Illiterate Addenbrooke's Cognitive Examination-III in Three Indian Languages: An Adaptation and Validation Study.

BACKGROUND: Literacy is an important factor that predicts cognitive performance. Existing cognitive screening tools are validated only in educated populations and are not appropriate for older adults with little or no education leading to poor performance on these tests and eventually leading to misdiagnosis. This challenge for clinicians necessitates a screening tool suitable for illiterate or low-literate older individuals. OBJECTIVES: The objective was to adapt and validate Addenbrooke's Cognitive Examination-III (ACE-III) for screening general cognitive functions in illiterate and low-literate older populations in the Indian context in three languages. METHOD: The Indian illiterate ACE-III was systematically adapted by modifying the original items of the Indian literate ACE-III to assess the cognitive functions of illiterates and low-literates with the consensus of an expert panel of professionals working in the area of dementia and related disorders. A total of 180 illiterate or low-literate participants (84 healthy-controls, 50 with dementia, and 46 with mild cognitive impairment [MCI]) were recruited from three different centers speaking Bengali, Hindi, and Kannada to validate the adapted version. RESULTS: The optimal cut-off score for illiterate ACE-III to distinguish controls from dementia in all 3 languages was 75. The optimal cut-off scores in distinguishing between controls and MCI ranged from 79 to 82, with a sensitivity ranging from 93% to 99% and a specificity ranging from 72% to 99%. CONCLUSION: The test is found to have good psychometric properties and is a reliable cognitive screening tool for identifying dementia and MCI in older adults with low educational backgrounds in the Indian context.

A deep-SIQRV epidemic model for COVID-19 to access the impact of prevention and control measures.

The coronavirus (COVID-19) has mutated into several variants, and evidence says that new variants are more transmissible than existing variants. Even with full-scale vaccination efforts, the theoretical threshold for eradicating COVID-19 appears out of reach. This article proposes an artificial intelligence(AI) based intelligent prediction model called Deep-SIQRV(Susceptible-Infected-Quarantined-Recovered-Vaccinated) to simulate the spreading of COVID-19. While many models assume that vaccination provides lifetime protection, we focus on the impact of waning immunity caused by the conversion of vaccinated individuals back to susceptible ones. Unlike existing models, which assume that all coronavirus-infected individuals have the same infection rate, the proposed model considers the various infection rates to analyze transmission laws and trends. Next, we consider the influence of prevention and control strategies, such as media marketing and law enforcement, on the spread of the epidemic. We employed the PAN-LDA model to extract features from COVID-19-related discussions on social media and online news articles. Moreover, the Long Short Term Memory(LSTM) model and Evolution Strategies(ES) are used to optimize transmission rates of infection and other model parameters, respectively. The experimental results on epidemic data from various Indian states demonstrate that persons infected with coronavirus had a more significant infection rate within four to nine days after infection, which corresponds to the actual transmission laws of the epidemic. The experimental results show that the proposed model has good prediction ability and obtains the Mean Absolute Percentage Error(MAPE) of 0.875%, 0.965%, 0.298%, and 0.215% for the next eight days in Maharashtra, Kerala, Karnataka, and Delhi, respectively. Our findings highlight the significance of using vaccination data, COVID-19-related posts, and information generated by the government's tremendous efforts in the prediction calculation process.

Possibility of the COVID-19 third wave in India: mapping from second wave to third wave.

After a consistent drop in daily new coronavirus cases during the second wave of COVID-19 in India, there is speculation about the possibility of a future third wave of the virus. The pandemic is returning in different waves; therefore, it is necessary to determine the factors or conditions at the initial stage under which a severe third wave could occur. Therefore, first, we examine the effect of related multi-source data, including social mobility patterns, meteorological indicators, and air pollutants, on the COVID-19 cases during the initial phase of the second wave so as to predict the plausibility of the third wave. Next, based on the multi-source data, we proposed a simple short-term fixed-effect multiple regression model to predict daily confirmed cases. The study area findings suggest that the coronavirus dissemination can be well explained by social mobility. Furthermore, compared with benchmark models, the proposed model improves prediction R 2 by 33.6%, 10.8%, 27.4%, and 19.8% for Maharashtra, Kerala, Karnataka, and Tamil Nadu, respectively. Thus, the simplicity and interpretability of the model are a meaningful contribution to determining the possibility of upcoming waves and direct pandemic prevention and control decisions at a local level in India.

PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning.

The recent outbreak of novel Coronavirus disease or COVID-19 is declared a pandemic by the World Health Organization (WHO). The availability of social media platforms has played a vital role in providing and obtaining information about any ongoing event. However, consuming a vast amount of online textual data to predict an event's trends can be troublesome. To our knowledge, no study analyzes the online news articles and the disease data about coronavirus disease. Therefore, we propose an LDA-based topic model, called PAN-LDA (Pandemic-Latent Dirichlet allocation), that incorporates the COVID-19 cases data and news articles into common LDA to obtain a new set of features. The generated features are introduced as additional features to Machine learning(ML) algorithms to improve the forecasting of time series data. Furthermore, we are employing collapsed Gibbs sampling (CGS) as the underlying technique for parameter inference. The results from experiments suggest that the obtained features from PAN-LDA generate more identifiable topics and empirically add value to the outcome.

Social media based surveillance systems for healthcare using machine learning: A systematic review.

BACKGROUND: Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain. METHODS: To study the landscape of research in health informatics performing surveillance of the various health-related data posted on social media or web-based platforms, we present a bibliometric analysis of the 1240 publications indexed in multiple scientific databases (IEEE, ACM Digital Library, ScienceDirect, PubMed) from the year 2010-2018. The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms. FINDINGS: Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by them. In the corpus of selected articles, we found 26 articles were using machine learning technique. These articles were studied to find commonly used ML techniques. The majority of studies (24%) focused on the surveillance of flu or influenza-like illness (ILI). Twitter (64%) is the most popular data source to perform surveillance research using social media text data, and Support Vector Machine (SVM) (33%) being the most used ML algorithm for text classification. CONCLUSIONS: The inclusion of online data in surveillance systems has improved the disease prediction ability over traditional syndromic surveillance systems. However, social media based surveillance systems have many limitations and challenges, including noise, demographic bias, privacy issues, etc. Our paper mentions future directions, which can be useful for researchers working in the area. Researchers can use this paper as a library for social media based surveillance systems in the healthcare domain and can expand such systems by incorporating the future works discussed in our paper.

Juvenile amyotrophic lateral sclerosis: Classical wine glass sign on magnetic resonance imaging.

Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig disease, is a chronic degenerative neurologic disease and is characterized by the selective involvement of the motor system. Usually, patients present with upper motor neuron (UMN) and lower motor neuron compromise. Degeneration of the UMN in the cerebral cortex is one of the main pathologic changes in ALS. These changes usually affect corticospinal tracts leading to degeneration of the fibers which show characteristic hyperintensities along the tracts leading to the "wine glass sign." Patients with ALS usually present in the sixth decade of life; presentation in pediatric age in the form of juvenile ALS being rare.

A natural plant growth promoter calliterpenone from a plant Callicarpa macrophylla Vahl improves the plant growth promoting effects of plant growth promoting rhizobacteria (PGPRs).

Experiments were conducted to evaluate the efficacy of calliterpenone, a natural plant growth promoter from a shrub Callicarpa macrophylla Vahl., in enhancing the growth and yield promoting effects of plant growth promoting rhizobacteria (PGPRs), in menthol mint (Mentha arvensis L).This study is based on our previous results indicating the microbial growth promotion by calliterpenone and assumption that application of calliterpenone along with PGPRs will improve the population of PGPRs resulting in higher impacts on plant growth and yield. Of the 15 PGPRs (identified as potent ones in our laboratory), 25 µl of 0.01 mM calliterpenone (8.0 µg/100 ml) was found to be useful in improving the population of nine PGPRs in culture media. The five selected strains of PGPRs exhibiting synergy with calliterpenone in enhancing growth of maize compared to PGPR or calliterpenone alone were selected and tested on two cultivars (cvs. Kosi and Kushal) of M. arvensis. Of the five strains, Bacillus subtilis P-20 (16S rDNA sequence homologous to Accession No NR027552) and B. subtilis Daz-26 (16SrDNA sequence homologuos to Accession No GU998816) were found to be highly effective in improving the herb and essential oil yield in the cultivars Kushal and Kosi respectively when co-treated with calliterpenone. The results open up the possibilities of using a natural growth promoter along with PGPRs as a bio-agri input for sustainable and organic agriculture.