ALBERT

Description: The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance, and determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding, and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling the genetic epidemiology of SARS-CoV-2.

Description: Alcoholic liver disease (ALD) is responsible for an average of 50.4% and 44.2%of liver disease deaths among males and females respectively. Driven by alcohol misuse, ALD is often reversible by cessation of consumption. However, abstinence programs can have limited success at curtailing abuse, and the loss of life. ALD, therefore, remains a significant clinical challenge. There is a need for effective treatments that prevent or reverse alcohol-induced liver damage to complement or supplant behavioral interventions. Metabolic syndrome, which is disproportionally prevalent in ALD patients, accelerates the progression of ALD and increases liver disease mortality. Current rodent models of ALD unfortunately do not account for the contribution of the western diet to ALD pathology. To address this, we have developed a rodent model of ALD that integrates the impact of the western diet and alcohol; the WASH-diet model. We show here that the WASH diet, either chronically or in small time-restricted bouts, accelerated ALD pathology with severe steatohepatitis, elevated inflammation and increased fibrosis compared to mice receiving chronic alcohol alone. We also validated our WASH-diet model as an in vivo system for testing the efficacy of experimental ALD treatments. The efficacy of the inverse-agonist SR9238, previously shown to inhibit both non-alcohol and alcohol-induced steatohepatitis progression, was conserved in our WASH-diet model. These findings suggested that the WASH-diet may be useful for in vivo pre-clinical assessment of novel therapies.

Description: Mutations in mitochondrial replicative polymerase PolγA lead to progressive external ophthalmoplegia (PEO). While PolγA is the known central player in mitochondrial DNA (mtDNA) replication, it is unknown whether a regulatory process exists on the mitochondrial outer membrane which controlled its entry into the mitochondria. We now demonstrate that PolγA is ubiquitylated by mitochondrial E3 ligase, MITOL (or MARCH5, RNF153). Ubiquitylation in wild-type (WT) PolγA occurs at Lysine 1060 residue via K6 linkage. Ubiquitylation of PolγA negatively regulates its binding to Tom20 and thereby its mitochondrial entry. While screening different PEO patients for mitochondrial entry, we found that a subset of the PolγA mutants is hyperubiquitylated by MITOL and interact less with Tom20. These PolγA variants cannot enter into mitochondria, instead becomes enriched in the insoluble fraction and undergo enhanced degradation. Hence, mtDNA replication, as observed via BrdU incorporation into the mtDNA, was compromised in these PEO mutants. However, by manipulating their ubiquitylation status by 2 independent techniques, these PEO mutants were reactivated, which allowed the incorporation of BrdU into mtDNA. Thus, regulated entry of non-ubiquitylated PolγA may have beneficial consequences for certain PEO patients.

Description: Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serving action. User sentiment appears to be an appropriate indicator for goal discrepancy that guides the agent to complete the user’s desired task with gratification. The negative sentiment expressed by the user about an aspect of the provided choice is treated as a discrepancy that is being resolved by the GDM depending upon the observed discrepancy and current dialogue state. The goal update capability and the VA’s interactiveness trait enable end-users to accomplish their desired task satisfactorily. Findings The obtained experimental results illustrate that DGDVA can handle dynamic goals with maximum user satisfaction and a significantly higher success rate. The interaction drives the user to decide its final goal through the latent specification of possible choices and information retrieved and provided by the dialogue agent. Through the experimental results (qualitative and quantitative), we firmly conclude that the proposed sentiment-aware VA adapts users’ dynamic behavior for its goal setting with substantial efficacy in terms of primary objective i.e., task success rate (0.88). Practical implications In real world, it can be argued that many people do not have a predefined and fixed goal for tasks such as online shopping, movie booking & restaurant booking, etc. They tend to explore the available options first which are aligned with their minimum requirements and then decide one amongst them. The DGDVA provides maximum user satisfaction as it enables them to accomplish a dynamic goal that leads to additional utilities along with the essential ones. Originality To the best of our knowledge, this is the first effort towards the development of A Dynamic Goal Adapted Task-Oriented Dialogue Agent that can serve user goals dynamically until the user is satisfied.

Description: Background The remarkable progress seen in maternal and child health (MCH) in India over the past two decades has been impacted by the COVID-19 pandemic. We aimed to undertake a rapid assessment to identify key priorities for public health research in MCH in India within the context and aftermath of the COVID-19 pandemic. Methods A web-based survey was developed to identify top research priorities in MCH. It consisted of 26 questions on six broad domains: vaccine preventable diseases, outbreak preparedness, primary healthcare integration, maternal health, neonatal health, and infectious diseases. Key stakeholders were invited to participate between September and November 2020. Participants assigned importance on a 5-point Likert scale, and assigned overall ranks to each sub-domain research priority. Descriptive statistics were used to examine Likert scale responses, and a ranking analysis was done to obtain an “average ranking score” and identify the top research priority under each domain. Results Amongst the 84 respondents from across 15 Indian states, 37% were public-health researchers, 25% healthcare providers, 20% academic faculty and 13% were policy makers. Most respondents considered conducting systems strengthening research as extremely important. The highest ranked research priorities were strengthening the public sector workforce (vaccine preventable diseases), enhancing public-health surveillance networks (outbreak preparedness), nutrition support through community workers (primary care integration), encouraging at least 4–8 antenatal visits (maternal health), neonatal resuscitation to reduce birth asphyxia (neonatal health) and screening and treatment of tuberculosis (infectious diseases). Common themes identified through open-ended questions primarily included systems strengthening priorities across domains. Conclusions The overall focus for research priorities in MCH in India during the COVID-19 pandemic is on strengthening existing services and service delivery, rather than novel research. Our results highlight pivotal steps within the roadmap for advancing and sustaining maternal and child health gains during the ongoing COVID-19 pandemic and beyond.