Willingness of university students to continue using e-Learning platforms after compelled adoption of technology: Test of an extended UTAUT model.

The COVID-19 pandemic has prompted the adoption of an e-Learning pedagogy. This forced teachers and students to shift to online learning and thus was compelled to adopt online educational technology. Educational institutes have been facing challenges like insufficient infrastructure and a shortage of quality teachers. Online learning can help to address these challenges as online classes can accommodate more students. However, before implementing e-Learning technology management of institutes wants to be sure whether students will adopt new technology. Therefore, the purpose of this study was to unveil which factors are important to adopt new technology if implemented mandatorily. We tested the most popular technology acceptance model the UTAUT to understand students' intentions to continue using the e-Learning system in a mandatory environment. The study used a quantitative approach of research. The participants for this study were selected from a private university in India. The questionnaire for the study was adapted from previous studies. The survey was conducted by sharing an online link while students were attending classes online during the pandemic. Thus, the study utilized a convenience sampling technique. The data were analyzed using structural equation modelling. The findings revealed that the UTAUT model can partially explain the forceful adoption of technology. The study found 'Performance expectancy' and the 'availability of resources' as significant indicators of 'intention for continued usage'. This study recommends educational institutes should ensure students attain academic goals by using e-Learning platforms and ensuring the availability of essential resources to use the e-Learning technology.

Mutational Analysis of EGFR Mutations in Non-Small Cell Lung Carcinoma-An Indian Perspective of 212 Patients.

Lung cancer is the world's leading cause of cancer-related deaths. Epidermal growth factor receptor (EGFR) is one of the critical oncogenes and plays a significant role in tumor proliferation and metastasis. Patients with sensitizing mutations in the EGFR gene have better clinical outcomes when treated with tyrosine kinase inhibitors (TKI). This study expands our knowledge of the spectrum of EGFR mutations among lung cancer patients in the Indian scenario. This is a retrospective descriptive study of all newly diagnosed patients with lung cancer in tertiary care hospital in India. All the samples were subjected to real-time PCR (q-PCR) analysis and confirmation of rare novel mutations was done using Sanger sequencing. Clinicopathological characteristics, mutational EGFR status, and location on the exon and metastatic sites were evaluated. An analysis of total 212 samples showed mutations in 38.67% of cases. Among these, five (5.9%) samples had mutations in exon 18, 41 (48.8%) samples had mutations in exon 19, 12 (14.28%) samples had mutations in exon 20, and 26 (30.95%) samples had mutations in exon 21. Eleven (13.41%) were found to be uncommon EGFR mutations. Additionally, six (21.4%) samples that had EGFR mutations were also positive for brain metastasis. Future testing on bigger panels will help to characterize the incidence of genetic mutations and to determine the appropriate targeted treatment choices for NSCLC patients.

CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media.

With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the text according to the sentimental polarities of opinions, which has positive or negative. Thus, this article concentrates on presenting a novel method, named CAVIAR-Dragonfly optimization with Extended Naive Bayes (CDNB), for performing sentiment classification and affective state classification. At first, the BITS review from Twitter is subjected to preprocessing, which includes stop word removal and stemming. Then, the next step is the feature extraction, in which all the reviews are converted to a feature vector. After that, all the individual feature vectors are collected to form the feature matrix, which is applied to the proposed C-Dragonfly optimization algorithm, to perform the sentiment classification and affective state classification. The performance of the proposed method is analyzed using the Twitter Sentiment Analysis Training Corpus Data Set based on true positive rate (TPR), true negative rate (TNR), and accuracy. From the analysis, it can be shown that the proposed method yields the maximum TPR, TNR, and accuracy of 89.0934%, 72.3064%, and 79.3591% for sentiment classification and 84.2122%, 66.2187%, and 76.6249% for the sentiment affective state classification.

Chasing the personalized medicine dream through biomarker validation in colorectal cancer.

Colorectal cancer (CRC) is a major health burden worldwide. The optimal approach to the diagnosis, management, and treatment of CRC involves multidisciplinary and integrated management practices. The field is rapidly changing because of recent advancements in delineating the molecular basis of tumorigenesis, introduction of targeted therapy, varied patient response to mainstay chemotherapeutics, biological drugs, and the effective combination regimes being used for treatment. Recent meta-analysis studies, which tend to establish few clinically useful predictor biomarkers, identify inconsistent results and limitations of the trials. Therefore, molecular pathological epidemiology discipline initiatives are promising. Here, we provide an overview of the potential of biomarker validation for personalized medicine by focusing largely on metastatic (m)CRC. We also highlight new candidate predictive and prognostic biomarkers.

KRAS gene mutations in correlation with clinicopathological features of colorectal carcinomas in Indian patient cohort.

KRAS gene mutations have been identified as a predictive molecular marker to predict the sensitivity of tumors to anti-EGFR therapeutics. The variability of clinical response to anti-EGFR agents has highlighted the need to select the appropriate patients who can benefit from the treatment. We examined the prevalence of KRAS mutations in 1,323 colorectal cancer patients from different regions of India and its correlation with geographic distribution and clinicopathological characteristics. DNA was extracted from formalin-fixed, paraffin-embedded tissue samples and was amplified by nested polymerase chain reaction at KRAS exon 2 and subjected to nucleotide sequencing using ABI 3100 Genetic Analyzer. The frequency of KRAS mutations was found to be 20.5 % (271/1,323). There was significant association (p < 0.05) between KRAS mutations, age and the tumor differentiation. Statistical analysis revealed significantly higher prevalence of colorectal cancer with mutated KRAS gene in northern regions of the country. No significant association was observed between KRAS mutations and gender (p > 0.05). Our study indicates that KRAS mutations in Indian colorectal cancer patients occur at lower level compared to that of Western population.