Vasopressins are evolutionarily conserved peptide hormones. Mammalian vasopressin functions systemically as an antidiuretic and regulator of blood and cardiac flow essential for adapting to terrestrial environments. Moreover, vasopressin acts centrally as a neurohormone involved in social and parental behavior and stress response. Vasopressin synthesis in several cell types, storage in intracellular vesicles, and release in response to physiological stimuli are highly regulated and mediated by three distinct G protein coupled receptors. Other receptors may bind or cross-bind vasopressin. Vasopressin is regulated spatially and temporally through transcriptional and post-transcriptional mechanisms, sex, tissue, and cell-specific receptor expression. Anomalies of vasopressin signaling have been observed in polycystic kidney disease, chronic heart failure, and neuropsychiatric conditions. Growing knowledge of the central biological roles of vasopressin has enabled pharmacological advances to treat these conditions by targeting defective systemic or central pathways utilizing specific agonists and antagonists.

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A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart classrooms to enhance faculty efficiency based on accumulated learning outcomes and interests. Smart classroom boards, audio-visual aids, and multimedia are directly related to the Smart classroom environment. Along with these facilities, more effort is required to monitor and analyze students' outcomes, teachers' performance, attendance records, and contents delivery in on-campus classrooms. One can achieve more improvement in quality teaching and learning outcomes by developing digital twins in on-campus classrooms. In this article, we have proposed DeepClass-Rooms, a digital twin framework for attendance and course contents monitoring for the public sector schools of Punjab, Pakistan. DeepClassRooms is cost-effective and requires RFID readers and high-edge computing devices at the Fog layer for attendance monitoring and content matching, using convolution neural network for on-campus and online classes.

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In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients' estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model's prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients' count of the proposed model is much closer to the actual patient count.