Stimuli-sensitive biomimetic nanoparticles for the inhibition of breast cancer recurrence and pulmonary metastasis.

Biomimetic nanoparticles represent a promising avenue for mitigating rapid clearance by the reticuloendothelial system (RES); however, current challenges include insufficient tumour targeting, suboptimal adhesion, and inadequate localized drug release within tumour regions. These shortcomings contribute to persistent contests, such as recurrence and pulmonary metastasis, even with advanced breast cancer therapies. Stimuli-sensitive drug release can furbish the membrane coated nanoparticles for their efficiency against the stated problems. To enhance the efficacy of biomimetic nanoparticles in addressing these issues, we proposed a versatile, stimuli-responsive drug delivery system by encapsulating doxorubicin (Dox) and perfluorohexane (PFH) within poly (lactic-co-glycolic acid) (PLGA) nanoparticles, subsequently coated with macrophage-derived cell membranes. Within this framework, PFH serves as the mediator for ultrasonic (US)-irradiation-triggered drug release specifically within tumour microenvironment, while the macrophage-derived cell membrane coating enhances cell adhesion, enables immune evasion, and natural tumour-homing ability. The characterization assays and in vitro evaluations yielded encouraging results, indicating enhanced targeting and release efficiencies. In vivo studies demonstrated marked inhibitory effects on both breast cancer recurrence and pulmonary metastasis. The resulting data indicate that these engineered nanoparticles have notable potential for targeted delivery and controlled release upon US irradiation, thereby offering significant therapeutic efficacy against primary breast cancer, pulmonary metastasis, and recurrent malignancies. Our findings lay the groundwork for a novel clinical approach, representing an intriguing direction for ongoing investigation by oncologists.

Bio-evaluations of sericin coated hesperidin nanoparticles for gastric ulcer management.

Gastric ulcers are worrying, and their worsening conditions may result in bleeding in the internal lining of the stomach. The problem is annoying, and both patients and professionals are still not satisfied with the available treatment options. Hesperidin, a flavonoid molecule with potent anti-inflammatory and antioxidant effects, can work like witchcraft to repair gastric ulcers and preserve the stomach lining. Here, we employed a strategy that involved covering the surface of the nano-lipid carriers (NLCs) with sericin before encasing the hesperidin within (Se-He-NLC). Sericin, a biodegradable polymer increases the muco-adhesion with stomach lining and deployment of hesperidin in controlled manner. Se-He-NLCs were physico-chemically characterized for drug loading, encapsulation, particle size, morphology, drug release, chemical stability, and chemical bonding. The nanocarriers showed first order drug release in a controlled manner. Se-He-NLCs showed better in vitro permeation and ex vivo mucoadhesion, thereby by promoting the in vivo bioavailability. Se-He-NLCs also promoted the reduced glutathione (GSH) and glutathione-S-transferase (GST) levels by 2.24- and 1.61-folds, respectively in the stomach lining, and also the regulation of superoxide dismutase (SOD) and catalase (CAT) activities parallel to the control group. In addition, tissues lipid hydroperoxides (LOOH) and myeloperoxidase (MPO) activity were reduced significantly with Se-He-NLCs administration. Se-He-NLC therapy of stomach ulcers in vivo demonstrated better binding ratio and ulcer healing potential. This approach reveals huge capacity for delivering therapies to treat gastric ulcers based on the clinical significance of sericin coated hesperidin nanocarriers in gastric ulcer treatment.

Loratadine oral bioavailability enhancement via solid dispersion loaded oro-dispersible films: Formulation, characterization and pharmacokinetics.

Loratadine (LRD) belongs to second-generation tricyclic H1 antihistamine class, known for its non-sedating properties in allergic reactions. H1 antihistamines avoid and block the responses to allergens or histamine in nose and conjunctivae, thereby abolishing itching, congestion and sneezing. LRD is a Biopharmaceutical Class System (BCS) class II drug with dissolution or solubility limited absorption which limited the oral bioavailability and therapeutic efficacy of LRD. To improve the oral bioavailability of LRD for allergic disease (urticaria) treatment, LRD solid dispersions (LRD-SDs) were integrating into oro-dispersible films (ODFs). LRD-SDs were prepared through hot-melt extrusion method (HME) using d-alpha-tocopherol polyethylene glycol 1000 succinate (TPGS-1000), and polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol graft copolymer (SP). Subsequently, LRD-SDs were incorporated in ODFs by solvent casting method. The physicochemical and mechanical properties of LRD solid dispersions-loaded oro-dispersible films (LRD-SDs-ODFs), were evaluated. The in-vitro dissolution, ex-vivo permeation, oral bioavailability, and pharmacodynamics studies were conducted to evaluate LRD-SDs-ODFs efficiency. LRD-SDs-ODFs showed superior solubility and in-vitro dissolution results compared to that of pure LRD (p < 0.05). The solubility of the LRD-SD coded as LTS-4 was 190 times higher than the pure drug in aqueous media. The average hydrodynamic particle size (PS), polydispersity index (PDI), and zeta potential (ZP) of SD particles were 76 ± 2.1 nm, 0.20 ± 0.08 and - 19.16 ± 1.4 mV, respectively. Moreover, differential scanning calorimetry (DSC) and X-ray diffraction (XRD) results confirmed the amorphousness of LRD in LRD-SDs-ODFs. The permeability flux of LRD was 44.6 ± 3.1 µg/cm2/h from DPF-5 formulation. Likewise, in vivo oral bioavailability of DPF-5 in Sprague-Dawley rats was significantly increased (p < 0.05) compared to free LRD. Further, wheal area was reduced 20 % higher than LRD in 8 h (p < 0.05). Overall, LRD-SDs-ODFs considerably enhanced LRD solubility, dissolution rate, bioavailability, and antihistaminic efficacy. Our findings show that SDs-ODFs is an effective carrier system for delivering poorly soluble LRD.

Eco-Friendly Sustainable Nanocarriers to Treat Oxidative Stresses and Skin Aging-Related Ailments, Valorization of a By-Product.

The peel from Citrus-sinensis L. is a medicinally significant food waste, and its extract (O-Ext) could be significant against oxidative stresses and skin aging, However, the penetration barriers, instability in formulation, undefined toxicities, and enzymatic activities make the O-Ext difficult to formulate and commercialize. The goal of this study was to evaluate O-Ext against oxidative stress, prepare O-Ext-loaded nano-lipid carriers (O-NLCs), and load them into topical O/W-emulsion (O-NLC-E) to improve O-Ext permeation and its in vivo antiaging effects. TPC, TFC, DPPH activity, and mineral/metal contents of O-Ext were determined via atomic-absorption spectroscopy. For bioactive compounds profiling, GC-MS analysis was carried out. O-NLCs were prepared and tested for physicochemical attributes, while HaCaT and fibroblast cells were used to study permeation and cytotoxicity. The kinetic characteristics of ex vivo permeation through rat skin were established, following the Higuchi model. Following written consent, safety investigations were conducted on human volunteers for three months, where optimized O-NLC-E and B-NLC-E were regularly applied on cheeks. Non-invasive procedures were used to assess the volunteer's skin erythema, TEWL, sebum level, melanin, hydration, pH, elasticity, and pore sizes after specified intervals. The results demonstrated that applying O-NLC-E formulation to the skin of volunteers directed significant antiaging benefits. The study offers nanotechnology-based sustainability approach against skin ageing.

In Vitro and Ex Vivo Evaluation of Mangifera indica L. Extract-Loaded Green Nanoparticles in Topical Emulsion against Oxidative Stress and Aging.

Although Mangifera indica L. extract (M-Ext) of the peel and kernel possesses potent antioxidant and excellent antiaging qualities, the effects are only partially seen because of the skin's limited ability to absorb it. M-Ext was loaded into nanolipid carriers (M-NLCs) in this work to create a green topical formulation that would boost antiaging efficacy and address penetration deficit. Compound identification was done using GCMS and atomic absorption spectroscopy for heavy metals in M-Ext. M-Ext was also evaluated against oxidative stress antioxidant enzymes. The M-NLCs were fabricated and evaluated for their physicochemical characterizations. Cytotoxicity and cell permeation analysis of M-Ext and M-NLCs were carried out in fibroblasts and HaCaT cell lines. An ex vivo permeation study of M-Ext and M-NLC-loaded emulsion was performed through rat skin and the kinetic parameters were determined. Kinetic data showed that the ex vivo permeation of M-NLC-loaded emulsion through rat skin followed the Higuchi model. The safety profile was evaluated in human volunteers after written consent. Three months' in vivo investigations were conducted using the optimized M-NLC-loaded emulsion and vehicle (B-NLC-loaded emulsion) on human cheeks for comparison. The volunteers' skin erythema level, melanin contents, TEWL index, moisture contents, sebum level, elasticity, pH, and pore size were examined after the first, second, and third month via noninvasive techniques. There were significant findings for physicochemical characterizations and in vitro and ex vivo studies. The findings demonstrate that the green nanolipid carriers amplified the overall antioxidant effectiveness and may represent an emerging treatment strategy for oxidative stresses and aging.

Hypomorphic GINS3 variants alter DNA replication and cause Meier-Gorlin syndrome.

The eukaryotic CDC45/MCM2-7/GINS (CMG) helicase unwinds the DNA double helix during DNA replication. The GINS subcomplex is required for helicase activity and is, therefore, essential for DNA replication and cell viability. Here, we report the identification of 7 individuals from 5 unrelated families presenting with a Meier-Gorlin syndrome-like (MGS-like) phenotype associated with hypomorphic variants of GINS3, a gene not previously associated with this syndrome. We found that MGS-associated GINS3 variants affecting aspartic acid 24 (D24) compromised cell proliferation and caused accumulation of cells in S phase. These variants shortened the protein half-life, altered key protein interactions at the replisome, and negatively influenced DNA replication fork progression. Yeast expressing MGS-associated variants of PSF3 (the yeast GINS3 ortholog) also displayed impaired growth, S phase progression defects, and decreased Psf3 protein stability. We further showed that mouse embryos homozygous for a D24 variant presented intrauterine growth retardation and did not survive to birth, and that fibroblasts derived from these embryos displayed accelerated cellular senescence. Taken together, our findings implicate GINS3 in the pathogenesis of MGS and support the notion that hypomorphic variants identified in this gene impaired cell and organismal growth by compromising DNA replication.

Physicochemical Characterizations and Pharmacokinetic Evaluation of Pentazocine Solid Lipid Nanoparticles against Inflammatory Pain Model.

Pentazocine (PTZ), a narcotic-antagonist analgesic, has been extensively used in the treatment of initial carcinogenic or postoperative pain. Hepatic first-pass metabolism results in low oral bioavailability and high dose wastage. Herein, 10 mg (-)-Pentazocine (HPLC-grade) was incorporated to solid lipid nanoparticles (SLNs) using a double water-oil-water (w/o/w) emulsion by solvent emulsification-evaporation technique, followed by high shear homogenization to augment its oral bioavailability, considering the lymphatic uptake. The resulting SLNs were characterized for zeta potential (ZP), particle size (PS), and polydispersity index (PDI) using a zetasizer. The entrapment efficiency (EE) and loading capacity (LC) were calculated. Chemical interactions, through the identification of active functional groups, were assessed by Fourier-transformed infrared (FTIR) spectroscopy. The nature (crystallinity) of the SLNs was determined by X-ray diffractometry (XRD). The surface morphology was depicted by transmission electron microscopy (TEM). In vitro (in Caco-2 cells) and in vivo (in male Wistar rats) investigations were carried out to evaluate the PTZ release behavior and stability, as well as the cellular permeation, cytotoxicity, systemic pharmacokinetics, antinociceptive, anti-inflammatory, and antioxidative activities of PTZ-loaded SLNs, mainly compared to free PTZ (marketed conventional dosage form). The optimized PTZ-loaded SLN2 showed significantly higher in vitro cellular permeation and negligible cytotoxicity. The in vivo bioavailability and pharmacokinetics parameters (t1/2, Cmax) of the PTZ-loaded SLNs were also significantly improved, and the nociception and inflammation, following carrageenan-induced inflammatory pain, were markedly reduced. Concordantly, PTZ-loaded SLNs showed drastic reduction in the oxidative stress (e.g., malonaldehyde (MDA)) and proinflammatory cytokines (e.g., Interleukin (IL)-1ß, -6, and TNF-α). The histological features of the paw tissue following, carrageenan-induced inflammation, were significantly improved. Taken together, the results demonstrated that PTZ-loaded SLNs can improve the bioavailability of PTZ by bypassing the hepatic metabolism via the lymphatic uptake, for controlled and sustained drug delivery.

Two Promising Anti-Cancer Compounds, 2-Hydroxycinnaldehyde and 2- Benzoyloxycinnamaldehyde: Where do we stand?

Natural bioactive compounds with anti-carcinogenic activity are gaining tremendous interest in the field of oncology. Cinnamon, an aromatic condiment commonly used in tropical regions, appeared incredibly promising as an adjuvant for cancer therapy. Indeed, its whole or active parts (e.g., bark, leaf) exhibited significant anti-carcinogenic activity, which is mainly due to two cinnamaldehyde derivatives, namely 2-hydroxycinnaldehyde (HCA) and 2- benzoyloxycinnamaldehyde (BCA). In addition to their anti-cancer activity, HCA and BCA exert immunomodulatory, anti-platelets, and anti-inflammatory activities. The highly reactive α,ßunsaturated carbonyl pharmacophore, called Michael acceptor, contributes to their therapeutic effects. The molecular mechanisms underlying their anti-tumoral and anti-metastatic effects are miscellaneous, strongly suggesting that these compounds are multi-targeting compounds. Nevertheless, unravelling the exact molecular mechanisms of HCA and BCA remains a challenging matter which is necessary for optimal controlled-drug targeting delivery, safety, and efficiency. Eventually, their poor pharmacological properties (e.g., systemic bioavailability and solubility) represent a limitation and depend both on their administration route (e.g., per os, intravenously) and the nature of the formulation (e.g., free, smart nano-). This concise review focused on the potential of HCA and BCA as adjuvants in cancer. We describe their medicinal effects as well as provide an update about their molecular mechanisms reported either in-vitro, ex-vivo, or in animal models.

Development of Cephradine-Loaded Gelatin/Polyvinyl Alcohol Electrospun Nanofibers for Effective Diabetic Wound Healing: In-Vitro and In-Vivo Assessments.

Diabetic wound infections caused by conventional antibiotic-resistant Staphylococcus aureus strains are fast emerging, leading to life-threatening situations (e.g., high costs, morbidity, and mortality) associated with delayed healing and chronic inflammation. Electrospinning is one of the most widely used techniques for the fabrication of nanofibers (NFs), induced by a high voltage applied to a drug-loaded polymer solution. Particular attention is given to electrospun NFs for pharmaceutical applications (e.g., original drug delivery systems) and tissue regeneration (e.g., as tissue scaffolds). However, there is a paucity of reports related to their application in diabetic wound infections. Therefore, we prepared eco-friendly, biodegradable, low-immunogenic, and biocompatible gelatin (GEL)/polyvinyl alcohol (PVA) electrospun NFs (BNFs), in which we loaded the broad-spectrum antibiotic cephradine (Ceph). The resulting drug-loaded NFs (LNFs) were characterized physically using ultraviolet-visible (UV-Vis) spectrophotometry (for drug loading capacity (LC), drug encapsulation efficiency (EE), and drug release kinetics determination), thermogravimetric analysis (TGA) (for thermostability evaluation), scanning electron microscopy (SEM) (for surface morphology analysis), and Fourier-transform infrared spectroscopy (FTIR) (for functional group identification). LNFs were further characterized biologically by in-vitro assessment of their potency against S. aureus clinical strains (N = 16) using the Kirby-Bauer test and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, by ex-vivo assessment to evaluate their cytotoxicity against primary human epidermal keratinocytes using MTT assay, and by in-vivo assessment to estimate their diabetic chronic wound-healing efficiency using NcZ10 diabetic/obese mice (N = 18). Thin and uniform NFs with a smooth surface and standard size (<400 nm) were observed by SEM at the optimized 5:5 (GEL:PVA) volumetric ratio. FTIR analyses confirmed the drug loading into BNFs. Compared to free Ceph, LNFs were significantly more thermostable and exhibited sustained/controlled Ceph release. LNFs also exerted a significantly stronger antibacterial activity both in-vitro and in-vivo. LNFs were significantly safer and more efficient for bacterial clearance-induced faster chronic wound healing. LNF-based therapy could be employed as a valuable dressing material to heal S. aureus-induced chronic wounds in diabetic subjects.

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.

DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder.

BACKGROUND: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. OBJECTIVE: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. METHODS: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via nSegmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2D-Convolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. RESULTS: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. CONCLUSION: In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

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.

Fabrication, physical characterizations and in vitro antibacterial activity of cefadroxil-loaded chitosan/poly(vinyl alcohol) nanofibers against Staphylococcus aureus clinical isolates.

Particular attention is devoting to the design of electrospun nanofibers (NFs) as new drug delivery nanosystems to overcome bacterial resistance and toxicological issues. Their advantages include high encapsulation efficiency, great drug-loading capacity, easiness in production, cost-effectiveness, and controlled targeted drug delivery. We aim to characterize electrospun chitosan (CS)/poly(vinyl alcohol) (PVA) NFs (CPNFs) loaded with cefadroxil monohydrate (CFX), a broad spectrum antibiotic. The biodegradable and biocompatible carrier system was greenly fabricated by electrospinning at various CS/PVA ratios. CPNFs were characterized using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and UV-spectrometry. Their potential toxicity was evaluated in human epidermal keratinocytes by 3-[4,5-dimethylthiazole-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay. Their antibacterial activity was tested by agar well diffusion method and MTT assay against clinical isolates of Staphylococcus aureus, a Gram-positive bacterium involved in serious skin infections. The thermostable CFX-loaded CPNFs at optimized 30:70 ratio revealed a burst and sustained release profile that occurred predominantly by diffusion following non-Fickian (anomalous) transport mechanism, as well as a more potent and safe antibacterial than free CFX. Thus, electrospun CFX-loaded CPNFs could be a new promising transdermal drug delivery system to activate the wound healing process and cost-effectively treat S. aureus-induced (resistant) skin infections.

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.

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.

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.