MR imaging appearances of different types of reconstructive muscle flaps following reconstructive surgery of the lower extremity with associated post-surgical changes due to altered anatomy, radiation, and potential complications, can be challenging. A multidisciplinary therapeutic approach to tumors allows for limb salvage therapy in a majority of the patients. Decision-making for specific types of soft tissue reconstruction is based on the body region affected, as well as the size and complexity of the defect. Hematomas and infections are early complications that can jeopardize flap viability. The local recurrence of a tumor within six months after a complete resection with confirmed tumor-free margins and adjuvant radiation therapy is rare. Identification of a new lesion similar to the initial tumor favors a finding of tumor recurrence.
Adult ; Female ; Hematoma/etiology ; Humans ; Limb Salvage ; Lower Extremity/anatomy & histology/radiography/*surgery ; *Magnetic Resonance Imaging ; Male ; Middle Aged ; Neoplasm Recurrence, Local ; Reconstructive Surgical Procedures ; Sarcoma/radiotherapy/*surgery ; Soft Tissue Infections/radiography/*surgery ; Soft Tissue Injuries/radiography/*surgery ; Soft Tissue Neoplasms/radiotherapy/*surgery ; Surgical Flaps/adverse effects

OBJECTIVE: To identify the precise locations of the motor branches and motor points of hamstring and triceps surae muscles to the bony landmarks. METHOD: Twenty-eight limbs of 14 adult cadavers were anatomically dissected. The adult cadavers were selected randomly without regard to gender and age. The cadravers which were unable to obtain a neutral position or which received a trauma to the posterior thighs or the lower legs were excluded from the study. The number and location of the motor branches and motor points from sciatic nerve to each hamstirng muscles and from tibial nerve to each triceps surae muscles were identified related to the bony landmarks. Bony landmarks were ischial tuberosity, medial and lateral epicondyles of femur, and medial and lateral malleolli of tibia. The length of femur was defined as the distance from the ischial tuberosity to the intercondylar line of femur and the length of lower leg was defined as the distance from the intercondylar line of femur to the intermalleolar line of tibia. The locations of the muscular branches and the motor points were expressed as the percentage of the length of femur and lower leg. RESULTS: One muscular branch from the sciatic nerve to the semimembranosus muscle and from the posterior tibial nerve to the soleus muscle, and one or two muscular branches to the biceps femoris, semitendinosus, and semimembranosus, medial gastrocnemius, lateral gastrocnemius and soleus muscle were located at 23.0+/-5.7%, 21.0+/-10.5%, 25.0+/-10.3% of the femur from the ischial tuberosity and 2.0+/-6.2%, 4.0+/-3.3% and 10.0+/-3.3% of the lower leg from the intercondylar line of femur. There were one to four motor points in the hamstring and triceps surae muscles. The motor points of biceps femoris, semitendinosus and semimembranosus were located at 33.0+/-7.8%, 28.0+/-14.5% and 48.0+/-19.0% of the femur. The motor points of the medial gastrocnemius, lateral gastrocnemius and soleus were located in 5.0+/-0.6%, 10.0+/-3.0% and 18.0+/-4.3% of the lower leg below the intercondylar line of femur. CONCLUSION: The identification of the locations of muscular branches and motor points related to the bony landmarks from this study would increase the accuracy of the motor branch blocks or motor point blocks to the hamstrings and triceps surae muscles.
Adult ; Cadaver ; Extremities ; Femur ; Humans ; Leg ; Muscle, Skeletal ; Muscles* ; Sciatic Nerve ; Thigh ; Tibia ; Tibial Nerve

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

PURPOSE: Herniated nucleus pulposus fragments are recognized by the immune system as a foreign-body, which results in an autoimmune reaction. Human activation-inducible tumor necrosis factor receptor (AITR) and its ligand, AITRL, are important costimulatory molecules in the pathogenesis of autoimmune diseases. Despite the importance of these costimulatory molecules in autoimmune disease, their role in the autoimmune reaction to herniated disc fragments has yet to be explored. The purpose of the present study is to investigate whether the overexpression of AITR and AITRL might be associated with lumbar disc herniation. MATERIALS AND METHODS: The study population consisted of 20 symptomatic lumbar disc herniation patients. Ten macroscopically normal control discs were obtained from patients with spinal fractures managed with anterior procedures that involved a discectomy. Peripheral blood samples from both the study patients and controls were collected. The expression levels of AITR and AITRL were investigated by flow cytometric analysis, confocal laser scanning microscopy, immunohistochemistry and by reverse transcriptase-polymerase chain reaction (RT-PCR). The soluble AITR and AITRL serum levels were measured by an enzyme-linked immunosorbent assay. RESULTS: Flow cytometric analysis revealed significantly higher levels of both AITR and AITRL in the lumbar disc herniation patients than in the controls. The AITRL expression levels were also increased in patients with lumbar disc herniation, shown by using confocal laser scanning microscopy, immunohisto-chemistry, and RT-PCR. Finally, soluble AITR and AITRL were elevated in the patients with lumbar disc herniations. CONCLUSION: The AITR and AITRL are increased in both the herniated disc tissue and the peripheral blood of patients with lumbar disc herniation.
Adult ; Female ; Flow Cytometry ; Humans ; Immunohistochemistry ; Interleukins/blood ; Intervertebral Disk Displacement/*immunology ; *Lumbar Vertebrae ; Male ; Microscopy, Confocal ; Middle Aged ; Receptors, Nerve Growth Factor/*blood ; Receptors, Tumor Necrosis Factor/*blood ; Reverse Transcriptase Polymerase Chain Reaction ; Tumor Necrosis Factor-alpha/blood ; Tumor Necrosis Factors/*blood

The purpose of our experiment is to examine the level of anti-Haemophilus influenza polysaccharide antibody titer in the Korean population. Using ELISA, the level of Hib-PS antibodies in 384 infants and children who were all free from Hib invasive diseases, was tested. And the blood of 50 mothers within 24 hours of delivery and cord blood from their respective full-term neonates was also tested. The transport of Hib-PS IgG and IgG subclasses in paired sera from mothers and neonates was also measured. The titer of Hib-PS IgG varies with age. At birth the mean optical density of cord blood was 1.028; however, it declined to 0.609 up to 6 months and further decline was noted up to 2 years to 0.488. Then the mean O.D. remained around 0.5 from 3 to 14 years of age. The mean O.D. of Hib-PS IgG in the mothers blood was 0.856. The ratio of mean O.D. of anti-Hib PS IgG antibody in the cord blood to that in the maternal blood was 1.20. The mean optical densities of IgG subclasses were: 1.18 for anti-Hib PS IgG1, 1.07 for anti-Hib PS IgG2, 1.01 for anti-Hib PS IgG3, and 1.09 for anti-Hib PS IgG4. The sera from Korean children of almost all age groups reacted to Hib-PS antigen on ELISA. Also the active transport of anti-Hib PS IgG antibody through placenta was observed. Among four IgG subclasses, only IgG1 transport had significant experimental meaning.
Adolescent ; Adult ; Antibodies, Bacterial/*immunology ; Antigens, Bacterial/*immunology ; Bacterial Capsules ; Child ; Child, Preschool ; Enzyme-Linked Immunosorbent Assay ; Female ; Fetal Blood/immunology ; Haemophilus Vaccines/*immunology ; Haemophilus influenzae/*immunology ; Humans ; Immunoglobulin G/classification/immunology ; Infant ; Infant, Newborn ; Korea ; Male ; Maternal-Fetal Exchange ; Polysaccharides, Bacterial/*immunology ; Pregnancy

Background: Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed. Objective: To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF. Methods: In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model. Results: In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set. Conclusions Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.

Objectives: This study aimed to identify the current patterns of cancer incidence and estimate the projected cancer incidence and mortality between 2020 and 2035 in Korea. Methods: Data on cancer incidence cases were extracted from the Korean Statistical Information Service from 2000 to 2017, and data on cancer-related deaths were extracted from the National Cancer Center from 2000 to 2018. Cancer cases and deaths were classified according to the International Classification of Diseases, 10th edition. For the current patterns of cancer incidence, age-standardized incidence rates (ASIRs) and age-standardized mortality rates were investigated using the 2000 mid-year estimated population aged over 20 years and older. A joinpoint regression model was used to determine the 2020 to 2035 trends in cancer. Results: Overall, cancer cases were predicted to increase from 265 299 in 2020 to 474 085 in 2035 (growth rate: 1.8%). The greatest increase in the ASIR was projected for prostate cancer among male (7.84 vs. 189.53 per 100 000 people) and breast cancer among female (34.17 vs. 238.45 per 100 000 people) from 2000 to 2035. Overall cancer deaths were projected to increase from 81 717 in 2020 to 95 845 in 2035 (average annual growth rate: 1.2%). Although most cancer mortality rates were projected to decrease, those of breast, pancreatic, and ovarian cancer among female were projected to increase until 2035. Conclusions These up-to-date projections of cancer incidence and mortality in the Korean population may be a significant resource for implementing cancer-related regulations or developing cancer treatments.