A study report on the effect of COVID-19 pandemic in providing in-and-out-patient psychiatric services in a level-3 COVID hospital.

Background: COVID-19 pandemic disrupted all routine and emergency hospital services, including our out-and-in-patient psychiatric services. Aim: To study the effect of the COVID-19 pandemic and subsequent lockdown in providing in-and-out-patient psychiatric services and the experience of tele-consultation services in our level-3 COVID hospital. Materials and Methods: We conducted a retrospective observational study using an administrative database at psychiatry in-and-out-patient department. All the cases that were reported to us, through emergency Out-Patient Department (OPD) and tele-consultation OPD, from April 2020 to October 2020, were included in the study. Data, thus obtained, were compared with the out-and-in-patient data during the same period on the previous year. Results: During the study period, there was a decline in out-patient registration of patients by 94.5%, and a reduction in admission rate was 75.5%, in comparison with the previous year. During 3 months of tele-consultation service provided, 23.5% of patients had the diagnosis of depression, 21.4% of them had various types of headaches, 15.9% of patients had psychosis, 15.3% had anxiety disorders, and 8.8% had a bipolar-affective disorder. Conclusion: Being a level-3 COVID hospital, our hospital suffered significantly in relation to psychiatric in-and-out-patients attendance and service recipients during the study period of COVID-19 pandemic.

Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images.

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal.

Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus.

Objective and Impact Statement. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. Introduction. Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans' index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. Methods. We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. Results. Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. Conclusion. Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.

Corrigendum: Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

[This corrects the article DOI: 10.3389/fnins.2019.01449.].

Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks.

Tramadol use in premature ejaculation: daily versus sporadic treatment.

AIM: Premature ejaculation (PME) is defined as ejaculation with the minimal sexual stimulation before, on or shortly after penetration and or before a person wishes it. It is a function of the time between intra-vaginal penetration and intra-vaginal ejaculation. Tramadol has shown efficacy in PME when used as sporadic basis. In this study, we compared the use of 100 mg of tramadol as sporadic treatment (administered 6-8 h before coitus) versus continued treatment with the objective of evaluating the therapeutic results of both modalities. We assumed our alternative hypothesis that they have similar effects. MATERIALS AND METHODS: A prospective study was carried out on 60 patients divided into two groups of 30 patients each. Intra-vaginal ejaculation latency time (IELT) and coital frequency were measured both prior to and after the treatment. Group A received tramadol 100 mg daily for 4 weeks and on request (sporadically) for 4 weeks more. Group B was given placebo in the same manner. Results were statistically analyzed using the Student t-test. RESULTS: Mean IELT prior to treatment was 59.2 s in Group A and 58.7 s in Group B. Mean pre-treatment coital frequency was 2.44 times/week for Group A and 2.13 times/week for Group B. Mean IELT was 202.5 s after continued tramadol treatment and 238.2 s after sporadic treatment in Group A. Mean IELT with daily placebo was 94.8 s and with sporadic placebo was 96.6 s. Coital frequency increased to 4.32 times/week with daily tramadol treatment and 4.86 times with sporadic treatment. Coital frequency increased to 2.88 times/week with daily placebo treatment and 3.23 times with sporadic treatment. CONCLUSIONS: The results of PME treatment with tramadol are similar with both continued and sporadic administration. The sex life of patients improved and they reported greater satisfaction with the sporadic treatment.

Deliberate self-harm in nondepressed substance-dependent patients.

OBJECTIVE: The lifetime diagnosis of substance dependence syndrome is a major risk factor for attempting suicide. The systematic study of various risk factors of suicide in substance-dependent patients in Indian population will have far-reaching implications about the understanding of disorder. The objective was to study the sociodemographic and clinical factors associated with deliberate self-harm (DSH) in nondepressed substance-dependent patients. METHODS: Participants included 60 male inpatients (30 patients with DSH and 30 without DSH) fulfilling International Classification of Diseases, Tenth Revision Diagnostic Criteria for Research for substance dependence syndrome, aged between 18 and 60 years, with Hamilton Depression Rating Scale score fewer than 7. They were assessed using Addiction Severity Index, Presumptive Stressful Life Event Scale, State-Trait Anger Expression Inventory, Lubben Social Network Scale, International Personality Disorder Examination, Risk Rescue Rating Scale, and Global Assessment of Functioning. RESULT: Patients with DSH had significantly higher rates of opioid dependence (P < .05), risk of isolation (P < 0.001), the number of life events (P < 0.001), anger trait and anger expression (P < 0.001), personality disorder (P < 0.05), the number of substance use problems and lower social functioning (P < 0.001), as compared with those without DSH (P < 0.001). There was no significant correlation between Risk Rescue Rating Scale with sociodemographic and clinical variables. CONCLUSIONS: The study demonstrated that patients with opioid dependence, high risk of isolation, the greater number of life events, higher anger trait and anger expression, personality disorder, low social functioning, and greater number of substance use problems have risk for DSH.