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The EndoRings device is a distal attachment consisting of two layers of circular flexible rings that evert mucosal folds. The aim of this study was to investigate whether EndoRing assisted colonoscopy (ER) improves polyp and adenoma detection compared to standard colonoscopy (SC).Multicenter, parallel group, randomized controlled trial.Total of 556 patients randomized to ER (n = 275) or SC (n = 281). Colonoscopy completed in 532/556 (96%) cases. EndoRings removed in 74/275 (27%) patients. Total number of polyps in ER limb 582 vs. 515 in SC limb, P = 0.04. Total number of adenomas in ER limb 361 vs. 343 for SC limb, P = 0.49. A statistically significant difference in the mean number of polyps per patient in both the intention to treat (1.84 SC vs. 2.10 ER, P = 0.027) and per protocol (PP) (1.84 SC vs. 2.25 ER, P = 0.004).Our study shows promise for the EndoRings device to improve polyp detection.

The discovery of cancer drivers and drug targets are often limited to the biological systems - from cancer model systems to patients. While multiomic patient databases have sparse drug response data, cancer model systems databases, despite covering a broad range of pharmacogenomic platforms, provide lower lineage-specific sample sizes, resulting in reduced statistical power to detect both functional driver genes and their associations with drug sensitivity profiles. Hence, integrating evidence across model systems, taking into account the pros and cons of each system, in addition to multiomic integration, can more efficiently deconvolve cellular mechanisms of cancer as well as learn therapeutic associations. To this end, we propose BaySyn - a hierarchical Bayesian evidence synthesis framework for multi-system multiomic integration. BaySyn detects functionally relevant driver genes based on their associations with upstream regulators using additive Gaussian process models and uses this evidence to calibrate Bayesian variable selection models in the (drug) outcome layer. We apply BaySyn to multiomic cancer cell line and patient datasets from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas, respectively, across pan-gynecological cancers. Our mechanistic models implicate several relevant functional genes across cancers such as PTPN6 and ERBB2 in the KEGG adherens junction gene set. Furthermore, our outcome model is able to make higher number of discoveries in drug response models than its uncalibrated counterparts under the same thresholds of Type I error control, including detection of known lineage-specific biomarker associations such as BCL11A in breast and FGFRL1 in ovarian cancers. All our results and implementation codes are freely available via an interactive R Shiny dashboard.

India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.

Summary Background Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. Methods In this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlation-based feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals. Findings The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlation-based feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84–0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83–0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74–0·84; sensitivity set at 90%; specificity of 58%). Interpretation Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients with symptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care. Funding Charles Wolfson Charitable Trust and Guts UK.

Introduction The outbreak of COVID-19 has differentially affected countries in the world, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of the COVID-19 spread. Vulnerability of a geographical region/country to COVID-19 has been a topic of interest, particularly in low- and middle-income countries like India to assess the multi-factorial impact of COVID-19 on the incidence, prevalence or mortality data. Datasets and Methods Based on publicly reported socio-economic, demographic, health-based and epidemiological data from national surveys in India, we compute contextual, COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These multi-resolution cVIs were used in regression models to assess their impact on indicators of the spread of COVID-19 such as the average time-varying instantaneous reproduction number. Results Our observational study was focused on 30 districts of the eastern Indian state of Odisha. It is an agrarian state, prone to natural disasters and one of the largest contributors of an unprotected migrant workforce. Our analyses identified housing and hygiene conditions, availability of health care and COVID preparedness as important spatial indicators. Conclusion Odisha has demonstrated success in containing the COVID-19 infection to a reasonable level with proactive measures to contain the spread of the virus during the first wave. However, with the onset of the second wave of COVID, the virus has been making inroads into the hinterlands and peripheral districts of the state, burdening the already deficient public health system in these areas. The vulnerability index presented in this paper identified vulnerable districts in Odisha. While some of them may not have a large number of COVID-19 cases at a given point of time, they could experience repercussions of the pandemic. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions leading to more effective mitigation strategies for the COVID-19 pandemic and beyond. WHAT IS ALREADY KNOWN Measuring vulnerability to COVID-19 and other pandemics is a complex and layered subject. In Low-to-Middle-Income Country (LMIC) like India, complete reliance on incidence, prevalence or mortality data of the disease may not be the best measure since this data from the health system and DHS in public domain is limited. ADDED VALUE OF THIS STUDY To our knowledge, this is the first study at the district level concerning the COVID-19 situation in Odisha, characterized by a large tribal and migrant population. We defined vulnerability through relevant socio-economic domains that have an influence on mitigation strategies. Although we applied our methods to the districts of Odisha, we believe they can be used in other LMIC regions. IMPLICATIONS OF THE FINDINGS Regions with higher overall or theme-specific vulnerability index might experience potentially severe consequences of the COVID-19 outbreak demanding precise, dynamic and nimble policy decisions to prevent a potentially dire situation.

India has seen a surge of SARS-CoV-2 infections and deaths in early part of 2021, despite having controlled the epidemic during 2020. Building on a two-strain, semi-mechanistic model that synthesizes mortality and genomic data, we find evidence that altered epidemiological properties of B.1.617.2 (Delta) variant play an important role in this resurgence in India. Under all scenarios of immune evasion, we find an increased transmissibility advantage for B.1617.2 against all previously circulating strains. Using an extended SIR model accounting for reinfections and wanning immunity, we produce evidence in support of how early public interventions in March 2021 would have helped to control transmission in the country. We argue that enhanced genomic surveillance along with constant assessment of risk associated with increased transmission is critical for pandemic responsiveness.One Sentence SummaryAltered epidemiological characteristics of B.1.617.2 and delayed public health interventions contributed to the resurgence of SARS-CoV-2 in India from February to May 2021.

Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15–June 30, 2020, we estimate the underreporting factor for cases at 34–53 (deaths: 8–13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27–July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30–42 for cases. Together, these imply approximately 96–98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13–22 (deaths: 3–7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15–23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17–21. Together, these updated estimates imply approximately 92–96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

Modeling the dynamics of COVID-19 pandemic spread is a challenging and relevant problem. Established models for the epidemic spread such as compartmental epidemiological models e.g. Susceptible-Infected-Recovered (SIR) models and its variants, have been discussed extensively in the literature and utilized to forecast the growth of the pandemic across different hot-spots in the world. The standard formulations of SIR models rely upon summary-level data, which may not be able to fully capture the complete dynamics of the pandemic growth. Since the disease spreads from carriers to susceptible individuals via some form of contact, it inherently relies upon a network of individuals for its growth, with edges established via direct interaction, such as shared physical proximity. Using individual-level COVID-19 data from the early days (January 30 to April 15, 2020) of the pandemic in India, and under a network-based SIR model framework, we performed state-specific forecasting under multiple scenarios characterized by the basic reproduction number of COVID-19 across 34 Indian states and union territories. We validated our short-term projections using observed case counts and the long-term projections using national sero-survey findings. Based on healthcare availability data, we also performed projections to assess the burdens on the infrastructure along the spectrum of the pandemic growth. We have developed an interactive dashboard summarizing our results. Our predictions successfully identified the initial hot-spots of India such as Maharashtra and Delhi, and those that emerged later, such as Madhya Pradesh and Kerala. These models have the potential to inform appropriate policies for isolation and mitigation strategies to contain the pandemic, through a phased approach by appropriate resource prioritization and allocation.

Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15-June 30, 2020, we estimate the underreporting factor for cases at 34-53 (deaths: 8-13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27-July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30-42 for cases. Together, these imply approximately 96-98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13-22 (deaths: 3-7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15-23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17-21. Together, these updated estimates imply approximately 92-96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures and other non-pharmaceutical interventions. We compare five epidemiological models for forecasting and assessing the course of the pandemic. We compare how the models analyze case-recovery-death count data in India, the country with second highest reported case-counts in a world where a large proportion of infections remain undetected. A baseline curve-fitting model is introduced, in addition to three compartmental models: an extended SIR (eSIR) model, an expanded SEIR model developed to account for infectiousness of asymptomatic and pre-symptomatic cases (SAPHIRE), another SEIR model to handle high false negative rate and symptom-based administration of tests (SEIR-fansy). A semi-mechanistic Bayesian hierarchical model developed at the Imperial College London (ICM) is also examined. Using COVID-19 data for India from March 15 to June 18 to train the models, we generate predictions from each of the five models from June 19 to July 18. To compare prediction accuracy with respect to reported cumulative and active case counts and cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) and mean squared relative prediction error (MSRPE) for each of the five models. For active case counts, SEIR-fansy yields an SMAPE value of 0.72, and the eSIR model yields a value of 33.83. For cumulative case counts, SMAPE values are 1.76 for baseline model, 23.10 for eSIR, 2.07 for SAPHIRE and 3.20 for SEIR-fansy. For cumulative death counts, the SEIR-fansy model performs the best, with an SMAPE of 7.13, as compared to 26.30 for the eSIR model. Using Pearson correlation coefficient and Lin concordance correlation coefficient, for cumulative case counts, the baseline model exhibits highest correlation (both Pearson as well as Lin coefficients), while for cumulative death counts, projections from SEIR-fansy exhibit the best performance: For cumulative cases, correlation coefficients computed for the baseline model are 1 (Pearson) and 0.991 (Lin). For eSIR, those values are 0.985 (Pearson) and 0.316 (Lin). For SAPHIRE, we compute 1 (Pearson) and 0.975 (Lin). Finally, for SEIR-fansy we have those values at 1 (Pearson) and 0.965 (Lin). Similarly, for cumulative deaths, correlation coefficients computed for eSIR is 0.978 (Pearson) and 0.206 (Lin), and for SEIR-fansy we have those values at 0.999 (Pearson) and 0.742 (Lin). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) counts as well. We compute underreporting factors on two specific dates (June 30 and July 10) and note that on both dates, the SEIR-fansy model reports the highest underreporting factor for active cases (June 30: 6.10 and July 10: 6.24) and cumulative deaths (June 30: 3.62 and July 10: 3.99) for both dates, while the SAPHIRE model reports the highest underreporting factor for cumulative cases (June 30: 27.79 and July 10: 26.74).

Lately there has been an increasing interest in learning object affordances. This is particularly to address human activity understanding and intention recognition for household robots. However, most existing approaches do not concentrate on imbibing new perceptual models of inferring affordances from visual input. Such models are key for object usage by household robots. Towards this goal, this paper introduces a knowledge based approach to inferring semantic object affordances. This is achieved by integrating ontology and qualitative reasoning within a statistical relational learning scheme. Encouraging results are obtained for the CAD-120 dataset consisting of indoor household activity videos involving human-object interactions.

ObjectivesTo evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.DesignCohort study (daily time series of case counts).SettingObservational and population based.ParticipantsConfirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.ExposureLockdown (non-medical intervention).Main outcomes and measuresWe illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.ResultsThe estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19–25 March) to 113 372 (25–31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.ConclusionsPatterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.

BackgroundMany popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM).MethodsUsing COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson’s and Lin’s correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model.ResultsFor active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63–8.80), while eSIR yields 8.35 (7.19–9.60), SAPHIRE returns 8.17 (7.90–8.52) and SEIR-fansy projects 8.51 (8.18–8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy.ConclusionsIn this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the “total” number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

Underreporting of COVID-19 cases and deaths is a hindrance to correctly modeling and monitoring the pandemic. This is primarily due to limited testing, lack of reporting infrastructure and a large number of asymptomatic infections. In addition, diagnostic tests (RT-PCR tests for detecting current infection) and serological antibody tests for IgG (to assess past infections) are imperfect. In particular, the diagnostic tests have a high false negative rate. Epidemiologic models with a latent compartment for unascertained infections like the Susceptible-Exposed-Infected-Removed (SEIR) models can provide predictions for unreported cases and deaths under certain assumptions. Typically, the number of unascertained cases is unobserved and thus we cannot validate these estimates for a real study except for simulation studies. Population-based seroprevalence studies can provide a rough estimate of the total number of infections and help us check epidemiologic model projections. In this paper, we develop a method to account for high false negative rates in RT-PCR in an extension to the classic SEIR model. We apply this method to Delhi, the national capital region of India, with a population of 19.8 million and a COVID-19 hotspot of the country, obtaining estimates of underreporting factor for cases at 34-53 times and that for deaths at 8-13 times. Based on a recently released serological survey for Delhi with an estimated 22.86% seroprevalence, we compute adjusted estimates of the true number of infections reported by the survey (after accounting for misclassification of the antibody test results) which is largely consistent with the model outputs, yielding an underreporting factor for cases from 30-42. Together with the model and the serosurvey, this implies approximately 96-98% cases in Delhi remained unreported and whereas only 109,140 cases were reported on July 10, the true number of infections varied somewhere between 4.4-4.6 million across different estimates. While repeated serological monitoring is resource intensive, model-based adjustments, run with the most up to date data, can provide a viable option to keep track of the unreported cases and deaths and gauge the true extent of transmission of this insidious virus.

With only 536 COVID-19 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25, 2020. The lockdown was first extended to May 3 soon after the analysis of this article was completed, and then to May 18 while this article was being revised. In this article, we use a Bayesian extension of the susceptible-infected-removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other, less severe nonpharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its health care and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown from 42–56 days is preferable to substantially ‘flatten the curve’ when compared to 21–28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and thus inform policymakers about one aspect of this multifaceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods, and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.

IntroductionIndia has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation.MethodsUsing data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, we illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case-fatality rates, doubling times of cases, effective reproduction numbers, and the scale of testing.ResultsThe estimated effective reproduction number R for India was 3.36 (95% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns.ConclusionsPatterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualization tools that are daily updated at covind19.org.

PURPOSE Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer. METHODS We developed TransPRECISE (personalized cancer-specific integrated network estimation model), a multiscale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intrapathway activities, to globally assess cell lines as representative models for patients, and to develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (> 7,700) from the Cancer Proteome Atlas across ≥ 30 tumor types, a set of 640 cancer cell lines from the MD Anderson Cell Lines Project spanning 16 lineages, and ≥ 250 cell lines’ response to > 400 drugs. RESULTS TransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: patients with head and neck tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines and had different prognostic patterns (456 v 654 days of median overall survival; P = .02). High predictive accuracy was observed for drug sensitivities in cell lines across multiple drugs (median area under the receiver operating characteristic curve > 0.8) using Bayesian additive regression tree models with TransPRECISE pathway scores. CONCLUSION Our study provides a generalizable analytic framework to assess the translational potential of preclinical model systems and to guide pathway-based personalized medical decision making, integrating genomic and molecular data across model systems.

ImportanceIndia has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India – a democracy of 1.34 billion people – took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3rd, soon after the analysis of this paper was completed.ObjectiveTo study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 cases in India compared to other less severe non-pharmaceutical interventions using epidemiological forecasting models and Bayesian estimation algorithms; to compare effects of hypothetical durations of lockdown from an epidemiological perspective; to study alternative explanations for slower growth rate of the virus outbreak in India, including exploring the association of the number of cases and average monthly temperature; and finally, to outline the pivotal role of reliable and transparent data, reproducible data science methods, tools and products as we reopen the country and prepare for a post lock-down phase of the pandemic.Design, Setting, and ParticipantsWe use the daily data on the number of COVID-19 cases, of recovered and of deaths from March 1 until April 7, 2020 from the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Additionally, we use COVID-19 incidence counts data from Kaggle and the monthly average temperature of major cities across the world from Wikipedia.Main Outcome and MeasuresThe current time-series data on daily proportions of cases and removed (recovered and death combined) from India are analyzed using an extended version of the standard SIR (susceptible, infected, and removed) model. The eSIR model incorporates time-varying transmission rates that help us predict the effect of lockdown compared to other hypothetical interventions on the number of cases at future time points. A Markov Chain Monte Carlo implementation of this model provided predicted proportions of the cases at future time points along with credible intervals (CI).ResultsOur predicted cumulative number of COVID-19 cases in India on April 30 assuming a 1-week delay in people’s adherence to a 21-day lockdown (March 25 – April 14) and a gradual, moderate resumption of daily activities after April 14 is 9,181 with upper 95% CI of 72,245. In comparison, the predicted cumulative number of cases under “no intervention” and “social distancing and travel bans without lockdown” are 358 thousand and 46 thousand (upper 95% CI of nearly 2.3 million and 0.3 million) respectively. An effective lockdown can prevent roughly 343 thousand (upper 95% CI 1.8 million) and 2.4 million (upper 95% CI 38.4 million) COVID-19 cases nationwide compared to social distancing alone by May 15 and June 15, respectively. When comparing a 21-day lockdown with a hypothetical lockdown of longer duration, we find that 28-, 42-, and 56-day lockdowns can approximately prevent 238 thousand (upper 95% CI 2.3 million), 622 thousand (upper 95% CI 4.3 million), 781 thousand (upper 95% CI 4.6 million) cases by June 15, respectively. We find some suggestive evidence that the COVID-19 incidence rates worldwide are negatively associated with temperature in a crude unadjusted analysis with Pearson correlation estimates [95% confidence interval] between average monthly temperature and total monthly incidence around the world being −0.185 [−0.548, 0.236] for January, −0.110 [−0.362, 0.157] for February, and −0.173 [−0.314, −0.026] for March.Conclusions and RelevanceThe lockdown, if implemented correctly in the end, has a high chance of reducing the total number of COVID-19 cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for the best outcome. We cannot heavily rely on the hypothetical prevention governed by meteorological factors such as temperature based on current evidence. From an epidemiological perspective, a longer lockdown between 42-56 days is preferable. However, the lockdown comes at a tremendous price to social and economic health through a contagion process not dissimilar to that of the coronavirus itself. Data can play a defining role as we design post-lockdown testing, reopening and resource allocation strategies.SoftwareOur contribution to data science includes an interactive and dynamic app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. Anyone can visualize the observed data for India and create predictions under hypothetical scenarios with quantification of uncertainties. We make our prediction codes freely available (https://github.com/umich-cphds/cov-ind-19) for reproducible science and for other COVID-19 affected countries to use them for their prediction and data visualization work.

Introduction The EndoringsTM is a distal attachment consisting of two layers of circular flexible rings that evert mucosal folds. Aims; to investigate if Endorings Colonoscopy (ER) improves polyp and adenoma detection compared to standard colonoscopy (SC). Methods This multi-centre, parallel group, randomized controlled trial included screening, surveillance and symptomatic patients. Primary outcome; number of polyps per patient. Secondary outcomes; number of adenomas per patient, adenoma/polyp detection rates and withdrawal times. Results Total of 556 patients (214 females, 342 males) randomized to ER (275) or SC (281). Mean age 67. Colonoscopy completed 532/556 (96%) cases. EndoRings removed in 74/275 (27%) patients. In 66/74 (89%) cases removal was performed due to difficulties with sigmoid intubation. Remainder removed to facilitate retroflexion or polyp removal/retrieval. Total number of polyps in ER limb 571 vs 444 in SC limb. Total number of adenomas in ER limb 361 vs 343 for SC limb. Our study showed a statistically significant difference in the mean number of polyps per patient in both the Intention to Treat (ITT) (1.8 SC vs 2.1 ER, p-value 0.02) and Per Protocol (PP) (1.8 SC vs 2.25 ER, p-value 0.009). There was a trend towards a greater polyp detection rate in the ER colonoscopy (67.5% SC vs 75.2% ER, p-value 0.05). Conclusions Despite the high removal rate of Endorings, there was a statistically significant increase in the mean number of polyps in the ER limb compared to the SC limb. Our study shows promise for the EndoRingsTM device to improve polyp detection.

Background and study aims: There have been significant advances in the management of complex colorectal polyps. Previous failed resection or polyp recurrence is associated with significant fibrosis, making endoscopic resection extremely challenging; the traditional approach to these lesions is surgery. The aim of this study was to evaluate the efficacy of a novel, knife-assisted snare resection (KAR) technique in the resection of scarred colonic polyps. Patients and methods: This was a prospective cohort study of patients, in whom the KAR technique was used to resect scarred colonic polyps > 2 cm in size. Patients had previously undergone endoscopic mucosal resection (EMR) and developed recurrence, or EMR had been attempted but was aborted as a result of technical difficulty. Results: A total of 42 patients underwent KAR of large (median 40 mm) scarred polyps. Surgery for benign disease was avoided in 38 of 41 patients (93 %). No life-threatening complications occurred. Recurrence was seen in six patients (16 %), five of whom underwent further endoscopic resection. The overall cure rate for KAR in complex scarred colonic polyps was 90 %. Conclusions: KAR of scarred colonic polyps by an expert endoscopist was an effective and safe technique with low recurrence rates.

For a collaborative assistive device, human intent recognition (IR) is one of the first and foremost requirements. Formalizing the complex process of human IR in a compact yet expressive mathematical model holds promise. We put forward a Hierarchical Finite State Machine (H-FSM) for human IR within a generalized framework for collaborative assistive devices. Visual and contextual observations drive the H-FSM to different levels of granularity.

Background and study aims Up to 25 % colorectal adenomas are missed during colonoscopy. The aim of this study was to investigate whether the endocuff could improve polyp detection in an organized bowel cancer screening program (BCSP). Patients and methods This parallel group, single-blinded, randomized controlled trial included patients with positive fecal occult blood test (FOBT) who were attending for BCSP colonoscopy. The primary outcome was the number of polyps per patient. Secondary outcomes included the number of adenomas per patient, adenoma and polyp detection rates, and withdrawal times. Results A total of 534 BCSP patients were randomized to endocuff-assisted or standard colonoscopy. The mean age was 67 years and the male to female ratio was 1.8:1. We detected no significant difference in the number of polyps per patient (standard 1.8, endocuff 1.6; P = 0.44), adenomas per patient (standard 1.4, endocuff 1.3; P = 0.54), polyp detection rate (standard 69.8 %, endocuff 70.3 %; P = 0.93), adenoma detection rate (standard 63.0 %, endocuff 60.9 %; P = 0.85), advanced adenoma detection rate (standard 18.5 %, endocuff 16.9 %; P = 0.81), and cancer detection rate (standard 5.7 %, endocuff 5.3 %; P = 0.85). The mean withdrawal time was significantly shorter among patients in the endocuff group compared with the standard colonoscopy group (16.9 vs. 19.5 minutes; P Conclusions This study did not find improved polyp or adenoma detection with endocuff-assisted colonoscopy in the FOBT-positive BCSP population. A shorter withdrawal time with endocuff may reflect improved views and stability provided by the endocuff.Trial registered at ClinicalTrials.gov (NCT02529007).

Object affordances provide useful information related to understanding of human activities. The aim of this paper is to create an ontology for object affordance reasoning that can be shared across different assistive robots operating within the household domain. A novel ontology called O-PrO (Object Property Ontology) consisting of 61 household objects is presented. The ontology can be used for computing cognitive and semantic object affordances.

Background and study aims - Mucosal views can be impaired by residual bubbles and mucus during gastroscopy. This study aimed to determine whether a pre-gastroscopy drink containing simethicone and N-acetylcysteine improves mucosal visualisation.Patients and methods - We conducted a randomized controlled trial recruiting 126 subjects undergoing routine gastroscopy. Subjects were randomized 1:1:1 to receive: A—pre-procedure drink of water, simethicone and N-acetylcysteine (NAC); B—water alone; or C—no preparation. Study endoscopists were blinded to group allocation. Digital images were taken at 4 locations (lower esophagus/upper gastric body/antrum/fundus), and rated for mucosal visibility (MV) using a 4-point scale (1 = best, 4 = worst) by 4 separate experienced endoscopists. The primary outcome measure was mean mucosal visibility score (MVS). Secondary outcome measures were procedure duration and volume of fluid flush required to achieve adequate mucosal views.Results - Mean MVS for Group A was significantly better than for Group B (1.35 vs 2.11, P Conclusions - A pre-procedure drink containing simethicone and NAC significantly improves mucosal visibility during gastroscopy and reduces the need for flushes during the procedure. Effectiveness in the lower esophagus demonstrates potential benefit in Barrett’s oesophagus surveillance gastroscopy.

Endoscopic mucosal resection (EMR) is widely practiced in western countries. Endoscopic submucosal dissection (ESD) is very effective for treating complex polyps but colonic ESD in the western setting remains challenging. We have developed a novel technique of knife-assisted snare resection (KAR) for the resection of these complex lesions. Here we aim to describe the technique, evaluate its outcomes, identify outcome predictors and define its learning curve.We conducted a prospective cohort study of patients who had large and refractory polyps resected by KAR at our institution from 2007 to 2013. Polyp characteristics and procedure details were recorded. Endoscopic follow-up was performed to identify recurrence.A total of 170 patients with polyps 20-170 mm in size were treated by KAR and followed up for a mean of 31.5 months (range 12-84 months). 29% of the polyps were50 mm, 22% had fibrosis from previous unsuccessful interventions and 25% were in the right colon. The perforation rate (1.2%) and bleeding rate (4.7%) were acceptable and managed conservatively in most patients. Recurrence rate after the first attempt was 13.1%. Recurrence was significantly increased by polyp size50 mm (p = 0.008; OR 5.03, 95% CI 1.54-16.48), presence of fibrosis (p = 0.002; OR 6.59, 95% CI 1.97-22.07) and piecemeal resection (p 0.001; OR 0.31, CI 0.078-1.12). Cure rates were 87% after the first attempt, improving to 95.6% with further attempts. En bloc resection rate showed a linear increase and reached almost 80% as the endoscopist's cumulative experience approached 100 cases.This is the largest reported Western series on KAR in the colon. We have demonstrated the feasibility, efficacy and safety of this technique in the treatment of complex polyps, with or without fibrosis and at all sites. KAR has shown better outcomes than either EMR or ESD. We have also managed to identify significant outcome predictors and define the learning curve.

A 54-year-old man presented to our institution with severe dysphagia. Gastroscopy revealed a 10-mm submucosal lesion, with a blue tinge, in the esophagus, 30 cm from the incisors (Fig. 1A). An EUS demonstrated a 10-mm, predominantly fat, hypoechoic, submucosal lesion extending to, but not infiltrating, the muscularis propria. Color Doppler did not identify blood flow through the lesion (Fig. 1B). Endoscopic follow-up was recommended. During follow-up the patient described having progressive dysphagia and odynophagia. Management options of surgery, endoscopic resection, and endoscopic follow-up were discussed. Endoscopic submucosal dissection (ESD) was performed to resect the lesion (Video 1, available online at www.giejournal. org). The procedure was successfully completed without any significant adverse events. The resection specimen showed benign squamous mucosa with an ectatic blood vessel, in keeping with a benign cavernous hemangioma. At follow-up, the patient’s symptoms have improved. The use of ESD of vascular lesions in the esophagus is high risk. However, if the correct submucosal plane is identified, large blood vessels can be avoided. In this case, it became apparent to us at a very early stage that it was a vascular lesion, so dissection was performed in the deeper planes. This case demonstrates the successful treatment of an esophageal submucosal cavernous hemangioma by ESD, avoiding the morbidity associated with esophagectomy.

Introduction One of the factors which make the removal of a polyp challenging is its location in the GI tract. We will present a series of video clips of polyps located in difficult locations to demonstrate the challenges facing the endoscopist and discuss the strategies to circumvent the problem. Three types of polyps are discussed in this abstract: 1. Polyps involving the dentate line 2. Polyps involving the ileo-caecal valve and terminal ileum and 3. Polyps involving the appendiceal orifice. Method Information regarding the nature, location, procedure, recurrence, complication and need for surgery was obtained from a prospective database of polyps more than 2 cm removed between 2010 and 2014. Results 1. Polyp involving the dentate line: Access to these polyps is very difficult. A rich sensory supply of the distal squamous epithelium, difficulty in identifying the distal edge of the polyp and the rich vascular supply in this region makes it challenging. Twelve cases [n = 12] underwent endoscopic resection. The size ranged from 12 mm–150 mm occupying 25% to 80% of the circumference. Laterally spreading tumour –Granular [n = 10] and two were sessile [1s and 1s/2c, n = 2]. 1. Polyp involving the ileo –caecal valve. Here the challenges include difficult access, identifying the margins, ileal extension and risk of perforation. Seven polyps, 30 mm–60 mm in size and involving the ileo- caecal valves were removed endoscopically. LST –G [5], 1s [1] and SSA [1]. 1. Polyp involving the appendicular orifice Decision to resect these polyps should be weighed against the risk of perforation since these polyps could extend into the appendicular canal. Nine cases of polyp involving the appendicular orifice were resected endoscopically with size ranging from 20 mm -100 mm. Six were LST –G[[7]sessile polyp [2]. Histology of the resected polyps was as follows. TVA with LGD [21], TVA with foci of HGD [2], sessile serrated adenoma [4], tubular adenoma with intra mucosal cancer [1], Tubular adenoma [1]. Conclusion Polyps located at the appendicular orifice and ileo -caecal valve have traditionally been managed with right hemicolectomy and polyps at the dentate line with TAR/TEMS. Our data shows that an expert endoscopist can resect these polyps with good clearance and avoid surgery. This makes a case for referral of these polyps to an expert centre. Disclosure of interest None Declared.