Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

Background:We compared the effects of adding dexmedetomidine to a 30 ml solution of 0.325% bupivacaine in supraclavicular brachial plexus block. Onset and duration of sensory and motor block along with the duration of analgesia were the primary endpoints.Materials and Methods:Fifty patients posted for upper limb surgeries were enrolled for a prospective, randomized, double-blind, placebo-controlled trial. Patients were divided into two groups, the control group S and the study group SD. In group S (n = 25), 30 ml of 0.325% bupivacaine + 1 ml normal saline; and in group SD (n = 25), 30 ml of 0.325% bupivacaine + 1 ml (100 μg) dexmedetomidine were given for supraclavicular brachial plexus block using the peripheral nerve stimulator. Onset and duration of sensory and motor blocks were assessed along with the duration of analgesia, sedation, and adverse effects, if any. Hemodynamic parameters, like heart rate (HR), systolic arterial blood pressure (SBP), and diastolic arterial blood pressure (DBP) were also monitored.Results:Demographic data and surgical characteristics were comparable in both the groups. The onset times for sensory and motor blocks were significantly shorter in SD than S group (P < 0.001), while the duration of blocks was significantly longer (P < 0.001) in SD group. Except for the initial recordings (at 0, 5, 10, and 15 min), heart rate levels in group SD were significantly lower (P < 0.001). SBP and DBP levels in SD group at 15, 30, 45, 60, 90 and 120 min were significantly lower than in S group (P < 0.001). In fact, when the percentage changes in HR/SBP/DBP were compared from 0-5/0-10/0-15/0-30/0-45/0-60/0-90/0-120 min in SD with S group, they came out to be highly significant (P < 0.001) in group SD. The duration of analgesia (DOA) was significantly longer in SD group than S group (P < 0.001). Except that, bradycardia was observed in one patient in the group SD, no other adverse effects were observed in either of the groups.Conclusion:Dexmedetomidine added as an adjuvant to bupivacaine for supraclavicular brachial plexus block significantly shortens the onset time and prolongs the duration of sensory and motor blocks and duration of analgesia. Patients in group SD were adequately sedated (modified Ramsay Sedation Score, RSS = 2/6 or 3/6) with no adverse effects except bradycardia in one patient of group SD.

Low-dose topiramate is efficacious in migraine prophylaxis as compared to both placebo and lamotrigine. Lamotrigine in low doses might be beneficial for headache frequency; however, longer trials are required to establish its efficacy on the intensity and frequency of migraine.

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Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 – suspected, confirmed and death.

Ketogenic diet (KD) is a high-fat, adequate-protein, and low-carbohydrate diet that leads to nutritional ketosis, long known for antiepileptic effects and has been used therapeutically to treat refractory epilepsy. This review attempts to summarize the evidence and clinical application of KD in diabetes, obesity, and other endocrine disorders. KD is usually animal protein based. An empiric vegetarian Indian variant of KD has been provided keeping in mind the Indian food habits. KD has beneficial effects on cardiac ischemic preconditioning, improves oxygenation in patients with respiratory failure, improves glycemic control in diabetics, is associated with significant weight loss, and has a beneficial impact on polycystic ovarian syndrome. Multivitamin supplementations are recommended with KD. Recently, ketones are being proposed as super-metabolic fuel; and KD is currently regarded as apt dietary therapy for “diabesity.”

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