Validated Chromatographic Methods for the Simultaneous Determination of Sodium Cromoglycate and Oxymetazoline Hydrochloride in a Combined Dosage Form

Two chromatographic methods were developed and validated for the simultaneous determination of Sodium Cromoglycate (SCG) and Oxymetazoline Hydrochloride (OXMT). SCG and OXMT are administered in combination for effective treatment of nasal congestion and allergy. The first chromatographic method was based on usingaluminum TLC plates pre-coated with silica gel GF254 as the stationary phase and chloroform: methanol: toluene: triethylamine (5: 2: 4:1, by volume) as the mobile phase followed by densitometric measurement of the separated bands at 235 nm. The second method is a high performance liquid chromatographic method for separation and determination of SCG and OXMT using reversed phase C18 column with isocratic elution. The mobile phase composed of acetonitrile: methanol (2: 1, v/v) at flow rate of 1.0 mL/ min. Quantitation was achieved with UV detection at 220 nm. The validity of the proposed methods was assessed using the standard addition technique. The obtained results were statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p = 0.05

Spectral resolution and simultaneous determination of oxymetazoline hydrochloride and sodium cromoglycate by derivative and ratio-based spectrophotometric methods

Sodium cromoglycate (SCG) and oxymetazoline hydrochloride (OXMT) are administered in combination for effective treatment of nasal congestion and allergy. In this work, SCG was determined using direct spectrophotometry by measuring its zero order absorption spectra at its λmax 320.6 nm where OXMT showed zero absorbance. On the other hand, four simple, sensitive and precise spectrophotometric methods were developed and validated for the determination of OXMT in the presence of SCG in their laboratory prepared mixtures and pharmaceutical formulation, without preliminary separation; Method A: first derivative spectrophotometric method [1D], Method B: first derivative of ratio spectra method [1DD], Method C: ratio difference spectrophotometric method [RDSM] and Method D: ratio subtraction method [RSM]. Ratio manipulating methods (Method B, C and D) were done using divisor of 10.00 µg/mL SCG. Linear correlation was obtained in range 4-22 µg/mL for OXMT by methods A, B and D and 6-22 µg/mL for method C. All methods were validated in compliance with the International Conference on Harmonization (ICH) guidelines and satisfactory results were obtained. No significant difference was noted between the developed methods and the official one with respect to accuracy and precision

Applications of Datamining Techniques for Predicting the Post - Covid 19 Symptoms in Saudi Arabia, Jazan

  Background The entire world was combating COVID-19; however, a significant proportion of patients demonstrate the persistence of some COVID-19 symptoms, new symptom development, or exaggeration of pre-existing disease after a negative viral load. They are referred to as a post-COVID-19 syndrome. According to various researches, COVID-19 has a wide range of long-term effects on virtually all systems, including the respiratory, cardiovascular, gastrointestinal, neurological, mental, and dermatological systems. Finding the various symptoms of post-acute and chronic is critical since they might have a significant impact on the patients' everyday functioning. As a result, we aimed to distinguish the symptoms immediately after the initial phase in which the symptoms affected them for more than three weeks using data mining techniques. Methodology: Post-COVID conditions do not affect everyone the same way. They can cause various types and combinations of symptoms in different people. The purpose of this research is to analyse the complications of post covid-19 syndrome. The purpose of Data mining is for discovering the knowledge from vast amount of database. To classify the symptoms of post covid-19, data mining techniques is used. In this study, ranking method was used in preprocessing to select subset of attributes for strengthening the rate of accuracy of classifiers. The data were collected through Google form of 384 household of students from Public Health College in Jazan University. The WEKA open-source software is used for this research work under Windows7 environment. An experimental study is carried out using data mining technique such as J48 and Random Forest tree. The data records are classified as six categories such as General symptoms, Nervous symptoms, Respiratory symptoms, Heart symptoms, Digestive symptoms and normal. Result: The performances of classifiers are evaluated through the confusion matrix in terms of accuracy, time taken to build the Model and error rate. It has been concluded that Random Forest Tree gives better accuracy, minimum time taken to build the model and less error rate than the J48 classifier

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Dual antineoplastic and photodynamic effects of methanolic extract of Tecoma stans yellow flowers for cancer treatment

Introduction: Tecoma stans is a fast-growing plant from the family Bignoniaceae. Various parts of T. stans have been used in different biological applications, especially in cancer treatment. Photodynamic therapy (PDT) is a promising modality for cancer treatment that depends on the interaction between a photosensitizer, light, and oxygen. Searching for photosensitizers from plant origin is crucial to provide nontoxic photosensitizers with high economic value. This study aims to evaluate the anticancer and photodynamic activities of T. stans methanolic flower extract (TSFE). Methods: The phytoconstituents of TSFE were analyzed by the UPLC/MS/MS technique. The cytotoxicity of TSFE was examined on the breast carcinoma (MCF-7) and lung carcinoma (A549) cell lines, in dark and after irradiation by blue light (400-450 nm). Results: TSFE contained various phytochemical components with antineoplastic activity. Moreover, TSFE contained coumarins and anthocyanins that may act as photosensitizers. TSFE showed negligible cytotoxicity against MCF-7 cell lines at all tested concentrations in dark. A non-significant cell viability change was observed upon radiation (P>0.05). TSFE showed significant dark cytotoxicity on A549 cells, which improved significantly after light radiation (P<0.05). Conclusion: TSFE is a promising anticancer and natural photosensitizer for PDT and this study may inspire further ethnobotanical investigations into promising new natural anti-cancers and photosensitizers

Evaluation of multiple digital elevation models for hypsometric analysis in the watersheds affected by the opening of the Red Sea

Climate change is increasingly affecting the Red Sea-related terrains in Egypt and Saudi Arabia with a notable increase in heavy precipitation events. This highly vulnerable region to flashfloods and other climate change-driven hazards encompasses rough terrains with more than 11,000 basins/subbasins, which necessitates the accurate estimation of their hydrological and geomorphological parameters among which the hypsometric analysis. In this regard, The study examines the accuracy of the hypsometric analysis extracted using open source SRTM-1, ASTER-GDEM, Copernicus-GLO30, ALOS-DEM against high-resolution Topo-1 m and Topo-2.8 m DEMs for Talat Hamdh basin in Egypt and Wadi El-Salwely basin in Saudi Arabia, respectively. Copernicus-GLO30 shows the highest accuracy among all DEMs with the root-mean-squared–error (RMSE), mean elevation error, standard deviation, maximum and minimum absolute errors of 3.03, 2.0, 2.3, 11.7 and 0.1 m, respectively for Talat Hamdh basin. The findings also show that, regardless of the geology and geomorphic evolution of the basin, the hypsometric analysis is sensitive to the DEM type rather than the spatial resolution as Copernicus DEM yields similar basin numbers (a single basin) and area (1.366 and 141.9 km2) compared to the reference DEMs (1.408 and 154.4 km2) for Talat Hamdh and Wadi El-Salwely basins, respectively. Contrariwise, other open source DEMs yield multiple basins and thus significantly smaller basin area. Given the DEM-type dependence of the hypsometric analysis, the study recommends that large-scale hydrological and geomorphological analyses should consider using a high-resolution reference DEM on a local-scale basin to examine the accuracy of open source DEMs prior to conducting the analysis

A Comparative Study for SDN Security Based on Machine Learning

In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies