A study of achievement motivation, Job satisfaction and Mental stress of Shikshansahayak Teachers

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CD40mAb adjuvant induces a rapid antibody response that may be beneficial in post-exposure prophylaxis

Active vaccination can be effective as a post-exposure prophylaxis, but the rapidity of the immune response induced, relative to the incubation time of the pathogen, is critical. We show here that CD40mAb conjugated to antigen induces a more rapid specific antibody response than currently used immunological adjuvants, alum and monophosphoryl lipid A™

Cloning and heterologous expression of a gene encoding lycopene-epsilon-cyclase, a precursor of lutein in tea (Camellia sinensis var assamica)

This report describes the cloning and expression of a gene lycopene epsilon cyclase, (LCYE) from Camellia sinensis var assamica which is a precursor of the carotenoid lutein in tea. The 1982 bp cDNA sequence with 1599 bp open reading frame of LCYE was identified from an SSH library constructed for quality trait in tea. 5’ and 3’ RACE (rapid-amplification of cDNA ends) was done to clone the full length cDNA of LCYE. Homology studies showed that the deduced amino acid sequence of LCYE gene had the highest sequence identity of up to 84% with Vitis vinefera. The cloned gene was successfully expressed in a PET based Escherichia coli expression system. The size of the expressed protein was 59615 Daltons. A suppression subtractive library was constructed using a quality clone H3111 (tester) and a garden series clone T3E3 (driver).Key words: Carotenoid, RACE, heterologous expression, lutein, tea

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Photopatterned antibodies for selective cell attachment

We present a phototriggerable system that allows for the spatiotemporal controlled attachment of selected cell types to a biomaterial using immobilized antibodies that specifically target individual cell phenotypes.o-Nitrobenzyl caged biotin was used to functionalize chitosan membranes and mediate site-specific coupling of streptavidin and biotinylated antibodies after light activation. The ability of this system to capture and immobilize specific cells on a surface was tested using endothelial-specific biotinylated antibodies and nonspecific ones as controls. Homogeneous patterned monolayers of human umbilical vein endothelial cells were obtained on CD31-functionalized surfaces. This is a simple and generic approach that is applicable to other ligands, materials, and cell types and shows the flexibility of caged ligands to trigger and control the interaction between cells and biomaterials.We thank Martina Knecht (MPIP) for help with the synthesis of caged biotin and Dr. Ron Unger and Prof. C. J. Kirkpatrick (University Clinic Mainz, RepairLab) for providing HUVECs. C.A.C. acknowledges funding support from the Portuguese Foundation for Science and Technology (FCT) (fellowship SFRH/BD/61390/2009) and from the International Max-Planck Research School in Mainz. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. REGPOT-CT2012-316331-POLARIS

Diversity of Endophytic Fungi in Banana Cultivars of Assam India

Endophytic fungal isolates (139 no.) were obtained from 143 (62 roots, 18 fruits and 54 leaves) samples of 15 different varieties of banana collected from 10 sites in Assam, India during 2018-2019. Overall isolation frequency from surface-sterilized tissue ranged from 10%-80% (as per site) and 6%-70% (as per variety of banana). All isolates were segregated into 40 different types on the basis of macromorphological and micro morphological characteristics. Forty different fungal taxa were isolated belonging to 14 genera including Absidia, Arthrinium, Aspergillus, Bipolaris, Cladosporium, Curvularia, Dendrophion, Fusarium, Humicola, Mortierella, Mucor, Penicillium, Paecilomyces, Verticillium and one mycelium sterile. Among them, Cladosporium cladosporioidies and Paecilomyces sp. frequently occurred in most of the sites surveyed whereas Cladospoirum cladosporioides and Aspergillus sp. 8, Fusarium graminseram were most frequently isolated from different varieties. However, all sites differed in their fungal diversity. Banana samples from Narigoan and Jorhat have been found with maximum fungal species followed by marigoan samples so as to Banana varieties Amrit Sagar endowed 27 no. of fungi followed by Jehaji and Honda which were associated with a maximum 14 fungal sp. Isolation frequency and relative abundance of Cladosporium cladosporiodes (80%, 4.6), Paecilomyces farinosus (80%, 4.6) followed by Penicillium ruburm, Aspergillus sp. 8 & 9 (70%, 4.02) were recorded as maximum comparatively in different sites. However, Aspergillus sp. 8, Mortieralla sp. and Pacilomyces farinosus are isolated frequently from different banana varieties (73.33%, 4.93)

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Research PaperThis study explored the farmers’ attitude towards back yard poultry farming and identified the factors associated with it. An attitude scale consisting of 12 items was developed and administered to 35 back yard poultry farmers of West Siang district in Arunachal Pradesh, India. The results revealed that were majority of the respondents were of medium ag e group and majority of them were literate and had middle and primary level of education and less number of respondents were illiterate, and had medium level of innovativeness. Almost an equal number of respondents practice agriculture as major occupation and lived in joint families. Most of the respondent had good contacts with the KVK’s personnel for receiving knowledge about new technology and interventions. When their attitude was assessed, majority belonged to 'favorable' category and among the independent variables 'family-type' had a negative value with attitude. Based on the findings, implications were drawn for the extension agencies to promote poultry farming as income generating venture in the tribal areas.Not Availabl