ABSTRACT
Andrographolide (AG-1) is identified as an attractive scaffold based on in silico/in vitro/in vivo (preclinical and clinical) studies against COVID-19 infection, for which hardly any effective drug is available to date. Due to complexity of its chemical structure, stereoselective and regioselective Heck arylation reactions at C-17 exocyclic double bond of AG-1 is a major challenge and we stepped forward to generate a small focused library of compounds. Among all the molecules, AG-12 and AG-13 were predicted to have better pharmacokinetic profiles than AG-1. Upon evaluation of in vivo efficacy of AG-12 and AG-13 in comparison to AG-1 using an LPS-induced acute lung injury model, AG-13 showed promising action towards reduction of the neutrophil count, minimization of oxidative stress, and inhibition of inflammatory cytokines. Further, lead optimization should be carried out towards developing potential natural product-driven therapeutics to combat acute respiratory distress syndrome (ARDS) situations during COVID-19. © 2023 Elsevier B.V.
ABSTRACT
Governments and healthcare organizations increasingly pay attention to social media for handling a disease outbreak. The institutions and organizations need information support to gain insights into the situation and act accordingly. Currently, they primarily rely on ground-level data, collecting which is a long and cumbersome process. Social media data present immense opportunities to use ground data quickly and effectively. Governments and HOs can use these data in launching rapid and speedy remedial actions. Social media data contain rich content in the form of people's reactions, calls-for-help, and feedback. However, in healthcare operations, the research on social media for providing information support is limited. Our study attempts to fill the gap mentioned above by investigating the relationship between the activity on social media and the quantum of the outbreak and further using content analytics to construct a model for segregating tweets. We use the case example of the COVID-19 outbreak. The pandemic has advantages in contributing to the generalizability of results and facilitating the model's validation through data from multiple waves. The findings show that social media activity reflects the outbreak situation on the ground. In particular, we find that negative tweets posted by people during a crisis outbreak concur with the quantum of a disease outbreak. Further, we find a positive association between this relationship and increased information sharing through retweets. Building further on this insight, we propose a model using advanced analytical methods to reduce a large amount of unstructured data into four key categories-irrelevant posts, emotional outbursts, distress alarm, and relief measures. The supply-side stakeholders (such as policy makers and humanitarian organizations) could use this information on time and optimize resources and relief packages in the right direction proactively.
ABSTRACT
Introduction: Coronavirus Disease-19 (COVID-19) infection is associated with high rates of pulmonary and extrapulmonary complications that may continue to incur morbidity, disability and delayed mortality in survivors. These include hyperglycaemia, cardiac injury, acute ischaemic or haemorrhagic stroke, neurological deficits, acute kidney injury and liver injury. Aim: To describe symptoms and complications being faced by COVID-19 recovered patients, as well their socio-demographic profile and co-morbidities. Materials and Methods: This was a cross-sectional descriptive study conducted for the period of 12 months from April 2020-March 2021. Out of nearly 10,000 recovered COVID-19 patients, 1000 (calculated sample size) patients were selected randomly. The patients were categorised gender-wise (male and female) and locality-wise (urban and rural) and an attempt was made to find if any significant difference exists in the symptoms and complications based on above categorisation. The test used for this purpose was Chi-square test and Fisher’s-exact test. Results: Mean age of participants was 50.2±15.7 years and 43.8% had co-morbidities. Common complications included hyperglycaemia (n=28), acute kidney injury (n=8), acute liver injury (n=5), cardio-vascular accident and stroke (n=5), septicaemia (n=8), ischaemic heart disease (n=7), deep vein thrombosis (n=2), cytokine release syndrome (n=10) and post COVID-19 fibrosis (n=3). For septicaemia, a statistically significant difference (p<0.001) was found between urban and rural areas whereas no significant difference in post COVID-19 complications between males and females was observed. conclusion: The most common co-morbidity was diabetes mellitus and most common complication reported was hyperglycaemia.