In patients with low HFC proportion ( less then 35%), severe anxiety could significantly decrease HFC proportion. The HFC proportion positively correlated with immediate and delayed memory in depressed patients. Further analysis showed that patients with low HFC proportion may have worse delayed memory. LIMITATIONS The lack of prior exposure to the monitoring equipment and procedure could have generated artefacts that would have disappeared after habituation. CONCLUSIONS These results support a positive correlation between the HFC proportion and memory in depressed patients. Further research is required to explore the clinical implications of these findings. V.BACKGROUND Anxiety disorders have been related to cardiovascular diseases via low-grade inflammation, but longitudinal studies on the association between generalized anxiety disorder (GAD) and inflammatory biomarkers are sparse. Furthermore, no studies have examined the association between GAD and the "cardio-protective" adipocytokine adiponectin in this context so far. METHODS In a Swiss population-based sample of 2,415 adults participating in baseline and follow-up exams (mean follow-up duration=5.5 years), we diagnosed a total of 55 persons (2.3%) with GAD using a validated semi-structured psychiatric interview. We prospectively examined the relation between GAD and circulating levels of inflammatory biomarkers (i.e., C-reactive protein, interleukin (IL)-1β, IL-6, tumor necrosis factor-α, and adiponectin), in linear regression models, statistically controlled for the baseline inflammatory marker, socioeconomic status, cardiovascular risk factors, health behaviors, and psychiatric disorders. RESULTS Compared to those without GAD, individuals with GAD had lower IL-6 (β=-0.249, 95%-CI -0.493-(-0.004), p=0.046), and adiponectin (β=-0.264, 95%-CI -0.482-(-0.045), p=0.018) levels at follow-up after adjustment for all covariates. Moreover, GAD was unrelated to several other inflammatory measures. CONCLUSION Individuals with GAD do not seem to exhibit chronic low-grade inflammation, suggesting different underlying biobehavioral mechanisms to those from other anxiety disorders. Low adiponectin levels may be linked to symptoms of GAD through brain areas directly involved in the processing of fear and anxiety. BACKGROUND Major depressive depression (MDD) is often accompanied by suicidal behavior. Increasing evidence shows that MDD patients display psychotic symptoms. However, the relationship between psychotic symptoms and suicide attempt in MDD has not been reported. Therefore, this study was to identify the prevalence and associated factors of suicide attempts in first-episode and untreated Chinese Han outpatients comorbid with psychotic major depression (PMD). METHODS 1718 first-episode and untreated MDD outpatients were assessed with Positive Symptom Scale of Positive and Negative Syndrome Scale (PANSS), the 17-item Hamilton Depression Scale, Hamilton Anxiety Scale, and clinical global impression of severity scale (CGI-S). https://www.selleckchem.com/products/DMXAA(ASA404).html Some glycolipid metabolism and thyroid hormone parameters were measured. RESULTS In MDD patients, the percentage of PMD was 10.0%. The incidence of attempted suicide in PMD patients was 51.5%, which was more than 3 times higher than that in non-PMD patients (16.7%). In PMD patients, compared to non-attempters, suicide attempters were older, had a longer course of disease, scored higher on all scales, as well as had higher levels of thyroid stimulating hormone (TSH), thyroid peroxidases antibody, anti-thyroglobulin and blood glucose. PANSS positive symptom, CGI, diastolic blood pressure and TSH were independently associated with suicide attempts of PMD. CONCLUSIONS PMD patients have a higher prevalence of suicide attempt than non-PMD patients. Some demographic and clinical parameters are relevant factors for suicide attempt in PMD patients. BACKGROUND Studies comparing objective and subjective cognition in major depressive disorder (MDD) are scarce, and available data concern different cognitive functions but not decision-making specifically. This cross-sectional study aimed to explore objective (O-DMI) and subjective decision-making impairment (S-DMI) as well as the discrepancy between both in people with MDD. Secondly, we assessed the association between S-DMI and O-DMI. Finally, we explored sociodemographic, clinical, and psychosocial factors related to decision-making impairment status. METHOD One hundred and nine people with MDD were assessed with the Iowa Gambling Task to identify "O-DMI". The item 9 of the Beck Depression Inventory was used to compose the variable "S-DMI". Psychiatric history, medication adherence, childhood trauma, physical and psychological pain, and negative life experiences were also collected. RESULTS Forty-six percent of participants have O-DMI and 65.1% S-DMI. From 49.5% showing a discrepancy between both, 68,6% have positive discrepancy and 31,4% negative (i.e. under and overestimation respectively). O-DMI and S-DMI were not associated. Binary logistic regressions showed that the number of negative life events in the past 6 months was predictor of O-DMI, while depression severity and medication adherence predicted S-DMI. Finally, medication adherence and depression severity predicted the type of discrepancy. LIMITATIONS The variable S-DMI was obtained through a single item. CONCLUSION These results confirm the lack of association between objective and subjective measures of cognition in MDD, and for the first-time concerning decision-making. This suggest that both assessments should be done in order to have a deeper knowledge of the cognitive functioning of each patient. BACKGROUND The purpose of this study was to develop and evaluate an algorithm for bone segmentation on whole-body CT using a convolutional neural network (CNN). METHODS Bone segmentation was performed using a network based on U-Net architecture. To evaluate its performance and robustness, we prepared three different datasets (1) an in-house dataset comprising 16,218 slices of CT images from 32 scans in 16 patients; (2) a secondary dataset comprising 12,529 slices of CT images from 20 scans in 20 patients, which were collected from The Cancer Imaging Archive; and (3) a publicly available labelled dataset comprising 270 slices of CT images from 27 scans in 20 patients. To improve the network's performance and robustness, we evaluated the efficacy of three types of data augmentation technique conventional method, mixup, and random image cropping and patching (RICAP). RESULTS The network trained on the in-house dataset achieved a mean Dice coefficient of 0.983 ± 0.005 on cross validation with the in-house dataset, and 0.