Cardiovascular diseases (CVDs) constitute an important cause of morbidity and mortality globally, and India is no exception to this trend. With the ongoing aging of the population in India, there is a notable surge in the prevalence and impact of CVDs among older adults. This review is aimed at providing a comprehensive overview of the current knowledge concerning the prevalence, risk factors, and management of CVDs in the context of Indian older adults. The incidence of CVDs in India is not only alarming but also exhibits an upward trajectory with advancing age. Primary risk factors contributing to the elevated incidence among older adults include hypertension (HT), diabetes, dyslipidemia, obesity, smoking, a sedentary lifestyle, and poor dietary habits. Additionally, stress and genetic predisposition emerge as noteworthy contributors to CVDs in this population. Effectively identifying and managing these risk factors among older adults in India is imperative to alleviate the burden of these diseases and enhance overall quality of life. Strategies aimed at mitigating the impact of CVDs in the country necessitate a comprehensive approach, integrating lifestyle interventions, public health initiatives, and a robust healthcare system. In summary, CVDs represent a significant health concern in both rural and urban areas of India. However, variations exist in the prevalence, risk factors, and accessibility to healthcare between these regions. Therefore, addressing the prevalence of CVDs in India necessitates a complex, multidimensional strategy that takes into account the unique opportunities and challenges that come with living in both rural and urban areas.

Psoriasis, due to its unique pathological manifestations and the limited success of existing therapeutic modalities, demands dedicated domain research. Our group has developed nanotherapeutics consisting of bioactives such as Thymoquinone (TQ) and Fulvic acid (FA), which have been successfully incorporated into a Nanoemulsion gel (NEG), taking kalonji oil as oil phase. The composition is aimed at ameliorating psoriasis with better therapeutic outcomes. TQ is a natural bio-active that has been linked to anti-psoriatic actions. FA has anti-inflammatory actions due to its free radical and oxidant-scavenging activity. Our previous publication reports the formulation development of the NEG, where we overcame the pharmaco-technical limitations of combining the above two natural bioactives. In vitro evaluation of the optimized NEG was carried out, which showed an enhanced dissolution rate and skin permeation of TQ. This work furthers the pharmaceutical progression of dual-targeted synergistic NEG to treat psoriasis. A suitable animal model, BALB/c mice, has been used to conduct the in vivo studies, which revealed the effective anti-psoriatic action of TQ. Molecular docking studies corroborated the results and revealed a good binding affinity for both the targets of TNF-α (Tumor necrosis factor) and IL-6 (Interlukin-6). Tissue uptake by Confocal laser scanning microscopy (CLSM), a skin interaction study of the gel formulation, and an antioxidant free radical scavenging assay (1-1 Diphenyl-2-picrylhydrazyl DPPH) were also carried out. It was concluded that the NEG may be effective in treating psoriasis with minimal side effects.

The study delves into the multifaceted potential of quercetin (Qu), a phytoconstituent found in various fruits, vegetables, and medicinal plants, in combination with silver nanoparticles (AgNPs). The research explores the synthesis and characterization of AgNPs loaded with Qu and investigates their pharmaceutical applications, particularly focusing on antibacterial properties. The study meticulously evaluates Qu’s identity, and physicochemical properties, reaffirming its suitability for pharmaceutical use. The development of Qu-loaded AgNPs demonstrates their high drug entrapment efficiency, ideal particle characteristics, and controlled drug release kinetics, suggesting enhanced therapeutic efficacy and reduced side effects. Furthermore, the research examines the antibacterial activity of Qu in different solvents, revealing distinct outcomes. Qu, both in methanol and water formulations, exhibits antibacterial activity against Escherichia coli, with the methanol formulation displaying a slightly stronger efficacy. In conclusion, this study successfully synthesizes AgNPs loaded with Qu and highlights their potential as a potent antibacterial formulation. The findings underscore the influence of solvent choice on Qu’s antibacterial properties and pave the way for further research and development in drug delivery systems and antimicrobial agents. This innovative approach holds promise for addressing microbial resistance and advancing pharmaceutical formulations for improved therapeutic outcomes.

Thymoquinone has a multitude of pharmacological effects and has been researched for a wide variety of indications, but with limited clinical success. It is associated with pharmaco-technical caveats such as hydrophobicity, high degradation, and a low oral bioavailability. A prudent approach warrants its usage through an alternative dermal route in combination with functional excipients to harness its potential for treating dermal afflictions, such as psoriasis. Henceforth, the present study explores a nanoformulation approach for designing a fulvic acid (peat-sourced)-based thymoquinone nanoemulsion gel (FTQ-NEG) for an enhanced solubility and improved absorption. The excipients, surfactant/co-surfactant, and oil selected for the o/w nanoemulsion (FTQ-NE) are Tween 80/Transcutol-P and kalonji oil. The formulation methodology includes high-energy ultrasonication complemented with a three-dimensional/factorial Box–Behnken design for guided optimization. The surface morphology assessment through scanning/transmission electron microscopy and fluorescence microscopy revealed a 100 nm spherical, globule-like structure of the prepared nanoemulsion. Furthermore, the optimized FTQ-NE had a zeta potential of −2.83 ± 0.14 Mv, refractive index of 1.415 ± 0.036, viscosity of 138.5 ± 3.08 mp, and pH of 5.8 ± 0.16, respectively. The optimized FTQ-NE was then formulated as a gel using Carbopol 971^® (1%). The in vitro release analysis of the optimized FTQ-NEG showed a diffusion-dominant drug release (Higuchi model) for 48 h. The drug permeation flux observed for FTQ-NEG (3.64 μg/cm²/h) was much higher compared to that of the pure drug (1.77 mg/cm²/h). The results were further confirmed by confocal microscopy studies, which proved the improved penetration of thymoquinone through mice skin. Long-term stability studies of the purported formulation were also conducted and yielded satisfactory results.

Trigonelline, an alkaloid found in the seeds of Trigonella foenum-graecum L. (fenugreek), has been recognized for its potential in treating various diseases. Notably, trigonelline has demonstrated a neuroprotective impact by reducing intrasynaptosomal calcium levels, inhibiting the production of reactive oxygen species (ROS), and regulating cytokines. Kainic acid, an agonist of kainic acid receptors, is utilized for inducing temporal lobe epilepsy and is a common choice for establishing kainic acid-induced status epilepticus, a widely used epileptic model. The neuroprotective effect of trigonelline in the context of kainic acid-induced epilepsy remains unexplored. This study aimed to induce epilepsy by administering kainic acid (10 mg/kg, single subcutaneous dose) and subsequently evaluate the potential anti-epileptic effect of trigonelline (100 mg/kg, intraperitoneal administration for 14 days). Ethosuccimide (ETX) (187.5 mg/kg) served as the standard drug for comparison. The anti-epileptic effect of trigonelline over a 14-day administration period was examined. Behavioral assessments, such as the Novel Object Recognition (NOR) test, Open Field Test (OFT), and Plus Maze tests, were conducted 2 h after kainic acid administration to investigate spatial and non-spatial acquisition abilities in rats. Additionally, biochemical analysis encompassing intrasynaptosomal calcium levels, LDH activity, serotonin levels, oxidative indicators, and inflammatory cytokines associated with inflammation were evaluated. Trigonelline exhibited significant behavioral improvements by reducing anxiety in open field and plus maze tests, along with an amelioration of memory impairment. Notably, trigonelline substantially lowered intrasynaptosomal calcium levels and LDH activity, indicating its neuroprotective effect by mitigating cytotoxicity and neuronal injury within the hippocampus tissue. Moreover, trigonelline demonstrated a remarkable reduction in inflammatory cytokines and oxidative stress indicators. In summary, this study underscores the potential of trigonelline as an anti-epileptic agent in the context of kainic acid-induced epilepsy. The compound exhibited beneficial effects on behavior, neuroprotection, and inflammation, shedding light on its therapeutic promise for epilepsy management.

Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models.