Dr. Tavpritesh Sethi is a physician-scientist and Assistant Professor of Computational Biology at Indraprastha Institute of Information Technology Delhi, India and a fellow of the Wellcome Trust/DBT India Alliance at All India Institute of Medical Sciences, New Delhi, India. Over the past two years, he has been a visiting faculty member at Stanford University, School of Medicine from February 2017 to January 2019. He received his M.B.B.S from Government Medical College, Amritsar and PhD from CSIR-Institute of Genomics and Integrative Biology, New Delhi, India. Dr. Sethi specializes in improving outcomes in neonatal, child and maternal health by bridging medicine and artificial intelligence. His research is focused on development and deployment of machine-learning based solutions to enable decisions and policy in pressing healthcare questions such as antimicrobial resistance, sepsis and health inequalities in intensive care and public health settings. He has authored over 20 research articles and has been a recipient of MIT-TR35 India Innovators under 35, Wellcome Trust/DBT India Alliance Early Career Award. He is an editorial board member of PLOS One, Systems Medicine and Journal of Genetics. Dr. Sethi is a member of the European Association of Systems Medicine and leads the Australasia region for International Association of Systems and Networks Medicine (IASyM).
AMRSense
AMRSense is a One Health Data Ecosystem for solving the challenge of Antimicrobial Resistance. Brought to reality by a collaboration between IIIT-Delhi, ICMR, CHRI-PATH, and Tata 1mg.com, AMRSense closes the gaps in data and AI for evidence-based implementation of AMR surveillance.
WashKaro is a free Android-based app which aims to spread awareness among the mass regarding Tuberculosis. The app provides a plethora of features like: Human curated information Success Stories Multilingual text and speech support Chatbot
We developed Strainflow for SARS-CoV-2 genome sequences, where sequences were treated as documents with words (codons) to learn the codon context of 0.9 million spike genes using the skip-gram algorithm.
Background: Antimicrobial resistance (AMR) has escalated to pandemic levels, posing a significant global health threat. This study examines the patterns and trends of AMR in Bloodstream Infections (BSIs) across India, aiming to inform better surveillance and intervention strategies. Methods: Six-year data from 21 tertiary care centers in the Indian Council of Medical Research's AMR Surveillance Network (IAMRSN) were retrospectively analyzed to estimate cluster-robust trends in resistance. Time-series analysis was used to discern lead/lag relationships between antibiotic pairs and the directional influence of resistance in community and hospital-acquired BSIs(CA/HA BSIs). A data-driven Bayesian network ensemble averaged over 301 bootstrap samples was modelled to uncover systemic associations between AMR and Sustainable Development Goals (SDGs).Findings: Our findings indicate significant (p < 0.001) monthly increases in Imipenem and Meropenem resistance for Klebsiella, E. coli, and Acinetobacter BSIs. Importantly, Carbapenem resistance in HA-BSIs preceded that in CA-BSIs for Klebsiella and Acinetobacter (p < 0.05). At a national level, Cefotaxime resistance emerged as a potential early indicator for emerging Carbapenem resistance, proposing a novel surveillance marker. In Klebsiella BSIs, states with higher achievement of SDG3 goals showed lower Imipenem resistance. A model-based AMR scorecard is introduced for focused interventions and continuous monitoring.Interpretation: The identified spatiotemporal trends and drug resistance associations offer critical insights for AMR surveillance aligning with WHO GLASS standards.The escalation of carbapenem resistance in BSIs demands vigilant monitoring and may be crucial for achieving SDGs by 2030. Implementing the proposed framework for data-driven evidence can help nations achieve proactive AMR surveillance.
The COVID-19 Infodemic had an unprecedented impact on health behaviors and outcomes at a global scale. While many studies have focused on a qualitative and quantitative understanding of misinformation, including sentiment analysis, there is a gap in understanding the emotion-carriers of misinformation and their differences across geographies. In this study, we characterized emotion carriers and their impact on vaccination rates in India and the United States. A manually labelled dataset was created from 2.3 million tweets and collated with three publicly available datasets (CoAID, AntiVax, CMU) to train deep learning models for misinformation classification. Misinformation labelled tweets were further analyzed for behavioral aspects by leveraging Plutchik Transformers to determine the emotion for each tweet. Time series analysis was conducted to study the impact of misinformation on spatial and temporal characteristics. Further, categorical classification was performed using transformer models to assign categories for the misinformation tweets. Word2Vec+BiLSTM was the best model for misinformation classification, with an F1-score of 0.92. The US had the highest proportion of misinformation tweets (58.02%), followed by the UK (10.38%) and India (7.33%). Disgust, anticipation, and anger were associated with an increased prevalence of misinformation tweets. Disgust was the predominant emotion associated with misinformation tweets in the US, while anticipation was the predominant emotion in India. For India, the misinformation rate exhibited a lead relationship with vaccination, while in the US it lagged behind vaccination. Our study deciphered that emotions acted as differential carriers of misinformation across geography and time. These carriers can be monitored to develop strategic interventions for countering misinformation, leading to improved public health.
Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online.
Urinary Tract Infections (UTIs) affect nearly 150 million people worldwide annually. High antimicrobial resistance (AMR) is reported in India. However, no studies provide multicentric, nationwide data on trends and associations of AMR in UTIs. To our knowledge, this study presents the most comprehensive estimates of trends and associations of AMR across India.
Evidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In massive and rapidly growing corpuses, such as COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeline that evaluates multiple aspects, such as network topological features, communities, and their temporal trends, can make this process more efficient.
Delays in identification of critical events in intensive care contribute to irreversible organ dysfunction and mortality(1). Shock Index(SI), calculated as the ratio of heart-rate and systolic blood pressure is a validated indicator for bedside examination, risk stratification, and prognostication(2,3). SI was originally designed for the evaluation of hemodynamic stability, with values >0.7 indicating shock. Shock is one of the highest contributors to higher mortality rates and SI has been used for the assessment of Shock in ICUs(4). The mortality rate in patients who develop shock in ICU is as high as 34% in developing countries(5). While in developing countries in Europe and North America the mortality rates are at 38% due to septic shock(6) Thus, prediction and early identification of Shock Index abnormality can help in initiating early life-saving therapies to prevent shock related mortality.(7). Early identification is critical for appropriate management(1), improved patient outcomes, and reduction of mortality(8–10).
Hypothermia is a life-threatening condition where the temperature of the body drops below 35°C and is a key source of concern in Intensive Care Units (ICUs). Early identification can help to nudge clinical management to initiate early interventions. Despite its importance, very few studies have focused on the early prediction of hypothermia. In this study, we aim to monitor and predict Hypothermia (30 min-4 h) ahead of its onset using machine learning (ML) models developed on physiological vitals and to prospectively validate the best performing model in the pediatric ICU. We developed and evaluated ML algorithms for the early prediction of hypothermia in a pediatric ICU. Sepsis advanced forecasting engine ICU Database (SafeICU) data resource is an in-house ICU source of data built in the Pediatric ICU at the All-India Institute of Medical Science (AIIMS), New Delhi. Each time-stamp at 1-min resolution was labeled for the presence of hypothermia to construct a retrospective cohort of pediatric patients in the SafeICU data resource. The training set consisted of windows of the length of 4.2 h with a lead time of 30 min-4 h from the onset of hypothermia. A set of 3,835 hand-engineered time-series features were calculated to capture physiological features from the time series. Features selection using the Boruta algorithm was performed to select the most important predictors of hypothermia. A battery of models such as gradient boosting machine, random forest, AdaBoost, and support vector machine (SVM) was evaluated utilizing five-fold test sets. The best-performing model was prospectively validated. A total of 148 patients with 193 ICU stays were eligible for the model development cohort. Of 3,939 features, 726 were statistically significant in the Boruta analysis for the prediction of Hypothermia. The gradient boosting model performed best with an Area Under the Receiver Operating Characteristic curve (AUROC) of 85% (SD = 1.6) and a precision of 59.2% (SD = 8.8) for a 30-min lead time before the onset of Hypothermia onset. As expected, the model showed a decline in model performance at higher lead times, such as AUROC of 77.2% (SD = 2.3) and precision of 41.34% (SD = 4.8) for 4 h ahead of Hypothermia onset. Our GBM(gradient boosting machine) model produced equal and superior results for the prospective validation, where an AUROC of 79.8% and a precision of 53% for a 30-min lead time before the onset of Hypothermia whereas an AUROC of 69.6% and a precision of 38.52% for a (30 min-4 h) lead time prospective validation of Hypothermia. Therefore, this work establishes a pipeline termed ThermoGnose for predicting hypothermia, a major complication in pediatric ICUs.
Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives.
A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and temporal separation of hot-spots, might be an effective way of halting the disease spread. We approach this problem by proposing a novel pipeline VacSIM that dovetails Deep Reinforcement Learning models into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine. Whereas the Reinforcement Learning models suggest better actions and rewards, Contextual Bandits allow online modifications that may need to be implemented on a day-to-day basis in the real world scenario. We evaluate this framework against a naive allocation approach of distributing vaccine proportional to the incidence of COVID-19 cases in five different States across India (Assam, Delhi, Jharkhand, Maharashtra and Nagaland) and demonstrate up to 9039 potential infections prevented and a significant increase in the efficacy of limiting the spread over a period of 45 days through the VacSIM approach. Our models and the platform are extensible to all states of India and potentially across the globe. We also propose novel evaluation strategies including standard compartmental model-based projections and a causality-preserving evaluation of our model. Since all models carry assumptions that may need to be tested in various contexts, we open source our model VacSIM and contribute a new reinforcement learning environment compatible with OpenAI gym to make it extensible for real-world applications across the globe.
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape or increased pathogenicity. Early prediction of emerging strains could be vital to pandemic preparedness but remains an open challenge. Here, we developed Strainflow, to learn the latent dimensions of 0.9 million high-quality SARS-CoV-2 genome sequences, and used machine learning algorithms to predict upcoming caseloads of SARS-CoV-2. In our Strainflow model, SARS-CoV-2 genome sequences were treated as documents, and codons as words to learn unsupervised codon embeddings (latent dimensions). We discovered that codon-level changes lead to a change in the entropy of the latent dimensions. We used a machine learning algorithm to find the most relevant latent dimensions called Dimensions of Concern (DoCs) of SARS-CoV-2 spike genes, and demonstrate their potential to provide a lead time for predicting new caseloads in several countries. The DoCs capture codons associated with global Variants of Concern (VOCs) and Variants of Interest (VOIs), and may be surveilled to predict country-specific emergence and spread of SARS-CoV-2 variants.
Early prediction of hemodynamic shock in the ICU can save lives. Several studies have leveraged a combination of vitals, lab investigations, and clinical data to construct early warning systems for shock. However, these have a limited potential of generalization to diverse settings due to reliance on non-real-time data. Monitoring data from vitals can provide an early real-time prediction of Hemodynamic shock which can precede the clinical diagnosis to guide early therapy decisions. Generalization across age and geographical context is an unaddressed challenge. In this retrospective observational study, we built real-time shock prediction models generalized across age groups (adult and pediatric), ICU-types, and geographies. We trained, validated, and tested a shock prediction model on the publicly available eICU dataset on 208 ICUs across the United States. Data from 156 hospitals passed the eligibility criteria for cohort building. These were split hospital-wise in a five-fold training-validation-test set. External validation of the model was done on a pediatric ICU in New Delhi and MIMIC-III database with more than 0.23 million and one million patient-hours vitals data, respectively. Our models identified 92% of all the shock events more than 8 hours in advance with AUROC of 86 %(SD= 1.4) and AUPRC of 93% (SD =1.2) on the eICU testing set. An AUROC of 87 % (SD =1.8), AUPRC 92 % (SD=1.6) were obtained in external validation on the MIMIC-III cohort. The New Delhi Pediatric SafeICU data achieved an AUROC of 87 % (SD =4) AUPRC 91% (SD=3), despite being completely different geography and age group. In this first, we demonstrate a generalizable model for predicting shock, and algorithms are publicly available as a pre-configured Docker environment at https://github.com/tavlab-iiitd/ShoQPred.
COVID-19 knowledge has been changing rapidly with the fast pace of information that accompanied the pandemic. Since peer-reviewed research is a trusted source of evidence, capturing and predicting the emerging themes in COVID-19 literature are crucial for guiding research and policy. Machine learning, natural language processing and dynamical networks have the potential to enable rapid distillation and prediction of actionable insights for ending the pandemic.
Shock is a major killer in the ICU and Deep learning based early predictions can potentially save lives. Generalization across age and geographical context is an unaddressed challenge. In this retrospective observational study, we built real-time shock prediction models generalized across age groups and continents. More than 1.5 million patient-hours of novel data from a pediatric ICU in New Delhi and 5 million patient-hours from the adult ICU MIMIC database were used to build models. We achieved model generalization through a novel fractal deep-learning approach and predicted shock up to 12 hours in advance. Our deep learning models showed a receiver operating curve (AUROC) drop from 78% (95%CI, 73-83) on MIMIC data to 66% (95%CI, 54-78) on New Delhi data, outperforming standard machine learning by nearly a 10% gap. Therefore, better representations and deep learning can partly address the generalizability-gap of ICU prediction models trained across geographies. Our data and algorithms are publicly available as a pre-configured docker environment at https://github.com/SAFE-ICU/ShoQPred.
The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artificial Intelligence. Hence, we have exploited the potential of Natural Language Processing for identifying relevant information that needs to be disseminated amongst the masses. In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by matching daily news with trusted guidelines from the World Health Organization. The proposed pipeline deploys various techniques of NLP such as summarizers, word embeddings, and similarity metrics to provide users with news articles along with a corresponding healthcare guideline. A total of 36 models were evaluated and a combination of LexRank based summarizer on Word2Vec embedding with Word Mover distance metric outperformed all other models. This novel open-source approach can be used as a template for proactive dissemination of relevant healthcare information in the midst of misinformation spread associated with epidemics.
The adoption of non-pharmaceutical interventions and their surveillance is critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions in terms of adoption can help shape public health decisions. Also, the efficacy of Non-Pharmaceutical Interventions can be affected by public behaviours in events such as election rallies, festivals and protest events, as captured from social media. Social media analytics can offer crucial public health insights Here we examined mask use and mask fit in the United States, especially during the first large scale public gathering post pandemic, the Black Lives Matter (BLM). This study aimed to analyze the utilization and fit of face masks and social distancing in the USA from social media and events of large physical gatherings through publicly available social media images from six cities and the BLM protests. 2.04 million publicly available social media images were collected and analyzed from the six cities between February 1, 2020, and May 31, 2020. We used correlation tests to examine the relationships between the online mask usage trends and the COVID-19 cases. We looked for significant changes in mask-wearing patterns and group posting before and after important policy decisions. For BLM protests, we analyze 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask-fit score. The average percentage of group pictures dropped from 8.05% to 4.65% post the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston and Minneapolis observed an increase of 5%, 7.4%, 7.4%, 6.5%, 5.6% and 7.1% in mask wearing online, respectively, between February 2020 and May 2020. Boston and Minneapolis observed a significant increase of 3% and 7.4% mask-wearing after the mask mandates. A difference of 6.2% and 8.3% were found in the group pictures between BLM posts and Non-BLM posts for New York City and Minneapolis. In contrast, the difference between BLM and NON-BLM posts in the percentage of masked faces in group pictures was 29% and 20.1% for New York City and Minneapolis, respectively. Of the masked faces in protests, 35% wore the mask with a fit score greater than 80%. The study finds a significant drop in the group posting when the stay-at-home laws were applied and a significant increase in mask wearing for two of the three cities when the mask mandates were applied. Although a general positive trend towards mask-wearing and social distancing is observed, a high percentage of posts did not adhere to the guidelines. BLM related posts were found to capture the lack of seriousness to safety measures through a high percentage of group pictures and low mask fit scores. Thus, the methodology used provides a directional indication of how government policies can be indirectly monitored through social media.
A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and temporal separation of hot-spots, might be an effective way of halting the disease spread. We approach this problem by proposing a novel pipeline VacSIM that dovetails Deep Reinforcement Learning models into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine. Whereas the Reinforcement Learning models suggest better actions and rewards, Contextual Bandits allow online modifications that may need to be implemented on a day-to-day basis in the real world scenario. We evaluate this framework against a naive allocation approach of distributing vaccine proportional to the incidence of COVID-19 cases in five different States across India (Assam, Delhi, Jharkhand, Maharashtra and Nagaland) and demonstrate up to 9039 potential infections prevented and a significant increase in the efficacy of limiting the spread over a period of 45 days through the VacSIM approach. Our models and the platform are extensible to all states of India and potentially across the globe. We also propose novel evaluation strategies including standard compartmental model-based projections and a causality-preserving evaluation of our model. Since all models carry assumptions that may need to be tested in various contexts, we open source our model VacSIM and contribute a new reinforcement learning environment compatible with OpenAI gym to make it extensible for real-world applications across the globe. (this http URL).
COVID-19 pandemic has deeply affected the health, economic, and social fabric of nations. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being.
The relationship between meteorological factors such as temperature and humidity with COVID-19 incidence is still unclear after 6 months of the beginning of the pandemic. Some literature confirms the association of temperature with disease transmission while some oppose the same. This work intends to determine whether there is a causal association between temperature, humidity and Covid-19 cases. Three different causal models were used to capture stochastic, chaotic and symbolic natured time-series data and to provide a robust & unbiased analysis by constructing networks of causal relationships between the variables. Granger-Causality method, Transfer Entropy method & Convergent Cross-Mapping (CCM) was done on data from regions with different temperatures and cases greater than 50,000 as of 13th May 2020. From the Granger-Causality test we found that in only Canada, the United Kingdom, temperature and daily new infections are causally linked. The same results were obtained from Convergent Cross Mapping for India. Again using Granger-Causality test, we found that in Russia only, relative humidity is causally linked to daily new cases. Thus, a Generalized Additive Model with a smoothing spline function was fitted for these countries to understand the directionality. Using the combined results of the said models, we were able to conclude that there is no evidence of a causal association between temperature, humidity and Covid-19 cases.
The novel coronavirus SARS-CoV-2, which originated in China months back, has dramatically enveloped the global population crossing all boundaries and borders, infecting more than 5 million people and causing more than 300,000 deaths as on 21 May 2020. The distinct difference in the disease burden (including infectivity and mortality) between the regions across the globe is an enigma. Despite harboring 60% of the global population, Asia accounts for only ~18% global cases and <10% global mortality due to COVID-192. Western Europe (Italy, France, Spain, United Kingdom) and USA account for about 50% and 70% of global cases and mortality despite the fact that China continued to contribute to >80% of global cases till the end of March first week. Currently, the two most populous countries in the World- India and China (accounting for 35% global population) together account for less than 4% and 3% global cases and mortality, respectively. These observations have displayed a temporal consistency with almost similar country-wise distribution of cases over the 1-month period, highlighting the impact of consistent factors which govern these epidemiologic associations
COVID-19 pandemic is an enigma with uncertainty caused by biological and health systems factors. Although many models have been developed all around the world, transparent models that allow interacting with the assumptions will become more important as we test various strategies for lockdown, testing and social interventions and enable effective policy decisions. In this paper we developed a suite of models to guide development of policies under different scenarios when the lockdown opens. These had been deployed to create an interactive dashboard called COVision which includes the Agent based Models (ABM) and classical compartmental models i.e. Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) approaches. Our tool allows simulation of scenarios by changing strength of lockdown, basic reproduction number(R0), asymptomatic spread, testing rate, contact rate (Beta), recovery rate (Gamma), incubation period and starting number of cases. We optimized ABMs and classical compartmental models to fit the actual data, both of which performed well in terms of R-squared, root mean squared error (RMSE) and mean absolute percentage error (MAPE). Out of the three models in our suite, ABM was able to capture the data better than SIR and SEIR and achieved an RSQ of 92.3% for India and 89% for Maharashtra for the next 30 days. We also computed R0 using SIR and SEIR models which were found to be decreasing over the different periods of lockdown indicating the effectiveness of policies and interventions. Finally, we formulated ICU bed requirements using our best models. Our evaluation suggests that ABM models were able to capture the dynamic nature of the epidemic for a longer duration of time while classical SIR and SEIR models performed inefficiently for longer terms. The visual interactivity and ability to simulate outcomes under different parameters will allow the policymakers to make informed decisions for estimating the strength of lockdown to be implemented and testing rates. Further, our models were able to highlight the differences at state level for the parameters such as R0 and contact rates and hence can be applied for state specific decision making. An interactive dashboard http://covision.tavlab.iiitd.edu.in have been hosted as a web-server for the war level monitoring of the covid19 pandemic in India in public domain
COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people's health and governance systems. Since social media is the largest source of information, managing the infodemic not only requires mitigating of misinformation but also an early understanding of psychological patterns resulting from it. During the COVID-19 crisis, Twitter alone has seen a sharp 45% increase in the usage of its curated events page, and a 30% increase in its direct messaging usage, since March 6th 2020. In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India. We look at these two sources with a psycho-linguistic lens of emotions and quantified the extent and coupling between the two. We modified path, a deep skip-gram based open-sourced lexicon builder for effective capture of health-related emotions. We were then able to capture the time-evolution of health-related emotions in social media and official bulletins. An analysis of lead-lag relationships between the time series of extracted emotions from official bulletins and social media using Granger's causality showed that state bulletins were leading the social media for some emotions such as Medical Emergency. Further insights that are potentially relevant for the policymaker and the communicators actively engaged in mitigating misinformation are also discussed. Our paper also introduces CoronaIndiaDataset2, the first social media based COVID-19 dataset at national and state levels from India with over 5.6 million national and 2.6 million state-level tweets. Finally, we present our findings as COVibes, an interactive web application capturing psychometric insights captured upon the CoronaIndiaDataset, both at a national and state level.
The flood of conflicting COVID-19 research has revealed that COVID-19 continues to be an enigma. Although more than 14,000 research articles on COVID-19 have been published with the disease taking a pandemic proportion, clinicians and researchers are struggling to distill knowledge for furthering clinical management and research. In this study, we address this gap for a targeted user group, i.e. clinicians, researchers, and policymakers by applying natural language processing to develop a CovidNLP dashboard in order to speed up knowledge discovery. The WHO has created a repository of about more than 5000 peer-reviewed and curated research articles on varied aspects including epidemiology, clinical features, diagnosis, treatment, social factors, and economics. We summarised all the articles in the WHO Database through an extractive summarizer followed by an exploration of the feature space using word embeddings which were then used to visualize the summarized associations of COVID-19 as found in the text. Clinicians, researchers, and policymakers will not only discover the direct effects of COVID-19 but also the systematic implications such as the anticipated rise in TB and cancer mortality due to the non-availability of drugs during the export lockdown as highlighted by our models. These demonstrate the utility of mining massive literature with natural language processing for rapid distillation and knowledge updates. This can help the users understand, synthesize, and take pre-emptive action with the available peer-reviewed evidence on COVID-19. Our models will be continuously updated with new literature and we have made our resource CovidNLP publicly available in a user-friendly fashion at http://covidnlp.tavlab.iiitd.edu.in/.
The flood of conflicting COVID-19 research has revealed that COVID-19 continues to be an enigma. Although more than 14,000 research articles on COVID-19 have been published with the disease taking a pandemic proportion, clinicians and researchers are struggling to distill knowledge for furthering clinical management and research. In this study, we address this gap for a targeted user group, i.e. clinicians, researchers, and policymakers by applying natural language processing to develop a CovidNLP dashboard in order to speed up knowledge discovery. The WHO has created a repository of about more than 5000 peer-reviewed and curated research articles on varied aspects including epidemiology, clinical features, diagnosis, treatment, social factors, and economics. We summarised all the articles in the WHO Database through an extractive summarizer followed by an exploration of the feature space using word embeddings which were then used to visualize the summarized associations of COVID-19 as found in the text. Clinicians, researchers, and policymakers will not only discover the direct effects of COVID-19 but also the systematic implications such as the anticipated rise in TB and cancer mortality due to the non-availability of drugs during the export lockdown as highlighted by our models. These demonstrate the utility of mining massive literature with natural language processing for rapid distillation and knowledge updates. This can help the users understand, synthesize, and take pre-emptive action with the available peer-reviewed evidence on COVID-19. Our models will be continuously updated with new literature and we have made our resource CovidNLP publicly available in a user-friendly fashion at http://covidnlp.tavlab.iiitd.edu.in/.
Cytosolic functions of Long non-coding RNAs including mRNA translation masking and sponging are major regulators of biological pathways. Formation of T cell-bounded hypoxic granuloma is a host immune defense for containing infected Mtb-macrophages. Our study exploits the mechanistic pathway of Mtb-induced HIF1A silencing by the antisense lncRNA-HIF1A-AS2 in T cells. Computational analysis of in-vitro T-cell stimulation assays in progressors(n=119) versus latent(n=221) tuberculosis patients revealed the role of lncRNA mediated disruption of hypoxia adaptation pathways in progressors. We found 291 upregulated and 227 downregulated lncRNAs that were correlated at mRNA level with HIF1A and HILPDA which are major players in hypoxia response. We also report novel lncRNA-AC010655 (AC010655.4 and AC010655.2) in cis with HILPDA, both of which contain binding sites for the BARX2 transcription factor, thus indicating a mechanistic role. Detailed comparison of infection with antigenic stimulation showed a non-random enrichment of lncRNAs in the cytoplasmic fraction of the cell in progressors. The lack of this pattern in non-progressors indicates the hijacking of the lncRNA dynamics by Mtb. The in-vitro manifestation of this response in the absence of granuloma indicates pre-programmed host-pathogen interaction between T-cells and Mtb regulated through lncRNAs, thus tipping this balance towards progression or containment of Mtb. Finally, we trained multiple machine learning classifiers for reliable prediction of latent to the active progression of patients, yielding a model to guide aggressive treatment.
The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation.
Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture body surface temperature with the potential to reveal underlying perfusion disturbance in shock. In this study, we automate early detection and prediction of shock using machine learning upon thermal images obtained in a pediatric intensive care unit of a tertiary care hospital. 539 images were recorded out of which 253 had concomitant measurement of continuous intra-arterial blood pressure, the gold standard for shock monitoring. Histogram of oriented gradient features were used for machine learning based region-of-interest segmentation that achieved 96% agreement with a human expert. The segmented center-to-periphery difference along with pulse rate was used in longitudinal prediction of shock at 0, 3, 6 and 12 hours using a generalized linear mixed-effects model. The model achieved a mean area under the receiver operating characteristic curve of 75% at 0 hours (classification), 77% at 3 hours (prediction) and 69% at 12 hours (prediction) respectively. Since hemodynamic shock associated with critical illness and infectious epidemics such as Dengue is often fatal, our model demonstrates an affordable, non-invasive, non-contact and tele-diagnostic decision support system for its reliable detection and prediction.
Shock is one of the major killers in ICUs and early interventions can potentially reverse it. In this study, we advance a non-contact thermal imaging modality to continuous monitoring of hemodynamic shock working on 406 patient videos of 256 seconds length for 22 patients longitudinally. Deep learning was performed upon these videos to extract Center-to-Peripheral Difference (CPD) in temperature values. CPD along with heart rate, was finally analysed to predict the shock status up to next 12 hours using Long-Short Term Memory models. Our models achieved best area under the receiver-operating-characteristics curve of 0.81 ± 0.06 and area under precision-recall curve of 0.78 ± 0.05 at 5 hours, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable prediction using an automated decision pipeline, that can save lives and provide better care.
Excessive oxygen use or hyperoxia among preterm neonates is associated with morbidities like retinopathy of prematurity (ROP) and chronic lung disease (CLD) [1]. Delivering optimal oxygen involves maintaining the oxygen saturation (SpO2) within a narrow range by adjusting the inspired oxygen concentration. In our neonatal intensive care unit (NICU), we observed that nurses faced difficulty in maintaining SpO2 of neonates within the unit’s target range, often resulting in fluctuating SpO2 values. We planned this quality improvement (QI) study to evaluate the existing compliance with oxygen saturation targeting in our NICU, and whether implementation of a unit policy on oxygen administration would improve the compliance.
Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensemble-averaged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stable-families within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work.
Emerging antimicrobial resistance (AMR) is a global threat to life. Injudicious use of antibiotics is the biggest driver of resistance evolution, creating selection pressures on micro-organisms. Intensive care units (ICUs) are the strongest contributors to this pressure, owing to high infection and antibiotic usage rates. Antimicrobial stewardship programs aim to control antibiotic use; however, these are mostly limited to descriptive statistics. Genomic analyses lie at the other extreme of the value-spectrum, and together these factors predispose to siloing of knowledge arising from AMR stewardship. In this study, we bridged the value-gap at a Pediatric ICU by creating Bayesian network (BN) artificial intelligence models with potential impacts on antibiotic stewardship. Methods, actionable insights and an interactive dashboard for BN analysis upon data observed over 3 years at the PICU are described. BNs have several desirable properties for reasoning from data, including interpretability, expert knowledge injection and quantitative inference. Our pipeline leverages best practices of enforcing statistical rigor through bootstrapping, ensemble averaging and Monte Carlo simulations. Competing, shared and independent drug resistances were discovered through the presence of network motifs in BNs. Inferences guided by these visual models are also discussed, such as increasing the sensitivity testing for chloramphenicol as a potential mechanism of avoiding ertapenem overuse in the PICU. Organism, tissue and temporal influences on drug co-resistances are also discussed. While the model represents inferences that are tailored to the site, BNs are excellent tools for building upon pre-learnt structures, hence the model and inferences were wrapped into an interactive dashboard not only deployed at the site, but also made openly available to the community via GitHub. Shared repositories of such models could be a viable alternative to raw-data sharing and could promote partnering, learning across sites and charting a joint course for antimicrobial stewardship programs in the race against AMR.
RNA is known to play diverse roles in gene regulation. The clues for this regulatory function of RNA are embedded in its ability to fold into intricate secondary and tertiary structure. Results: We report the transcriptome-wide RNA secondary structure in zebrafish at single nucleotide resolution using Parallel Analysis of RNA Structure (PARS). This study provides the secondary structure map of zebrafish coding and non-coding RNAs. The single nucleotide pairing probabilities of 54,083 distinct transcripts in the zebrafish genome were documented. We identified RNA secondary structural features embedded in functional units of zebrafish mRNAs. Translation start and stop sites were demarcated by weak structural signals. The coding regions were characterized by the three-nucleotide periodicity of secondary structure and display a codon base specific structural constrain. The splice sites of transcripts were also delineated by distinct signature signals. Relatively higher structural signals were observed at 3' Untranslated Regions (UTRs) compared to Coding DNA Sequence (CDS) and 5' UTRs. The 3' ends of transcripts were also marked by unique structure signals. Secondary structural signals in long non-coding RNAs were also explored to better understand their molecular function. Conclusions: Our study presents the first PARS-enabled transcriptome-wide secondary structure map of zebrafish, which documents pairing probability of RNA at single nucleotide precision. Our findings open avenues for exploring structural features in zebrafish RNAs and their influence on gene expression.
The simplest definition of a disease is based on symptoms and the best definition of a disease is based on cause. Asthma is variously defined as a disorder of recurrent breathlessness and wheezing [1] and as a complex chronic inflammatory airway disease [2]. It is now mostly agreed upon that asthma is a heterogeneous clinical syndrome, which lacks singular pathophysiological explanation. Discovery of asthma endotypes—specific disease phenotype clusters, with a specific biological mechanism [3]—is a critical step towards personalized therapy. The discovery of such endotypes may proceed top down, from clinical phenotype to molecular signatures, or bottom up—from molecular signatures to clinical phenotypes. Studies reflecting the airway milieu, such as exhaled breath condensate (EBC) composition, appear to be a good place to start for a bottom up approach. A problem with EBC is that it is an unknown dilution of the airway lining fluid and while various suggestions have been made for normalization, none are reliable [4]. We previously reported the presence of a characteristic trident peak signature in nuclear magnetic resonance (NMR) of EBC found through visual inspection of the spectra. This peak signature at 7 parts per million (ppm), which was shown to be attributed to the concentration of ammonium ions in the airway milieu was absent in a majority of asthmatics while being present in healthy controls [5]. Many other informative patterns may exist in the NMR spectra but these are not obvious to the naked eye. Here, we considered the possibility that NMR signatures of EBC, taken together as a whole, rather than broken down into individual metabolites, could serve as a fingerprint for endotypes of asthma. While there have been initial studies about the local metabolomic patterns in the airway that could reflect the disease pathogenesis, these have been focused on identifying metabolites and comparing them [6,7,8]. Given the difficulties in compensating for variable dilutions and the limitations in accurately identifying all metabolites from mixed spectral signatures, we considered the possibility of directly using the global spectral pattern. This has the advantage of internal relative referencing of all peaks within a single spectrum, minimizing the impact of dilution. However, this has the disadvantage of creating a high-dimension dataset with likely strong internal correlations, requiring newer forms of statistical and computational analyses. Here, we show for the first time how global spectral signatures can be used to yield not only classifiers between asthma and healthy subjects, but also to discover clinically relevant metabolome clusters within asthma.
Bacterial dormancy is a major impediment to the eradication of tuberculosis (TB), because currently used drugs primarily target actively replicating bacteria. Therefore, decoding of the critical survival pathways in dormant tubercle bacilli is a research priority to formulate new approaches for killing these bacteria. Employing a network-based gene expression analysis approach, we demonstrate that redox active vitamin C (vit C) triggers a multifaceted and robust adaptation response in Mycobacterium tuberculosis (Mtb) involving ~ 67% of the genome. Vit C-adapted bacteria display well-described features of dormancy, including growth stasis and progression to a viable but non-culturable (VBNC) state, loss of acid-fastness and reduction in length, dissipation of reductive stress through triglyceride (TAG) accumulation, protective response to oxidative stress, and tolerance to first line TB drugs. VBNC bacteria are reactivatable upon removal of vit C and they recover drug susceptibility properties. Vit C synergizes with pyrazinamide, a unique TB drug with sterilizing activity, to kill dormant and replicating bacteria, negating any tolerance to rifampicin and isoniazid in combination treatment in both in-vitro and intracellular infection models. Finally, the vit C multi-stress redox models described here also offer a unique opportunity for concurrent screening of compounds/combinations active against heterogeneous subpopulations of Mtb. These findings suggest a novel strategy of vit C adjunctive therapy by modulating bacterial physiology for enhanced efficacy of combination chemotherapy with existing drugs, and also possible synergies to guide new therapeutic combinations towards accelerating TB treatment.
n Ayurveda system of medicine individuals are classified into seven constitution types, “Prakriti”, for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.
Knowledge of long-term outcomes following an index episode of acute severe colitis (ASC) can help informed decision-making at a time of acute exacerbation especially when colectomy is an option. We aimed to identify long-term outcomes and their predictors after a first episode of ASC in a large North Indian cohort. Methods: Hospitalized patients satisfying Truelove and Witts' criteria under follow-up at a single centre from January 2003-December 2013 were included. Patients avoiding colectomy at index admission were categorized as complete (≤3 non bloody stool per day) or incomplete responders (CR, IR), based upon response to corticosteroids at day 7. Random Forest based machine learning models were constructed to predict the long term risk of colectomy or steroid dependence following an index episode of ASC. Results: Of 1731 patients with ulcerative colitis, 179(10%) had an index episode of ASC. Nineteen(11%) patients underwent colectomy at index admission and 42(26%) over a median follow-up of 56(1-159) months. Hazard ratio for colectomy for IR was 3.6(1.7-7.5, p=0.001) compared to CR. Modeling based on four variables: response at day 7 of hospitalization, steroid use during first year of diagnosis, longer disease duration prior to ASC and number of extra-intestinal manifestations, was able to predict colectomy with an accuracy of 77%. Conclusions: Disease behavior of ASC in India is similar to the West, with a third undergoing colectomy at 10 years. Clinical features, especially response at day 7 hospitalization for index ASC, can predict both colectomy and steroid dependence with reasonable accuracy.
Smartphones combined with affordable technologies has a huge potential in Telemedicine and for better delivery of healthcare. We have used an affordable thermal camera combined with intensive patient monitoring and analytics to successfully enable digitization of whole body, non-contact sensing of temperature patterns. The utility of relative and absolute temperature patterns was tested for early diagnosis of various ailments such as circulatory shock. Image analytics combined with the development of computer algorithms enabled us to validate these patterns which can be deployed for remote community based care in the absence of adequate clinical staff and scarcity of resources. The use of this approach can help health workers such as ASHA workers to visualize, record and share whole body temperature patterns with the specialists via telemedicine helping them to make an informed decision. These techniques can be extended for use in adults and we plan to validate these for early detection of severe conditions eventually saving more lives through telemedicine.
The potential for whole body thermal patterns in diagnosis of hemodynamic perfusion disturbances in critical care as well as community settings is unexplored. In this study we have combined an in-house digitized Big-data resource from ICU settings with Infra-red thermography to derive novel inferences about the tele-diagnostic potential of IR thermography in diagnosis of shock and perfusion disturbances. While Data-science and Big-data are expected to revolutionize the next generation medicine and healthcare, the scientific efforts towards building Big-data resources for enhancing patient safety and healthcare governance are missing, especially in developing countries. We addressed this challenge and describe our experience on deployment of Big-data warehousing and data-analytics software using lean pipelines developed using open-source technologies and their utility in deriving knowledge and high utility patterns from Affordable Infrared Thermography. These knowledge frameworks and potentially translatable technology were developed in the Pediatric Intensive Care environment through extensive cross-talk between expert clinicians and data-scientists. In this work, we first demonstrate the successful creation of a unique Pediatric ICU resource of over 60,000 hours of continuous multivariate monitoring data followed by validation of the potential of whole body IR thermography in diagnosis of hemodynamic compromise. These patterns were validated through linear mixed models, a state-of-the-art statistical method for longitudinal data. The validated technology is affordable, and can be coupled to smartphones thus providing a huge potential in tele-medicine and electronic governance in healthcare and has the potential to be deployed in a tele-medicine setting with capturing of whole body temperature patterns by Accredited Social Health Activist (ASHA) workers. Therefore, this can enable early diagnosis of critical conditions such as sepsis and shock that are commonly associated with epidemics such as Dengue hemorrhagic fever in developing countries such as India. These images can be remotely shared with expert physicians and data-analysts via telemedicine thus aiding decisions in the Critical Care as well as Community settings.
Dengue virus, a mosquito-borne flavivirus, is a causative agent for dengue infection, which manifests with symptoms ranging from mild fever to fatal dengue shock syndrome. The presence of four serotypes, against which immune cross-protection is short-lived and serotype cross-reactive antibodies that might enhance infection, pose a challenge to further investigate the role of virus and immune response in pathogenesis. We evaluated the viral and immunological factors that correlate with severe dengue disease in a cohort of pediatric dengue patients in New Delhi. Severe dengue disease was observed in both primary and secondary infections. Viral load had no association with disease severity but high viral load correlated with prolonged thrombocytopenia and delayed recovery. Severe dengue cases had low Th1 cytokines and a concurrent increase in the inflammatory mediators such as IL-6, IL-8 and IL-10. A transient increase in CD14+CD16+ intermediate monocytes was observed early in infection. Sorting of monocytes from dengue patient peripheral blood mononuclear cells revealed that it is the CD14+ cells, but not the CD16+ or the T or B cells, that were infected with dengue virus and were major producers of IL-10. Using the Boruta algorithm, reduced interferon-α levels and enhanced aforementioned pro-inflammatory cytokines were identified as some of the distinctive markers of severe dengue. Furthermore, the reduction in the levels of IL-8 and IL-10 were identified as the most significant markers of recovery from severe disease. Our results provide further insights into the immune response of children to primary and secondary dengue infection and help us to understand the complex interplay between the intrinsic factors in dengue pathogenesis.
Tubercular lymphadenitis (TL) is the most common form of extra-pulmonary tuberculosis (TB) consisting about 15–20% of all TB cases. The currently available diagnostic modalities for (TL), are invasive and involve a high index of suspicion, having limited accuracy. We hypothesized that TL would have a distinct cytokine signature that would distinguish it from pulmonary TB (PTB), peripheral tubercular lymphadenopathy (LNTB), healthy controls (HC), other lymphadenopathies (LAP) and cancerous LAP. To assess this twelve cytokines (Tumor Necrosis Factor (TNF)—α, Interferon (IFN) -γ, Interleukin (IL)-2, IL-12, IL-18, IL-1β, IL-10, IL-6, IL-4, IL-1Receptor antagonist (IL-1Ra), IL-8 and TNF-β, which have a role in pathogenesis of tuberculosis, were tested as potential peripheral blood biomarkers to aid the diagnosis of TL when routine investigations prove to be of limited value.
India has one of the highest disease burdens in the world. A better understanding of what ails India will help policy makers plan appropriate health-care services and infrastructure development, design medical education curricula, and identify health research priorities that are relevant to the needs of the country. The POSEIDON study aimed to record the prevalence of symptoms and medical conditions for which patients visit a primary health-care practitioner in India.
Objective: To determine whether fractional exhaled nitric oxide (FENO) has a utility as a diagnostic or predictive maker in acute exacerbations of asthma in children. Design: Analysis of data collected in a pediatric asthma cohort. Setting: Pediatric Chest Clinic of a tertiary care hospital. Methods: A cohort of children with asthma was followed up every 3 months in addition to any acute exacerbation visits. Pulmonary function tests (PFT) and FENO were obtained at all visits. We compared the FENO values during acute exacerbations with those at baseline and those during the follow up. Results: 243 asthmatic children were enrolled from August 2009 to December 2011 [mean (SD) follow up — 434 (227) days]. FENO during acute exacerbations was not different from FENO during follow up; however, FENO was significantly higher than personal best FENO during follow up (P < 0.0001). FENO during acute exacerbation did not correlate with the severity of acute exacerbation (P=0.29). The receiver operating characteristics curve for FENO as a marker for acute exacerbation had an area under the curve of 0.59. Cut-off of 20 ppb had a poor sensitivity (44%) and specificity (68.7%) for acute exacerbation. Conclusions: FENO levels during acute exacerbation increase from their personal best levels. However, no particular cut off could be identified that could help in either diagnosing acute exacerbation or predicting its severity.
MicroRNAs (miRNAs) are small noncoding RNAs of 22 to 25 nucleotides in length that act through an RNA-induced silencing complex to posttranscriptionally regulate mRNAs that contain complementary sequences. Highly stable circulating miRNAs are found in biological fluids and are potential biomarkers.1, 2 These are often enclosed in small secretory membrane vesicles called exosomes, which permit transfer of miRNA between cells.3 Exhaled breath condensate (EBC), which can be obtained noninvasively and conveniently and is representative of the airway lining fluid, could be an ideal substrate for discovery of pulmonary disease biomarkers.4, 5, 6 For the first time, we report that miRNAs can be reliably detected in EBC by using quantitative PCR analysis and are suitable as biomarkers.
Dynamic variations in mitochondrial shape have been related to function. However, tools to automatically classify and enumerate mitochondrial shapes are lacking, as are systematic studies exploring the relationship of such shapes to mitochondrial stress. Here we show that during increased generation of mitochondrial reactive oxygen species (mtROS), mitochondria change their shape from tubular to donut or blob forms, which can be computationally quantified. Imaging of cells treated with rotenone or antimycin, showed time and dose-dependent conversion of tubular forms to donut-shaped mitochondria followed by appearance of blob forms. Time-lapse images showed reversible transitions from tubular to donut shapes and unidirectional transitions between donut and blob shapes. Blobs were the predominant sources of mtROS and appeared to be related to mitochondrial-calcium influx. Mitochondrial shape change could be prevented by either pretreatment with antioxidants like N-acetyl cysteine or inhibition of the mitochondrial calcium uniporter. This work represents a novel approach towards relating mitochondrial shape to function, through integration of cellular markers and a novel shape classification algorithm.
Exhaled breath condensate (EBC) holds promise as a noninvasive method of collecting airway-lining fluid, although at an unknown dilution [1]. While metabolomic studies of EBC using nuclear magnetic resonance (NMR) spectroscopy have previously shown promise in asthma diagnosis and subtyping [2, 3], a study that was recently published in the European Respiratory Journal that failed to find usable NMR signature in EBC collections from disposable systems [4]. This led the authors to conclude that NMR metabolomics lacks sufficient sensitivity for metabolic fingerprinting of EBC. Interestingly, they were able to obtain high quality results from the same samples with mass spectroscopy, which they recommended for future use. As this is a nascent and technically complex field, we present our very different early experiences, which suggest that reproducible, valid and useful NMR metabolomic fingerprinting of EBC is indeed possible. Specifically, we found that the presence or absence of a trident peak at 7 ppm during NMR spectroscopy reliably distinguished between EBC samples collected from normal and asthmatic subjects, respectively. This peak probably represents ammonium ion, loss of which in asthma is consistent with reduced expression of glutaminase, an enzyme that converts glutamine to glutamate and ammonia [5].
Inhaled therapies for pulmonary tuberculosis are in development and appear promising at first look. A fundamental premise of such therapy is efficient delivery of drug at high concentrations to the active disease site, while minimizing systemic delivery. This assumes that inhaled drug will actually reach the diseased lung, which while intuitive for healthy lungs, may be untrue for diseased lungs with abnormal structure or function. This review discusses the structural and functional aspects of respiratory physiology that are likely to impact local drug delivery and presents the available evidence on how this pertains to tuberculous lungs.
Ayurveda is an ancient system of personalized medicine documented and practiced in India since 1500 B.C. According to this system an individual's basic constitution to a large extent determines predisposition and prognosis to diseases as well as therapy and life-style regime. Ayurveda describes seven broad constitution types (Prakriti s) each with a varying degree of predisposition to different diseases. Amongst these, three most contrasting types, Vata, Pitta, Kapha, are the most vulnerable to diseases. In the realm of modern predictive medicine, efforts are being directed towards capturing disease phenotypes with greater precision for successful identification of markers for prospective disease conditions. In this study, we explore whether the different constitution types as described in Ayurveda has molecular correlates.
Analyses of frequency profiles of markers on disease or drug-response related genes in diverse populations are important for the dissection of common diseases. We report the results of analyses of data on 405 SNPs from 75 such genes and a 5.2 Mb chromosome, 22 genomic region in 1871 individuals from diverse 55 endogamous Indian populations. These include 32 large (>10 million individuals) and 23 isolated populations, representing a large fraction of the people of India. We observe high levels of genetic divergence between groups of populations that cluster largely on the basis of ethnicity and language. Indian populations not only overlap with the diversity of HapMap populations, but also contain population groups that are genetically distinct. These data and results are useful for addressing stratification and study design issues in complex traits especially for heterogeneous populations.
