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Recent Submissions
Smart System: Wireless Communication Using AI and IoT in 5G and 6G
(Technical Press, 2026) Meena Chavan, Somdotta Roy Choudhury, Imran Ullah Khan, Jigarkumar Dineshbhai Patel, Arjuna Muduli
A Machine Learning-based Approach for Smart Agriculture Monitoring and Decision Support
(Bentham Science, 2026) Ankit Jain, Anita Shukla, Imran Ullah Khan
The field of machine learning is expanding and has a wide range of potential uses in agriculture. Machine learning is used to forecast pests and illnesses, decrease water usage, and increase crop yields, which is a topic of investigation for farmers and agricultural experts. Machine learning is capable of improving resource efficacy along with food production sustainability for farmers in the future. Numerous factors either directly or indirectly affect crop growth. Among these are the climate parameters. We can boost productivity by employing machine learning to monitor and regulate these parameters. In addition, there is a need for technological solutions to address a number of issues, including fire alerts, maintaining humidity levels and appropriate temperatures, and meeting the needs of sophisticated plants while monitoring unauthorized entry into agricultural areas. The significance of an appropriate and satisfactory supply of power cannot be understated. By using a NodeMCU Wi-Fi module, the technology offers a practical and effective solution to the issues identified in the Indian farming system. Various sensors such as those for temperature, fire, light, PIR, humidity, and soil moisture, have been utilized to monitor and regulate a variety of technological issues. Using IOT and machine learning, the projected system uses a Wi-Fi module to display real-time data that can be watched online from any location in the world. The farmer is automatically notified by this module about the need for water, site temperature, moisture and humidity, light, fire warning, and unwelcome occupancy or encroachment. Using the machine learning principle, an experiment was conducted with varying soil and plant levels. It was established that the sensor exhibited sufficient sensitivity to yield consistent results under varying water level situations for diverse combinations of plants and soil.
The role of sirtuins in cancer progression
(Elsevier Inc., 2026) Anas Islam, Usama Ahmad, Mohd Muazzam Khan, Talib Hussain
Sirtuins represent a highly conserved family of nicotinamide adenine dinucleotide (NAD⁺)-dependent deacetylases that have emerged as pivotal regulators at the intersection of cellular metabolism, epigenetic regulation, and cancer biology. First identified as the mammalian orthologs of yeast Silent Information Regulator 2 (Sir2), the seven mammalian sirtuins (SIRT1–SIRT7) share a conserved catalytic core domain of approximately 275 amino acids yet exhibit remarkable functional diversity through distinct subcellular localizations, enzymatic activities, and substrate specificities. This evolutionary conservation from bacteria to mammals highlights their fundamental importance in cellular homeostasis and organismal survival. In the context of cancer, sirtuins have garnered substantial attention over the past two decades not merely as metabolic sensors, but as master regulators whose dysregulation profoundly influences tumor initiation, progression, and therapeutic response.
Sirtuins in cardiovascular health
(Elsevier (Academic Press), 2026) Anas Islam, Badruddeen, Mohammad Irfan Khan, Juber Akhtar, Asad Ahmad
Liver Targeting Strategies with Nanocarriers
(Bentham Science, 2026) Afreen Usmani, Mohd Aftab Siddiqui, Mohd Nazam Ansari, Rania I.M. Almoselhy
Fatty liver diseases, including nonalcoholic fatty liver disease (NAFLD) and alcoholic fatty liver disease (AFLD), have emerged as some of the most prevalent causes of chronic liver disorders worldwide. Treatment options remain limited due to poor drug bioavailability and nonspecific targeting, resulting in suboptimal therapeutic outcomes. In this context, nanocarriers have shown significant promise by improving drug delivery, enabling precise targeting, and thereby enhancing therapeutic efficacy in the management of fatty liver diseases. This chapter offers a comprehensive review of liver-targeting strategies using nanocarriers, with particular attention to the complexity of fatty liver disease. It examines various nanocarrier systems, such as liposomes, polymeric nanoparticles, dendrimers, and lipid-based carriers, focusing on their structural features and potential for targeted drug delivery. Emerging strategies, including receptor-mediated and stimulus-responsive delivery systems, are critically analyzed. Furthermore, this chapter explores the integration of nanocarriers with cutting-edge technologies, such as RNA-based therapeutics and CRISPR-Cas9 gene editing. Issues related to scalability, safety, and regulatory challenges are also discussed, alongside the latest advancements in preclinical and clinical research. Overall, this chapter serves as a valuable resource, outlining current knowledge and future directions for researchers, clinicians, and pharmaceutical developers working to advance liver-targeted therapies for fatty liver diseases.