Alwar Pharmacy College

Diabetes mellitus poses a substantial global health burden, with diabetic retinopathy (DR) being a prevalent and potentially devastating microvascular complication. Platelet activation has been implicated in the pathogenesis of DR, suggesting platelet indices such as mean platelet volume (MPV), platelet distribution width (PDW), platelet-large cell ratio (P-LCR), and plateletcrit (PCT) as potential noninvasive markers for predicting its onset. We conducted a cross-sectional study involving 300 patients diagnosed with type 2 diabetes mellitus (T2DM) attending a tertiary care center. Demographic data, duration of diabetes, and HbA1c levels were recorded. Platelet indices were measured using complete blood counts, and DR was diagnosed based on fundus examination findings. Among the study participants, group B (n = 140) comprising patients with DR had significantly higher levels of MPV (13.28 ± 2.14 fL), PDW (14.56 ± 2.37), P-LCR (29.59 ± 6.018%), and PCT (0.29 ± 0.06) compared to group A (n = 160) without DR (MPV: 9.99 ± 1.64 fL, PDW: 12.81 ± 2.28, P-LCR: 27.64 ± 8.36%, PCT: 0.26 ± 0.09) (p < 0.001 for all comparisons). Subgroup analysis within poorly controlled diabetics (HbA1c > 7%) also showed significantly higher platelet indices in those with DR compared to those without. Our findings underscore a significant association between elevated platelet indices and the presence of DR in patients with T2DM, independent of glycemic control status. These indices could serve as valuable surrogate markers for identifying individuals at risk of developing DR, facilitating early intervention strategies in clinical practice.

Image enhancement is a vital stage in computer vision and medical imaging because it improves the clarity, comprehensibility, and quality of the images, allowing for further analysis. Traditional enhancement approaches are limited in their usefulness in real-world applications due to issues such as noise, resolution limitations, and structural degradation. This paper investigates the application of deep learning-driven augmentation to address these difficulties, focusing on three cutting-edge convolutional neural network models: MobileNetV2, Xception, and DenseNet121. We assessed the performance of each model using a range of criteria after it was used to improve the input photos. These measures included MSE, PSNR, RMSE, MAE, SSIM, FSIM, and the Universal Image Quality Index (UIQI). We used SSIM to assess structural similarity, and FSIM to assess feature similarity. In terms of perceptual quality and structural integrity, Xception consistently outperformed the other groups in the trials, especially when using SSIM and PSNR measurements. However, all three models did an excellent job of upgrading photographs. MobileNetV2 and DenseNet121 shown commendable efficacy with minimal computational resources. The study shows that deep learning approaches provide a more reliable and resilient method of image reconstruction than traditional methods. This study shows that combining lightweight architectures with deep CNNs produces highquality images for a wide range of real-time applications.

Fast dissolving tablets (FDTs) are designed to disintegrate rapidly in the oral cavity without the need for water, enhancing patient compliance. Piroxicam, a non-steroidal anti-inflammatory drug (NSAID) with poor solubility, can be formulated as FDTs to improve its bioavailability and therapeutic efficacy. In this study, FDTs of piroxicam were prepared using direct compression and sublimation techniques. Various sublimating agents, such as camphor, was employed to create a porous structure, enhancing the dissolution rate. The prepared tablets were evaluated for post-compression parameters, including weight variation, friability, hardness, wetting time, disintegration time, and in vitro drug release. The results demonstrated that tablets formulated using sublimation exhibited faster disintegration and improved dissolution compared to those prepared by direct compression alone.

Anti-aging creams have gained widespread popularity as a solution to combat the visible signs of aging, such as wrinkles, sagging, and uneven skin tone. These creams typically contain a variety of active ingredients designed to address different aspects of skin aging, including moisturizing, antioxidant effects, and promoting biological activity. Common ingredients found in anti-aging formulations include retinol, hyaluronic acid, peptides, and various plant extracts, each offering unique benefits in enhancing skin health. Retinol, for instance, is known for stimulating collagen production and improving skin texture, while hyaluronic acid helps retain moisture, reducing the appearance of fine lines. Furthermore, the use of antioxidants protects the skin from oxidative stress caused by free radicals, one of the primary contributors to premature aging. The effectiveness of anti-aging creams is determined through a range of evaluations, such as pH measurement, viscosity testing, spreadability, stickiness, and stability tests. These tests ensure that the product not only delivers the desired results but also remains safe and effective over time. Despite their popularity, the efficacy of these creams can vary depending on the formulation and the active ingredients used. This review explores the current understanding of anti-aging creams, their active components, and the methods used to assess their effectiveness, providing insights into their potential benefits and limitations

Fungi are increasingly recognized as significant human pathogens, making effective diagnosis and treatment crucial. While the introduction of new antifungal agents over the past two decades has advanced treatment options, rising drug resistance presents a challenge. Nanoparticles (NPs) have emerged as a promising solution for delivering antifungal drugs, offering greater inhibitory effects at lower concentrations compared to traditional antibiotics. Key issues such as reduced efficacy, limited tissue penetration, poor solubility, and decreased bioavailability often hinder antifungal treatments. Incorporating antifungal drugs into various NP delivery systems aims to overcome these limitations. This review discusses different types of NPs used for antifungal delivery and highlights their advantages over conventional drugs.

Abstract: Dielectric constant (real and imaginary parts) of moist soils is calculated at microwave frequency with an empirical model. The model equation is generated by curve fitting experimental data with second order polynomial expression. Two types of sandy loam samples are used to estimation of dielectric constant at frequencies range from 4.0 GHz to 18.0 GHZ. Both soil samples (sandy loam) are differentiated according to their textural composition. The real part of dielectric constant is negatively corelated with microwave frequency while imaginary parts of soil increase as frequency increases.

Conventional learning methodologies in chemistry had limited its efficacy with respect to the subject adaptation by the learner. Innovative teaching learning methodologies such as experiential learning, blended learning, and problem-based learning are considered the evolutionary methods for the subject adaptation by the learner. Implementation of blended learning technology in analytical chemistry had not been explored at undergraduate level. Design of blended learning methodology of the subject instrumental methods of analysis during the classroom sessions and its efficiency over conventional teaching was assessed using feedback from questionnaires. The questionnaire included feedback that was centered on the two main goals of assisting students in adopting the subject and improving their learning. A questionnaire based on students' learning-centric behavior was created in order to examine their affirmative answers to the same.