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The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

Biofuel is the only novel solution to the increase in the greenhouse effect and bursting energy demand. The catalytic cracking of non-edible vegetable oils, namely castor and mustard was studied to yield gasoline range (C5-C9) hydrocarbons. Hβ (Microporous; pore size2 nm) and AlMCM-41 (Mesoporous; pore size 2 nm-50 nm) materials with different Si/Al ratios were used as catalysts for cracking purposes. Characterization of these catalysts was done by X-ray diffraction, Surface area analyzer, nitrogen sorption studies, TPD and inductively coupled plasma techniques. Used mustard oil was cracked over AlMCM-41 catalysts in a fixed bed catalytic cracking unit at optimized reaction condition (400 °C, 4.6 h

Face log detection (FLD) in the surveillance video extracts a new face image from the video sequences (VS). FLD utilizes biometric techniques for humans’ recognition. To improve the precise FLD with less complexity of our proposed method is Prewitt Logistic Deep Recurrent Neural Learning (PLDRNL) used. The input VS was received from the video database. Next, the keyframes are extracted from the VS. This proposed deep recurrent neural learning method uses four hidden layers to remove the facial features such as the face, eyes, nose, and mouth in the form of an edge. The edges of each element are derived using the Prewitt edge detector through the horizontal and vertical mask. Finally, the relevant features are fed into the output layer. The PLDRNL uses a logistic activation function at the output layer for matching the extracted related elements with the pre-stored testing feature vector. If two features are matched, then the face in the given VF is detected. The error in the FD is minimized using gradient descent function at the output layer. Based on the results, the human face effectively identified with the minimum false-positive rate (FPR). Experimental evaluation is carried out using different factors such as FLD, FPR, and time complexity.

• We performed median and tibial nerve somatosensory evoked potentials (SEPs), pattern-shift visual evoked potentials (PSVEPs), and brain-stem auditory evoked potentials (BAEPs) on 27 patients with motor neuron disease (MND). Median and tibial nerve SEPs were abnormal in 8 (30%) of 27 and 3 (14%) of 21 patients tested, respectively. Central and peripheral abnormalities were recorded in the absence of spondylosis. As a group, patients with MND and no evidence of cervical spondylosis had normal conduction to Erb's point following median nerve stimulation, but conduction times beyond this point were prolonged. The PSVEPs and BAEPs were within normal limits in all patients, excluding abnormalities attributable to other disease, but the group P100 latency was significantly prolonged in the group with MND. The BAEPs were normal in the group with MND. This study provides neurophysiological evidence of sensory system involvement in MND.

Nineteen patients with spasmodic torticollis, unresponsive to standard therapy, were administered local injections of botulinum‐A toxin into the affected muscles. During an average follow‐up period of 11.5 months, a more than 25% improvement was noted in 14 of 19 patients. All those with purely focal dystonia and 9 of 10 patients with a disease history of less than three years benefited from treatment. Side effects were insignificant and transient. Botulinum toxin is a very effective and safe method of treatment for spasmodic torticollis.

The chemoprotection extended by eugenol against carbon tetrachloride (CCl4) intoxication was established by studies on drug-metabolizing phase I and phase II enzymes. An overall decrease in drug-metabolizing enzymes, namely NADPH-cytochrome c reductase, NADH-cytochrome reductase, coumarin hydroxylase, 7-ethoxy coumarin-O-deethylase, UDP-glucuronyltransferase and glutathione-S-transferase, was observed with CCl4 intoxication, with a subsequent decrease in cytochrome P450 and cytochrome b5 content. CCl4 caused a significant decrease in microsomal phospholipids and the marker enzymes glucose-6-phosphatase and 5'-nucleotidase, and an increase in thiobarbituric acid reactive substances (TBARS). Simultaneous administration of eugenol with CCl4 inhibited the accumulation of TBARS and the decrease in the microsomal phospholipids and marker enzymes. Further, the chemical onslaught imposed by CCl4 on the drug-metabolizing system was removed successfully by eugenol. Eugenol appears to act as an in vivo antioxidant and as a better inducer of phase II enzymes than phase I enzymes. It is therefore suggested that eugenol could be an interesting basic structure for drug design.

In a double-blind, placebo-controlled study, 23 patients suffering from intractable spasmodic torticollis (ST) were given successively either botulinum toxin A (BTA) or normal saline by intramuscular injections in the affected muscles. Evaluation was carried out by three blinded observers, using a clinical and video assessment of the severity of torticollis, employing a scoring system described by Tsui (1). Patients were also asked to subjectively comment on changes in the amount of pain and on changes in the activities of daily living (ADL). BTA was proven to be superior on all forms of assessment to placebo, and these results were statistically significant. Side effects mainly consisted of pain at the injection site. Tiredness occurred at equal frequency with BTA and placebo. No serious or systemic side effects were noted. Botulinum toxin is a safe, effective and relatively simple treatment for spasmodic torticollis.