No abstract available.
Obesity* ; PPAR delta*

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.
Drug Therapy ; Filtration ; Mortality ; Ovarian Neoplasms ; Prognosis ; RNA ; Sequence Analysis, RNA ; Survival Rate

PURPOSE: Avascular necrosis (AVN) of the femoral head is a major complication after internal fixation of a femoral neck fracture and determines the functional prognosis. We investigated postoperative bone single-photon emission computed tomography/computed tomography (SPECT/CT) for assessing the risk of femoral head AVN.METHODS: We retrospectively reviewed 53 consecutive patients who underwent bone SPECT/CT within 2 weeks of internal fixation of a femoral neck fracture and follow-up serial hip radiographs over at least 12 months.RESULTS: Nine patients developed femoral head AVN. In 15 patients who showed normal uptake on immediate postoperative SPECT/CT, no AVN occurred, whereas 9 of 38 patients who showed cold defects of the femoral head later developed AVN. The negative predictive value of immediate postoperative SPECT/CT for AVN was 100 %, whereas the positive predictive value was 24 %. Among 38 patients with cold defects, 1 developed AVN 3 months postoperatively. A follow-up bone SPECT/CT was performed in the other 37 patients at 2??0 months postoperatively. The follow-up bone SPECT/CT revealed completely normalized femoral head uptake in 27, partially normalized uptake in 8, and persistent cold defects in 2 patients. AVN developed in 3.7 % (1/27), 62.5 % (5/8), and 100 % (2/2) of each group, respectively.CONCLUSION: According to the time point of imaging, radiotracer uptake patterns of the femoral head on postoperative bone SPECT/CT indicate the risk of AVN after internal fixation of femoral neck fractures differently. Postoperative bone SPECT/CT may help orthopedic surgeons determine the appropriate follow-up of these patients.
Femoral Neck Fractures ; Femur Neck ; Follow-Up Studies ; Head ; Hip ; Humans ; Necrosis ; Orthopedics ; Prognosis ; Retrospective Studies ; Surgeons

BACKGROUND AND PURPOSE: Hemispherectomy reportedly produces remarkable results in terms of seizure outcome and quality of life for medically intractable hemispheric epilepsy in children. We reviewed the neuroradiologic findings, pathologic findings, epilepsy characteristics, and clinical long-term outcomes in pediatric patients following a hemispheric disconnection. METHODS: We retrospectively studied 12 children (8 males) who underwent a hemispherectomy at Asan Medical Center between 1997 and 2005. Clinical, EEG, neuroradiological, and surgical data were collected. Long-term outcomes for seizure, motor functions, and cognitive functions were evaluated at a mean follow-up of 12.7 years (range, 7.6-16.2 years) after surgery. RESULTS: The mean age at epilepsy onset was 3.0 years (range, 0-7.6 years). The following epilepsy syndromes were identified in our cohort: focal symptomatic epilepsy (n=8), West syndrome (n=3), and Rasmussen's syndrome (n=1). Postoperative histopathology of our study patients revealed malformation of cortical development (n=7), encephalomalacia as a sequela of infarction or trauma (n=3), Sturge-Weber syndrome (n=1), and Rasmussen's encephalitis (n=1). The mean age at surgery was 6.5 years (range, 0.8-12.3 years). Anatomical or functional hemispherectomy was performed in 8 patients, and hemispherotomy was performed in 4 patients. Eight of our 12 children (66.7%) were seizure-free, but 3 patients with perioperative complications showed persistent seizure. Although all patients had preoperative hemiparesis and developmental delay, none had additional motor or cognitive deficits after surgery, and most achieved independent walking and improvement in daily activities. CONCLUSIONS: The long-term clinical outcomes of hemispherectomy in children with intractable hemispheric epilepsy are good when careful patient selection and skilled surgical approaches are applied.
Child ; Chungcheongnam-do ; Cohort Studies ; Electroencephalography ; Encephalitis ; Encephalomalacia ; Epilepsy* ; Follow-Up Studies ; Hemispherectomy ; Humans ; Infant ; Infant, Newborn ; Infarction ; Paresis ; Patient Selection ; Quality of Life ; Retrospective Studies ; Seizures ; Spasms, Infantile ; Sturge-Weber Syndrome ; Walking

Encephalocraniocutaneous lipomatosis (ECCL) is a rare neurocutaneous syndrome that affects ectomesodermal tissues (skin, eyes, adipose tissue, and brain). The neurologic manifestations associated with ECCL are various including seizures. However, ECCL patients very rarely develop brain tumors that originate from the neuroepithelium. This is the first described case of ECCL in combination with dysembryoplastic neuroepithelial tumor (DNET) that presented with intractable seizures. A 7-year-old girl was admitted to our center because of ECCL and associated uncontrolled seizures. She was born with right anophthalmia and lipomatosis in the right temporal area and endured right temporal lipoma excision at 3 years of age. Seizures began when she was 3 years old, but did not respond to multiple antiepileptic drugs. Brain magnetic resonance (MR) imaging performed at 8 and 10 years of age revealed an interval increase of multifocal hyperintense lesions in the basal ganglia, thalamus, cerebellum, periventricular white matter, and, especially, the right temporal area. A nodular mass near the right hippocampus demonstrated the absence of N-acetylaspartate decrease on brain MR spectroscopy and mildly increased methionine uptake on brain positron emission tomography, suggesting low-grade tumor. Twenty-four-hour video electroencephalographic monitoring also indicated seizures originating from the right temporal area. Right temporal lobectomy was performed without complications, and the nodular lesion was pathologically identified as DNET. The patient has been seizure-free for 14 months since surgery. Although ECCL-associated brain tumors are very rare, careful follow-up imaging and surgical resection is recommended for patients with intractable seizures.
Adipose Tissue ; Anophthalmos ; Anticonvulsants ; Basal Ganglia ; Brain ; Brain Neoplasms ; Cerebellum ; Child ; Drug Resistant Epilepsy ; Female ; Follow-Up Studies ; Hippocampus ; Humans ; Lipoma ; Lipomatosis* ; Magnetic Resonance Spectroscopy ; Methionine ; Neoplasms, Neuroepithelial* ; Neurocutaneous Syndromes ; Neurologic Manifestations ; Positron-Emission Tomography ; Seizures* ; Thalamus ; White Matter

The laparoscopy-assisted hepatic resection has been introduced for solitary benign or malignant hepatic tumors. We successfully had undergone left lateral sectionectomy with assistance of laparoscopy. Forty-three year old male patient was admitted for diagnosis of liver mass. Abdominal computed tomography showed a 2 cm sized single nodular tumor in segment 3 which was consistent with hepatoma. He underwent laparoscopy-assisted left lateral sectionectomy using Cavitron Ultrasonic Surgical Aspirator and Ultrasonic Shears. The patient discharged without any complications in the 8th postoperative day. The pathologic findings of nodule were consistent with the eosinophilic abscess of liver. Left lateral sectionectomy of liver can be conducted safely with assistance of laparoscopy.
Abscess ; Carcinoma, Hepatocellular ; Diagnosis ; Eosinophils* ; Humans ; Laparoscopy ; Larva Migrans, Visceral ; Liver Abscess* ; Liver* ; Male ; Toxocariasis ; Ultrasonics

Objective: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer’s disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. Methods: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. Results: Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. Conclusion This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Objective: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer’s disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. Methods: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. Results: Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. Conclusion This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Objective: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer’s disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. Methods: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. Results: Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. Conclusion This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Objective: Borderline personality disorder (BPD) is known to share characteristics with a variety of personality disorders (PDs) and exhibits diverse patterns of defense mechanisms. To enhance our understanding of BPD, it’s crucial to shift our focus from traditional categorical diagnostics to the dimensional traits shared with other PDs, as the borderline personality organization (BPO) model suggests. This approach illuminates the nuanced spectrum of BPD characteristics, offering deeper insights into its complexity. While studies have investigated the comorbidity of BPD with other PDs, research exploring the relationship between various personality factors and defense mechanisms within BPD itself has been scarce. The present study was undertaken to investigate the complex interrelationships between various personality factors and defense styles in individuals diagnosed with BPD. Methods: Using a network analysis approach, data from 227 patients diagnosed with BPD were examined using the Defense Style Questionnaire and Personality Disorder Questionnaire-4+ for assessment. Results: Intricate connections were observed between personality factors and defense styles. Significant associations were identified between various personality factors and defense styles, with immature defense styles, such as maladaptive and image-distorting being particularly prominent in BPD in the centrality analysis. The maladaptive defense style had the highest expected influence centrality. Furthermore, the schizotypal, dependent, and narcissistic personality factors demonstrated relatively high centrality within the network. Conclusion Network analysis can effectively delineate the complexity of various PDs and defense styles. These findings are expected to facilitate a deeper understanding of why BPD exhibits various levels of organization and presents with heterogeneous characteristics, consistent with the perspectives proposed by the BPO.