It is generally accepted that there are two spectrophotometric techniques for quantifying ceftazidime (CFT) in bulk medications and pharmaceutical formulations.  The methods  are described as simple, sensitive, selective, accurate and efficient techniques. The first method used an alkaline medium to convert ceftazidime to its diazonium salt, which is then combined with the 1-Naphthol (1-NPT) and 2-Naphthol (2-NPT) reagents. The azo dye that was produced brown  and red in color with absorption intensities of ƛmax 585 and 545nm respectively. Beer's law was followed in terms of concentration ranging from  (3-40) µg .ml^-1 For (CFT-1-NPT) and (CFT-2-NPT), the detection limits were 1.0096 and 0.8017 µg.ml^-1, respec

          Simple, cheap, sensitive, and accurate kinetic- spectrophotometric method has been developed for the determination of naringenin in pure and supplements formulations. The method is based on the formation of Prussian blue. The product dye exhibits a maximum absorbance at 707 nm. The calibration graph of naringenin was linear over the range 0.3 to 10 µg ml^-1 for the fixed time method (at 15 min) with a correlation coefficient (r) and percentage linearity (r²%) were of 0.9995 and 99.90 %, respectively, while the limit of detection LOD was 0.041 µg ml^-1. The method was successfully applied for the determination of naringenin in supplements with satisfac

This study was aimed to determine bone formation markers (OST and BALP) and lysyl oxidase in diabetes and non-diabetes Iraqi acromegaly patients in addition to find the relationship among these parameters. The present study conducted 60 acromegalic patients (30 diabetes & 30 non diabetes) attending National Diabetes Center / AL-Mustansiriya University/Baghdad, and 30 healthy individuals as a control group aged (35-60) years. All patients were administrated Sandostatin drug, and they were diagnosed by physician in the hospital.FBG, GH, IGF-1, OST, BALP, and LOX were determined in all groups. The results showed a highly significant rise in all parameters (GH, IGF-1, FBG, OST, BALP, and LOX values in serum of all patients when compared with n

A rapid, sensitive and without extraction spectrophotometric method for determination of clonazepam (CLO) in pure and pharmaceutical dosage forms has been described. The proposed method was simply depended on charge transfer reaction between reduced CLO (n-donor) and metol (N-methyl-p-aminophenol sulfate) as a chromogenic reagent (π- acceptor). The reduced drug, with zinc and concentrated hydrochloric acid, produced a purple colored soluble charge-transfer complex with metol in the presence of sodium metaperiodate in neutral medium, which has been measured at λmax 532 nm. All the variables which affected the developed and the stability of the colored product such as concentration of reagent and oxidant, temperature and time of rea

A simple, accurate, precise, rapid, economical and a high sensitive spectrophotometric method has been developed for the determination of tadalafil in pharmaceutical preparations and industrial wastewater samples, which shows a maximum absorbance at 204 nm in 1:1 ethanol-water. Beer's law was obeyed in the range of 1-7?g/ mL ,with molar absorptivity and Sandell ? s sensitivity of 0.783x105l/mol.cm and 4.97 ng/cm2respectively, relative standard deviation of the method was less than 1.7%, and accuracy (average recovery %) was 100 ± 0. 13. The limits of detection and quantitation are 0.18 and 0.54 µg .ml-1, respectively. The method was successfully applied to the determination of tadalafil in some pharmaceutical formulations

This approach was developed to achieve an accurate, fast, economic and sensitivity to estimation of diphenhydramine Hydrochloride. The dye that produced via reaction between diphenhydramine HCl with thymol blue in acidic medium pH ≈ 4.0. The ion pair method include an optimization study to formed yellowcolored that extraction by liquid – liquid method. The product separated of complexes by using by chloroform solution measured spectrophotometry at 400 nm. The analysis data at optimum conditions showed that linearity concentration in a range of calibration curve 1.0 – 50 μg /mL, limit of detectionand limit of quantification 0.0786 and 0.2358 μg/mL respectively. The molar absorptivity and Sandell’s sensitivity were 1.8 × 10 -4 L/mo

The role of filamentous bacteria represented by Streptomycessp was studied as biological treatment for activated sludge AL- Restomia treatment unit in Baghdad city. The result shows reducing in phosphate concentration where apprise in started entrance the treatment unit 12.083 mg/L fast the unit stages reached to 8.426 mg /L where nitrate concentration apprises 3.59 mg/l and ending in 2.43 mg/L The concentration of ammonia apprises 1358 mg/L and reached to 140 mg/L. also the TDS concentration reduced from 1426 to 1203 mg/L where nutrient which represented (SO4, Mg, Ca, Na, K) reduced by range 30.883- 23.337 , 194- 121 , 440- 321 , 109.03- 101.53 and 16.85- 15.4mg/L respectively COD reduce from427.263- 82mg/L with absorbance0.018- 0.027

In this study, measuring effectiveness Alauriz in some seeds powder and found that the seeds of-sophistication sex had the highest effective enzymatic reach 353 units / gProtein and Alkabbatah study enzyme extract under different storage conditions and Altaj showed that the enzyme loses Thbatih and whether Mrkbhetwan adding Alklasrin concentration of 10% for the systematic solution

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a