Knowledge, Attitude and Practice towards COVID-19 Pandemic among Patients Attending Dental Outpatient Department of M.G.M. Medical College and Hospital, Jamshedpur, Jharkhand, India.

Background: The 2019 coronavirus disease (COVID-19) is an extremely contagious illness that spreads mostly via the dentistry practice. Patients in need of dental care are at a higher risk of becoming infected with and becoming carriers of the illness. Aim: To assess the COVID-19-related knowledge, attitude, and practice (KAP) of patients visiting dental outpatient department of M.G.M. Medical College and Hospital, Jamshedpur, Jharkhand, India. Materials and Methods: In this cross-sectional study, patients visiting dental outpatient department of M.G.M. Medical College and Hospital, Jamshedpur, Jharkhand, India were included. The questionnaire had four sections including demographic information, knowledge, attitudes, and practice in relation to COVID-19. Along with computation of descriptive statistics, data analysis was performed using unpaired t-test. Results: A total of 332 people took part in the research. The female respondents were higher in number (53.01%) and the highest percentage of the respondents belongs to the age group 18-40 years (55.72%). Nearly, 98% of respondents were vaccinated. The highest percentage of KAP level was recorded for knowledge (74.95%) followed by that of attitude (50.84%) and practices (37.05%). Conclusion: While respondents' overall knowledge was high but their enthusiasm for taking preventative measures was low, and their efforts to stem the pandemic were lagging at best. Future campaigns should focus more on reaching out to marginalized populations, such as those with less education or higher poverty rates.

Genome-Wide Single-Nucleotide Polymorphism-Based Genomic Diversity and Runs of Homozygosity for Selection Signatures in Equine Breeds.

The horse, one of the most domesticated animals, has been used for several purposes, like transportation, hunting, in sport, or for agriculture-related works. Kathiawari, Marwari, Manipuri, Zanskari, Bhutia, Spiti, and Thoroughbred are the main breeds of horses, particularly due to their agroclimatic adaptation and role in any kind of strong physical activity, and these characteristics are majorly governed by genetic factors. The genetic diversity and phylogenetic relationship of these Indian equine breeds using microsatellite markers have been reported, but further studies exploring the SNP diversity and runs of homozygosity revealing the selection signature of breeds are still warranted. In our study, the identification of genes that play a vital role in muscle development is performed through SNP detection via the whole-genome sequencing approach. A total of 96 samples, categorized under seven breeds, and 620,721 SNPs were considered to ascertain the ROH patterns amongst all the seven breeds. Over 5444 ROH islands were mined, and the maximum number of ROHs was found to be present in Zanskari, while Thoroughbred was confined to the lowest number of ROHs. Gene enrichment of these ROH islands produced 6757 functional genes, with AGPAT1, CLEC4, and CFAP20 as important gene families. However, QTL annotation revealed that the maximum QTLs were associated with Wither's height trait ontology that falls under the growth trait in all seven breeds. An Equine SNP marker database (EqSNPDb) was developed to catalogue ROHs for all these equine breeds for the flexible and easy chromosome-wise retrieval of ROH along with the genotype details of all the SNPs. Such a study can reveal breed divergence in different climatic and ecological conditions.

Analytical code sharing practices in biomedical research.

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten papers organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability (p=2.71×10-9). Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses (p=1.15*10-07). In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

Comparative evaluation of periodontal health status of pre and postmenopausal females.

BACKGROUND: Menopause in females is a corporeal stage that gives rise to a number of adaptive changes both at systemic and oral levels, prototypically occurring in the late fourth or fifth decade of life. Though physiological aging affects the oral tissue, the hormonal changes due to menopause also act as a major contributing factor in deteriorating the health of oral tissues. Thus, the main aim of our study was to compare the overall periodontal status and alkaline phosphatase levels in the saliva of females in their pre and postmenopausal ages. MATERIAL AND METHODS: This study was conducted on 200 female subjects coming to the Department of Periodontology at Rajendra Institute of Medical Science for oral prophylaxis. The subjects were arbitrarily selected in the age group ranging from 15 to 70 years and were further divided based on the inclusion criteria. Group A included 100 subjects with age ranging from 15 to 45 years, and group B, 100 subjects with age 54 to 70 years. Signs of periodontitis including clinical attachment loss, furcation involvement, and probing depth and salivary alkaline levels were obtained, evaluated, compared, and analyzed. RESULTS: In group A 65% of patients had grade 0 and 28% had grade 1 of clinical attachment level. Similarly in group B, 44% of the total had grade 0 of clinical attachment loss, 38% had grade 1, and 18% were of grade 2. On evaluating grades of furcation involvement, around 45% of the total patients in group A were grade 1 (incipient, pocket formation), while in group B, 51% of the total patients were grade 1. At least 46% in group A and only 20% in group B had no signs of furcation involvement. Salivary alkaline phosphatase levels in pre and postmenopausal patients showed a significant difference between the two groups was obtained. CONCLUSION: The study concluded a major difference in the periodontal health of pre and postmenopausal women with many influencing factors apart from menopause itself.

DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery.

We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity of boundaries and incomplete sequences. It scored consistently high for performance metrics while breaching accuracy of >98% when tested across a large number of validated instances. During multiple benchmarkings DeepPlnc consistently outperformed all the compared tools and maintained a highly significant lead in the range of 2.5%- 4.6% from the second best performing tool (p-value << 0.01). DeepPlnc was used to annotate a de novo assembled transcriptome of a himalayan species where again it suggested its much better suitability for genome and transcriptome annotation purposes than the existing tools. DeepPlnc has been made freely available as a web-server and stand-alone program at https://scbb.ihbt.res.in/DeepPlnc/.

RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery.

Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies.

miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

The first draft genome of Picrorhiza kurrooa, an endangered medicinal herb from Himalayas.

Picrorhiza kurrooa is an endangered medicinal herb which is distributed across the Himalayan region at an altitude between 3000-5000 m above mean sea level. The medicinal properties of P. kurrooa are attributed to monoterpenoid picrosides present in leaf, rhizome and root of the plant. However, no genomic information is currently available for P. kurrooa, which limits our understanding about its molecular systems and associated responses. The present study brings the first assembled draft genome of P. kurrooa by using 227 Gb of raw data generated by Illumina and PacBio RS II sequencing platforms. The assembled genome has a size of n = ~ 1.7 Gb with 12,924 scaffolds. Four pronged assembly quality validations studies, including experimentally reported ESTs mapping and directed sequencing of the assembled contigs, confirmed high reliability of the assembly. About 76% of the genome is covered by complex repeats alone. Annotation revealed 24,798 protein coding and 9789 non-coding genes. Using the assembled genome, a total of 710 miRNAs were discovered, many of which were found responsible for molecular response against temperature changes. The miRNAs and targets were validated experimentally. The availability of draft genome sequence will aid in genetic improvement and conservation of P. kurrooa. Also, this study provided an efficient approach for assembling complex genomes while dealing with repeats when regular assemblers failed to progress due to repeats.

First wave of covid-19 in India using IoT for identification of virus.

Internet of Things (IoT) are evolving rapidly and making it possible for many uses, such as manufacturing, military, education and health, to link different intelligent objects. Coronavirus has recently spread widely around the globe and no effective therapies are currently available. It is also very necessary to avoid infection and to control the symptoms, such as fever and shortness of breath. As Coronavirus is a disease that is circulating very rapidly and the social distancing to deter an outbreak is very significant, it is essential to provide a system that is intelligent enough to monitor the effects of individuals with little direct contact. This document contains an IoT-based and wireless sensor network architecture and simulation of the COVID-19 Monitoring Mechanism (CSMM) for the monitoring of people in their quarantine, particularly the elderly who are living under chronic diseases and immune failure, and are therefore more likely to contract severe diseases. The mechanism relies on patient health data remotely. A doctor or care practitioner may carry out the monitoring process. For starters, where there is high fire or trouble breathing, this can conveniently be used for a detected urgent or irregular situation. The process will then give a warning to the health care provider or practitioner, sending urgent SMS with time and condition to act without any delays to save the patient's life.

Comparative transcriptome analysis of Rheum australe, an endangered medicinal herb, growing in its natural habitat and those grown in controlled growth chambers.

Rheum australe is an endangered medicinal herb of high altitude alpine region of Himalayas and is known to possess anti-cancerous properties. Unlike many herbs of the region, R. australe has broad leaves. The species thrives well under the environmental extremes in its niche habitat, therefore an understanding of transcriptome of R. australe to environmental cues was of significance. Since, temperature is one of the major environmental variables in the niche of R. australe, transcriptome was studied in the species growing in natural habitat and those grown in growth chambers maintained at 4 °C and 25 °C to understand genes associated with different temperatures. A total of 39,136 primarily assembled transcripts were obtained from 10,17,74,336 clean read, and 21,303 unigenes could match to public databases. An analysis of transcriptome by fragments per kilobase of transcript per million, followed by validation through qRT-PCR showed 22.4% up- and 22.5% down-regulated common differentially expressed genes in the species growing under natural habitat and at 4 °C as compared to those at 25 °C. These genes largely belonged to signaling pathway, transporters, secondary metabolites, phytohormones, and those associated with cellular protection, suggesting their importance in imparting adaptive advantage to R. australe in its niche.

De novo transcriptome provides insights into the growth behaviour and resveratrol and trans-stilbenes biosynthesis in Dactylorhiza hatagirea - An endangered alpine terrestrial orchid of western Himalaya.

This is the first report on de novo transcriptome of Dactylorhiza hatagirea, a critically-endangered, terrestrial orchid of alpine Himalayas. The plant is acclaimed for medicinal properties but little is known about its secondary-metabolites profile or cues regulating their biosynthesis. De novo transcriptome analysis was therefore, undertaken to gain basic understanding on these aspects, while circumventing the acute limitation of plant material availability. 65,384 transcripts and finally, 37,371 unigenes were assembled de novo from a total of 236 million reads obtained from shoot, tuber and leaves of the plant. Dominance of differentially-expressing-genes (DEGs) related to cold-stress-response and plant-hormone-signal-transduction; and those involved in photosynthesis, sugar-metabolism and secondary-metabolite-synthesis provided insights into carbohydrate-partitioning in the plant during its preparation for freezing winter at natural habitat. DEGs of glucomannan, ascorbic acid, carotenoids, phylloquinone/naphthoquinones, indole alkaloids, resveratrol and stilbene biosynthesis revealed the secondary-metabolite profile of D. hatagirea. UHPLC results confirmed appreciable amounts of resveratrol and trans-stilbene in D. hatagirea tubers, for the first time. Expression analysis of 15 selected genes including those of phenylpropanoid pathway confirmed the validity of RNA-seq data. Opportunistic growth, temperature- and tissue-specific-differential-expression of secondary metabolite biosynthesis and stress tolerant genes were confirmed using clonal plants growing at 8, 15 and 25 °C.

Transcriptome and Co-Expression Network Analyses Identify Key Genes Regulating Nitrogen Use Efficiency in Brassica juncea L.

Nitrate is the main source of inorganic nitrogen for plants, which also act as signaling molecule. Present study was aimed to understand nitrate regulatory mechanism in Brassica juncea cultivars, with contrasting nitrogen-use-efficiency (NUE) viz. Pusa Bold (PB, high-NUE) and Pusa Jai Kisan (PJK, low-NUE), employing RNA-seq approach. A total of 4031, 3874 and 3667 genes in PB and 2982, 2481 and 2843 genes in PJK were differentially expressed in response to early, low (0.25 mM KNO3), medium (2 mM KNO3) and high (4 mM KNO3) nitrate treatments, respectively, as compared to control (0 mM KNO3). Genes of N-uptake (NRT1.1, NRT1.8, and NRT2.1), assimilation (NR1, NR2, NiR, GS1.3, and Fd-GOGAT) and remobilization (GDH2, ASN2-3 and ALaT) were highly-upregulated in PB than in PJK in response to early nitrate treatments. We have also identified transcription factors and protein kinases that were rapidly induced in response to nitrate, suggesting their involvement in nitrate-mediated signaling. Co-expression network analysis revealed four nitrate specific modules in PB, enriched with GO terms like, "Phenylpropanoid pathway", "Nitrogen compound metabolic process" and "Carbohydrate metabolism". The network analysis also identified HUB transcription factors like mTERF, FHA, Orphan, bZip and FAR1, which may be the key regulators of nitrate-mediated response in B. juncea.