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BackgroundLupus is a heterogenous diseases which results in significant premature mortality. Most studies have evaluated risk factors for lupus mortality using regression models which considers the phenotype in isolation. Identifying clusters of patients on the other hand may help overcome the limitations of such analyses.ObjectivesThe objectives of this study were to describe the causes of mortality and to analyze survival across clusters based on clinical phenotype and autoantibodies in patients of the Indian SLE Inception cohort for Research (INSPIRE)MethodsOut of all patients, enrolled in the INSPIRE database till March 3st 2022, those who had <10% missing variables in the clustering variables were included in the study. The cause of mortality and duration between the recruitment into the cohort and mortality was calculated. Agglomerative unsupervised hierarchical cluster analysis was performed using 25 variables that define SLE phenotype in clinical practice. The number of clusters were fixed using the elbow and silhouette methods. Survival rates were examined using Cox proportional hazards models: unadjusted, adjusted for age at disease onset, socio-economic status, steroid pulse, CYC, MMF usage and cluster of the patients.ResultsIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting.Out of 2211 patients in the cohort, 2072 were included into the analysis. The median (IQR) age of the patients was 26 (20-33) years and 91.7% were females. There were 288 (13.1%) patients with juvenile onset lupus. The median (range) duration of follow up of the patients was 37 (6-42) months. There were 170 deaths, with only 77 deaths occurring in a health care setting. Death within 6 months of enrollment occured in in 80 (47.1%) patients. Majority (n=87) succumbed to disease activity, 23 to infections, 24 to coexisting disease activity and infection and 21 to other causes. Pneumonia was the leading cause of death (n=24). Pneumococcal infection led to death in 11 patients and SARS-COV2 infection in 7 patients. The hierarchical clustering resulted in 4 clusters and the characteristics of these clusters are represented in a heatmap (Figure-1A,B). The mean (95% confidence interval [95% CI] survival was 39.17 (38.45-39.90), 39.52 (38.71-40.34), 37.73 (36.77-38.70) and 35.80 (34.10-37.49) months (p<0.001) in clusters 1, 2, 3 and 4, respectively with an HR (95% CI) of 2.34 (1.56, 3.49) for cluster 4 with cluster 1 as reference(Figure 1C). The adjusted model showed an HR (95%CI) for cluster 4 of 2.22 (1.48, 3.22) with an HR(95%CI) of 1.78 (1.29, 2.45) for low socioeconomic status as opposed to a high socioeconomic status (Table 1).ConclusionIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting. Disease activity as determined by the traditional activity measures may not be sufficient to understand the true magnitude of organ involvement resulting in mortality. Clinically relevant clusters can help clinicians identify those at high risk for mortality with greater accuracy.Table 1.Univariate and multivariate Cox regression models predicting mortalityUnivariateMultivariateVariablesHazard ratio (95% Confidence interval)P valueHazard ratio (95% Confidence interval)P valueCluster1Reference-Reference-20.87 (0.57, 1.34)0.5320.89 (0.57, 1.38)0.59831.22 (0.81, 1.84)0.3371.15 (0.76, 1.73)0.51342.34 (1.56, 3.49)<0.0012.22(1.48, 3.22)<0.001Socioeconomic statusLower1.78 (1.29, 2.45)<0.001Pulse steroidYes1.6 (0.99, 2.58)0.051MMFYes0.71 (0.48, 1.05)0.083CYCYes1.42 (0.99, 2.02)0.052Proliferative LNYes0.99 (0.62, 1.56)0.952Date of birth age0.99 (0.98, 1.01)0.657CYC- cyclophosphamide, MMF- Mycophenolate mofetilFigure 1.A. Agglomerative clustering dendrogram depicting the formation of four clusters. B.Heatmap depicting distribution of variables used in clustering C. Kaplan-Meier curve showing the survival function across the 4 clusters[Figure omitted. See PDF]REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone eclared.
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Religious and faith-based communities can play a significant role in building a resilient society during and after a pandemic. According to the World Health Organization (WHO), religious institutions, faith-based organizations, and faith communities can provide important healthcare information to a community's most vulnerable population through their service networks. Currently, India ranks second in terms of the total number of confirmed COVID-19 cases. In March 2020, India adopted a travel restriction for visitors from outside India, followed by a nationwide lockdown to contain the spread of the disease. During this time, many religious institutions and faith-based communities provided aid to needy people, such as wage workers and migrant laborers who did not have jobs to support themselves. Against this backdrop, this paper examines the role of two well-known Hindu faith-based organizations in Lucknow-Brahma Kumaris and Sri Ramakrishna Math and Ramakrishna Mission Sevashrama in providing various tangible and intangible services to the citizens. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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The CoVID-19 infections began rising worldwide during the initial weeks of March 2020, reacting to which the Government of India called for nationwide lockdown for ~ 3 weeks. The concentration of pollutants during the lockdown were compared with pollution levels recorded during the preceding year for the same time frame. A direct relationship was established between the high level of air pollutants (PM2.5, PM10, NO2 and SO2) and CoVID-19 infections being reported in the Indian cities. The correlation indicates that the air pollutants like PM2.5, PM10, NO2 and SO2 are aggravating the number of casualties due to the CoVID-19 infections. The transmission of the virus in the air is in the form of aerosols;and hence places which are highly polluted may see a proportionate rise in CoVID-19 cases The high-level exposure of PM2.5 over a long period is found to be significantly correlated with the mortality per unit confirmed CoVID-19 cases as compared to other air pollutant parameters like PM10, NO2 and SO2. © 2023, The Author(s), under exclusive licence to Korea Spatial Information Society.
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The study of tautomerism in biologically relevant heterocycles is essential, as it directly affects their chemical properties and biological function. Lactam-lactim tautomerization in pyridine/pyrazine derivatives is such a phenomenon. Favipiravir, a pyrazine derivative, is an essential antiviral drug molecule having notable performance against SARS-CoV-2. Along with a better yielding synthetic method for favipiravir, we have also investigated the lactam-lactim tautomerization of favipiravir and its analogous molecules. Most of these molecules were crystalized and studied for various interactions in their lattice. Many interesting supramolecular interactions such as hydrogen bonding, pi-pi stacking and halogen bonding were revealed during the analysis. Some of these structures show interesting F-F halogen bonding and water channels in their solid state.Copyright © 2022 The Royal Society of Chemistry.
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Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Blended learning mixes offline learning activities and resources with online learning activities and resources. The goal of blended learning is to minimise the time spent sitting in class, which is a significant advantage for a College / university. It might aid university officials with enhancing programmes that have low enrollment, reducing expenditures, and fulfilling staff teaching responsibilities. The study is focusing on the perception of the college students towards the online learning program during the pandemic. The blended learning is the unavoidable and safety method of teaching learning process during the pandemic. It is a qualitative study made in Chennai city. The students of higher educational institutions (Arts & Science and Engineering colleges) are considered as samples. 250 sample respondents are selected from the study are using simple random technique. The Google forms was circulated and collected the primary data. Assessment is a very important instrument for measuring the degree of knowledge that a student has in relation to the topic in which they are enrolled in any level of education. Teachers are able to give the lecture and measure student learning via the use of unique and inventive approaches when they use blended learning strategies.Copyright © 2022 Ubiquity Press. All rights reserved.
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The whole world has witnessed the global pandemic situation caused and hampered very badly due to COVID-19. We had seen the adverse effect globally, in terms of health, economy, social lifestyle. So, it's an urgent need to find a rapid detection technique/test to avoid the spread of the virus. The most effective and world-wide accepted detection method of COVID-19 is the RT-PCR. But due to its slow detection time and False-negative rates, researchers and scientists are trying different detection methods such as use of GC-MS, E-nose, Electrochemical method, use of nanomaterial-based sensor arrays. But all these have limitations in terms of real time sensing, detection time, sample preparation, etc. In order to overcome said drawbacks and to get real-time analysis, we are proposing a concept for COVID-19 detection based on the reported literature. As per recent advancement researchers have evident the presence of VOCs in COVID-19 infected person's breath by GC-MS method. A real time system is very much necessary to detect the VOCs in the Exhaled breath of the COVID-19 infected person to minimize the burden of healthcare system. In this article we will discuss and propose the probable detection techniques for real time sensing of the VOCs presence in the Exhaled breath of the COVID-19 infected person. © Published under licence by IOP Publishing Ltd.
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Rapid development and distribution of vaccines have been a hallmark of the battle against COVID-19. While the efficacy, clinical trials, adverse health effects, and sociodemographic and clinical factors determining the distribution of vaccines have been studied extensively, there has been little effort to design cost-effective vaccine provisioning schemes. We introduce a vaccine provisioning scheme that leverages coalitional game theory to improve the cost of vaccines while meeting the epidemiological demand of neighboring zones. The proposed approach incentivizes bulk purchases by groups (or coalitions) of zones at lower prices while penalizing large coalitions to avoid logistical challenges. Moreover, it enables the policymaker to model the vaccine demand of zones based on their epidemiological profiles, such as susceptible, infected numbers or population density, or a combination thereof. We carry out experiments using the SEIRD (susceptible, exposed, infected, recovered, death) epidemic model as well as the daily confirmed cases in the five boroughs of New York City to show the efficacy of the approach. © 2022 IEEE.
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Rapid development and distribution of vaccines have been a hallmark of the battle against COVID-19. While the efficacy, clinical trials, adverse health effects, and sociodemographic and clinical factors determining the distribution of vaccines have been studied extensively, there has been little effort to design cost-effective vaccine provisioning schemes. We introduce a vaccine provisioning scheme that leverages coalitional game theory to improve the cost of vaccines while meeting the epidemiological demand of neighboring zones. The proposed approach incentivizes bulk purchases by groups (or coalitions) of zones at lower prices while penalizing large coalitions to avoid logistical challenges. Moreover, it enables the policymaker to model the vaccine demand of zones based on their epidemiological profiles, such as susceptible, infected numbers or population density, or a combination thereof. We carry out experiments using the SEIRD (susceptible, exposed, infected, recovered, death) epidemic model as well as the daily confirmed cases in the five boroughs of New York City to show the efficacy of the approach. © 2022 IEEE.
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Introduction: Coronavirus Disease-2019 (COVID-19) pandemic exposed the health workforce to an unprecedented occupational hazard. While taking care of patients they always had to be conscious simultaneously for safeguarding themselves and their family members against the highly infectious virus. In West Bengal, cases were first reported in the last week of March-2020 and reached the peak around October-2020 in the first wave, once the lockdown was lifted. During the initial months, the staggering number of cases, prevailing uncertainty over case management, and untimely demise of colleagues and relatives, took their toll on the physical and mental health of doctors, paramedics, or support staff, both in the government and private sectors. Aim(s): To measure perceived stress, resilience and psychological well-being of healthcare providers using standard psychometric tools. Material(s) and Method(s): This was a cross-sectional observational study carried out among healthcare workers in hospitals located in West Bengal, India. A self-administered questionnaire was circulated through a digital platform between June-November 2020. The questionnaire was designed using Perceived Stress Scale (PSS-10), Kessler-6 (K6), and Brief Resilient Coping Scale (BRCS) to assess perceived stress, psychiatric morbidity, and resilience of the person. It had three parts, one to capture socio-demographic details of the participants including age, sex, marital status, occupation, family history of psychiatric morbidity, place of stay etc. Second part consisted of psychometric scales and third was designed to capture the views of participants on the coping strategies. Calculated sample size was 189. Result(s): Based on standard cut-off values, it was found that 65.6% subjects were under moderate or severe stress;56.6% had compromised mental well-being and 64% were not coping well with the pandemic situation. PSS were significantly poor for females(p-value<0.001),single(p-value<0.001)andthosewithout history of psychiatric morbidity (p-value <0.001) and low resilient copers (p<0.0001). Mental well-being was compromised more among married (p-value=0.01), doctors (p-value=0.008), aged <40 years (p-value=0.003), high resilient copers (p-value=0.02). Popular means of stress reliever were music and yoga/exercise. Correct and updated knowledge on disease transmission, availability of personal protective equipment, pursuing hobbies like music and gardening were few suggested measures to improve coping with stress associated with patient care. Conclusion(s): The study revealed that majority of the health workers experienced moderate to heavy degree of stress and compromised psychological well-being during the first wave of pandemic. Relationship of stress and psychological wellbeing with resilience and socio-demographic variables was not always linear. Copyright © 2022 Journal of Clinical and Diagnostic Research. All rights reserved.
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Background and Objectives: Pregnancy, an altered physiological state, is specifically vulnerable to psychological distress (PD), more so during the coronavirus disease 2019 (COVID-19) pandemic. This can impart detrimental consequences to both mother and child. This study assessed the magnitude of PD and associated factors among pregnant women in rural West Bengal. The proportion of pregnant women with COVID-19 symptomatology and its relationship with PD was also determined. Methods: This cross-sectional study was done among 130 pregnant women availing antenatal care in health centers during August-October 2020. Patient Health Questionnaire (PHQ-4) was used to assess PD and a 'Perception on COVID-19 Pandemic' (PCP) Scale was used to assess the perception of the COVID-19 pandemic (Cronbach's alpha = 0.75). Statistical analyses were done in SPSS Inc., SPSS for Windows, Chicago, USA. Results: The proportion of study participants with PD was 49.2%. Unsatisfactory antenatal care (AOR = 19.4, CI = 2.5-152.7), COVID-19 case within family/neighborhood (AOR = 6.3, CI = 1.2-34.9), strenuous spousal relationship (AOR = 7.3, CI = 1.1-50), increasing score in perceived susceptibility domain of PCP Scale (AOR = 1.3, CI = 1.1-1.6), and decreased daily sleep duration (AOR = 2.8, CI = 1.6-4.9) were found to be associated with PD. 38.5% of participants reported COVID-19 related symptoms. Conclusion: The magnitude of PD among pregnant women in the study area is high. Thus, proper counseling of pregnant mothers during antenatal visits by public health nursing personnel to alleviate fears about the pandemic would go a long way to reduce the negative impact of PD on mother and child. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.
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COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.
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Blended learning mixes offline learning activities and resources with online learning activities and resources. The goal of blended learning is to minimise the time spent sitting in class, which is a significant advantage for a College / university. It might aid university officials with enhancing programmes that have low enrollment, reducing expenditures, and fulfilling staff teaching responsibilities. The study is focusing on the perception of the college students towards the online learning program during the pandemic. The blended learning is the unavoidable and safety method of teaching learning process during the pandemic. It is a qualitative study made in Chennai city. The students of higher educational institutions (Arts & Science and Engineering colleges) are considered as samples. 250 sample respondents are selected from the study are using simple random technique. The Google forms was circulated and collected the primary data. Assessment is a very important instrument for measuring the degree of knowledge that a student has in relation to the topic in which they are enrolled in any level of education. Teachers are able to give the lecture and measure student learning via the use of unique and inventive approaches when they use blended learning strategies. Copyright © 2022 Ubiquity Press. All rights reserved.
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Techno-economics and Life Cycle Assessment of Bioreactors: Post-Covid19 Waste Management Approach covers the emerging trends in bioreactor research, including techno-economics and life cycle assessment perspectives, both key considerations in making the anaerobic-digestion process technically feasible, economically viable and environmentally sustainable. The book is divided into three sections, with an introductory chapter on the impact of COVID-19 on existing practices of waste and resource management. Sections cover advances in bioreactor development for enhanced valorization of waste, the techno-economics of the different bioreactor systems, the life cycle assessment of bioreactors, their methodological challenges and future perspectives. Providing a holistic overview of bioreactors and taking into account recent trends in their design, the chapters also highlight the advances needed to manage COVID-19 waste in a sustainable manner. With contributions from leading experts in bioreactor and life cycle assessment, this book will be an invaluable reference source for academics working on anaerobic digesters and energy sustainability, as well as for research and development professionals in the renewable energy industry, and scientists and engineers working on clean and efficient energy generation from wastes. © 2022 Elsevier Inc. All rights reserved.
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Covid-19 is an aggregate productivity shock but the magnitude of the shock varies widely across sectors and households. As a result, it is also a shock to the income distribution. A second feature of the pandemic is that the virus creates a classic negative externality-greater economic activity spreads the disease faster but agents do not internalize this beyond personal health risk. Borrowing techniques from the optimal taxation literature, this chapter analyses how these twin problems can be addressed using tax-transfer policies and economic incentives. I show that there is no conflict between efficiency and equity-policies which reduce the disease burden optimally must also redistribute from agents and sectors that have suffered relatively smaller losses to those that have been beset with big shocks. Lags in income assessment can be overcome by paying a universal basic income upfront and postponing tax liabilities. Even from a public health perspective, an egalitarian response to the heterogeneous impact of Covid-19 can be more effective than blanket measures like lockdowns coupled with fiscal conservatism. © 2022 selection and editorial matter, Rajib Bhattacharyya, Ananya Ghosh Dastidar and Soumyen Sikdar;individual chapters, the contributors.
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The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning. This challenge is an open call for researchers to analyze a dataset of sound recordings, collected from COVID-19 infected and non-COVID-19 individuals, for a two-class classification. These recordings were collected via crowdsourcing from multiple countries, through a website application. The challenge features two tracks, one focusing on cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings. In this paper, we introduce the challenge and provide a detailed description of the task, and present a baseline system for the task.
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COVID-19 unleashed a global pandemic that has resulted in human, economic, and social crises of unprecedented scale. While the efficacy of mobility restrictions in curbing contagion has been scientifically and empirically acknowledged, a deeper understanding of the human behavioral trends driving the mixed adoption of mobility restrictions will aid future policymaking. In this paper, we employ associative rule-mining and regression to pinpoint socioeconomic and demographic factors influencing the evolving mobility trends. We compare and contrast short-distance and long-distance trips by analyzing Chicago county-level and US state-level mobility. Our study yields rules that explain the changing propensity in trip length and the collective effect of population density, economic standing, COVID testing, and the number of infected cases on mobility decisions. Through regression and correlation analysis, we show the influence of ethnic and demographic factors and perception of infection on short and long-distance trips. We find that the new mobility rules correspond to reduced long-And short-distance trip frequencies. We graphically demonstrate a marked decline in the proportion of long county-level trips but a minor change in the distribution of state-level trips. Our correlation study highlights it is hard to characterize the effect of perception of infection spread on mobility decisions. We conclude the paper with a discussion on the overlap between the analysis in the existing literature on both during-And post-lockdown mobility trends and our findings. © 2022 ACM.
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The outbreak of the coronavirus has resulted in unprecedented action, which has led authorities to decide to begin the blockade of the areas most hit by the infectious disease. Social media has been an important support for people during this difficult time. On November 9, 2020, when the first vaccine with an infection rate of 90% or higher was announced, social media responded with, and people around the world began to express the feelings of vaccination. It was no longer a hypothesis, but closer to,every day to become a reality Therefore, it becomes imperative to verify some of the information posted on social media during the pandemic situation, specially related to Covid vaccines. To this end, it is necessary to correctly identify fact-checkable posts, so that their information content can be verified.In this work, we have addressed the problem to identify 3 types of classification on the Twitter microblogging site. We organized a shared task in the FIRE 2021 conference to study the problem of identifyefficient classifier for prediction tweets posted during a particular pandemic scenario (the Covid 19). This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutraal for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective posts.Using this support vector machine in order to solve the antivax, provax,neutral classification of twets .We’re going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot. © 2021 Forum for Information Retrieval Evaluation, December 13-17, 2021, India.
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The molecular structure of 2-Deoxy D-Glucose is exactly like D-Glucose except for the fact that the former has no hydroxyl group on the carbon atom at the position 2 but instead has a hydrogen atom. Glucose can be of two types: one which turns the plane of the polarized light, when passed through its solution in water, towards right is called Dextrorotatory or D-Glucose and the other which turns it towards left is called Levorotatory or LGlucose. Chemists call glucose as an aldohexose, indicating that it is made up of six carbon atoms, one of which is an aldehyde group and the rest contain hydrogen atoms and hydroxyl groups. In other words, it has one oxygen atom less than D-Glucose and therefore, is called 2-Deoxy-D-Glucose. The Drugs Controller General of India granted approval (May 17 2021) for use of 2 DG drug after it went through too extensive successful clinical trials. Now, the drug has been recommended to be used in moderate cases which have to be taken orally mixed with water, 2 times a day for 5 to 7 days. It showed that the drug was able to reduce oxygen dependency by the third day and reduce the hospital stay considerably.
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The overall goal of this award is to find ways to prolong the efficacy of cabazitaxel chemotherapy in patients with castration resistant prostate cancer (CRPC) who have previously been treated with and developed resistance to Abiraterone Acetate (ABI) or enzalutamide (ENZ). In months 1-12 of this award, we aimed to determine whether a novel galectin-1 (Gal-1) inhibitor, S-LLS30 developed by the applicant, prevents ABI/ENZ resistance and/or sensitizes the cells to cabazitaxel (Major task 1). We have shown that indeed S-LLS30 sensitizes CRPC cells to ENZ and strongly affected cells expressing Gal-1. The experiments with cabazitaxel are continuing despite prolonged operational shutdown at the University due to COVID-19 restrictions. We have also started to investigate the role of Gal-1 nuclear localization, and its binding partners Gemin4 and HSP90 in this process (Major task 2, subtask 1). It appears that Gemin4 plays a substantial role in Gal-1 activity in this context but the role of HSP90 is unclear. Finally, we conducted preliminary experiments to evaluate the toxicity of S-LLS30 and determine the maximum tolerated dose (Major task 3, subtask 1). S-LLS30 was deemed to be of limited toxicity and very well tolerated in mice up to 30 mg/Kg doses. S-LLS30 is a viable potential drug candidate to overcome resistance to ABI/ENZ in models of CRPC.