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Background: The approval of atezolizumab + bevacizumab for untreated advanced HCC was a significant benefit for patients, but with an increased risk of potentially severe bleeding complications. Tivozanib (a selective VEGFR 1, 2, & 3 tyrosine kinase inhibitor [TKI]) has been combined with durvalumab in the DEDUCTIVE study;preliminary results presented in January 2022 showed that this combination was well tolerated with comparable efficacy to other immune checkpoint and VEGF containing regimens in patients with previously untreated HCC (J Clin Oncol 40, no. 4 suppl 462-462). We now report the final results of this cohort (cohort A) of patients as well as those with previously treated HCC, including safety results for all the patients. Method(s): Major eligibility criteria included age .18 yrs with documented advanced HCC, Child-Pugh Class A, ECOG 0 or 1, and creatinine clearance .40 ml/min. Major exclusion criteria included co-infection with HBV and HCV and significant organ dysfunction. Patients were treated with 0.89 mg tivozanib p.o., 21 days on followed by 7 days off and 1500 mg durvalumab i.v. every 28 days. The primary objective was to determine the safety and tolerability of this combination in patients with advanced HCC;secondary objectives included assessing objective response rate (ORR), progression free survival, and overall survival (OS). The study was amended in 2021 to include a cohort of patients previously treated with atezolizumab and bevacizumab (cohort B). Result(s): 21 patients were enrolled in cohort A and 6 in cohort B;the median age was 67, 88% of patients were male, and 24% were Asian. The median followup time was 13.2 mos and 3.4 mos for cohorts A and B, respectively. Data were available for 25 of the 27 patients enrolled. For cohort A, the ORR was 25% (5/20) and 1-year OS was 76%. For safety analysis, 24 (96%) patients had at least 1 treatment-emergent adverse event (TEAE);92% were attributed possibly to either tivozanib or durvalumab;32% were serious TEAEs and there was 1 TEAE leading to death (unrelated). Of the 8 (32%) serious TEAE, 2 were coronavirus infection. 2 patients had serious (grade 3) treatment-related AEs: 1 pneumonitis and 1 with gastrointestinal hemorrhage and anemia. There were no grade 4 or 5 treatment-related AEs. Conclusion(s): Treatment with the combination of tivozanib and durvalumab in patients with either untreated advanced HCC or those previously treated with atezolizumab and bevacizumab was well tolerated;no severe bleeding events occurred in this study. Efficacy outcomes were comparable to other IO-TKI combinations in HCC. Data for PDL1 status, HBV/HCV status was collected and will be presented along with final safety and efficacy results for both cohorts.
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This paper presents a factor graph-based model that takes comorbidities and clinical measurements as inputs and predicts intensive care unit (ICU) admissions 3 days and 7 days in advance for hospitalized COVID-19 patients. We applied the proposed model on a COVID-19 cohort from a large medical center in Chicago (with records from March 2020 to August 2021). We used the first occurrence of the Delta variant in the U.S., February 2021, as the threshold to divide the dataset into pre-Delta data (533 patients) and post-Delta data (56 patients). Our model demonstrated 0.82 AUC on the pre-Delta data and 0.87 AUC on the post-Delta data in 7-day predictions. Our contribution is a model that (i) explains relationships between different clinical features and provides interpretations for ICU admissions, (ii) outperforms existing methods for 7-day predictions, and (iii) maintains more robustness than existing models in predictions under the influence of the Delta variant. The proposed model could be used as a predictive tool in clinical practice to help clinicians in decision-making by predicting which patients will need ICU support in the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This white paper summarizes the key outcomes, topics, and recommendations from the Canada-India Healthcare Summit 2021 Conference, Biotechnology Session, held on May 20-21, 2021. In particular, the authors have focused their attention on topics ranging from research and development into the etiology and treatment of COVID-19 to novel approaches, such as ultraviolet-C disinfection and cell and gene therapy. The paper also deals with important topics around the effects of food distribution and nutrition on COVID-19 and vice versa, as well as key considerations around research and development, innovation, policy, grants, and incentives, and finally, summarizes the ways in which Canada and India, being close allies, have already begun to partner to fight the pandemic (as well as future strategies to continue this excellent progress). We also include key points raised during the summit and summarize them as part of this white paper.
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Closed-loop, analytical solution of Linear, algebraic equation containing many unknown variables are found in many mathematical modeling equations and network analysis involved in healthcare and neuroscience research. Finding unique, analytical solution for linear, algebraic equations has diverse application in many fields. Computation of unique, non-trivial solutions to large array containing several variables is a computationally overwhelming task. The paper begins by introducing the concept of network analysis in modelling for healthcare and neuroscience research. A simple example of network modelling is the logistics of delivering vaccine like COVID19 from a company to Primary health center while maintaining cold-storage of the vaccine. This is followed by explanation of the working of computationally efficient, best-practice LU Decomposition with partial pivoting algorithm to solve dense linear equations. This is followed by narration of building, testing, obfuscation, compiling and release of an Android Graphic user interface application implementing the above methods. The final part of the paper examines the exceptional accuracy and efficiency of solving, dense matrix equations on Android Run Time machine using this approach. The calculated Poisson modelling of probability of Stochastic, Singularity event is = 3. 33 × 33−9per floating point operation a number derived after running 33, 333, 333, 333 floating-point operation runs. The mean execution time was 88.534 seconds, for solving matrix equation [A]withN=60 variables in performing N9=216000 computations. The whole working Android application containing many other tools is hosted on the GitHub and Figshare platforms along with additional graphs, dataset, Java, and Python programs used to complete this study.
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There is a growing need for spatial privacy considerations in the many geo-spatial technologies that have been created as solutions for COVID-19-related issues. Although effective geo-spatial technologies have already been rolled out, most have significantly sacrificed privacy for utility. In this paper, we explore spatial k-anonymity, a privacy-preserving method that can address this unnecessary tradeoff by providing the best of both privacy and utility. After evaluating its past implications in geo-spatial use cases, we propose applications of spatial k-anonymity in the data sharing and managing of COVID-19 contact tracing technologies as well as heat maps showing a user's travel history. We then justify our propositions by comparing spatial k-anonymity with several other spatial privacy methods, including differential privacy, geo-indistinguishability, and manual consent based redaction. Our hope is to raise awareness of the ever-growing risks associated with spatial privacy and how they can be solved with Spatial K-anonymity.
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In the present study Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellite derived Aerosol Optical Depth (AOD) and the Ozone Monitoring Instrument (OMI) onboard Aura satellite derived Single Scattering Albedo (SSA) data sets were used to demonstrate the regional variation in aerosol radiative forcing during covid-19 imposed lockdown over the urban climate of Ahmedabad city. An analysis of short-wave (0.25um to 4.0 um) Instantaneous Direct Aerosol Radiative forcing (IDARF) is done using these satellite data as inputs to the Radiative Transfer model - SBDART. Result shows reduction in IDARF by the month of April-2020 and highest reduction in the month of May. Value of IDARF for May is around 22.785 Wm-2, which is 40.21% less than the mean value of IDARF from pre lockdown to post lockdown. Which indicates Negative Radiative Forcing (Net Cooling Effect). Magnitude of IDARF during lockdown and post lockdown are found to be 34.49 Wm-2 and 71.62 Wm-2 which is 87.94% higher than the mean value of IDARF from pre lockdown to post lockdown. Which suggest Positive Radiative Forcing (Net Warming Effect). © 2021 IEEE.
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In the present study, the atmospheric concentrations of Carbon Monoxide (CO) over India during COVID-19 (2020) were studied by comparing it with 2019 and 2021. COVID-19 has created an undesirable impact all over the world. However, as a blessing in disguise, these measures have a positive effect on the environment due to closing the mass gathering places. The work has undergone using the TROPOMI instrument, on-board Sentinel-5 Precursor. The results, evidence that human activities like transportation in Delhi, Industrial activities near Indo-Gangetic Plain have sharply fallen during the lockdown phase. On Contrary, there is a sharp increment in the area of Thermal power plants being coal-based. On the whole, the mean concentration of CO over India has minimal change due to long lifetime (1~2 months), indicating the duration of the (68 days) lockdown did not capture prompt and short-term atmospheric change. © 2021 IEEE.
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The present study focuses over Ahmedabad City of Gujarat State, India for the time period 1st March to 30th June comprising of the Pre-Lockdown Phase (PLP), the National Lockdown Phase - 1 (NLP1) and the Unlock Phase - 1 (ULP1). We have considered this time period over the years 2019, 2020 and 2021 to explore the effect of COVID induced lockdown on LST and understanding its variation. Satellite data acquired from AQUA - MODIS with a spatial and temporal resolution of 1 Km and 1-2 days respectively was used for the analysis of the LST. The average LST over Ahmedabad was 314.18 K, 311.79 K and 315.67 K for PLP over the years 2019, 2020 and 2021. For NLP1 the average LST over those years were 321.68 K, 318.73 K and 319.39 K respectively. And for the ULP1 the average LST over those years were 319.87 K, 314.07 K and 312.19 K respectively. We observe a 2.38 %, 2.22 % and 1.17 % increase in LST from the PLP to NLP1 during the years 2019, 2020 and 2021. The increase of LST during the NLP1 in 2020 showed that as the pollution decreased, the active elements that were present in the atmosphere which caused disturbance to the sensor on the satellite while calculating LST were reduced and we got a brighter top of surface. The decrease in LST from 2019 levels for the ULP1 is also observed indicating the effects of lockdown and onset of monsoon in 2020 and 2021. © 2021 IEEE.
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Nitrogen Dioxide (NO2) monitoring is a necessary step towards the understanding of climate change and public health. In this study, we tried to understand the comparative analysis of variation of NO2 over the region of Ahmedabad city. We have extracted NO2 concentration data for the year 2019 and 2020. The data was collected from both ground-based measurements and satellite based measurements of NO2 concentrations values. The results highlighted complete dynamics of seasonal NO2 concentration during the year 2019 and 2020 including the lockdown effect of COVID-19 outbreak. The validation approach of satellite data, based on cross-correlation analysis with ground data, it provided value of the Pearson correlation factor of 0.613 and correlation coefficients (R2) of 0.376. The huge fall in seasonal trend of NO2 concentration because of the pandemic is also shown in this study. © 2021 IEEE.
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Background: With the advent of COVID19, Infection control practices were followed rampantly in all health institutions. Whether these practices were able to restrict Multi-drug resistant organisms (MDROs) was an interesting thought. So a retrospective study was conducted to find out the prevalence of the MDROs before and after April 2020. Methods: Total number of specimens sent for culture were reviewed from October 2019 till March 2020.The number of isolates were calculated and the number of MDROs were recognised. The same was done from April 2020 till August 2020 in a tertiary care hospital. Results: A total of 1328 specimens were received from October 2019 to March 2020.228 isolates were recovered from 221 culture positive specimens. 111 isolates from228 were MDROs giving a prevalence of 48%. 500 specimens were received from April 2020 to August 2020 and 100 isolates were recovered from 96 culture positive specimens. Out of 100 isolates 41 were MDROs giving a prevalence of 41%. There was a reduction of 7% in the prevalence of MDROs. Conclusions: Strict infection control practices like mask, social distancing and hand hygiene were followed after COVID19 outbreak. These practices seemed to have helped to reduce the prevalence of MDROs as seen in our study. If these are continued ever we may be able to control MDROs further.
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Background: Integrated palliative care through equipped health care workers is essential in supporting those affected by the global COVID-19 pandemic. We developed & disseminated a Palliative Care in COVID-19 Resource Toolkit for LMICs focussing on those affected by co-morbidities & comprising an e-book, webinars x5, ECHO platform interactive sessions for HCW. Core competencies were ethics & goals of care, communication, self-care, symptom control, end of life care, bereavement & making sense of distress. To date delivery of 26 courses to >1200 participants in 15 countries. Quantitative data from participants show impact on all areas of competence. Objective: To evaluate the faculty experience in the development and delivery of the Resource Tool kit . Methods: A google forms survey to all faculty rating their experience of preparation, planning & delivery + pre & post levels of confidence & knowledge. Results: 19 faculty ( 95%), median age 51(29-63), 84% doctors, 10% nurses, 21% & 31% >20 years experience in PC & teaching respectively. 20% no prior experience of virtual teaching & 95% no experience of flipped classrooms. >90% agreed WhatsApp communication, co-facilitation & teaching methodology using flipped classrooms, algorithms & case narrative helpful.Faculty reported feeling valued, supported,engaged & a sense of solidarity and purpose. There was improvement in knowledge (pre 6.55 post 7.64) & confidence (pre 6.06 post 7.76) in teaching competency domains. 95% would recommend the Toolkit. Conclusion: The experience of developing & delivering a novel online training package showed benefits for this experienced Faculty in developing knowledge & confidence as well as a sense of purpose & solidarity when working in isolation in the midst of a pandemic. Using novel teaching methods & co-facilitation with peer support offered learning relevant for future training. Further qualitative study planned to explore the impact for Faculty & Participants.
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In this position paper, we discuss the potential use of a reinforcement learning (RL)-based human-in-the-loop recommender system to support clinical management of COVID-19. COVID-19 is a disease of extraordinary complexity that even the most experienced clinicians are struggling to understand. There is an urgent need for an evidence-based model for predicting the severity of the COVID-19 disease and its complications that can guide individual clinical management decisions. Such a model will utilize a diverse set of information to determine a patient's disease severity and associated risk of complications. An immediate application would be a clinical protocol tailored for COVID-19 patient care;this is a critical need both today and for future studies of potential treatments. © 2020 Copyright for the individual papers remains with the authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).