Production and characterization of an organic solvent activated protease from haloalkaliphilic bacterium Halobiforma sp. strain BNMIITR.

An unusual haloalkaliphilic bacterium known as Halobiforma sp. strain BNMIITR, which was noticed to produce an extracellular alkaline protease, was found in a soil sample from Northern India's Sambhar Lake. On the generation of protease, the effects of dietary elements including nitrogen and carbon sources, amino acids, and growth conditions like temperature and pH were investigated. When low-cost agricultural by-products were employed as nitrogen sources, the manufacturing of enzymes was significantly boosted. In the present study, protease production was enhanced by 2.94 fold and 2.17 fold. By solvent precipitation and Hydrophobic interaction chromatography (HIC) on Phenyl Sepharose 6 Fast Flow matrix, the enzyme was purified 31.67 fold. It was determined that the apparent molecular mass was 21 kDa. The pH range where the enzyme was most stable was 6.0-12.0, with a temperature of 50 °C as optimum. When there was alkaline earth metals and heavy metals, protease was discovered to be active. It was evident that the enzyme was a serine type of protease because it was active in the presence of a variety of surfactants, oxidizing and reducing chemicals, and phenylmethylsulfonyl fluoride (PMSF) completely inhibited activity. Enzyme exhibited a wide range of substrate specificity. Amazingly, enzyme remained stable both in polar and nonpolar solvents. The most interesting aspect of this enzyme is enhanced activity in polar solvents like dimethylformamide (DMF) and dimethyl sulfoxide (DMSO). It was discovered that the protease was stable and compatible with a number of widely available detergents.

SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.

BACKGROUND: Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification. OBJECTIVE: This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques. METHODS: This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets. RESULTS: SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets. CONCLUSION: These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.

A review article on the dosimetry in intraluminal HDR brachytherapy of lung carcinoma.

Lung cancer is one of the most common cancers in the world. Intraluminal brachytherapy (BT) is one of the most adopted treatment modalities for lung malignancies with Ir-192 source in radiotherapy. In intraluminal BT, treatment delivery is required to be very accurate and precise with respect to the plan created in the treatment planning system (TPS). The BT dosimetry is necessary for better treatment outcomes. Therefore in this review article, some relevant studies were identified and analyzed for dosimetric outcomes in intraluminal BT in lung malignancies. The dosimetry in BT for plan verification is not presently in practice, which needs to be performed to check the variation between the planned and measured doses. The necessary dosimetric work done by the various researchers in intraluminal BT such as the Monte Carlo CYLTRAN code was used to calculate and measure the dose rate in any medium. Anthropomorphic phantom was used to measure doses at some distance from the source with Thermo luminescence dosimeters (TLDs). The dosimetric influence of air passage in the bronchus was evaluated with the GEANT4 Monte Carlo method. A pinpoint chamber was used to measure and quantify the impact of inhomogeneity in wax phantom for the Ir-192 source. The Gafchromic films and Monte Carlo methods were used to find the phantom and heterogeneities, which were found to underestimate the dose for the lungs and overestimated for the bones in TPS. The exact tool to quantify the variation in planned and delivered doses should be cost-effective and easy to use possibly with tissue equivalent phantoms and Gafchromic films in lung malignancies treatment.

Angle of concavity in planning target volume can be adopted as selection criteria for intensity-modulated radiation therapy or three-dimensional conformal radiotherapy technique in brain tumors.

Introduction: With innovation of medical imaging, radiotherapy attempts to conform the high dose region to the planning target volume (PTV). The present work aimed to assess the angle of concavity in PTV can be adopted as selection criteria for intensity-modulated radiation therapy (IMRT) or three-dimensional conformal radiotherapy (3DCRT) technique in Brain tumors. Materials and Methods: Thirty previously irradiated patients with brain tumors were replanned with both 3DCRT and IMRT technique. Angle of concavity (dip) in the PTV near the organs at risk was measured in the contoured structure set images of each patient. These cases were divided into three groups where angles were 0°, >120° and <120°. Dose of 60 Gy/30# was fixed. Results: In Group 1, the IMRT plan had better TV95% as compared to 3DCRT respectively with significant P value (P = 0.002). Mean of conformity index (CI) and Homogeneity Index (HI) were comparable. For Group 2 (angle >120°), the IMRT plan had better TV95% as compared to 3DCRT respectively with a significant P value (P = 0.021). HI and CI were not significant. For Group 3 (<120°), IMRT plan had better TV95% as compared to 3DCRT respectively with a significant P value (P = 0.001). HI and CI were better in IMRT arm with significant P value. Conclusion: The results from this study showed that the angle of concavity can be considered as an additional objective tool for selection criteria whether tumor can be treated with IMRT or 3DCRT. Tumors where angle of concavity was <120°, HI and CI provided more uniformity and conformity of dose distribution inside PTV with significant P values.

Effect of bioagents on cucumber seed mycoflora, seed germination, and seedling vigour.

The effect of different bioagents such as Trichoderma harzianum, T. viride, T. virens, Pseudomonas fluorescens, and Bacillus subtilis was studied on seed mycoflora, seed germination, root/shoot length, and seedling vigour of cucumber var. Solan Srijan under in vitro conditions. Alternaria sp., Aspergillus sp., and Fusarium spp. were observed on cucumber as seed mycoflora, with T. harzianum showing the greatest inhibition for Alternaria sp. and Fusarium spp., and T. viride showing the greatest inhibition for Aspergillus sp. Cucumber var. Solan Srijan seeds were treated with various bio agents, with T. harzianum being the most effective in increasing seed germination (88.75%), root length (13.58 cm), shoot length (14.58 cm), and seedling vigour (2501.31).

Deep neural network for food image classification and nutrient identification: A systematic review.

Technology impacts human life in both the aspects such as positive and negative, which helps in better communication and eliminating geographical boundaries. However, social media and mobile devices may lead to severe health conditions such as sleep problems, depression, obesity, etc. A systematic review is conducted to analyze health issues by tracking food intake by considering positive aspects using Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Guidelines. The major scientific databases (such as Web of Science, Scopus, and IEEE explore) are explored to search the image recognition and analysis articles. The search query is applied to the databases using keywords like "Food Image," "Food Image Classification," "Nutrient Identification," "Nutrient Estimation," and using "Machine Learning," etc. 771 articles are extracted from these databases, and 56 are identified for final consideration after rigorous screening. A few investigations are extracted based on available food image datasets, hyperparameters tuning, a technique used, performance metrics, and challenges of Food Image Classification (FIC). This study discusses different investigations with their proposed FIC and nutrient estimation solution. Finally, this intensive research presents a case study using FIC and object detection techniques to estimate nutrition with food image analysis.

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network.

The outbreak of COVID-19 has engulfed the entire world since the end of 2019, causing tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability to accurately predict the spread of disease as the arrangements for the essential supply of medical items largely depend on prior predictions. The objective of the study is to train a reliable model for predicting the spread of Coronavirus. The prediction capabilities of various powerful models such as the Autoregression Model (AR), Global Autoregression (GAR), Stacked-LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), Facebook Prophet (FBProphet), and Residual Recurrent Neural Network (Res-RNN) were taken into consideration for predicting COVID-19 using the historical data of daily confirmed cases along with Twitter data. The COVID-19 prediction results attained from these models were not up to the mark. To enhance the prediction results, a novel model is proposed that utilizes the power of Res-RNN with some modifications. Gated Recurrent Unit (GRU) and LSTM units are also introduced in the model to handle the long-term dependencies. Neural Networks being data-hungry, a merged layer was added before the linear layer to combine tweet volume as additional features to reach data augmentation. The residual links are used to handle the overfitting problem. The proposed model RNN Convolutional Residual Network (RNNCON-Res) showcases dominating capability in country-level prediction 20 days ahead with respect to existing State-Of-The-Art (SOTA) methods. Sufficient experimentation was performed to analyze the prediction capability of different models. It was found that the proposed model RNNCON-Res has achieved 91% accuracy, which is better than all other existing models.

Definitive chemoradiation in nonmetastatic squamous cell carcinoma anal canal: A single-institution experience.

Purpose: The purpose of the study was to analyze the survival outcomes and toxicities in squamous cell carcinoma anal canal treated with definitive chemoradiotherapy. Materials and Methods: Retrospective analysis of 51 patients with squamous cell carcinoma anal canal treated with chemoradiotherapy was done. Data were collected and analyzed for disease-free survival (DFS), colostomy-free survival (CFS), overall survival (OS), and acute/late toxicities. Results: Out of total 51 patients, only 44 patients had a follow-up of more than 36 months and were analyzed. After a median follow-up of 46 months (range 10-68 months), the 3-year DFS was 73.9%. Three patients developed locoregional recurrence, while one patient developed distant metastasis. At 3-year OS rate was 77%. Out of 44 patients, six patients lost to follow-up, while two patients died due to progressive disease and two due to noncancer causes. 3-year CFS rate was 59%. Most common grade >3 acute toxicities were skin reactions in nine (18%), followed by hematological in eight (16%) patients. Conclusion: Definitive chemoradiotherapy in anal canal results in good oncological outcomes with sphincter preservation. No severe treatment-related toxicities were observed.

Mental health issues assessment using tools during COVID-19 pandemic.

COVID-19 has brought distress among people as pandemic has impacted the globe not only economically or physically, but also psychologically by degrading their mental health. Several research were done in the past which tried to capture these issues but post-covid situation needs to be critically handled and analyzed so that corrective measures for cure and support can be taken. The current work is an attempt to observe the mental health issues (anxiety and depression) that occurred during the lockdown by combining a few pre-designed questionnaires. The online survey included 244 respondents (females = 126, males = 118) and when we thoroughly examined gender, age group, and occupational activity as three main factors, the results showed that female students aged 21-35 were affected more than male students of the same age group. In this study, we used a 4-item Geriatric Depression Scale (GDS-4) as a depression screening instrument and discovered that 225 out of total respondents were depressed. Using the Generalized Anxiety Disorder (GAD-7), a self-administered anxiety tool, we found 103 responders with mild, 87 with moderate, 12 with severe, and 42 with no anxiety symptoms. Patient Health Questionnaire (PHQ-9) showed the symptoms of mental disorders where 68 individuals had mild, 85 had moderate, 37 had moderately severe, 12 had severe, and 42 had no symptoms. With the help of multiple linear regression analysis, demographic data were evaluated, and later results were compared between GDS-4, GAD-7, and PHQ-9 using correlation coefficients. This will help practitioners and individuals to focus on their physiological health and adopt diagnostic measures.

Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial intelligence.

BACKGROUND: An unhealthy diet or excessive amount of food intake creates obesity issues in human beings that further may cause several diseases such as Polycystic Ovary Syndrome (PCOS), Cardiovascular disease, Diabetes, Cancers, etc. Obesity is a major risk factor for PCOS, which is a common disease in women and is significantly correlated with weight gain. METHODS: This study is providing a one-step solution for predicting the risk of obesity using different Machine Learning (ML) algorithms such as Gradient Boosting (GB), Bagging meta-estimator (BME), XG Boost (XGB), Random Forest (RF), Support Vector Machine (SVM), and K Nearest Neighbour (KNN). A dataset is collected from the UCI ML repository having features of physical description and eating habits of individuals to train the proposed model. RESULTS: The model has been experimented with different training and testing data ratios such as (90:10, 80:20, 70:30,60:40). At a data ratio of 90:10, the GB classifier achieved the highest accuracy i.e., 98.11%. Further, at the 80:20 ratio, the GB and XGB provide the same result i.e., 97.87%. For the 70:30 data ratio, XGB achieves the highest accuracy i.e., 97.79%. Further, the Nearest Neighbour (NN) learning method is applied to meal planning to overcome obesity. CONCLUSION: This method predicts the meal which includes breakfast, morning snacks, lunch, evening snacks, and dinner for the individual as per caloric and macronutrient requirements. The proposed research work can be used by practitioners to check obesity levels and to suggest meals to reduce the obese in adulthood.

DTLMV2-A real-time deep transfer learning mask classifier for overcrowded spaces.

Through the commencement of the COVID-19 pandemic, the whole globe is in disarray and debating on unique approaches to stop this viral transmission. Masks are being worn by people all around the world as one of the preventative measures to avoid contracting this sickness. Although some people are following and adopting this precaution, others are not, despite official recommendations from the administration and public health organisations has been announced. In this paper DTLMV2 (Deep Transfer Learning MobileNetV2 for the objective of classification) is proposed - A face mask identification model that can reliably determine whether an individual is wearing a mask or not is suggested and implemented in this work. The model architecture employs the peruse of MobileNetV2, a lightweight Convolutional Neural Network (CNN) that requires less computing power and can be readily integrated into computer vision and mobile systems. The computer vision with MobileNet is required to formulate a low-cost mask detection system for a group of people in open spaces that can assist in determining whether a person is wearing a mask or not, as well as function as a surveillance system since it is effective on both real-time pictures and videos. The face recognition model obtained 97.01% accuracy on validation data, 98% accuracy on training data and 97.45% accuracy on testing data.

Optical coherence tomography image based eye disease detection using deep convolutional neural network.

Over the past few decades, health care industries and medical practitioners faced a lot of obstacles to diagnosing medical-related problems due to inadequate technology and availability of equipment. In the present era, computer science technologies such as IoT, Cloud Computing, Artificial Intelligence and its allied techniques, etc. play a crucial role in the identification of medical diseases, especially in the domain of Ophthalmology. Despite this, ophthalmologists have to perform the various disease diagnosis task manually which is time-consuming and the chances of error are also very high because some of the abnormalities of eye diseases possess the same symptoms. Furthermore, multiple autonomous systems also exist to categorize the diseases but their prediction rate does not accomplish state-of-art accuracy. In the proposed approach by implementing the concept of Attention, Transfer Learning with the Deep Convolution Neural Network, the model accomplished an accuracy of 97.79% and 95.6% on the training and testing data respectively. This autonomous model efficiently classifies the various oscular disorders namely Choroidal Neovascularization, Diabetic Macular Edema, Drusen from the Optical Coherence Tomography images. It may provide a realistic solution to the healthcare sector to bring down the ophthalmologist burden in the screening of Diabetic Retinopathy.

Pattern of nutritional status in node-negative versus node-positive head and neck cancer patients undergoing treatment: a prospective cohort study.

PURPOSE: The aim of this study was to study the nutritional profile of node-negative and node-positive patients undergoing treatment for head and neck squamous cell cancer (HNSCC). METHODS: This prospective cohort study was conducted between 2018 and 2020. Patients diagnosed with HNSCC, planned for treatment, were enrolled after written informed consent. In node-negative (N0) and node-positive (N +) cohorts of patients, nutritional status was determined using anthropometric measures and Subjective Global Assessment (SGA) scale pre-treatment, and during and after treatment. Statistical analysis was performed using SPSS version 22. Data was analyzed using parametric and non-parametric tests, and p value of 0.05 was considered significant. RESULTS: In total, 161 patients were analyzed, 73 N0 and 88 N + cohorts. Pre-treatment, 9.6 to 20.4% patients in N0 and 23.9 to 32.8% patients in N + cohorts were malnourished. Incidence of malnutrition at completion of treatment was 40.8 to 52.5% overall, 20.5 to 41.1% N0, and 39.5 to 62.8% N + . Mean reduction in weight (11.1% ± 7.82 vs 6.26% ± 8.3, p = 0.000), mean reduction in BMI (2.57 ± 1.87 vs 1.29 ± 1.62, p = 0.000), median reduction in MUAC (2 cm vs 1 cm, p = 0.000), and median increase in SGA score (13 vs 6, p = 0.000) were higher in multi-modality as compared to those in a single-modality treatment. Similar findings were noted in N0 and N + cohorts. CONCLUSION: As compared to N0, N + patients had higher burden of malnutrition at diagnosis, and more worsening of nutritional parameters during treatment. More decline in nutritional status was seen in patients receiving multi-modality as compared to single-modality treatment.

Evaluation of metastatic neck nodes search for primary and subsequent management.

Background: Cancers in the head-and-neck region are the sixth most common cancers worldwide with an increasing incidence in developing countries. Methods: This study was carried out in the department of ENT and head-and-neck surgery in collaboration with the department of radiotherapy for a period of 1 year extending from May 01, 2014, to April 30, 2015. A total of 48 cases of metastatic secondary nodes were included in the study. Results: The male-to-female ratio in the present study was 4.33:1. Maximum number of patients were seen in the seventh decade. The youngest patient was a female 30 years old and the oldest was a male of 80 years. About 95.84% of primary tumors were squamous cell carcinomas and 40.47% of the patients of head-and-neck cancer with metastatic lymph nodes had well-differentiated squamous cell carcinomas. Majority of cases presented with N2 nodes, while N1 nodes were highest in cases of carcinomas larynx. Conclusions: Metastatic neck disease is a major problem in patients with head-and-neck cancer. The therapeutic goal includes not only known disease but also the elimination of possible subclinical disease. The judicious use of moderate doses of irradiation and modified surgical procedures should be used in specific clinical situations to significantly decrease neck recurrences while eliminating morbidity.

CNN-LSTM Hybrid Real-Time IoT-Based Cognitive Approaches for ISLR with WebRTC: Auditory Impaired Assistive Technology.

In the era of modern technology, people may readily communicate through facial expressions, body language, and other means. As the use of the Internet evolves, it may be a boon to the medical fields. Recently, the Internet of Medical Things (IoMT) has provided a broader platform to handle difficulties linked to healthcare, including people's listening and hearing impairment. Although there are many translators that exist to help people of various linguistic backgrounds communicate more effectively. Using kinesics linguistics, one may assess or comprehend the communications of auditory and hearing-impaired persons who are standing next to each other. When looking at the present COVID-19 scenario, individuals are still linked in some way via online platforms; however, persons with disabilities have communication challenges with online platforms. The work provided in this research serves as a communication bridge inside the challenged community and the rest of the globe. The proposed work for Indian Sign Linguistic Recognition (ISLR) uses three-dimensional convolutional neural networks (3D-CNNs) and long short-term memory (LSTM) technique for analysis. A conventional hand gesture recognition system involves identifying the hand and its location or orientation, extracting certain essential features and applying an appropriate machine learning algorithm to recognise the completed action. In the calling interface of the web application, WebRTC has been implemented. A teleprompting technology is also used in the web app, which transforms sign language into audible sound. The proposed web app's average recognition rate is 97.21%.

Deep learning based detection and analysis of COVID-19 on chest X-ray images.

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

Study of dosimetry and clinical factors for assessment of xerostomia in head and neck squamous cell carcinoma treated by intensity-modulated radiotherapy: A prospective study.

AIM: Clinical and dosimetric factors related to toxicity in terms of xerostomia in patients with head and neck squamous cell cancer (HNSCC) treated with intensity-modulated radiotherapy (IMRT). MATERIALS AND METHODS: Patients older than 18 years, with the WHO Performance Status Score <2 with primary diagnosis of HNSCC Stage II, III, and IV who had undergone primary or postoperative radiotherapy (RT) treated by IMRT at the center, from November 2015 to November 2016 were included in the study. Patients were assessed by physical examination and questioned to score their quality of life for dryness (HNDR) and stickiness (HNSS) by EORTC-HN-35 (Hindi or English version) at baseline (before treatment), at 3, 6, and 12 months following treatment. The validation of EORTC-HN-35 for HNDR and HNSS in patients was handed. RESULTS: Thirty patients were included in the study. The mean symptom score values for HNSS at baseline, 3, 6, and 12 months' post-RT treatment were 17.8, 62.2, 64.4, and 20.8, respectively. Dryness and stickiness also increased over 3-6 months in follow-up but slightly relieved at 12 months, but it could not reach to baseline. In subgroup analysis, at baseline mean score of dryness of mouth in elderly patients (≥60 years) (P = 0.248), poor performance status (Eastern Cooperative Oncology Group 2) (P = 0.80) and patients with advanced stage (Stage III and IVA) (P = 0.185) was higher. Correlation of normal tissue complication probability for xerostomia with contralateral mean parotid gland showed insignificant linearity with shallow curve. CONCLUSION: Patients remained symptomatic for xerostomia chiefly till 6 months' postirradiation, but it was slightly relieved in 12 months but could not reach the baseline. Dosimetric sparing ofcontralateral parotid resulted in decreased probability of developing xerostomia.

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.

A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

COBERT: COVID-19 Question Answering System Using BERT.

In the current situation of worldwide pandemic COVID-19, which has infected 62.5 Million people and caused nearly 1.46 Million deaths worldwide as of Nov 2020. The profoundly powerful and quickly advancing circumstance with COVID-19 has made it hard to get precise, on-request latest data with respect to the virus. Especially, the frontline workers of the battle medical services experts, policymakers, clinical scientists, and so on will require expert specific methods to stay aware of this literature for getting scientific knowledge of the latest research findings. The risks are most certainly not trivial, as decisions made on fallacious, answers may endanger trust or general well being and security of the public. But, with thousands of research papers being dispensed on the topic, making it more difficult to keep track of the latest research. Taking these challenges into account we have proposed COBERT: a retriever-reader dual algorithmic system that answers the complex queries by searching a document of 59K corona virus-related literature made accessible through the Coronavirus Open Research Dataset Challenge (CORD-19). The retriever is composed of a TF-IDF vectorizer capturing the top 500 documents with optimal scores. The reader which is pre-trained Bidirectional Encoder Representations from Transformers (BERT) on SQuAD 1.1 dev dataset built on top of the HuggingFace BERT transformers, refines the sentences from the filtered documents, which are then passed into ranker which compares the logits scores to produce a short answer, title of the paper and source article of extraction. The proposed DistilBERT version has outperformed previous pre-trained models obtaining an Exact Match(EM)/F1 score of 80.6/87.3 respectively.

Study of volumetric and dosimetric changes during fractionated radiotherapy in head and neck cancers: A single centre experience.

AIMS AND OBJECTIVE: The assessment of volumetric and dosimetric changes in the head-and-neck cancer during fractionated radiotherapy by intensity-modulated radiotherapy (IMRT) technique. MATERIALS AND METHODS: A single-center prospective observational hospital-based study with a sample size of 20 cases of the head-and--neck squamous cell carcinoma over 1 year treated with chemoradiotherapy 66-70 Gy/33-35#@2 Gy/fraction with weekly cisplatin 35 mg/m2. After contouring of target volumes (TVs) and organs at risk (OARs) in initial computed tomography (CT) scan, all patients were planned and treated by the IMRT technique. We re-delineated the TVs and OARs in the second (CT15#) and third (CT30#) planning CT scan, and the initial plan was implemented in the re-CT scan dataset with the same optimization and doses. The volumetric and dosimetric changes during fractionated radiotherapy of TVs and OARs were evaluated and compared. Nonparametric Wilcoxon-signed-rank test was used to compare the means between each plan. RESULTS: For all 20 patients, plans were compared for volumetric and dosimetric parameters on repeat CT scans. The mean variation in gross tumor volume (GTV) and planning TV (PTV) was significant after 15 and 30 fractions of radiotherapy. On dosimetric evaluation, there was a significant increase in doses to GTV and OARs (parotid, spinal cord, and cochlea) with a significant P value. However, doses to the OARs were not exceeded the maximum tolerance limit. CONCLUSION: This prospective single-center study concluded that two repeat imaging, along with re-planning improved TV coverage and decreased doses to the normal tissue. Larger studies with more sample sizes are required to set the criteria for replanning.