Sustainable Synthesis of Indole-Substituted Densely Functionalized Pyrrole.

A novel chromatography- and catalyst-free methodology has been developed for the synthesis of poly substituted pyrrole in good yields via a multicomponent reaction of arylglyoxal, 1,3-dicarbonyl, indole, and aromatic amine. This strategy provides various advantages such as simple experimental and workup procedures, mild reaction conditions, no added catalyst, use of green solvent, and simple purification procedure of pure product without using column chromatography. This green method offers a simple and highly effective strategy to synthesize a wide range of indole-pyrrole conjugates in a one-pot operation.

A Scoping Review of Technology-Facilitated Gender-Based Violence in Low- and Middle-Income Countries Across Asia.

Technology-facilitated gender-based violence (GBV) is a pervasive issue. Yet, most research focuses on high-income countries and few studies comprehensively summarize its prevalence, manifestations, and implications in the Global South. This scoping review sought to examine technology-facilitated GBV in low- and middle-income countries across Asia, specifically focusing on trends, common behaviors, and characteristics of perpetrators and survivors. A comprehensive search of peer-reviewed and gray literature published between 2006 and 2021 yielded 2,042 documents, of which 97 articles were included in the review. Across South and Southeast Asia, findings indicate that technology-facilitated GBV is a widespread phenomenon, with increased incidence during the COVID-19 pandemic. Technology-facilitated GBV comprises various forms of behaviors and prevalence varies by type of violence. Women, girls, and sexual and gender minorities, especially those with other intersecting marginalized identities, are often more vulnerable to experiencing online violence. Alongside these findings, the review revealed gaps in the literature including a lack of evidence from Central Asia and the Pacific Islands. There is also limited data on prevalence which we attribute to underreporting, in part due to disjointed, outdated, or nonexistent legal definitions. Findings from the study can be leveraged by key stakeholders such as researchers, practitioners, governments, and technology companies to develop prevention, response, and mitigation efforts.

Study of Echocardiogram Parameters from PPG Signal Using Self-Organized Operational Map-based Network.

Two crucial echocardiography parameters -left ventricular ejection fraction (LVEF) and myocardial performance index (MPI) are referred by clinicians to diagnose heart health. Here, an attempt was made to study the possibility of predicting values of LVEF and MPI from Photoplethysmography (PPG) signals. The classification of patients based on the LVEF and MPI values was also evaluated. After PPG signal feature extraction, the Dual Attention-Self Organised Operational Map-LSTM-Conv Network (DASLCN) was used to find the necessary results. Self-organized operational maps (SOOM) helped map the features before sending them to BiLSTM and 1D CNN layers. The results obtained were regression=0.86 with error% of 5.32±8.9 for MPI and accuracy=0.90 & sensitivity=0.89 for LVEF. This technique might help diagnose heart conditions from PPG signals without routine echocardiography.Clinical relevance- PPG is an easy cost-effective portable technique. Whereas, clinical echocardiography is possible only in specialized hospitals. Thus, exploring PPG signals to predict LVEF and MPI values were tried here. This study has been made on whether the grouping of patients based on the range of LVEF and MPI values was possible or not. Newly designed DASLCN helped to perform regression and classification in the same network.

Computer-aided Detection of Lesions from Coronary Angiograms Based on Contrast Learning Technique.

Coronary artery disease is one of the prevalent cardiovascular diseases in the world. Clinically, coronary artery angiography (CAG) is the most efficient diagnostic tool for detecting the stenosis caused by the presence of coronary lesions. Here, we proposed a simple but efficient methodology for predicting the coronary arterial block. The technique of classifying the angiograms collected from 369 patients is implemented using the contrast learning approach. ResNet 152 V2 is used as the deep network. Region of interest (ROI) is found for the diseased arteries for deciding the type of treatment procedure. Four different losses were implemented in this two-level classification technique. This framework achieved an accuracy of 0.81 recall of 0.76, precision of 0.86, specificity of 0.87, and F-score of 0.80. A comparative study with the state-of-the-art is carried out to establish the advantage of the proposed method. This computer-aided approach could be implemented by clinicians quite easily. Clinical relevance-An easy, simple, and fast technique of detecting and deciding treatment of coronary artery stenosis was studied using contrast learning and ROI. A computerised model was designed which can draw conclusion from CAGs even in the absence of clinical annotations.

A Computational Study of Graphene as a Prospective Material for Microelectrodes in Retinal Prosthesis and Electric Crosstalk Analysis.

The computational model plays a vital role in the design and optimization of microelectrodes for efficient electrical stimulation and recording in the retinal prosthesis. Moreover, the material choice acts decisively in ensuring that the electronic device delivers sufficient stimulating current to the retina without harming the neighboring tissue. Recently, due to the enhanced electrical and electrochemical properties of graphene, it has become a viable material in biomedical applications. In this study, we analyzed the computational model for the retinal prosthesis by the novel use of graphene-based microelectrodes. For this, different topologies of the electrode arrangement were investigated. The most suitable configuration involves the arrangement of electrodes serving as the ground in a hexagonal fashion around the central stimulating electrode. We observed that the performance of graphene as the stimulating electrodes is comparable to the existing noble metal-based electrodes. Moreover, we found that optimizing the microelectrode design resulted in uniform electric potential distribution, and this eventually led to an increased electric field intensity at the desired activation point. Additionally, we analyzed the crosstalk phenomenon, and we observed from the results that it is better to implant such an electrode array in the vicinity of the targeted volume to minimize the effect of crosstalk.Clinical relevance- The present study can help in the improvement of the retinal prosthesis by analyzing the quantitative and qualitative effects of graphene-based microelectrodes on producing the threshold electric field in the retina tissue. The results obtained can be used to optimize the implantable microelectrode design and thus is a step forward in finding a cure to vision impairment diseases.

Screening of Ischemic Heart Disease based on PPG Signals using Machine Learning Techniques.

The increasing rate of cardiac ailments has led to the rise in the scrutinization of ones cardiac health. The prevalent techniques for detecting heart diseases are costly and require expert supervision as well as modern equipment. Thus there is a need for an alternative low cost and easily available technique. Finger-tip photoplethysmography (PPG) signals can be used for identifying Ischemic Heart Disease (IHD). This technique of screening the disease will be very helpful to the inhabitants of remote, underdeveloped and unprivileged areas. Time-domain analysis of the signal was done for extracting different features. Segregation of diseased and healthy subjects was performed using Decision Trees, Discriminant Analysis, Logistic Regression, Support Vector Machine, KNN, and Boosted trees. Ten different performance metrics was studied using the confusion matrix. After analysis, the accuracy, sensitivity, specificity, and precision of 0.94, 0.95, 0.95 and 0.97 respectively was obtained using Boosted tress classifier. ROC and AUC were calculated to establish the robustness of the classification methods for determining IHD patients.