strategies toward development and implementation of personalized screening for breast cancer in Korea.

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.

Lymphomas arising in the central nervous system (CNS) of immunocompromised hosts are most commonly non-Hodgkin’s lymphomas and are highly associated with Epstein-Barr virus (EBV). Here we report an autopsy case of EBV-associated CNS diffuse large B-cell lymphoma (DLBCL) in a host suffering from systemic lupus erythematosus who underwent immunosuppressive therapy. After autopsy, EBV-associated CNS DLBCL as well as pulmonary mixed aspergillosis and Pneumocystis jirovecii pneumonia were added to the cause of clinical manifestations of complicated pneumonia and cerebral hemorrhage in this immunocompromised patient. In conclusion, complex disease processes were revealed by autopsy in this case, indicating that the clinicopathological correlations observed through autopsy can improve our understanding of disease progression and contribute to the management of similar patients in the future.
Aspergillosis ; Autopsy ; B-Lymphocytes ; Central Nervous System ; Cerebral Hemorrhage ; Disease Progression ; Herpesvirus 4, Human ; Humans ; Immunocompromised Host ; Lupus Erythematosus, Systemic ; Lymphoma ; Lymphoma, B-Cell ; Pneumocystis jirovecii ; Pneumonia

This study analyzes the role of the Sorting Hat in structuring the identity of the characters in the Harry Potter series written by J. K. Rowling. In the different stages of adolescence, one explores and re-establishes one's identity. One's sense of identity is determined by the commitments made regarding personal and social traits. However, it is difficult to establish a concrete identity formation process theory that is communicable to adolescents. In Harry Potter, the characters' identities are reflected upon the Sorting Hat and are continuously molded throughout the book. The Sorting Hat provides nurturing experiences based on temperament. Based primarily on their temperament, it sorts the students into four houses, each with their own distinct characteristics. Once sorted, the houses become the living and learning communities in which the students share the same dormitory and classes until their graduation. Within the community, the students seek connections, supportive relationships, and understanding within the group. The taking on of the group identity is an explanatory variable in the formation of individual identity. The Sorting Hat provides the students with stability and a safe boundary. After being sorted based on their temperament, the inexperienced and immature adolescents can explore different options under the guidance of the Hat before making a definite commitment. By presenting them with an appropriate environment (such as a mentor, friend, or family member), the Hat further shapes their identity and integrates the identity elements ascribed in the beginning. By providing experiences and interactions based on their unique temperament and environment, the Sorting Hat plays a crucial role in establishing the students' identities. The Sorting Hat can be an ideal model for finding one's identity during adolescence.
Adolescent* ; Friends ; Fungi ; Humans ; Learning ; Mentors ; Sociological Factors ; Temperament