FORMULATION AND DEVELOPMENT OF FENOFIBRATE LOADED LIPOSPHERE SYSTEM

Lipospheres offers a new approach to improve the solubility of poorly soluble drug. Fenofibrate is a third-generation fibric acid derivative belonging to BCS class-II, employed clinically as a hypolipidemic agent to lessen the risk caused by atherosclerosis. An attempt was made to improve aqueous solubility of FNO by aid of stearic acid and Paraffin oil.  The liposphere of FNO was prepared by melt dispersion technique using ultra turrax with %yield of 38% to 46% followed by their evaluation for saturation solubility, IR spectra, DSC, in-vitro study. Saturation solubility of FNO (92µg/ml) had improved to 184.31µg/ml with physical mixture of stearic and paraffin oil. Therefore, lipospheres of FNO were prepared using melt dispersion technique. The factorial batches were formulated using 32 factorial design with variables X1- concentration of stearic acid and X2- concentration of paraffin oil and responses Y1 - % Drug Entrapment (%DE) and Y2 - % Drug Release (% DR). The optimized batch was formulated and evaluated for Saturation Solublity, % DR, Invivo Study Thus from the present study it can be concluded that solubility of BCS class-II drugs can be improved by liposphere system. aid } �tapf�pÍ©and Paraffin oil.  The liposphere of FNO was prepared by melt dispersion technique using ultra turrax with %yield of 38% to 46% followed by their evaluation for saturation solubility, IR spectra, DSC, in-vitro study. Saturation solubility of FNO (92µg/ml) had improved to 184.31µg/ml with physical mixture of stearic and paraffin oil. Therefore, lipospheres of FNO were prepared using melt dispersion technique. The factorial batches were formulated using 32 factorial design with variables X1- concentration of stearic acid and X2- concentration of paraffin oil and responses Y1 - % Drug Entrapment (%DE) and Y2 - % Drug Release (% DR). The optimized batch was formulated and evaluated for Saturation Solublity, % DR, Invivo Study Thus from the present study it can be concluded that solubility of BCS class-II drugs can be improved by liposphere system.Keyword: Fenofibrate, Melt dispersion Technique, Liposphere.Â

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

© 2023 Tech Science Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model is trained from scratch to determine the classification success rate. 10,000 tomato leaf images (1000 images per class) from the publicly accessible dataset, covering one healthy category and nine disease categories, are utilized in training the proposed DTomatoDNet approach. More specifically, we classified tomato leaf images into Target Spot (TS), Early Blight (EB), Late Blight (LB), Bacterial Spot (BS), Leaf Mold (LM), Tomato Yellow Leaf Curl Virus (YLCV), Septoria Leaf Spot (SLS), Spider Mites (SM), Tomato Mosaic Virus (MV), and Tomato Healthy (H). The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%, demonstrating excellent accuracy in differentiating between tomato diseases. The model could be used on mobile platforms because it is lightweight and designed with fewer layers. Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.Peer reviewe

Tyrosinase inhibition: conformational analysis based studies on molecular dynamics calculations of bipiperidine based inhibitors.

Two series of variably N-substituted biperidines were synthesized by condensing various acid chlorides, alkyl halides and anhydrides with 1,4-bipiperidine. The new compounds were tested as tyrosinase inhibitors and a structure-activity relationship (SAR) study was carried out. Potent inhibition was observed in the case of the 4'-methylbenzyl substitution on this atom (IC50 = 1.72 microM) with this compound being a lead for future drug design. Additionally, calculations of the important QSAR molecular descriptors were done on the biperidine analogues after their 2 ps molecular dynamics (MD) simulations using molecular mechanics force field (MMFF) approaches. Using MD simulations potential and total energies were calculated for the energy minimized models of bipiperidine and the most active analogs 2, 3, 4, 6, 8 and 10

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy