ABSTRACT
Availability of well-tolerated novel agents that can slow or stop disease progression and improve quality of life remain an unmet medical need in IPF management. GB0139, a novel inhaled galectin-3 inhibitor, has shown good tolerability and antifibrotic potential via changes in biomarkers associated with IPF progression in an animal model (Delaine, T. et al. Chembiochem 2016;17:1759-70) and a Phase I study (Hirani, N. et al. Eur Respir J 2021;57(5):2002559) in healthy participants and IPF patients. We report the design of a Phase IIb study of GB0139 in IPF. This randomised, double-blind, placebo-controlled, parallel-group, multicentre study (NCT03832946) was initiated in April 2019. The primary endpoint is rate of decline in forced vital capacity (FVC) over 52 weeks. Key secondary endpoints are proportion of participants with an absolute decline from baseline in FVC % predicted of <=10%, change from baseline in St. George's Respiratory Questionnaire total score, time to first respiratory-related hospitalisation, and time to death (all-causes). Systemic GB0139 pharmacokinetics are included as an exploratory endpoint. Despite the COVID-19 pandemic, study recruitment has continued in ~100 centres across 15 countries, with over 400 participants randomised as of February 2022. Initially, participants treated with or without standard of care (SOC) were included. Following a protocol amendment in 2021, the current target is to randomise 141 participants who are not treated with SOC, with study completion in mid-2023.
ABSTRACT
Because India's economy has shrunk to a low level during COVID-19, building an emergency decision support model (EDSM) for economic growth factors is the main objective of this study. We develop the TODIM-VIKOR method under Pythagorean fuzzy information. For dealing with comparison problems, the Pythagorean fuzzy scoring function is presented. We also include a new entropy metric for assessing the degree of fuzziness in PyFS. We also present a new Jensen Shannon divergence metric for PyFS that can be used to compare the discrimination information of two PyFSs. In this article, we introduced entropy and divergence measures to derive objective weight in the TODIM-VIKOR approach. Establishes a novel emergency decision making (EDM) strategy under the Pythagorean fuzzy atmosphere, using economic growth considerations. We used TODIM to determine the overall dominance degree, which takes into account the bounded rationality of decision makers, and VIKOR to calculate the compromise ranking of alternatives. © 2023 IEEE.
ABSTRACT
Background: Gal-3 is a protein that binds specifically to N-acetylglucosamine-expressing carbohydrates, which are upregulated on key tumorigenic cell surface proteins. Gal-3 is widely over-expressed in the tumor microenvironment and is generally linked to poor outcomes. Gal-3 regulates immune cell function of T cells and macrophages, and promotes neovascularization and fibrosis [Peng Cancer Res 2008;Markowska J Biol Chem 2011;Kouo Cancer Immunol Res 2015]. Gal-3 sequesters interferon gamma, reduces T-cell influx, and contributes to tumor cell evasion of the immune system via LAG-3 activation [Chen PNAS 2009;Gordon-Alonso Nat Commun 2017]. Gal-3 has been identified as a marker of resistance to checkpoint inhibitors (CPIs);patients with stage IV NSCLC with high Gal-3 levels (> 70% Gal-3 immunohistochemical staining) have been shown to be resistant to the CPI pembrolizumab [Capalbo Int J Mol Sci 2019]. Animal data indicate synergy between CPI therapy and Gal-3 inhibition [Vuong Cancer Res 2019;Zhang FEBS Open Bio 2021]. Thus, inhibiting Gal-3 together with CPI-based immunotherapy may enhance tumor-specific immune responses, and overcome CPI resistance. Methods: GALLANT-1 (NCT05240131) is a 3-part, placebo-controlled phase Ib/IIa trial that will investigate safety and efficacy of GB1211 (a Gal-3 inhibitor) + atezo vs placebo + atezo in patients with advanced NSCLC. Part A will include 8-12 patients and study safety and tolerability of 200 mg and 400 mg GB1211 twice-daily + atezo (open-label). Primary endpoint is number of adverse events (AEs) after 12 weeks' treatment and will determine the dosage for Part B. Part B will include 75-94 patients, and is a randomized, double-blind study of GB1211 + atezo or placebo + atezo. Primary endpoints are safety (number of AEs) and efficacy (percentage change from baseline in the sum of longest diameter of target lesions after 12 weeks' treatment). Part C is an expansion study including patients from Parts A and B, with safety and efficacy assessments. Eligibility criteria: advanced or metastatic stage IIIB or IV NSCLC adenocarcinoma;measurable disease per RECIST v1.1;expression of programmed death ligand-1 on ≥50% of tumor cells;eligible for 1200 mg atezo every 3 weeks. Exclusion criteria: symptomatic, untreated, or actively progressing central nervous system metastases;prior systemic chemotherapy for treatment of recurrent advanced or metastatic disease, except if part of neoadjuvant/ adjuvant therapy;prior treatment with immune CPIs and/or GB1211;presence of EGFR mutation and ALK, ROS1, and RET alterations;treatment with antineoplastic or systemic immunotherapeutic agents prior to first GB1211 dose;severe infectious disease < 4 weeks prior to first GB1211 dose;active hepatitis B or C, HIV, or COVID-19. The study is being initiated;updated enrollment status will be presented at the meeting.
ABSTRACT
The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime. © 2021, Springer Nature Switzerland AG.
ABSTRACT
COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people's health and governance systems. Managing this infodemic not only requires mitigating misinformation but also an early understanding of underlying psychological patterns. In this study, we present a novel epidemic response management strategy. We analyze the psychometric impact and coupling of COVID-19 infodemic with official COVID-19 bulletins at the national and state level in India. We looked at them from the psycholinguistic lens of emotions and quantified the extent and coupling between them. We modified Empath, a deep skipgram-based lexicon builder, for effective capture of health-related emotions. Using this, we analyzed the lead-lag relationships between the time-evolution of these emotions in social media and official bulletins using Granger's causality. It showed that state bulletins led the social media for some emotions such as Medical Emergency. In contrast, social media led the government bulletins for some topics such as hygiene, government, fun, and leisure. Further insights potentially relevant for policymakers and communicators engaged in mitigating misinformation are also discussed. We also introduce CoronaIndiaDataset, the first social-media-based Indian COVID-19 dataset at the national and state levels with over 5.6 million national and 2.6 million state-level tweets for the first wave of COVID-19 in India and 1.2 million national tweets for the second wave of COVID-19 in India.</p>