Direct UPLC-MS-MS validated method for the quantification of 5-aminolevulinic acid: application to in-vitro assessment of colonic-targeted oral tablets

A reliable, sensitive, specific, and rapid ultra-performance liquid chromatography-tandem mass spectrometric (UPLC-MS-MS) method was developed for the determination of 5-aminolevulinic acid (5-ALA) in orally-administered colonic delivery system. The prepared system is a compression-coated tablet using granulated chitosan as the coat layer. L-Tyrosine (TYR) was used as an internal standard with no need for derivatization. The chromatographic system consisted of Acquity UPLC BEH C18 column and isocratic mobile phase composed of acetonitrile and 0.1% formic acid with a flow rate of 2.5 min. The assay was based on ESI+ mode in a multiple reaction monitoring (MRM) transitions at m/z 132.08 > 86.0 and m/z 132.08 > 114.0 and m/z 182.1 > 91.2 for 5-ALA and TYR, respectively. Limit of quantification was 5.0 ng/mL and the calibration curve was linear (r(2) = 0.994). Within-run precision and between-run repeatability were expressed as relative standard deviation and were lower than 2.5%. The recoveries from control samples were > 95%. The method was successfully applied for evaluation in assay and release profile of 5-ALA colon targeted tablets media containing suspended rat cecal contents pH 6.8 medium (colonic) for colonic delivery.     

Inference for a progressive stress model from Weibull distribution under progressive type-II censoring

Based on progressively type-II censored samples, this paper considers progressive stress accelerated life tests when the lifetime of an item under use condition follows the Weibull distribution with a scale parameter satisfying the inverse power law. It is assumed that the progressive stress is directly proportional to time and the cumulative exposure model for the effect of changing stress holds. Point estimation of the model parameters is obtained graphically by using Weibull probability paper plot that serves as a tool for model identification and also by using the maximum likelihood method. Interval estimation is performed by finding approximate confidence intervals (CIs) for the parameters as well as the studentized-t and percentile bootstrap CIs. Monte Carlo simulation study is carried out to investigate the precision of the estimates and compare the performance of CIs obtained. Finally, two examples are presented to illustrate our results.     

Flutter analysis of a viscoelastic tapered wing under bending–torsion loading

The dynamic stability of a tapered viscoelastic wing subjected to unsteady aerodynamic forces is investigated. The wing is considered as a cantilever tapered Euler–Bernoulli beam. The beam is made of a linear viscoelastic material where Kelvin–Voigt model is assumed to represent the viscoelastic behavior of the material. The governing equations of motion are derived through the extended Hamilton’s principle. The resulting partial differential equations are solved via Galerkin’s method along with the classical flutter investigation approach. The developed model is validated against the well-known Goland wing and HALE wing and good agreement is obtained. Different solution methods, namely; the k method, the p-k method, and the flutter determinant method are compared for the case of elastic wing. However, when the viscoelastic damping is introduced, the k and p-k methods become less effective. The flutter determinant method is modified and employed to carry out non-dimensional parametric study on the Goland wing. The study includes the effects of parameters such as the taper ratio, the density ratio, the viscoelastic damping of wing structure and many other parameters on the flutter speed and flutter frequency. The study reveals that a tapered wing would be more dynamically stable than a uniform wing. It is also observed that the viscoelastic damping provides wider stability region for the wing. The investigation shows that the density ratio, bending-to-torsion frequency ratio, and the radius of gyration have significant effects on the dynamic stability of the wing. Based on the obtained results, a wing with an elastic center and inertial center that are located closer to the mid-chord would be more dynamically stable.     

Utility of 2,3-dichloro-5,6-dicyano-p-benzoquinone in assay of codeine, emetine and pilocarpine

A simple and sensitive spectrophotometric method for the assay of codeine, emetine and pilocarpine is described, based on the interaction of these drugs (as n-electron donors) with 2,3-dichloro-5,6-dicyano-p-benzoquinone (as pi -acceptor) to give a highly coloured radical anion which exhibits maximum absorption at 460 nm. Formation of the radical anion has been established by electron spin resonance measurements. Beer's law is obeyed for the alkaloids investigated. The assay results are in accord with pharmacopoeial assay results. The procedure is sufficiently sensitive to permit unit dose assay of the individual alkaloids in pharmaceutical formulations.     

Use of p-chloranilic acid for the colorimetric determination of some antimalarials

A simple and sensitive colorimetric method for the assay of quinine sulphate, primaquine diphosphate, amodiaquine hydrochloride and pyrimethamine is described. The method is based on the interaction of the drugs and p-chloranilic acid to give a stable product with an intense colour which can be used for the determination of these antimalarials in their pharmaceutical preparations.     

Changes in Whey Proteome between Mediterranean and Murrah Buffalo Colostrum and Mature Milk Reflect Their Pharmaceutical and Medicinal Value

Milk represents an integrated meal for newborns; its whey protein is rich in many health beneficial components and proteins. The current study aimed to investigate the differences between colostrum and mature milk from Mediterranean and Murrah buffaloes using labeled proteomics and bioinformatics tools. In the current work, LC-MS/MS analysis led to identification of 780 proteins from which 638 were shared among three independent TMT experiments. The significantly changed proteins between the studied types were analyzed using gene ontology enrichment and KEGG pathways, and their interactions were generated using STRING database. Results indicated that immunological, muscular development and function, blood coagulation, heme related, neuronal, translation, metabolic process, and binding proteins were the main terms. Overall, colostrum showed higher levels of immunoglobulins, myosins, actin, neurofascin, syntaxins, thyroglobulins, and RNA-binding proteins, reflecting its importance in the development and activity of immunological, muscular, cardiac, neuronal, and thyroid systems, while lactoferrin and ferritin were increased in mature milk, highlighting its role in iron storage and hemoglobin formation.     

A Hybrid Deep Learning Model for Brain Tumour Classification

A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.