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Synthesis, and Computational Studies of Methyl 6’ -AMINO-5’-CYANO-2-OXO-2’H- SPIRO[INDOLINE-3’4’- PYRANO(2,3-C) PYRAZOLE]-3’-CARBOXYLATE
(Book Rivers, 2026) Umme Habiba Malika, Ayesha Anwera, Mohammad Amira, Malik Nasibullaha
Pyrazole derivatives are important heterocyclic compounds known for their wide range of biological activities, including antibacterial, antifungal, anti-inflammatory, analgesic, anticancer, and antidiabetic properties. In this study, a novel spiro pyranopyrazole derivative, methyl 6'-amino-5'-cyano-2-oxo-2'H-spiro[indoline-3,4'-pyrano[2,3-c]pyrazole]-3'-carboxylate, was synthesized through a one-pot multicomponent reaction using dimethyl acetylene dicarboxylate (DMAD), hydrazine monohydrate, malononitrile, and isatin in PEG:EtOH medium with TBAI as a catalyst at room temperature. The synthesized compound was obtained in good yield and characterized by spectroscopic methods. Pharmacokinetic and ADMET studies indicated favourable drug-likeness, oral bioavailability, and acceptable absorption properties according to Lipinski's rule of five. Biological activity prediction also suggested promising pharmacological potential.
Trends In Medicinal and Environmental Science: An Introduction
(978-93-6884-737-3, 2026) Iqbal Azad, Jamal Akhtar Ansari, Malik Nasibullah, Abdul Rahman Khan
Convolutional Neural Networks for Foliar Disease Diagnosis in Horticulture Crops: A Critical Review
(Springer Nature Switzerland, 2026) Anshu Khare, Naimur Rahman Kidwai, Shrish Bajpai
The research aims to examine further how to apply advanced machine learning models and deep learning models, especially convolutional neural network models (CNN), in detecting disease in both tomato and potato plants in advance. It emphasizes how various factors, such as disease-producing micro-organisms and environmental components in addition to less varying datasets, especially when using datasets such as PlantVillage, which have shown vast usage in various models in various research worldwide, can particularly influence the accuracy of disease detection models in crops such as potatoes and tomatoes using CNN models talked of in this study. CNN models, especially AlexNet models, ResNet models, and EfficientNet models, which show high accuracy in classifying different kinds of disease in various kinds of crops using 94–99.75% accuracy levels, are discussed in this study as a basis of further research to come up with more efficient models in promoting both machine learning and various uses of CNN models in disease management in promoting food security in many countries worldwide.
Sirtuins in inflammation and autoimmunity
(Academic Press, 2026) Mirunalini Gobinath , Parikshit Roychowdhury , Sagarika Mukherjee , Mohd Aftab Siddiqui
Sirtuins, a conserved family of nicotinamide adenine dinucleotide-dependent deacetylases, are key regulators of immune balance, inflammation, and autoimmunity. This chapter examines the dual functions of sirtuin isoforms (SIRT1–SIRT7) in controlling innate and adaptive immunity via transcriptional and posttranslational mechanisms. They affect critical inflammatory pathways, such as NF-κB, STAT3, and NLRP3 inflammasome activation, influencing cytokine, chemokine, and adhesion molecule expression. SIRT1 suppresses proinflammatory genes by deacetylating NF-κB p65, while SIRT6 impacts TNF-α secretion and macrophage polarization. The chapter discusses the variation in sirtuin expression and function in autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, and inflammatory bowel disease. Animal models lacking sirtuins offer insights into autoimmune development and inflammation resolution. The potential of sirtuin modulators such as resveratrol, SRT2104, and other compounds is evaluated using preclinical and clinical data. The chapter also addresses challenges such as tissue specificity, isoform selectivity, and context-dependent effects that hinder clinical translation. Overall, it emphasizes the promising yet complex role of sirtuins as therapeutic targets for inflammation and autoimmunity, emphasizing the need for further research.
From STEM to STREAM: Integrating Aesthetics through Indian Knowledge Systems in Contemporary Education
(Shiksha Shodh Manthan, 2026) Dhriti Tiwari, Azkiya Waris
Education in the STEM (Science, Technology, Engineering, and Mathematics) has grown in prominence in the 21st century as a means to propel technological advancement and monetary prosperity. Although this approach has made it easy for people to solving problems, think analytically, and use technology, it often ignores the creative, moral, and emotional parts of learning that are equally important for developing a holistic personality. To remove this imbalance, there has been a shift in educational priorities away from STEM (Science, Technology, Engineering, and Mathematics) and towards STEAM (Science, Technology, Engineering, Arts, and Mathematics), which places an emphasis on the value of aesthetics, communication, and creativity alongside scientific inquiry. IKS, is a generic term covering a huge diversity of indigenous methods of knowing, doing, and thinking. Knowledge in IKS is always considered as an integrated, and worthwhile pursuit. According to IKS science, art, logic and intuition, practicality and aesthetics all work together in perfect harmony, making learning an organic process.