Surgical excision of the subcutaneous tissues, with or without skin excision in the axillary hair-bearing area, has been the treatment of choice for Treating axillary a osmidrosis for the several decades. However, long periods of postoperative immobilization of a shoulder joint, partial necrosis of skin flaps or hematoma and the possibility of unsightly scars are frequent complications. For the purpose of reducing these complications, we performed ultrasonic aspiration of subcutaneous fat of the axilla, including the apocrine gland, using ultrasonic liposuction technique under confirmation of endoscopy via one small skin incision. From November 1997 to December 1998, a total of 134 patients (93 women and 41 men) received surgery for bilateral axillary osmidrosis on an outpatient basis. Sixty patients were evaluated more than 6 months after surgery. Among these patients, 6patients complained of a persistent foul odor(10%). Five patients received secondary ultrasonic aspiration for persistent foul odor and were then cured. We concluded that our method has several advantages such as 1) preservation of skin flap vascularity for the prevention of flap necrosis and axillary hair, 2) minimal scarring and bleeding, 3) shorter operation time and postoperative immobilization of the shoulder joint, 4) increased patient comfort, and 5) a safer operative method for recurred cases as a secondary method.
Apocrine Glands ; Axilla ; Cicatrix ; Endoscopy ; Female ; Hair ; Hematoma ; Hemorrhage ; Humans ; Immobilization ; Lipectomy ; Necrosis ; Odors ; Outpatients ; Shoulder Joint ; Skin ; Subcutaneous Fat ; Subcutaneous Tissue ; Ultrasonics*

No abstract available.
Respiratory Distress Syndrome, Adult*

Sacral pressure sores have been treated by a variety of surgical methods. complete treatment needs wide excision and coverage with healthy tissue which has constant and sufficient blood supply. Use of gluteus maximus muscle flap with or without overlying skin is a revolutionary method because of the reliability of blood supply. However, it is technically a little bit complicated, and future reconstruction for recurrent decubitus is especially limited in paraplegic patients. The development of gluteal perforator-based flap with para-sacral perforator introduce a new treatment modality for the sacral pressure sores. Total 10 cases of sacral pressure sores were treated with gluteal perforator-based flap. There were minimal postoperative complications except wound dehiscence in one case. This flap has a many advantage of no transection or sacrifice of the gluteus maximus muscle, elevation time for the flap is short, reliable blood flow of the perforator, large rotation arc and no post-operative hindrance to walking in patients who are not paraplegic. The disadvantages of this perforator-based flaps are the anatomical variation in the location of perforators and the need for technically careful dissection.
Humans ; Postoperative Complications ; Pressure Ulcer* ; Skin ; Walking ; Wounds and Injuries

We analyzed ERCP findings of 15 patients with clonorchiasis, that were confirmed by stool examination and operation from May, 1976 to September, 1980. The results were as follows. 1) Filling defects due to adult worm of clonorchis sinensis were significant direct findings of clonorchiasis. 2) Irregular stenosis, fuzziness, raggedness, peripheral dilation and disturbance of peripheral filling of intrahepatic bile ducts were significant indirect findings of clonorchiasis. 3) ERCP was excellent method to evaluate biliary tree in patients with clonorchiasis, who showed obstructive jaundice.
Adult ; Bile Ducts, Intrahepatic ; Biliary Tract ; Cholangiopancreatography, Endoscopic Retrograde* ; Clonorchiasis* ; Clonorchis sinensis ; Constriction, Pathologic ; Humans ; Jaundice, Obstructive

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

BACKGROUND: It is well known that when macrophages are stimulated with endotoxin, they produce a wide variety of cytokine mediators, including TNF-α and IL-1β. However, there is an alterationnin the macrophages responsiveness when they are challenged with repeated bouts of endotoxin, termed 'endotoxin tolerance' which is regarded as a self-protective phenomenon from continuous stimulation. In this study, endotoxin tolerance in the peripheral blood monocytes of sepsis patients was evaluated. METHODS: Fourteen patients with organism-documented sepsis were included. The severity of illness was evaluated by APACHE IIscore. Peripheral blood monocytes were isolated from the patients and diluted to 1×105/well. After stimulation with endotoxin(LPS of E. coli O114:B4, 100 ng/ml), they were incubated at 37℃ in 5% CO2 incubator for 24 hours. Supernatant was collected for the measurement of TNF-αand IL-1β with ELISA method. Peripheral blood monocytes of seven healthy volunteers were used as control. RESULTS: The APACHE IIscore(mean±SD) of the patients at the time of blood sampling was 12.2±5.7. The primary infection foci were urinary tract infection, pneumonia, subacute bacterial endocarditis, and catheter related infection, etc. The causative organisms were gram negative rods(10 cases), gram positive cocci(6 cases) with two cases of mixed infection. Serum TNF-α could be measured in 4 cases with 29.9±27.7 pg/ml. Serum IL-1β was measureable in only one patient. The TNF-α level of supernatant of cultured peripheral blood monocytes was 2,703±2,066 pg/ml in patients and 2,102±1,914 pg/ml in controls. The IL-1β level of supernatant was 884±1,050 pg/ml in patients and 575±558 pg/ml in controls. There was no difference of TNF-α and IL-1β level between patients and controls. CONCLUSION: We cannot prove the phenomenon of endotoxin tolerance in this study. Future study needs to be focused on the more severe sepsis patients who were taken for sampling earlier. Addition of serum to the culture medium could be an another valuable option for the success of this study.
APACHE ; Catheters ; Coinfection ; Endocarditis, Subacute Bacterial ; Enzyme-Linked Immunosorbent Assay ; Healthy Volunteers ; Humans ; Incubators ; Macrophages ; Monocytes ; Pneumonia ; Sepsis* ; Tumor Necrosis Factor-alpha* ; Urinary Tract Infections