Electroconvulsive Therapy in Patients With Cardiac Implantable Electronic Devices: A Case Report and Systematic Review of Published Cases.

OBJECTIVE: The aims of the study were to report the case of a 54-year-old man with recurrent depressive disorder with multiple medical comorbidities having a dual-chamber pacemaker, treated successfully with 11 sessions of electroconvulsive therapy, and to conduct a systematic review of published cases documenting the use of electroconvulsive therapy (ECT) in patients with cardiac implantable electronic devices (CIEDs) for treating major psychiatric disorders. METHODS: We searched electronic databases (MEDLINE, PubMed, Google Scholar, Embase, Cochrane Library, PsycINFO, and Crossref) and included studies reporting on the use of electroconvulsive therapy in patients with CIEDs. RESULTS: Thirty-five publications across 53 years (1967-2021) reported on 76 patients (including current report) who received a pooled total of 979 modified ECT sessions. The most common adverse events were premature ventricular contraction and hypertension. There have been no reports of serious adverse effects that necessitated the cessation of ECT. CONCLUSIONS: Electroconvulsive therapy is a safe and efficacious treatment for major psychiatric disorders, and the presence of CIEDs should not delay or deter the use of ECT in these patients.

DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers.

Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol-1 (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.

A close critical look of India's National Mental Health Survey 2016.

The National Mental Health Survey 2016 (NMHS 2016) was a large epidemiological study, one of its kind, conducted by the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru to overcome the shortcomings of the previous surveys. The detailed report of the study is available in two parts- 'mental health systems' and 'prevalence, pattern and outcomes'. Though done comprehensively, there are some inevitable limitations. The private sector, a substantial health care provider in the country was not a participant in the survey. Though MINI version 6.0 is a standard and structured instrument, it does not cover many commonly encountered mental illnesses like somatoform disorders. Further, the methodology of the survey makes it difficult for an accurate calculation of the prevalence of individual major psychiatric disorders. The survey has been appraised using a standard checklist for prevalence studies. The detailed qualitative data has not been shared in the report. The contribution of the traditional indigenous systems of healthcare and accessibility of services in rural areas have not been elaborated. Thus, the need for a comprehensive and culturally sensitive assessment tool, involvement of the private sector, and enhancing funding provision to improve the infrastructure are emphasized as future directions for the subsequent phases of the survey.