Risperidone is an atypical antipsychotic drug that is used for treating schizophrenia, bipolar mania, and autism. Risperidone rebalances dopamine and serotonin to improve thinking, mood, and behavior by working on dopamine and serotonin α₂receptor antagonism. Risperidone has poor solubility and high permeability through the intestine, so it belongs to Biopharmaceutical Classification System (BCS) class II exhibits poor oral biopharmaceutical properties.

 The aim of the present work was to improve solubility and dissolution of Risperidone by preparing nanosuspension using different stabilizers and different solvents‎ in a method known as solvent-antisolvent precipitation method.  Twenty-eight formulas were prepared

     Pseudomonas aeruginosa produces an extracellular biofilm matrix that consists of nucleic acids, exopolysaccharides, lipid vesicles, and proteins. Alginate, Psl and Pel are three exopolysaccharides that constitute the main components in biofilm matrix, with many biological functions attributed to them, especially concerning the protection of the bacterial cell from antimicrobial agents and immune responses. A total of 25 gentamicin-resistant P. aeruginosa selected isolates were enrolled in this study. Biofilm development was observed in 96% of the isolates. In addition, the present results clarified the presence of pelA and pslA in all the studied isolates. The expression of these genes was very low. Even though all biof

            Atorvastatin (ATR) is poorly soluble anti-hyperlipidemic drug; it belongs to the class II group according to the biopharmaceutical classification system (BCS) with low bioavailability due to its low solubility. Solid dispersions adsorbate is an effective technique for enhancing the solubility and dissolution of poorly soluble drugs.

          The present study aims to enhance the solubility and dissolution rate of ATR using solid dispersion adsorption technique in comparison with ordinary solid dispersion. polyethylene glycol 4000 (PEG 4000), polyethylene glycol 6000 (PEG 6000), Poloxamer188 and Poloxam

Abstract Bilastine, a second-generation antihistamine, is commonly prescribed for managing allergic rhinoconjunctivitis and urticaria due to its prolonged action. However, its therapeutic potential is constrained by poor water solubility and low oral bioavailability. This study aimed to enhance bilastine dissolution and patient compliance by formulating a nanosuspension-based orodispersible film (ODF). An anti-solvent precipitation method was employed to produce nanosuspension using different hydrophilic stabilizers (Soluplus®, Poloxamer 188, and PEG 6000). The influence of formulation parameters, such as the stabilizer ratio, the anti-solvent ratio, stirring speed, and the stabilizer type, on particle size and polydispersity index (PDI)

Fifteen local isolates of Pseudomonas were obtained from several sources such as soil, water and some high-fat foods (Meat, olives, coconuts, etc.). The ability of isolates to produce lipase was measured by the size of clear zone on Tween 20 solid medium and by measuring the enzymatic activity and specific activity. Isolate M3 (as named in this study) was found to be the most efficient for the production of the lipase with enzymatic activity reached 56.6 U/ml and specific activity of 305.94 U/mg. This isolate was identified through genetic analysis of the 16S rRNA gene. and it was shown that the isolate M3 belongs to Pseudomonas aeruginosa with 99% similarity. The DNA of isolate M3 was extracted and lipase gene was amplified through PCR tec

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD