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Background there is a high prevalence of depressive and anxiety disorders in the world, though the diagnosis is mostly insufficient. The Lithuanian-speaking population does not have a validated and open-access screening tool for depression. Due to appealing diagnostic superiority, universality within different populations, and open access the validation of Patient Health Questionnaire 9 (PHQ-9) is a must. Methods PHQ-9 was translated. Face, content, criterion-related, and construct validity checked by a group of psychiatrists. Two groups, clinical (N43) and non-clinical (N416), filled out the questionnaire. Reliability, internal consistency, parallel form variability, factor analysis, and diagnostic cut-off points were measured. Results PHQ-9 translation has confirmed psychometric validity as high reliability of the questionnaire was estimated with a Cronbach α of 0.858–0.877. Exploratory factor analysis indicated a one-factor structure. Questionnaire performed with great accuracy to distinguish the presence of illness in the clinical sample. Preliminary cut-off points were determined to be 8 with a sensitivity of 86.5%, specificity of 100.0% and accuracy of 89.1% across the psychiatric patient population. Conclusions The translated version of PHQ-9 is a reliable and suitable tool to screen for depressive symptoms.

Introduction Deep convolutional neural networks (CNNs) have been shown to be reliable in evaluating geometrically assessed systolic heart function, however, studies evaluating diastolic heart function, which reflects hemodynamic status, are scarce. This study presents a novel CNNs approach for recognition and evaluation of different diastolic left ventricular (LV) function measurements. Purpose To create a new CNNs approach in the assessment of LV diastolic function and compare the results with clinicians' measurements. Methods A total of 4586 2D echocardiographic images were extracted from the studies of 330 patients referred with varying indications. The ensemble of four CNNs was trained (80/20% training/validation patient split) for the assessment of transmitral inflow (E and A wave peak velocities), mitral septal and lateral annular velocities (e'), tricuspid regurgitation systolic velocity (Vmax) and detection the left atrial endocardium in apical four-chamber views. Additionally, E/A ratio, average E/e' ratio and left atrial volume index (LAVi) values were calculated for the evaluation of diastolic dysfunction according to the 2016 ASE/EACVI recommendations. CNNs performance in detecting diastolic dysfunction was compared to expert cardiologists on a set of 20 separate cases. Results Study results on the validation data showed that CNNs accurately predicted peak E/A ratio, average E/e' ratio (R2=0.88 and R2=0.89, respectively) and tricuspid regurgitation Vmax (R2=0.82). Figure 1 illustrates different functional LV measurements among the obtained echocardiographic images. Regarding the geometrical assessment of diastolic function, the segmentation model traced the left atrial endocardial border (intersection over union = 0.94) and subsequently was used in predicting the LAVi (R2=0.92). The ensemble of four CNNs had the area under the ROC curve of 0.93 for the detection of diastolic dysfunction when compared to expert cardiologists. Conclusion Deep CNNs demonstrate the capacity to detect peak velocities across different Doppler imaging modes and delineate left atrial endocardium border while using a relatively small dataset. Combining multiple CNNs has the potential of performing an accurate assessment of the diastolic LV function. Funding Acknowledgement Type of funding source: None

IntroductionPrevious studies suggest that one of the possible depression pathophysiological pathways is autoimmune inflammation increasing inflammatory mediators’ levels and thus affecting mood.ObjectivesTo compare depression and anxiety symptoms among inflammatory bowel disease patients receiving TNF-α inhibitors and those receiving treatment as usual (TAU).MethodsInstruments: Ulcerative colitis activity index, Crohn’s disease activity index, the subscale of neurovegetative symptoms of the Beck depression inventory, Hospital anxiety and depression scale. Active ulcerative colitis or Chron‘s disease patients not using antidepressants were included in the study and divided into an experimental group (receiving TNF-α inhibitors) and control group (receiving TAU).Results46 patients’ data were analyzed. Between the experimental group and the control group, the disease activity index was not significantly different (Chron’s disease 3.54 ±4.20; ulcerative colitis 5.70 ±5.00; p > 0.05) as well as the mean scores of the neurovegetative depression symptoms subscale of the Beck depression inventory (2.52 experimental ±3.91 control; p > 0.05). The mean score of the hospital anxiety and depression scale were significantly different between both groups (5.22 ±8.13; p < 0.05). The mean anxiety subscale scores’ p=0,06, which shows trend for significance. The mean depressive subscale score was significantly different in the control group (1.43 ±2.65; p < 0.05).ConclusionsPatients treated with biological therapy experienced fewer depression symptoms than patients showing similar disease activity, but receiving TAU.

Funding Acknowledgements Type of funding sources: None. INTRODUCTION  Deep learning (DL) has been successfully applied in the automated assessment of some transthoracic echocardiography (TTE) parameters such as left-ventricular ejection fraction. Nevertheless, automation of the right-sided heart assessment has not been widely studied, partially due to the relative difficulty involved in some of the right-sided heart measurement evaluation and time constraints in routine practice. Here we have explored the feasibility of a DL-based system capable of performing different tasks involved in the right-sided heart functional and geometric evaluation. PURPOSE  To develop a DL-based system assessing right atrium (RA) and right ventricle (RV) functional and geometric parameters and compare its accuracy to board-certified cardiologists. METHODS A total of 2,014 frames from 349 patients (with various indications for TTE) were used to train and validate four convolutional neural networks (CNNs) to perform either segmentation or landmark detection across four different TTE views: apical four-chamber (A4Ch), parasternal long-axis (PLAX), M-mode of tricuspid annulus and tissue Doppler imaging (TDI) of the right ventricular lateral wall. The CNNs were optimised to perform different right-sided heart measurements, namely, right atrial area in end-systole (RAA) and fractional area change (FAC) of RV in A4Ch view, proximal right ventricular outflow tract diameter (pRVOT) in PLAX view, tricuspid annular plane systolic excursion (TAPSE) in M-mode and S’ in TDI. Model performance was compared with two board-certified cardiologists using their average measurements on 20 test set patients.  RESULTS CNN predicted pRVOT diameter with a mean absolute error (MAE) of 1.02 mm and root mean squared error (RMSE) of 3.08 mm. The intersection over union (IoU) for the segmentation of RV and RA was 0.89 and 0.87, respectively. We then used  RV and RA segmentation predictions to calculate additional parameters which resulted in RMSE of 8.34% for FAC and 4.93cm2 for RAA. In the M-mode and TDI, the model achieved RMSE of 4.48 mm and 0.84 cm/s for the detection of TAPSE and S’, respectively. CONCLUSIONS We have demonstrated the feasibility of a DL-based system performing different measurements involved in right-sided heart evaluation. In a routine practice, where limited time resources might be available, such could assist in the thorough assessment of the right-sided heart geometry and function. Additional studies using cardiac magnetic resonance imaging to establish more precise accuracy of such systems is needed.