MCred: multi-modal message credibility for fake news detection using BERT and CNN.

Online social media enables low cost, easy access, rapid propagation, and easy communication of information, including spreading low-quality fake news. Fake news has become a huge threat to every sector in society, and resulting in decrements in the trust quotient for media and leading the audience into bewilderment. In this paper, we proposed a new framework called Message Credibility (MCred) for fake news detection that utilizes the benefits of local and global text semantics. This framework is the fusion of Bidirectional Encoder Representations from Transformers (BERT) using the relationship between words in sentences for global text semantics, and Convolutional Neural Networks (CNN) using N-gram features for local text semantics. We demonstrate through experimental results a popular Kaggle dataset that MCred improves the accuracy over a state-of-the-art model by 1.10% thanks to its combination of local and global text semantics.

XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks.

COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

A case study on the management of dry gangrene by Kaishore Guggulu, Sanjivani Vati and Dashanga Lepa.

Gangrene is a type of tissue death caused by lack of blood supply. Symptoms may include a change in skin color red or black, numbness, swelling, pain, ulceration and detachment of local skin. The feet and hands are most commonly affected. This condition may arise because of an injury, infection or other health conditions, majorly diabetes. Gangrene and its associated amputations are clinically challenging, but Ayurveda offers therapy options. In Ayurveda, gangrene can be considered as Kotha under Dushta Vrana due to Margavarana and Dhatu Kshaya. This case study aims to investigate whether Ayurvedic intervention can manage Kotha (gangrene) without amputation of the gangrenous part. A 45-year old woman came to the outpatient department (OPD) of Regional Ayurveda Research Institute for Drug Development, Gwalior, with clinical features indicative of dry gangrene. She was examined and diagnosis was confirmed by previous reports of X-ray and arterial color doppler. This case of dry gangrene was managed by Ayurvedic intervention of Kaishore Guggulu 1 g twice a day and Sanjivani Vati 250 mg thrice a day and Dashanga Lepa local application as a Lepa twice a day with lukewarm cow Ghrita for 2 months at OPD level. The response to the treatment was found highly effective. At the end of the treatment period of 2 months as compared with baseline, a highly significant change was observed. Kaishore Guggulu, Sanjivani Vati and Dashanga Lepa in the above-mentioned dose were found very effective and safe in a patient of dry gangrene.

An in vitro study to compare the accuracy of the master cast fabricated by four different transfer impression techniques for single-tooth implant replacement.

Single tooth implant retained crowns have become a recognized technique for the replacement of the missing teeth. With the predictable integration of implants, the emphasis is shifted towards precise prosthesis. Minor movement of the impression coping retained inside the impression material can occur during all the procedures, leading to the three-dimensional spatial inaccuracies in the master casts. Therefore, the present study was undertaken with the purpose to evaluate the accuracy of single-tooth implant impression techniques using four different impression copings, so as to obtain a precise definitive cast for a single-unit implant restoration. A maxillary acrylic resin model with a standard single implant in the first molar region was used to simulate a clinical situation. A total of 60 impressions were made with polyvinylsiloxane impression material, which were divided into four groups of 15 impressions each. Group I used non-modified square impression coping, while in group II, III and IV square impression coping were modified differently. Master casts fabricated for all the groups were analyzed to detect rotational position change of the hexagon on the implant replicas in the master casts in reference to the resin model. The master casts obtained with the roughened and adhesive-coated impression copings showed a lower amount of rotational movement than the masters casts achieved with the non-modified impression copings. Hence, the clinician should use sandblasted and adhesive coated impression copings to achieve a more accurate and precise orientation of the implant replicas in the laboratory master casts in single-tooth implant restorations.