In this study, hydroxyapatite (HAp) and gelatin (GEL) scaffolds were prepared to mimic the mineral and organic component of natural bone. The raw material was first compounded and resulting composite were molded into the petridishes. Using Solvent casting process, it is possible to produce scaffolds with mechanical and structural properties close to natural trabecular bone.The mechanical properties of composites were investigated by Thermo-mechanical analyzer (TMA), Vickers microhardness tester, Universal testing machine. It was observed that the composite has maximum tensile strength of 37.13MPa ( oven drying) and % elongation of 7.68 (Oven drying) and 2.04 (Natural drying) at 15% of Hap respectively. These results demonstrate that the prepared composite scaffold is a potential candidate for bone tissue engineering.
Microcrystalline cellulose (MCC) is an important ingredient in pharmaceutical, food, cosmetic and other industries. Microcrystalline cellulose was synthesized from the alpha cellulose content of pretreated cotton, Bombax ceiba L. by hydrochloric acid hydrolysis. The prepared microcrystalline cellulose was characterized by determining some physicochemical properties such as pH, angle of response, Carr's index, Hausner ratio, moisture content etc and compared with commercial-grade microcrystalline cellulose that is used in pharmaceutical industry as excipient. Scanning electron microscope (SEM) and FTIR data represented the structure and particle characterization of sample. Thermal gravimetric analysis (TGA) showed the thermal stability of the sample. The results showed that the yield of microcrystalline cellulose was about 85% and compared favorably with the commercial grade microcrystalline cellulose as well as conformed official specifications for microcrystalline cellulose in British Pharmacopeia. It was also found that the duration of acid hydrolysis affected the polymeric form of the processed alpha cellulose.
In this study, two chemometric techniques, partial least-square regression (PLSR) and artificial neural network (ANN) were developed and compared for the simultaneous assay of paracetamol (PCT) and caffeine (CAF) in pharmaceutical formulations by using spectrophotometric data. Six different concentrations of paracetamol and caffeine were considered to make mixture solutions of standard samples by using orthogonal experimental design (OED). UV spectra of these mixtures were recorded in the wavelength range of 205-300 nm versus a solvent blank and digitized absorbance was sampled at 1 nm intervals. Drug concentrations and instrumental spectra of 36 mixture solutions were used for model development and validation and finally 6 commercially available tablets were used to test the developed models. ANN shows better prediction efficiencies than that of PLSR with R2 value 99.28% for prediction and 99.13% for validation set. These two models were successfully applied to commercial pharmaceutical formulations, and it is found by ANN that the drugs contain 75 to 86% of paracetamol and 77 to 92% of caffeine of their label claim. Either of the proposed methods is simple and rapid and can be easily used in the quality assessment of drugs as an alternative analytical method. Bangladesh J. Sci. Ind. Res.54(3), 215-222, 2019
The in vitro evaluation of the physical characteristics of the pharmaceutical products ensures their quality as well as bioavailability and impart optimum therapeutic activity. Ciprofloxacin HCl, a widely used antibiotic to treat different types of bacterial infections, was chosen for this in vitro comparative study of different pharmaceutical company. The present study compared the content uniformity, weight variation, hardness, friability, thickness, diameter, disintegration and dissolution ability of five brands of ciprofloxacin HCl tablets marketed in Bangladesh to confirm whether they follow USP guidelines. All five brands of ciprofloxacin HCl tested meet the specification of the USP for content uniformity, weight variation, hardness, friability, thickness, diameter, disintegration and dissolution. The amount of active ciprofloxacin HCl varies from 244.46 mg to 248.46 mg among the products. The average hardness and friability of the products varies 73.9 N to 77.6 N and 0.013% to 0.031%, respectively. All the brands had shown disintegration time 5 to 8 minutes while they showed 80 to 95 % release of active ingredient within 30 minutes in dissolution testing. This may confirm the absorption of the drug from gastrointestinal tract for optimum therapeutic effect.Bangladesh J. Sci. Ind. Res. 50(4), 251-256, 2015
There is a large variety and trademarks of vegetable oils in Bangladesh. The oils have characteristics very similar to each other and often cannot be classified by the observation of color, odor or taste. This paper proposes a vibrational spectroscopic method like FTIR in association with chemometric techniques to classify vegetable oils like: sunflower, mustard, sesame, soybean, castor, olive and palm oils from different manufacturers. In the FTIR spectra of oil, as information about fatty acid composition is concentrated in the range of 4400-200 cm-1 principal component analysis (PCA) was applied on the standardized full FTIR spectral data of this region for vegetable oils to totally capture the FTIR spectral pattern; seven varieties of vegetable oils could be successfully classified from their PCA scores. PCA of FTIR spectra of different known vegetable oils is used to determine the identity of several unknown vegetable oils. The unknowns are then analyzed, plotted, and identified based on their proximity to the known in principal component space. For the multivariate analysis PCA and soft independent modeling of class analogy (SIMCA) and support vector machines (SVMs) were used. 85% and 14% variability of data was explained by PC1 and PC2 respectively. PC1 has strong positive correlation with soybean, sunflower, palm and olive oil while strong negative correlation with mustard, castor and sesame oil. Soybean oils are positively and sesame oils are negatively correlated with PC2. Unknown oil samples can be identified properly by used supervised methods i.e. SIMCA, SVM by developing model with the help of PCA. The major interest of this method using chemometric analysis of spectral data is in their rapidity, since no chemical treatment of samples is required.
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