Potential of plant mediated biosynthesis of iron nanoparticles and their application in dye degradation process.

In recent years, nanotechnology has emerged as cutting-edge technology with multifarious applications in a wide array of fields. Green synthesis of iron nanoparticles (FeNP) are an upcoming cost effective and eco-friendly technique and recently gained significant importance. In the present study, green FeNPs were prepared using leaf litter which is one of the major seasonal waste contributors in urban built-up areas. Shedding trees during winter months (January - March) were selected. Most abundant trees were Pongamia pinnata (Indian beech), Morus alba (mulberry), Prosopis juliflora (mesquite) and Kigelia africana (sausage tree). Synthesized FeNPs were further used for degrading two commercial dyes, eosin yellow and fuchsin basic, via Fenton's mechanism. The study showed that the prepared nanoparticles were of iron oxides, but also reported presence of polyphenols as a capping agent. Dye degradation efficiency of nanoparticles synthesized by P. pinnata leaf litter was recorded to be highest, whereas the efficiency of nanoparticles synthesized by K. africana leaf litter was lowest. Chances of iron leaching during dye degradation process was also tested and observed that Fe was present in treated water below the standard guidelines. Thus, FeNPs can serve as a low-cost solution to remediate water pollutants with a green approach. Implications: Nanoparticles prepared in the study were showed as a promising adsorbent and demonstrating high surface area and well-developed porosity. The prepared adsorbent will have a great impact on wastewater treatment technology and possible applications at a large scale. There are several applications of nanoparticles in pollution remediation and at the same time it can solve solid waste issues as it required to prepare nanoparticles. One of the major applications at policy level can be water pollution remediation which is urgently needed.

Mul-FaD: attention based detection of multiLingual fake news.

The latest buzzword in today's world is fake news. The circulation of false information influences elections, public health, brand reputations, and violence. Hence, the severity of the threat of fake news is increasing. The danger for fake news exists everywhere globally and is not specific to one language or nation. The creators of fake news layer the facts in the news with misinformation to confuse the readers. Hence, a need arises for creating a model for detecting fake news in multiple languages. This paper proposes a unified attention-based model Mul-FaD to detect fake news in various languages. We have created our dataset with around 40000 articles in English, German, and French. This paper also shows an exploratory analysis of the dataset created. In this paper, we perform experiments from a multilingual perspective in which we use an altered hierarchical attention-based network to detect fake news. Our model is able to achieve an accuracy of 93.73 and an F1 score of 92.9 for the combined corpus of the three languages.

Multivariate Models of Blood Glucose Prediction in Type1 Diabetes: A Survey of the State-of-the-art.

Diabetes mellitus is a long-term chronicle disorder with a high prevalence rate worldwide. Continuous blood glucose and lifestyle monitoring enabled the control of blood glucose dynamics through machine learning applications using data created by various popular sensors. This survey aims to assess various classical time series, neural networks and state-of-the-art regression models based on a wide variety of machine learning techniques to predict blood glucose and hyper/hypoglycemia in Type 1 diabetic patients. The analysis covers blood glucose prediction modeling, regression, hyper/hypoglycemia alerts, diabetes diagnosis, monitoring, and management. However, the primary focus is on evaluating models for the prediction of Type 1 diabetes. A wide variety of machine learning algorithms have been explored to implement precision medicine by clinicians and provide patients with an early warning system. The automated pancreas may benefit from predictions and alerts of hyper and hypoglycemia.

Clinicoepidemiological Features and Mortality Analysis of Deceased Patients with COVID-19 in a Tertiary Care Center.

BACKGROUND AND OBJECTIVE: A large number of studies describing the clinicoepidemiological features of coronavirus disease-2019 (COVID-19) patients are available but very few studies have documented similar features of the deceased. This study was aimed to describe the clinicoepidemiological features and the causes of mortality of COVID-19 deceased patients admitted in a dedicated COVID center in India. METHODOLOGY: This was a retrospective study done in adult deceased patients admitted in COVID ICU from April 4 to July 24, 2020. The clinical features, comorbidities, complications, and causes of mortality in these patients were analyzed. Pediatric deceased were analyzed separately. RESULTS: A total of 654 adult patients were admitted in the ICU during the study period and ICU mortality was 37.7% (247/654). Among the adult deceased, 65.9% were males with a median age of 56 years [interquartile range (IQR), 41.5-65] and 94.74% had one or more comorbidities, most common being hypertension (43.3%), diabetes mellitus (34.8%), and chronic kidney disease (20.6%). The most common presenting features in these deceased were fever (75.7%), cough (68.8%), and shortness of breath (67.6%). The mean initial sequential organ failure assessment score was 9.3 ± 4.7 and 24.2% were already intubated at the time of admission. The median duration of hospital stay was 6 days (IQR, 3-11). The most common cause of death was sepsis with multi-organ failure (55.1%) followed by severe acute respiratory distress syndrome (ARDS) (25.5%). All pediatric deceased had comorbid conditions and the most common cause of death in this group was severe ARDS. CONCLUSION: In this cohort of adult deceased, most were young males with age less than 65 years with one or more comorbidities, hypertension being the most common. Only 5% of the deceased had no comorbidities. Sepsis with multi-organ dysfunction syndrome was the most common cause of death. HOW TO CITE THIS ARTICLE: Aggarwal R, Bhatia R, Kulshrestha K, Soni KD, Viswanath R, Singh AK, et al. Clinicoepidemiological Features and Mortality Analysis of Deceased Patients with COVID-19 in a Tertiary Care Center. Indian J Crit Care Med 2021; 25(6):622-628.

"Rapid diagnosis of Cucumber mosaic virus in banana plants using a fluorescence-based real-time isothermal reverse transcription-recombinase polymerase amplification assay".

Cucumber mosaic virus (CMV) is a widespread plant virus infecting important vegetables, plantation and flower crops. Currently, CMV is detected by enzyme-linked immunosorbent assays (ELISA) and reverse transcription-polymerase chain reaction (RT-PCR) assays. ELISA requires polyclonal antibodies and is time-consuming. PCR requires skilled manpower and complex procedures of RNA isolation as well as a thermal cycler. To overcome these difficulties, a portable rapid, simple and visual fluorescence-based reverse transcription-recombinase polymerase amplification (portable RT-exo-RPA) assay for the detection of CMV was developed. A specific primer pair of 30-33 bp targeting a conserved region of the coat protein (CP) gene of CMV and a probe to function in the RT-exo-RPA assays were designed and synthesized. A total of 62 symptomatic as well as 58 asymptomatic banana plant samples, collected from banana orchards located in Jalgaon, Maharashtra, India, were evaluated for CMV infections using crude leaf extracts as templates by a reverse transcription-recombinase polymerase amplification (RT-RPA) assay as well as a real-time RT-exo-RPA assay and the results were compared with those of a reverse transcription-polymerase chain reaction (RT-PCR) assay using purified total plant RNAs as templates. CMV was as efficiently detected using the crude leaf extract template in the RT-RPA and real-time RT-exo-RPA assays as using the purified RNA template in the RT-PCR assay. To dispense with the use of real-time PCR, a portable RT-exo-RPA assay was developed and the alternative methods for the visualization of CMV detection using either a fluorometer or direct viewing with a UV transilluminator were evaluated. To our knowledge, this is the first report of the rapid and reliable diagnosis of CMV infections by a real-time RT-exo-RPA assay using a crude leaf extract as template.

Development of a recombinase polymerase amplification assay for the diagnosis of banana bunchy top virus in different banana cultivars.

Recombinase polymerase amplification (RPA) is a rapid, isothermal amplification method with high specificity and sensitivity. In this study, an assay was developed and evaluated for the detection of banana bunchy top virus (BBTV) in infected banana plants. Three oligonucleotide primer pairs were designed from the replicase initiator protein gene sequences of BBTV to function both in RPA as well as in polymerase chain reaction (PCR). A total of 133 symptomatic as well as asymptomatic banana leaf samples from various cultivars were collected from the different regions of India and evaluated for BBTV infection using the RPA assay. BBTV was efficiently detected using crude leaf sap in RPA and the results obtained were consistent with PCR-based detection using purified DNA as template. To our knowledge, this is the first report of reliable diagnosis of BBTV infection by RPA using crude leaf sap as a template.