Self-emulsifying micelles as a drug nanocarrier system for itraconazole oral bioavailability enhancement; in vitro and in vivo assessment.

Itraconazole (ITZ) is a renowned antifungal medication, however its therapeutic efficacy is limited by low solubility and oral bioavailability. The current research work attempted to augment the oral bioavailability of ITZ by incorporating into self-emulsifying micelles (SEMCs). To fabricate the SEMCs, various preparation techniques including physical mixture, melt-emulsification, solvent evaporation and kneading, were opted by using different weight ratio of drug and solubilizers i.e. Gelucire-50/13 or Gelucire-44/14 and characterized both in vitro and in vivo. The prepared SEMCs were found to be in the size range from 63.4 ± 5.2 to 284.2 ± 19.5 nm with surface charges ranging from -16 ± 1.2 to -27 ± 2.0 mV. The drug solubility was improved to a reasonable extent with all investigated formulations, however, SEMCs in group 6 prepared by kneading method (KMG6) using Gelucire-44/14: drug (10:1 presented 87.6 folds' increase (964.93 ± 2 µg/mL) compared to solubility of crystalline ITZ (11 ± 2 µg/mL) through kneading method. In addition, KMG6 SEMCs shows the fast drug release compared to other SEMCs. Further, KMG6 SEMCs also exhibited 5.12-fold higher relative intestinal serosal fluid absorption compared to crystalline ITZ. The pharmacokinetic parameters such Cmax, AUC and Tmax of KMG6 SEMCs significantly improved compared to crystalline ITZ. In conclusion, the manipulation of ITZ solubility, dissolution rate and absorption using SEMCs is a promising strategy for bioavailability enhancement.

Reimagining global food value chains through effective resilience to COVID-19 shocks and similar future events: A dynamic capability perspective.

The restructuring of global value/supply chains gained increasing attention as the unprecedented COVID-19 echoed around the world. Yet, the COVID-19 related theory-driven, large scale quantitative, and empirical studies are relatively scarce. This study advances the extant literature by empirically investigating how do firms in the global food value chains (GFVCs) re-imagine their businesses structure in response to the COVID-19-becoming more resilient and competitive to the current pandemic and similar future events. We leverage a unique data of 231 senior managers of the Australian GFVCs and examine their firms' response strategies. Drawing upon key insights from the dynamic capability view, we find that GFVCs' competitiveness is achieved when exposure to COVID-19 shocks elicits dynamic capabilities-readiness, response, recovery-and these capabilities work jointly and sequentially to cultivate resilience. A key finding of this study is that firms with domestic plus global value chain partners are more resilient than those having only global business partners. This finding implies that excessive reliance on offshoring sometimes becomes lethal, especially amid unexpected and prolonged global shocks and, therefore, companies should strike a balance between domestic and global business partners to remain competitive. These findings offer important contributions to theory, practice, and UN sustainable development goals.

DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information.

Phosphorylation is a ubiquitous type of post-translational modification (PTM) that occurs in both eukaryotic and prokaryotic cells where in a phosphate group binds with amino acid residues. These specific residues, i.e., serine (S), threonine (T), and tyrosine (Y), exhibit diverse functions at the molecular level. Recent studies have determined that some diseases such as cancer, diabetes, and neurodegenerative diseases are caused by abnormal phosphorylation. Based on its potential applications in biological research and drug development, the large-scale identification of phosphorylation sites has attracted interest. Existing wet-lab technologies for targeting phosphorylation sites are overpriced and time consuming. Thus, computational algorithms that can efficiently accelerate the annotation of phosphorylation sites from massive protein sequences are needed. Numerous machine learning-based methods have been implemented for phosphorylation sites prediction. However, despite extensive efforts, existing computational approaches continue to have inadequate performance, particularly in terms of overall ACC, MCC, and AUC. In this paper, we report a novel deep learning-based predictor to overcome these performance hurdles, DeepPPSite, which was constructed using a stacked long short-term memory recurrent network for predicting phosphorylation sites. The proposed technique expediently learns the protein representations from conjoint protein descriptors. The experimental results indicated that our model achieved superior performance on the training dataset for S, T and Y, with MCC values of 0.608, 0.602, and 0.558, respectively, using a 10-fold cross-validation test. We further determined the generalization efficacy of the proposed predictor DeepPPSite by conducting a rigorous independent test. The predictive MCC values were 0.358, 0.356, and 0.350 for the S, T, and Y phosphorylation sites, respectively. Rigorous cross-validation and independent validation tests for the three types of phosphorylation sites demonstrated that the designed DeepPPSite tool significantly outperforms state-of-the-art methods.

COVID-19 and digitalization: The great acceleration.

Inspired by burgeoning scholarly interest in the role of digitalization in the COVID-19 pandemic, this paper examines how the COVID-19 pandemic is driving or constraining the digitalization of businesses around the globe. We contend that COVID-19 is "the great accelerator" in fast-tracking the existing global trend towards embracing modern emerging technologies ushering in transformations in lifestyle, work patterns, and business strategies. Thus, COVID-19 has evolved to be a kind of "catalyst" for the adoption and increasing use of digitalization in work organization and the office, alongside presenting foreseen and unforeseen opportunities, challenges, and costs-leading to negative and positive feedback loops. In this article, we develop and advance a conceptual model by linking the different forces for and against digitalization in response to the pandemic. Our analysis indicates that adoption of emerging technologies may be hindered by vested external interests, nostalgia, and employer opportunism, as well as negative effects on employee well-being that undermine productivity, work-life balance, and future of work. Whilst digitalization may bring new opportunities, the process imparts risks that may be hard to mitigate or prepare for. Finally, we draw out the wider theoretical and practical implications of our analysis.

COVID-19 and business renewal: Lessons and insights from the global airline industry.

The emergence of the COVID-19 pandemic has adversely affected the fortunes of multiple companies around the globe. Accordingly, questions are increasingly being asked about how organizations can revitalize during and after a crisis. Yet, we have limited understanding of how organizations renew themselves during crises over time. We explore this question through the lens and examination of two South-Asian airlines: Pakistan International Airlines and Sri Lankan Airlines. The cases offer important insights into the reasons behind underperformance of state-controlled enterprises and renewal activities. We shed light on strategic renewal (SR) in the wake of increasing liberalization and deregulations in the global airline industry. To this end, we propose a four-stage approach towards renewing such underperforming organizations to respond effectively to black swan events and external shocks.

Few-Unit-Cell MFI Zeolite Synthesized using a Simple Di-quaternary Ammonium Structure-Directing Agent.

Synthesis of a pentasil-type zeolite with ultra-small few-unit-cell crystalline domains, which we call FDP (few-unit-cell crystalline domain pentasil), is reported. FDP is made using bis-1,5(tributyl ammonium) pentamethylene cations as structure directing agent (SDA). This di-quaternary ammonium SDA combines butyl ammonium, in place of the one commonly used for MFI synthesis, propyl ammonium, and a five-carbon nitrogen-connecting chain, in place of the six-carbon connecting chain SDAs that are known to fit well within the MFI pores. X-ray diffraction analysis and electron microscopy imaging of FDP indicate ca. 10 nm crystalline domains organized in hierarchical micro-/meso-porous aggregates exhibiting mesoscopic order with an aggregate particle size up to ca. 5 µm. Al and Sn can be incorporated into the FDP zeolite framework to produce active and selective methanol-to-hydrocarbon and glucose isomerization catalysts, respectively.

SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM.

Identification of DNA-binding proteins (DNA-BPs) is a hot issue in protein science due to its key role in various biological processes. These processes are highly concerned with DNA-binding protein types. DNA-BPs are classified into single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). SSBs mainly involved in DNA recombination, replication, and repair, while DSBs regulate transcription process, DNA cleavage, and chromosome packaging. In spite of the aforementioned significance, few methods have been proposed for discrimination of SSBs and DSBs. Therefore, more predictors with favorable performance are indispensable. In this work, we present an innovative predictor, called SDBP-Pred with a novel feature descriptor, named consensus sequence-based K-segmentation position-specific scoring matrix (CSKS-PSSM). We encoded the local discriminative features concealed in PSSM via K-segmentation strategy and the global potential features by applying the notion of the consensus sequence. The obtained feature vector then input to support vector machine (SVM) with linear, polynomial and radial base function (RBF) kernels. Our model with SVM-RBF achieved the highest accuracies on three tests namely jackknife, 10-fold, and independent tests, respectively than the recent method. The obtained prediction results illustrate the superlative prediction performance of SDBP-Pred over existing studies in the literature so far.

Stability Studies and Characterization of Glutathione-Loaded Nanoemulsion.

Glutathione reduced (GSH) is the mother of all the antioxidants and has an antimelanogenic effect. It is extremely vulnerable to oxidation in the solution form which limits its use. The GSH in nano-oil droplets present a potential solution to this problem. The aim of this study was to formulate glutathione-loaded nanoemulsion and assess its stability studies over a 90-day testing period. To formulate GSH-loaded nanoemulsion pseudo-ternary phase diagram, it was built with various concentrations of water, liquid paraffin oil, and surfactant mixture (Tween 80 and Span 80). The oily phase was prepared by dissolving the GSH (450 mg) in liquid paraffin oil through stirring. High-energy homogenization was used to prepare the nanoemulsion. From preformulation stability studies of the 28-day testing period, nanoemulsion (NE-19) with oil and surfactant mixture ratio (1:1) of hydrophilic lipophilic balance (HLB) value 10 was selected. The samples of NE-19 and its respective base (B-19) were kept at four different storage conditions for a period of 90 days and evaluated for physical characteristics, droplet size and distribution analysis, zeta potential analysis, electrical conductivity, mobility, polydispersity, pH, phase separation, and flow analysis at different time intervals. Glutathione in nano-oil droplets with nonionic surfactants produced oil-in-water nanoemulsions that were thermodynamically stable over the 90-day testing period at different storage conditions. NE-19 was formulated having non-Newtonian flow and pseudo-plastic behavior. pH was found in the range of 5-6. Polydispersity was less than 0.3. The droplet size of fresh nanoemulsion was 96.05 nm, whereas the zeta potential was -37.1. Mobility and electrical conductivity were -2.726 µm cm/Vs and 0.0141 mS/cm, respectively. Glutathione-loaded nanoemulsions have excellent stability, promising the solution in nano-oil droplets and are suggested for in-vivo release studies for oxidative skin related diseases.

Phytochemical screening and antioxidant potential of Parthenocissus quinquefolia (L.) planch extracts of bark and stem.

The phytochemical screening and antioxidant potential of bark and stem of Parthenocissus quinquefolia (L.) planch was assessed in order to verify its ethnopharmacological significance. All major secondary metabolites e.g. alkaloids, flavonoids, saponins, terpenoids, tannins, reducing sugars, cardiac glycosides and anthraquinones were present. Antioxidant activity was analysed by using five techniques which included DPPH Free Radical Scavenging Activity, FRAP (Ferric Reducing Antioxidant Power), TAA (Total Antioxidant Activity, TPC (Total Phenolic Content and MC (Metal Chelating) Activity. Ethanolic extract of bark showed the highest scavenging effects of 90.01±0.01%, with IC50 value of 24.32mg/ml. Aqueous stem extract showed best activity with IC50 value of 13.6±0.34mg/ml. The significance antioxidant potential indicates the effectiveness of bark and stem of P. quinquefolia in treatment of many diseases.

Phytochemical screening, GC-MS analysis and in vitro antioxidant activity of pollen of Centella asiatica (Linn) urban a traditional medicinal plant.

In the present study the crude extracts of pollen of Centella asiatica (Linn.) Urban were explored for their antioxidant potential using Ferric Reducing Power, Metal Chelating Activity and Trolox Equivalent Antioxidant Capacity assays. In crude extracts of pollen antioxidant components were initially extracted in methanol and further fractionated in solvents of different polarity, such as n-Hexane, Chloroform, Ethyl Acetate and Water exhibited reasonable antioxidant activity. The extract was found to contain large amounts of phenolic and flavonoid contents ranged from 143-1155 mg/l of gallic acid equivalent (GAE) and 911-2488 mg/l of quercetin (QE) respectively. Moreover, Super oxide Anion Radical Scavenging Activity and GS-MS analysis were also carried out.

Analysis of Insect toxicity and repellent activity of Phytochemicals from "Skimmia laureola, Nair" against "Black garden ant, Lasius niger" of Pakistan.

A study was conducted to evaluate the toxicity and repellency of essential oils from root, stem and leaves of Nazar panra, Skimmia laureola (DC.) Zucc. Ex Walp. of family (Sapindales: Rutaceae) ver. Nair of Pakistan. The oils were tested at three concentrations i.e. 1, 5 and 10%. Black garden ant, Lasius niger L. (Hymenoptera: Formicidae) of Pakistan were selected and exposed to essential oils at room temperature. All essential oils showed Insecticidal activity with LC(50)=10.15, while dose dependant effect was significant with R(2)=0.98. It can be concluded that the three Essential oils in this study have both Insecticidal as well as repellent effect.

Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model.

Enzyme catalysis is one of the most essential and striking processes among of all the complex processes that have evolved in living organisms. Enzymes are biological catalysts, which play a significant role in industrial applications as well as in medical areas, due to profound specificity, selectivity and catalytic efficiency. Refining catalytic efficiency of enzymes has become the most challenging job of enzyme engineering, into acidic and alkaline. Discrimination of acidic and alkaline enzymes through experimental approaches is difficult, sometimes impossible due to lack of established structures. Therefore, it is highly desirable to develop a computational model for discriminating acidic and alkaline enzymes from primary sequences. In this study, we have developed a robust, accurate and high throughput computational model using two discrete sample representation methods Pseudo amino acid composition (PseAAC) and split amino acid composition. Various classification algorithms including probabilistic neural network (PNN), K-nearest neighbor, decision tree, multi-layer perceptron and support vector machine are applied to predict acidic and alkaline with high accuracy. 10-fold cross validation test and several statistical measures namely, accuracy, F-measure, and area under ROC are used to evaluate the performance of the proposed model. The performance of the model is examined using two benchmark datasets to demonstrate the effectiveness of the model. The empirical results show that the performance of PNN in conjunction with PseAAC is quite promising compared to existing approaches in the literature so for. It has achieved 96.3% accuracy on dataset1 and 99.2% on dataset2. It is ascertained that the proposed model might be useful for basic research and drug related application areas.

Recombinant Tissue Plasminogen Activator in the Treatment of Neonates with Intracardiac and Great Vessels Thrombosis.

Life-threatening intracardiac and great vessels thrombi are rare in neonates. Recombinant tissue plasminogen activator (rTPA) is used in adults to stimulate fibrinolysis and facilitate thrombus resolution. Its use in neonates, along with heparin, remains controversial because of potential risk of serious bleeding. We aim to present our experience with the use of thrombolytic agents in seven neonates and young infants. In a retrospective study, over a period of 6 years, the medical records of neonates and young infants, who were diagnosed with intracardiac and great vessels thrombi, were reviewed. The following factors were collected: demographic data, primary diagnosis, thrombus site, risk factors, method of diagnosis, thrombolytic and/or anticoagulation agent, route, dose and duration of treatment, complications, and outcome. Six neonates and one 45-day-old infant were analyzed. Age ranged from 5 to 45 days (median age 12 days), and median weight was 2.9 kg (range 0.9-3.8 kg). The thrombi were diagnosed by echocardiography in five and by angiography in two cases. All patients had life-threatening thrombi; four were treated with rTPA (0.5 mg kg(-1) h(-1)) and heparin infusions with complete dissolution of the thrombi, within a median time of 60 h (6-72 h), and without complications. The remaining three patients (two who were premature, at 28 and 34 weeks of gestation, and the third who had a deranged coagulation profile) were treated with unfractionated heparin due to fear of bleeding. The thrombi dissolved in the premature babies (within 2 weeks and 3 months, respectively) but embolized and resulted in the death of the third infant after 2 weeks of treatment. The current case series confirmed the effectiveness and safety of intravenous rTPA infusion, at the dosages used, in neonates and young infants with life-threatening thrombi.

Biosynthesis of silver nanoparticles and its antibacterial and antifungal activities towards Gram-positive, Gram-negative bacterial strains and different species of Candida fungus.

Biomimetic and economic method for the synthesis of silver nanoparticles (AgNPs) with controlled size has been reported in presence of shape-directing cetlytrimethylammonium bromide (CTAB). Biochemical reduction of Ag(+) ions in micellar solution with an aqueous lemon extract produced spherical and polyhedral AgNPs with size ranging from 15 to 30 nm. The influence of [CTAB] and [lemon extract] on the size of particles, fraction of metallic silver and their antimicrobial properties is discussed. The AgNPs were evaluated for their antimicrobial activities (antibacterial and antifungal) against different pathogenic organisms. For this purpose, AgNPs were tested against two model bacteria (Staphylococcus aureus (MTCC3160) and Escherichia coli (MTCC405)) and three species of Candida fungus (Candida albicans (ATCC90028), Candida glabrata (ATCC90030) and Candida tropicalis (ATCC750). AgNPs were found to be highly toxic towards both bacteria. The inhibition action was due to the structural changes in the protein cell wall.

Green synthesis of biogenic silver nanomaterials using Raphanus sativus extract, effects of stabilizers on the morphology, and their antimicrobial activities.

The present study explores the reducing and capping potentials of aqueous Raphanus sativus root extract for the synthesis of silver nanomaterials for the first time in the absence and presence of two stabilizers, namely, water-soluble starch and cetyltrimethylammonium bromide (CTAB). The surface properties of silver nanoparticles (AgNPs) were determined by dynamic light scattering (DLS), transmission electron microscopy (TEM), energy dispersion X-ray spectroscopy (EDX), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) techniques. The mean size of AgNPs, ranging from 3.2 to 6.0 nm, could be facilely controlled by merely varying the initial [extract], [CTAB], [starch], and [Ag(+)] ions. The agglomeration number, average number of silver atoms per nanoparticle, and changes in the fermi potentials were calculated and discussed. The AgNPs were evaluated for their antimicrobial activities against different pathogenic organisms. The inhibition action was due to the structural changes in the protein cell wall.

Extracellular biosynthesis of silver nanoparticles: effects of shape-directing cetyltrimethylammonium bromide, pH, sunlight and additives.

The work reported in this paper describes the preparation, morphology, stability and sensitivity of Ag-nanoparticles towards sunlight using Allium sativum, garlic extract for the first time. The synthesized silver particles show an intense surface plasmon resonance band in the visible region at 410 nm. The position of the wavelength maxima, blue and red shift, strongly depends on the sunlight and pH. TEM analysis revealed the presence of spherical, different size (from 5.0 to 30 nm) and garlic constituents bio-conjugated, stabilized and/or layered silver nanoparticles. The concentrations of garlic extract, cetyltrimethylammonium bromide, Ag(+) ions and reaction time play vital roles for nucleus formation and the growth processes. Sulfur-containing biomolecules of extract, especially cysteine, are responsible for the reduction of Ag(+) ions into metallic Ag(0). The agglomeration number of the silver nanoparticles (N Ag) and the average number of free electrons per particle (n fe) are calculated and discussed.

Epigallocatechin-3-gallate-capped Ag nanoparticles: preparation and characterization.

We used an aqueous leaf extract of Camellia sinensis to synthesize Ag nanoparticles (AgNPs). A layer of ca. 6 nm around a group of the AgNPs in which the inner layer is bound to the AgNPs surface via the hydroxyl groups of catechin has been observed. TEM analysis of AgNPs showed the formation of truncated triangular nanoplates and/or nanodisks and spherical with some irregular-shaped polydispersed nanoparticles in absence and presence of shape-directing cetyltrimethylammonium bromide. The polyphenolic groups of epigallocatechin-3-gallate (EGCG) are responsible for the rapid reduction of Ag(+) ions into metallic Ag(0). The free -OH groups are able to stabilize AgNPs by the interaction between the surface Ag atoms of AgNPs and oxygen atoms of EGCG molecules.

Surfactant-assisted bio-conjugated synthesis of silver nanoparticles (AgNPs).

A simple one-spot synthetic route for the production of Ag-nanoparticles using aqueous extract of citrus lemon is being reported in presence of shape-directing cetyltrimethylammonium bromide (CTAB). To our knowledge, this is the first report where the biomolecules form a layer around a group of the Ag-nanoparticles in which the inner layer is bound to the AgNPs surface via the hydroxyl groups of citric acid. The appearance of a sharp surface plasmon resonance band in the UV-visible region might be due to the formation of spherical Ag-nanoparticles. Agglomeration number (N Ag), the average number of silver atoms per nanoparticle (N), molar concentrations of nanoparticle (C) in solution, extinction coefficient (ε) and increase in the Fermi energy (ΔE F) were calculated using Mie theory and discussed. Interestingly, reaction mixture became turbid at higher [CTAB] due to the uncontrolled growth of Ag-nanoparticles. The transmission electron microscopic images of nanoparticles, recorded at different magnifications.

A standardized chamuangone enriched extract from Garcinia cowa Roxb. leaves shows acute and sub-acute safety.

ETHNOPHARMACOLOGICAL RELEVANCE: The safety assessment of herbal products is critical for their appropriate pharmacological applications. Garcinia cowa Roxb., commonly known as Cha-muang in Thai, has ethnopharmacological relevance in inflammation, infectious diseases, and diabetes. The leaf extracts of G. cowa have been extensively reported for their anticancer, anti-inflammatory, antimicrobial and antioxidative effects. Notably, chamuangone is their major active constituent that contributes to various pharmacological properties. AIM OF THE STUDY: The current study aims to establish a standardized chamuangone enriched extract (CEE) and assess its acute and sub-acute toxicities in animal models. METHODOLOGY: CEE was established from G. cowa leaves using microwave-assisted extraction (MAE), followed by fractionation and enrichment through silica gel vacuum and column chromatography. The concentration of chamuangone in the leaves was quantified using validated quantitative high-performance liquid chromatography (HPLC) method. The safety profile of CEE was thoroughly evaluated in rodents according to the Organization for Economic Cooperation and Development (OECD) guidelines, specifically 425 and 407. The effect on oxidative stress markers such as superoxide dismutase (SOD), reduced glutathione (GSH), catalase (CAT), and malondialdehyde (MDA) levels were also evaluated in various organs. RESULTS: Base on the quantitative HPLC analysis the CEE containing 73.0 ± 2% w/w of chamuangone in the extract. In the acute toxicity study, following up and down procedure the female rats were dosed with CEE at 1750 and 550 mg/kg body weight (b.w.), with CEE 1750 mg/kg b.w. identified as toxic, causing mortality, while CEE 550 mg/kg b.w. was deemed safe. The LD50 value was calculated according to the standard protocols, resulting in 970 mg/kg b.w. In histopathological examination, CEE 550 mg/kg b.w. were safe in all the selected organs while the CEE 1750 mg/kg b.w. treated rats exhibited toxic effects in histological tissues sections in the form of necrosis in brain, cardiac muscle hypertrophy, liver inflammation, mild untoward in the spleen, fibrosis in lungs, pancreatitis, pyelonephritis, and ovaries cyst. Administration of CEE at doses of 550 mg/kg b.w. (single dose) in acute and 100 mg/kg b.w. (regularly 28-days) in sub-acute toxicities studies significantly (p < 0.05) decreased levels of uric acid, triglycerides, and cholesterol. Importantly, the CEE 550 and 100 mg/kg b.w. also significantly increased the levels of antioxidant enzymes (SOD, GSH and CAT) and decreased malondialdehyde levels. Normal histopathology was observed in the sub-acute toxicity study in all treated groups. CONCLUSION: The study successfully concludes that CEE at a dose of 100 mg/kg b.w. is safe for therapeutic application or use as a chemo preventive functional food utilizing green extraction methods. However, chronic toxicity studies are recommended to validate safety concerns over an extended period.