OBJECTIVE: Schizophrenia (SZ) has been associated with the inflammatory-related and immunological pathogenesis. This study investigates the aberration of cytokines in patients with SZ. METHODS: Thirty patients with SZ without antipsychotic treatment for at least two weeks participated. We measured the serum levels of fourteen cytokines at hospital admission and after 8-week antipsychotic treatment. Severity was measured by expanded version of 24-items brief psychiatric rating scale (BPRS-E). Repeated measure analyses of variance were conducted. RESULTS: The interleukin-1 receptor antagonist (IL-1ra) was significantly decreased after 8-week antipsychotic treatment than those of before antipsychotic treatment (F=12.15, df=1/30, p=0.002). Neural cell adhesion molecule 1/CD56 (NCAM-1/CD56) was significantly decreased (F=6.61, df=1/30, p=0.016) among those with second-generation antipsychotics but not first-generation antipsychotics treatment. The changes of BPRS-E-manic and BPRS-E-anxiety scores correlated with the baseline IL-1ra (r=-0.393), IL-6 (r=-0.407), and insulin like growth factor binding protein 3 (r=-0.446). Additionally, the changes of BPRS-E and BPRS-E-negative scores correlated with the changes of brain-derived neurotrophic factor (r=0.372) and interferon-gamma (r=0.375). CONCLUSION: Our study supports that IL-1ra and NCAM-1/CD56 may be considered as markers of developing SZ.
Antipsychotic Agents ; Brain-Derived Neurotrophic Factor ; Brief Psychiatric Rating Scale ; Carrier Proteins ; Cytokines ; Humans ; Insulin ; Interferon-gamma ; Interleukin 1 Receptor Antagonist Protein* ; Interleukin-1 ; Interleukin-6 ; Neural Cell Adhesion Molecules ; Schizophrenia*

OBJECTIVE: Postoperative delirium (POD) is a highly prevalent complex neuropsychiatric syndrome in elderly patients. However, its pathophysiology is currently unknown. Early detection and prevention of POD is important; therefore, the aim of this study was to investigate the link between preoperative insulin growth factor 1 (IGF-1) levels in the serum and POD in the Chinese elderly patients. METHODS: One hundred and three patients who were undergoing an orthopedic operation took part in the study. Preoperative serum IGF-1 levels were measured. POD was determined daily using the Confusion Assessment Method (CAM) and DSM-IV TR. Baseline serum IGF-1 levels were compared between patients who did and did not develop POD. Correlation coefficients were calculated to evaluate relationship between baseline characteristics and serum IGF-1 levels. The relationship between baseline biomarkers and delirium status was investigated using logistic regression analysis, adjusting for potential confounding variables. RESULTS: Twenty-three patients developed POD. The POD group had lower MMSE scores and higher CCI scores and proportions of acute admission. Preoperative serum IGF-1 levels were correlated with MMSE scores and age (MMSE: r=0.230, p<0.05; age: r=-0.419, p<0.001). Baseline serum IGF-1 levels did not differ between patients who did and did not develop POD, even after adjusting for potential confounding factors, MMSE score, and age. CONCLUSION: No association was found between preoperative IGF-1 levels and POD, suggesting that they are not direct biomarkers of the incidence of POD among the Chinese elderly population. Further research with larger sample sizes is warranted to clarify the relationship.
Aged* ; Asian Continental Ancestry Group* ; Biomarkers ; Confounding Factors (Epidemiology) ; Delirium* ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Incidence ; Insulin ; Insulin-Like Growth Factor I ; Logistic Models ; Orthopedics ; Sample Size

Adolescent ; Deglutition ; Dermoid Cyst ; Epithelium ; Floors and Floorcoverings ; Humans ; Keratins ; Mouth ; Mouth Floor

Bipolar disorder (BD) is a major psychiatric disorder that is easily misdiagnosed. Patient adherence to a treatment regimen is of utmost importance for successful outcomes in BD. Several trials of antipsychotics suggested that depot antipsychotics, including long-acting first- and second-generation agents, are effective in preventing non-adherence, partial adherence, and in reducing relapse in BD. Various long-acting injectable (LAI) antipsychotics are available, including fluphenazine decanoate, haloperidol decanoate, olanzapine pamoate, risperidone microspheres, paliperidone palmitate, and aripiprazole monohydrate. Due to the increasing number of BD patients receiving LAI antipsychotics, treatment guidelines have been developed. However, the clinical applicability of LAI antipsychotics remains a global cause for concern, particularly in Asian countries. Expert physicians from Taiwan participated in a consensus meeting, which was held to review key areas based on both current literature and clinical practice. The purpose of this meeting was to generate a practical and implementable set of recommendations for LAI antipsychotic use to treat BD; target patient groups, dosage, administration, and adverse effects were considered. Experts recommended using LAI antipsychotics in patients with schizophrenia, rapid cycling BD, BD I, and bipolar-type schizoaffective disorder. LAI antipsychotic use was recommended in BD patients with the following characteristics: multiple episodes and low adherence; seldom yet serious episodes; low adherence potential per a physician's clinical judgment; preference for injectable agents over oral agents; and multiple oral agent users still experiencing residual symptoms.
Antipsychotic Agents* ; Asian Continental Ancestry Group ; Bipolar Disorder* ; Consensus* ; Fluphenazine ; Haloperidol ; Humans ; Judgment ; Microspheres ; Patient Compliance ; Psychotic Disorders ; Recurrence ; Risperidone ; Schizophrenia ; Taiwan ; Aripiprazole ; Paliperidone Palmitate

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.