Development of innovative methodologies in environmental modelling using machine learning and artificial intelligence techniques for forecasting and prediction of surface ozone (O3), at ground level is needed to anticipate future climate projections and for effective air quality management. Several complexities like physical and chemical phenomena are involved in O3 formation from its precursors like nitrogen oxide (NO), carbon monoxide (CO), nitrogen dioxide (NO2) and oxides of nitrogen (NOx). It is important to understand and formulate nonlinear relationships in O3 formation using robust data-driven predictive models from time to time despite the existence of several O3 prediction models. This study focuses on the development of back-propagation artificial neural networks (BPANNs) for the prediction of seasonal and diurnal O3 concentrations using the data monitored at a specified location in Hyderabad, India (TIFR-NBF: Tata Institute of Fundamental Research-National Balloon Facility) during the period 2014–2016. The efficiency and performance of BPANNs in modelling with 80% of monitored data for training and 20% of data for validation of seasonal and diurnal ozone concentration and showed higher R2 (0.9999). The evaluation statistics of all the three seasonal models measured in terms of root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE) showed a better predictive ability against the monitored data fell within a 10% margin over response surface methodology (RSM) which indicate that it is much more confident and accurate in prediction of O3.
Photo-Fenton degradation of methylene blue by (Al 0.6 Mn 0.6 Fe 0.6 Co 0.6 Ni 0.6 )O 4 HEO.
Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is still one of the most common chronic liver diseases worldwide. The build-up of triglycerides in the liver is a key feature of MASLD and can lead to oxidative stress and inflammation, thereby progressing to metabolic dysfunction-associated steatohepatitis (MASH). The recent FDA approval of Resmetirom (Rezdiffra) for adults with non-cirrhotic MASH and moderate-to-advanced liver fibrosis marks a significant step forward in treatment and has once again sparked interest in THRβ1 as a target for metabolic therapies. THRβ1 is a major regulator of hepatic metabolism, influencing fatty acid oxidation, cholesterol homeostasis, mitochondrial function, and other pathways that maintain the liver’s lipid balance. In MASH, disruption of THRβ1 signaling, including changes in receptor expression and the local availability of thyroid hormone, may contribute to lipid accumulation in the liver, the development of metabolic dysfunction, inflammation, and disease progression. As a result, liver-selective activation of THRβ1 has become a promising therapeutic approach for restoring metabolic homeostasis while avoiding the systemic effects associated with thyroid hormone. Clinical and preclinical studies involving Resmetirom and other thyromimetics, such as VK2809, ASC41, and TERN-501, have shown beneficial effects on hepatic steatosis, lipid metabolism, and the histological features of MASH. This review offers a detailed look at the structure of THRβ1, how ligands bind to it, the mechanisms of its transcriptional regulation, and its roles in the metabolism of lipids, cholesterol, glucose, and mitochondria in the liver, before going on to analyze the dysregulation of THRβ1 in MASH and the pharmacological and structural reasons for the selectivity of THRβ1 agonists. The review also examines Resmetirom and other emerging thyromimetics, liver-targeted drug-design strategies, combination therapies, and unresolved mechanistic questions, highlighting the potential to develop next-generation therapies directed at THRβ1 for the treatment of MASH.
Sovateltide (SOVA) is a peptide-based therapeutic whose quality and safety depend on the accurate detection of related substances (RS). During hydrolysis studies, eight degradants were identified, which compromised the method’s selectivity. The co-elution of these degradants with the active pharmaceutical ingredient (API) and established impurities indicated that the initial approach was not stability-indicating. The study aimed to develop a robust, stability-indicating analytical method for SOVA using an Analytical Quality by Design (AQbD) approach, enabling reliable detection of RS, including chiral isomeric impurities formed during degradation. Critical method attributes (CMAs) and critical method variables (CMVs) were systematically evaluated through the design of experiments (DoE). Resolution was defined as the CMA, while mobile phase pH, column temperature, column selection, gradient slope, and flow rate were CMVs. Screening using DoE identified a mobile phase (MP) pH of 2.5 and selected the YMC Meteoric Core C18 BIO column. Global optimisation was performed using Response Surface Methodology (RSM), with trifluoroacetic acid (TFA) concentration and column oven temperature as key factors. Monte Carlo simulations and capability analysis further refined the design space. The optimised method included TFA at 0.07
To reach a sustainable future and meet the UN’s Sustainable Development Goals (UN SDGs), business model innovation (BMI) needs to explore theoretical and practical intersections of the traditional innovation management (IM) and new product development (NPD) processes with sustainability considerations. New environmental and health policy ambitions such as those presented in the European Green Deal and the EU Chemicals Strategy for Sustainability (CSS) challenge traditional IM theories on BMI and NPD processes. The Safe-and-Sustainable-by-Design (SSbD) concept is a central element of the CSS and demands a novel approach that integrates innovation with safety and sustainability (including circularity) of materials, products, and processes without compromising their functionality and/or commercial viability. Importantly, adopting such a concept can also prevent regrettable substitutions, future liability, and brand image issues for companies. To achieve this, companies must design products with minimal environmental impact, adopt circular economy principles, and ensure social responsibility throughout the value chain, while maintaining economic viability. By doing so, companies contribute to environmental, social, and economic sustainability. In this perspective, a conceptual framework is proposed on how to achieve sustainable BMI and NPD by integrating traditional IM tools with SSbD using life cycle thinking principles while considering external (changing legislation, new business standard requirements, competitive environments, technological developments, societal views) and internal drivers (company-specific targets, company culture, corporate strategy, management capabilities). SSbD and life cycle thinking should be embedded in newly developed training for IM professional designation. This is because innovation managers can play a key role in bringing this transition into practice.
