Identification of novel and safe fungicidal molecules against fusarium oxysporum from plant essential oils: in vitro and computational approaches.

Phytopathogenic fungi are serious threats in the agriculture sector especially in fruit and vegetable production. The use of plant essential oil as antifungal agents has been in practice from many years. Plant essential oils (PEOs) of Cuminum cyminum, Trachyspermum ammi, Azadirachta indica, Syzygium aromaticum, Moringa oleifera, Mentha spicata, Eucalyptus grandis, Allium sativum, and Citrus sinensis were tested against Fusarium oxysporum. Three phase trials consist of lab testing (MIC and MFC), field testing (seed treatment and foliar spray), and computer-aided fungicide design (CAFD). Two concentrations (25 and 50 μl/ml) have been used to asses MIC while MFC was assessed at four concentrations (25, 50, 75, and 100 μl/ml). C. sinensis showed the largest inhibition zone (47.5 and 46.3 m2) for both concentrations. The lowest disease incidence and disease severity were recorded in treatments with C. sinensis PEO. Citrus sinensis that qualified in laboratory and field trials was selected for CAFD. The chemical compounds of C. sinensis PEO were docked with polyketide synthase beta-ketoacyl synthase domain of F. oxysporum by AutoDock Vina. The best docked complex was formed by nootkatone with -6.0 kcal/mol binding affinity. Pharmacophore of the top seven C. sinensis PEO compounds was used for merged pharmacophore generation. The best pharmacophore model with 0.8492 score was screened against the CMNP database. Top hit compounds from screening were selected and docked with polyketide synthase beta-ketoacyl synthase domain. Four compounds with the highest binding affinity and hydrogen bonding were selected for confirmation of lead molecule by doing MD simulation. The polyketide synthase-CMNPD24498 showed the highest stability throughout 80 ns run of MD simulation. CMNPD24498 (FW054-1) from Verrucosispora was selected as the lead compound against F. oxysporum

Prevalence, patterns and predictive factors of non-alcoholic fatty liver disease among morbidly obese patients undergoing sleeve gastrectomy

BackgroundObesity related non-alcoholic fatty liver disease (NAFLD) is increasingly recognized worldwide.AimsWe aim to describe prevalence, histologic patterns, and risk factors of NAFLD in morbidly obese patients undergoing sleeve gastrectomy.MethodsA prospective study included 49 obese patients undergoing sleeve gastrectomy, with concomitant true cut liver biopsy. Exclusion criteria included history of alcohol intake, liver disease, or hepatotoxic agents’ intake. Clinical, biochemical, and histological features were evaluated. Histological patterns were classified based on the NIH-sponsored NASH Clinical Research Network NAFLD Activity Score (NAS).ResultsSeventy-three per cent were females, mean age 34 (range 17–58). Mean BMI was 43 (35–52). 45 patients (91.8 per cent) showed NAFLD. Nineteen (39 per cent) showed non-alcoholic steatohepatitis (NASH) and 5 (10 per cent) showed fibrosis. 4 biopsies (8 per cent) were normal. About 31 per cent of NAFLD patients had metabolic syndrome as defined by the international diabetes federation consensus. While nineteen patients (38.5 per cent) had abnormality in one or both transaminase levels, 71 per cent of patients with elevated AST had NASH. The prevalence of dyslipidaemia (abnormal lipid profile) in all study patients was found to be 47 per cent. 24 per cent of NAFLD patients and 16 per cent of NASH patients had DM.ConclusionNAFLD has a very high prevalence among our morbidly obese patients. Multiple biochemical abnormalities were evident in association with the histological changes detected in NAFLD categories. Intraoperative liver biopsy is safe during sleeve gastrectomy for the diagnosis of NAFLD

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

DETERMINATION OF ACYCLOVIR IN RABBIT PLASMA BY HIGH PERFORMANCE LIQUID CHROMATOGRAPHIC (HPLC) TECHNIQUE

A rapid, sensitive and simple reversed-phase high-performance liquid chromatographic (HPLC) method has been developed and validated for the determination of acyclovir (ACV) in rabbit plasma. BDS C18 column was used to conduct analysis using ammonium dihydrogen phosphate buffer (50mM) and methanol as mobile phase (98:2), with pH adjusted to 2.5 using orthophosphoric acid. Flow rate was kept at 1 mL/min. Selective precipitation of plasma proteins were done by adding 5% perchloric acid. Precipitated plasma proteins were separated by centrifugation. ACV moves in a supernatant, which was snapped and passed through a syringe filtration assembly. Direct injection of supernatant was given into a BDS C18 column and ACV was detected at 256 nm. The limit of detection for ACV in plasma was estimated as 15ng/mL whereas the limit of quantitation was calculated as 25 ng/mL. Moreover, the developed method has been found to be selective and linear into concentration range of 25 – 2000 ng/mL. The present method could be successfully applied to samples from bioavailability and bioequivalence studies

USE OF GLUTARIC ACID TO IMPROVE THE SOLUBILITY AND DISSOLUTION PROFILE OF GLIPIZIDE THROUGH PHARMACEUTICAL COCRYSTALLIZATION

The purpose of current study was to improve the solubility and dissolution profile of BCS class-II drug Glipizide using glutaric acid as a coformer via various cocrystalization techniques i.e., dry grinding, liquid assisted grinding, slurry and solvent evaporation. Fourier Transform Infrared Spectroscopy (FTIR) was performed to determine the interaction between components of glipizide-glutaric acid (GPZ-GLU) cocrystals. Powder X-ray Diffraction (PXRD) studies confirmed the crystalline nature of formulated cocrystals. Scanning Electron Microscopy (SEM) revealed cylindrical to rectangular shape of cocrystals. Flow properties of GPZ-GLU cocrystals were evaluated by micromeritics analysis. Size and surface morphology was determined by zeta sizer analysis and optical microscopy. Differential scanning calorimetry (DSC) and Thermogravimetric (TGA) analysis were performed to determine the melting points as well as thermal stability of pure components and formulated GPZ-GLU cocrystals. In-vitro drug release studies were carried out using dissolution apparatus-II. GPZ-GLU cocrystals showed higher drug release at pH 6.8 as compared to pH 1.2. However, percent drug release of optimum formulations at pH 6.8 was determined as; 24%-92.2% (F3) and 12.0%-93.5% (F7). Solubility studies revealed improved solubility as compared to pure drug in water i.e., 53 folds and 54.27 folds from F3 and F7 cocrystals, respectively. Finally it was concluded that glutaric acid has improved the solubility and dissolution profile of glipizide. However, many cocrystal formers have been reported in literature that can be used to enhance the physicochemical properties as well as bioavailability of poorly soluble drugs via cocrystalization technique

Machine Vision Approach for Diagnosing Tuberculosis (TB) Based on Computerized Tomography (CT) Scan Images

Tuberculosis is curable, still the world’s second inflectional murderous disease, and ranked 13th (in 2020) by the World Health Organization on the list of leading death causes. One of the reasons for its fatality is the unavailability of modern technology and human experts for early detection. This study represents a precise and reliable machine vision-based approach for Tuberculosis detection in the lung through Symmetry CT scan images. TB spreads irregularly, which means it might not affect both lungs equally, and it might affect only some part of the lung. That’s why regions of interest (ROI’s) from TB infected and normal CT scan images of lungs were selected after pre-processing i.e., selection/cropping, grayscale image conversion, and filtration, Statistical texture features were extracted, and 30 optimized features using F (Fisher) + PA (probability of error + average correlation) + MI (mutual information) were selected for final optimization and only 6 most optimized features were selected. Several supervised learning classifiers were used to classify between normal and infected TB images. Artificial Neural Network (ANN: n class) based classifier Multi-Layer Perceptron (MLP) showed comparatively better and probably best accuracy of 99% with execution time of less than a second, followed by Random Forest 98.83%, J48 98.67%, Log it Boost 98%, AdaBoostM1 97.16% and Bayes Net 96.83%