Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

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.

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

In this cross-sectional study, we aimed to identify distinguishing factors between populations with suicidal ideation recruited from hospitals and communities to make an efficient allocation of limited anti-suicidal resources according to group differences. We analyzed the baseline data from 120 individuals in a community-based cohort (CC) and 137 individuals in a hospital-based cohort (HC) with suicidal ideation obtained from the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) study. First, their sociodemographic factors, histories of medical and psychiatric illnesses, and suicidal behaviors were compared. Second, diagnosis by the Korean version of the Mini International Neuropsychiatric Interview, scores of psychometric scales were used to assess differences in clinical severity between the groups. The results revealed that the HC had more severe clinical features: more psychiatric diagnosis including current and recurrent major depressive episodes (odds ratio [OR], 4.054; P < 0.001 and OR, 11.432; P < 0.001, respectively), current suicide risk (OR, 4.817; P < 0.001), past manic episodes (OR, 9.500; P < 0.001), past hypomanic episodes (OR, 4.108; P = 0.008), current alcohol abuse (OR, 3.566; P = 0.020), and current mood disorder with psychotic features (OR, 20.342; P < 0.001) besides significantly higher scores in depression, anxiety, alcohol problems, impulsivity, and stress. By comparison, old age, single households, and low socioeconomic status were significantly associated with the CC. These findings indicate the necessity of more clinically oriented support for hospital visitors and more socioeconomic aid for community-dwellers with suicidality.
Alcoholism ; Anxiety ; Cohort Studies* ; Community Mental Health Centers ; Cross-Sectional Studies ; Depression ; Diagnosis ; Epidemiologic Studies ; Family Characteristics ; Impulsive Behavior ; Korea ; Mental Disorders ; Mood Disorders ; Psychometrics ; Social Class ; Suicidal Ideation ; Suicide* ; Weights and Measures

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

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