Phase Evolution and Stability of Aluminum Titanate Prepared by Solid -State Route and Solution Combustion Route

Aluminum titanate (Al2TiO5) powder was synthesized using two different route, namely, solid-state route and solution combustion route. In solid-state route, two different types (micron and nano-size powder) of Al2O3 precursor powder was used. Phase evolution and powder morphology were investigated by X-ray diffraction method and Field emission scanning electron microscope (FESEM) respectively. In case of large particle Al2O3 precursor powder, Al2TiO5 (AT) phase was formed at 1500oC with a yield of 97%. Whereas using nano-Al2O3 precursor powder, AT phase was formed at 1350oC with a yield of 91% and almost phase pure powder can be prepared at 1500oC. Average particle size of the AT powder was 11 µm (using large particle Al2O3) and 10.2 µm (using nano-size Al2O3) for calcination at 1500oC. AT powder was also synthesized by citrate-nitrate solution combustion method. The molar ratio of metal (Al:Ti) and citric acid was varied to get a uniform and complete combustion. A significant amount Al2TiO5 phase was formed at 1350°C. The stability of the solid state route prepared AT powder was investigated by keeping the powder at 1150°C/6hr. It was observed from the XRD analysis that only 40% AT-phase was retained in samples prepared with micron size Al2O3, but, in case of sample prepared using nano size Al2O3, 93% AT-phase was retained

Age at natural menopause and factors affecting its onset: A cross-sectional study among postmenopausal females in District Dehradun

Background: Menopause has become a popular topic of study as life expectancy rises around the world. The average age at menopause in both developed and developing countries ranges from 45 to 53 years. Factors influencing the extent of ovarian follicle reserve dwindling, such as sociodemographic, menstrual, reproductive, and dietary factors. Aim and Objective: The present study aimed to determine the mean age of menopause and to find out the various factors affecting menopause onset. Methods: A community based cross-sectional study conducted in rural and urban areas of District Dehradun for a period of 1 year starting 1st August 2020 to 31st July 2021. Data were collected using multistage stratified random sampling from 211 cases of natural menopause. Chi-square was applied as statistical test of significance and p < 0.05 was considered statistically significant. Results: According to current study, average age menopausal age is 46 years. Variables like sociodemographic characteristics, lifestyle habits, reproductive and menstrual factors were found to have a significant relationship with onset of menopause. Conclusion: Menopause, whether it occurs early or late in life, several factors are responsible for deciding its onset. As a result, it is critical to identify the factors influencing the onset of menopause

Defining the interval for monitoring potential adverse events following immunization (AEFIs) after receipt of live viral vectored vaccines

Live viral vectors that express heterologous antigens of the target pathogen are being investigated in the development of novel vaccines against serious infectious agents like HIV and Ebola. As some live recombinant vectored vaccines may be replication-competent, a key challenge is defining the length of time for monitoring potential adverse events following immunization (AEFI) in clinical trials and epidemiologic studies. This time period must be chosen with care and based on considerations of pre-clinical and clinical trials data, biological plausibility and practical feasibility. The available options include: (1) adapting from the current relevant regulatory guidelines; (2) convening a panel of experts to review the evidence from a systematic literature search to narrow down a list of likely potential or known AEFI and establish the optimal risk window(s); and (3) conducting “near real-time“ prospective monitoring for unknown clustering's of AEFI in validated large linked vaccine safety databases using Rapid Cycle Analysis for pre-specified adverse events of special interest (AESI) and Treescan to identify previously unsuspected outcomes. The risk window established by any of these options could be used along with (4) establishing a registry of clinically validated pre-specified AESI to include in case-control studies. Depending on the infrastructure, human resources and databases available in different countries, the appropriate option or combination of options can be determined by regulatory agencies and investigators

Defining the interval for monitoring potential adverse events following immunization (AEFIs) after receipt of live viral vectored vaccines

Live viral vectors that express heterologous antigens of the target pathogen are being investigated in the development of novel vaccines against serious infectious agents like HIV and Ebola. As some live recombinant vectored vaccines may be replication-competent, a key challenge is defining the length of time for monitoring potential adverse events following immunization (AEFI) in clinical trials and epidemiologic studies. This time period must be chosen with care and based on considerations of pre-clinical and clinical trials data, biological plausibility and practical feasibility. The available options include: (1) adapting from the current relevant regulatory guidelines; (2) convening a panel of experts to review the evidence from a systematic literature search to narrow down a list of likely potential or known AEFI and establish the optimal risk window(s); and (3) conducting “near real-time“ prospective monitoring for unknown clustering's of AEFI in validated large linked vaccine safety databases using Rapid Cycle Analysis for pre-specified adverse events of special interest (AESI) and Treescan to identify previously unsuspected outcomes. The risk window established by any of these options could be used along with (4) establishing a registry of clinically validated pre-specified AESI to include in case-control studies. Depending on the infrastructure, human resources and databases available in different countries, the appropriate option or combination of options can be determined by regulatory agencies and investigators

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.Peer Reviewe

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

Analyse intégrative et modélisation des voies moléculaires dérégulées dans la polyarthrite rhumatoïde

Rheumatoid arthritis (RA) is a complexautoimmune disease that results in synovial inflammationand hyperplasia leading to bone erosion and cartilagedestruction in the joints. The aetiology of RA remainspartially unknown, yet, it involves a variety of intertwinedsignalling cascades and the expression of pro-inflammatorymediators. In the first part of my PhD project, we present asystematic effort to construct a fully annotated, expertvalidated, state of the art knowledge-base for RA. The RAmap illustrates significant molecular and signallingpathways implicated in the disease. Signal transduction isdepicted from receptors to the nucleus systematically usingthe systems biology graphical notation (SBGN) standardrepresentation. Manual curation based on strict criteria andrestricted to only human-specific studies limits theoccurrence of false positives in the map. The RA map canserve as an interactive knowledge base for the disease butalso as a template for omic data visualization and as anexcellent base for the development of a computationalmodel. The static nature of the RA map could provide arelatively limited understanding of the emerging behaviorof the system under different conditions. Computationalmodeling can reveal dynamic network properties throughin silico perturbations and can be used to test and predictassumptions.In the second part of the project, we present a pipelineallowing the automated construction of a large Booleanmodel, starting from a molecular interaction map. For thispurpose, we developed the tool CaSQ (CellDesigner asSBML-qual), which automates the conversion ofmolecular maps to executable Boolean models based ontopology and map semantics. The resulting Booleanmodel could be used for in silico simulations to reproduceknown biological behavior of the system and to furtherpredict novel therapeutic targets. For benchmarking, weused different disease maps and models with a focus onthe large molecular map for RA.In the third part of the project we present our efforts tocreate a large scale dynamical (Boolean) model forrheumatoid arthritis fibroblast-like synoviocytes (RAFLS).Among many cells of the joint and of the immunesystem involved in the pathogenesis of RA, RA FLS playa significant role in the initiation and perpetuation ofdestructive joint inflammation. RA-FLS are shown toexpress immuno-modulating cytokines, adhesionmolecules, and matrix-modelling enzymes. Moreover,RA-FLS display high proliferative rates and an apoptosisresistantphenotype. RA-FLS can also behave as primarydrivers of inflammation, and RA FLS-directed therapiescould become a complementary approach to immunedirectedtherapies. The challenge is to predict the optimalconditions that would favour RA FLS apoptosis, limitinflammation, slow down the proliferation rate andminimize bone erosion and cartilage destruction.La polyarthrite rhumatoïde (PR) est unemaladie auto-immune complexe qui entraîne uneinflammation synoviale et une hyperplasie pouvantprovoquer une érosion osseuse et une destruction ducartilage dans les articulations. L'étiologie de la PR restepartiellement inconnue, mais elle implique de multiplescascades de signalisation croisées et l'expression demédiateurs pro-inflammatoires. Dans la première partie demon projet de doctorat, nous présentons un effortsystématique pour construire une base de connaissancessur la PR, entièrement annotée et validée par des experts.Cette carte de la PR illustre les voies moléculaires et designalisation importantes impliquées dans la maladie. Latransduction du signal est systématiquement représentéedes récepteurs au noyau en utilisant la représentationstandard de notation graphique en biologie des systèmes(SBGN). La curation manuelle est basée sur des critèresstricts et spécifique aux études sur l'homme, limitantl'apparition de faux positifs sur la carte. Cette carte peutservir de base de connaissances interactive pour la maladiemais aussi de tableau pour la visualisation des donnéesomiques. De plus, c’est une excellente base pour ledéveloppement d'un modèle informatique. La naturestatique de la carte PR pourrait fournir une compréhensionrelativement limitée du comportement émergeant dusystème dans différentes conditions. La modélisationinformatique pourra révéler les propriétés dynamiques duréseau par le biais de perturbations in silico et peut êtreutilisée pour tester et prédire des hypothèses.Dans la deuxième partie du projet, nous présentons unpipeline permettant la construction automatisée d'un grandmodèle booléen, à partir d'une carte d'interactionsmoléculaires. Pour cela, nous avons développé l'outilCaSQ (CellDesigner as SBML-qual), qui automatise laconversion des cartes moléculaires en modèles booléensexécutables basés sur la topologie et la sémantique descartes. Le modèle booléen résultant pourrait être utilisépour des simulations in silico afin de reproduire lecomportement biologique connu du système et de prédirede nouvelles cibles thérapeutiques. Pour l'analyse deperformance de l’outil, nous avons utilisé différentescartes et modèles de maladies en mettant l'accent sur lagrande carte moléculaire de la PR.Dans la troisième partie du projet, nous présentons nosefforts pour créer un modèle dynamique (booléen) àgrande échelle pour les synoviocytes de type fibroblastede polyarthrite rhumatoïde (RA-FLS). Parmi denombreuses cellules de l'articulation et du systèmeimmunitaire impliquées dans la pathogenèse de la PR, lesRA-FLS joue un rôle important dans l'initiation et laperpétuation de l'inflammation articulaire destructrice.Les RA-FLS expriment des cytokinesimmunomodulatrices, des molécules d'adhésion et desenzymes de modélisation matricielle. De plus, les RAFLSprésentent des taux de prolifération élevés et unphénotype résistant à l'apoptose. Les RA-FLS peuventégalement se comporter comme les principaux moteurs del'inflammation, et les thérapies dirigées contre les RA FLSpourraient devenir une approche complémentaire auximmunothérapies. Le défi est de prédire les conditionsoptimales qui favoriseraient l'apoptose des RA FLS,limiteraient l'inflammation, ralentiraient le taux deprolifération et minimiseraient l'érosion osseuse et ladestruction du cartilage