Background: Bilastine is a non-sedating, second-generation antihistamine used to treat urticaria and allergic conjunctivitis. Objective: to formulate and test bilastine as a mucoadhesive ophthalmic in situ gel in order to extend its presence at site for longer time and help treat conjunctivitis and allergic rhinitis. Methods: We prepared formulations using different concentrations of poloxamers (Poloxamer 407 (P407) and Poloxamer 188 (P188)) in combination with hydroxypropyl methyl cellulose (HPMC). The prepared formulas were evaluated for their physicochemical properties, sol-gel transition temperature, viscosity, mucoadhesive strength, drug release, and kinetic modeling. Results: The prepared in situ gels were clear and transparen

         Darifenacin hydrobromide (DH) is the more recent uroselective M3 receptor antagonist for treating uncomplicated overactive bladder (OAB). This study was aimed to formulate DH as fast dissolving buccal films (FDBFs) using a solvent casting method to enhance patient’s compliance.

         Films were prepared by using polyvinyl alcohol (PVA) as a film forming polymer. Different types and concentrations of superdisintegrants (croscarmellose sodium, sodium starch glycolate, indion 414) were used to select the best formula by studying the physicochemical properties of the films, disintegration time (DT) and percent drug release.

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“Orodispersible Tablet” a tablet that is to be placed in oral cavity where it disperses rapidly by saliva with no need for water before swallowing. Zaltoprofen (ZLP) is one of NSAIDs which is used in the treatment of rheumatoid arthritis and osteoarthritis as well as to relieve inflammation and pain after surgery, injury and tooth extraction. The present study was aimed to prepare rapidly dissolved lyophilized Zaltoprofen tablet with different pharmaceutical excipients and studying the factors affecting pharmaceutical properties like (solubility, disintegration time DT, dissolution, etc.) of tablets. The lyophilized disintegrating tablets (LDTs) were prepared using Zydis technique by lyophilization an aqueous

Experiment was conducted in Baghdad, three factor were used in this research included Two types of Plows included moldboard and disk plows which represented the main plot, Three forward speeds of the tillage was the second factor included 1.85, 3.75 and 5.62 km / h which represented sup plot , and Three levels of Soil Moisture was third factor included 21, 18 and 14 % in all of Vertical and Lateral Plowing Deviation, Practical and specific productivity, actual time for plowing one donam and appearance (goodness) of Tillage represented by the number of clods > 10 cm in silt clay loam soil with depth 22 cm were studied. the experiment was used Split – split plot design under randomized complete block design with three replications and Le

In this paper, we consider a new approach to solve type of partial differential equation by using coupled Laplace transformation with decomposition method to find the exact solution for non–linear non–homogenous equation with initial conditions. The reliability for suggested approach illustrated by solving model equations such as second order linear and nonlinear Klein–Gordon equation. The application results show the efficiency and ability for suggested approach.

              The current paper proposes a new estimator for the linear regression model parameters under Big Data circumstances.  From the diversity of Big Data variables comes many challenges that  can be interesting to the  researchers who try their best to find new and novel methods to estimate the parameters of linear regression model. Data has been collected by Central Statistical Organization IRAQ, and the child labor in Iraq has been chosen as data. Child labor is the most vital phenomena that both society and education are suffering from and it affects the future of our next generation. Two methods have been selected to estimate the parameter

The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T