Fast Detection of Heavy Metal Content in Fritillaria thunbergii by Laser-Induced Breakdown Spectroscopy with PSO-BP and SSA-BP Analysis.

Fast detection of heavy metals is important to ensure the quality and safety of herbal medicines. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to detect the heavy metal content (Cd, Cu, and Pb) in Fritillaria thunbergii. Quantitative prediction models were established using a back-propagation neural network (BPNN) optimized using the particle swarm optimization (PSO) algorithm and sparrow search algorithm (SSA), called PSO-BP and SSA-BP, respectively. The results revealed that the BPNN models optimized by PSO and SSA had better accuracy than the BPNN model without optimization. The performance evaluation metrics of the PSO-BP and SSA-BP models were similar. However, the SSA-BP model had two advantages: it was faster and had higher prediction accuracy at low concentrations. For the three heavy metals Cd, Cu and Pb, the prediction correlation coefficient (Rp2) values for the SSA-BP model were 0.972, 0.991 and 0.956; the prediction root mean square error (RMSEP) values were 5.553, 7.810 and 12.906 mg/kg; and the prediction relative percent deviation (RPD) values were 6.04, 10.34 and 4.94, respectively. Therefore, LIBS could be considered a constructive tool for the quantification of Cd, Cu and Pb contents in Fritillaria thunbergii.

Adsorption of recalcitrant phosphorus compounds using the phosphate-selective binding-protein PstS.

Currently available wastewater phosphorus (P) treatment technologies target removal of reactive forms of P. Selective adsorption of more recalcitrant soluble non-reactive phosphorus (sNRP) can improve P removal and recovery. A phosphate-selective phosphate-binding protein (PBP), PstS, was immobilized onto NHS-activated beads to assess the ability of this novel bioadsorbent to remove (adsorb) and subsequently recover (desorb) a range of sNRP compounds. Four sNRP compounds representative of wastewater sNRP were selected for use in this study: phytic acid (PA), sodium triphosphate (TrP), beta-glycerol phosphate (BGP), and sodium hexametaphosphate (HMP). Using PBP, adsorption of all sNRP compounds was thermodynamically favorable. The PBP had nearly equivalent binding affinity for PA compared to PBP's typical target, orthophosphate, although it had less affinity for the other sNRP compounds. Adsorption followed pseudo-second order reaction kinetics, with 95% of maximum adsorption occurring within 4 min. This was substantially faster sNRP adsorption compared to other adsorbents in the literature. Adsorption was modeled using the Langmuir isotherm, reflecting that one phosphate molecule attached to one PBP binding site. Notably, this selective 1:1 attachment resulted in higher total P removal for sNRP molecules with high P content. The binding site lost activity with increasing pH, and as such, highest desorption was achieved at pH 12, making the system amenable to sNRP removal as well as controlled recovery.

Nano-PSO Administration Attenuates Cognitive and Neuronal Deficits Resulting from Traumatic Brain Injury.

Traumatic Brain Injury (TBI), is one of the most common causes of neurological damage in young populations. It is widely considered as a risk factor for neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's (PD) disease. These diseases are characterized in part by the accumulation of disease-specific misfolded proteins and share common pathological features, such as neuronal death, as well as inflammatory and oxidative damage. Nano formulation of Pomegranate seed oil [Nano-PSO (Granagard TM)] has been shown to target its active ingredient to the brain and thereafter inhibit memory decline and neuronal death in mice models of AD and genetic Creutzfeldt Jacob disease. In this study, we show that administration of Nano-PSO to mice before or after TBI application prevents cognitive and behavioral decline. In addition, immuno-histochemical staining of the brain indicates that preventive Nano-PSO treatment significantly decreased neuronal death, reduced gliosis and prevented mitochondrial damage in the affected cells. Finally, we examined levels of Sirtuin1 (SIRT1) and Synaptophysin (SYP) in the cortex using Western blotting. Nano-PSO consumption led to higher levels of SIRT1 and SYP protein postinjury. Taken together, our results indicate that Nano-PSO, as a natural brain-targeted antioxidant, can prevent part of TBI-induced damage.

Comparing anti-aging hallmark activities of Metformin and Nano-PSO in a mouse model of genetic Creutzfeldt-Jakob Disease.

Advanced age is the main risk factor for the manifestation of late onset neurodegenerative diseases. Metformin, an anti-diabetic drug, was shown to extend longevity, and to ameliorate the activity of recognized aging hallmarks. Here, we compared the clinical, pathologic and biochemical effects of Metformin to those of Nano-PSO (Granagard), a brain targeted anti-oxidant shown by us to delay disease advance in transgenic mice mimicking for genetic Creutzfeldt Jacob disease (CJD) linked to the E200KPrP mutation. We demonstrate that both Metformin and Nano-PSO reduced aging hallmarks activities such as activated AMPK, the main energy sensor of cells as well as Nrf2 and COX IV1, regulators of oxidation, and mitochondrial activity. Both compounds reduced inflammation and increased stem cells production, however did not decrease PrP accumulation. As opposed to Nano-PSO, Metformin neither delayed clinical disease advance in these mice nor reduced the accumulation of sulfated glycosaminoglycans, a pathologic feature of prion disease. We conclude that elevation of anti-aging markers may not be sufficient to delay the fatal advance of genetic CJD.

Efficient focusing of microwave hyperthermia for small deep-seated breast tumors treatment using particle swarm optimization.

Focused microwave hyperthermia is a technique with advantage of high accuracy and low side effects for breast tumor treatments. In this study, an efficient focusing technique for noninvasive microwave hyperthermia treatment for breast tumors is presented. Particle Swarm Optimization (PSO) is used to find the optimum excitations (phases and amplitudes) of a three dimension (3D) Micro-Strip Patch (MSP) antenna array operating at 2.45 GHz. The antenna excitations are optimized to maximize the power loss density and the Specific Absorption Rate (SAR) at the tumor location, to reach the required hyperthermia temperature (above 42 °C) at the tumor location without causing hot spots in healthy tissues. The technique is tested on a challenging scenario of a 3D realistic breast model having a tumor less than 1 cm3 volume and embedded in different locations deep in the glandular tissue of a very dense breast. The results confirmed the capability of the focusing technique.

Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics.

The purpose of the study is to present a nondestructive qualitative and quantitative approach of hard-shell capsule using near-infrared (NIR) spectroscopy combined with chemometrics. The Yaobitong capsule (YBTC) was used for demonstration of the proposed approach and the NIR spectra were collected using a handheld fiber probe (FP) without the damage of capsule shell. By comparing the differences and similarities of the NIR spectra of capsule shells, contents and intact capsules, a preliminary conclusion can be drawn that the NIR spectra contained the information of the contents. Characteristic variables were selected by competitive adaptive weighted resampling (CARS) method, and least squares support vector machine (LSSVM) method based on particle swarm optimization (PSO) algorithm was applied to the construction of quantitative models. The relative standard error of prediction (RSEP) values of five saponins including notoginsenoside R1, ginsenoside Rg1, Re, Rb1, and Rd were 3.240%, 5.468%, 5.303%, 5.043%, and 3.745%, respectively. In addition, for qualitative model, three different types of adulterated capsules were designed. The model established by data driven version of soft independent modeling of class analogy (DD-SIMCA) demonstrated a satisfactory result that all adulterated capsules were identified accurately after an appropriate number of principal components (PCs) were chosen. The results indicated that although the NIR spectra collection was affected by capsule shell, sufficient content information can be obtained for quantitative and qualitative analysis after combining with chemometrics. It further proved that acquired NIR spectra do contain the effective component information of the capsule. This study provided a reference for the rapid nondestructive quality analysis of traditional Chinese medicine (TCM) capsule without damaging capsule shell.

A polyoxyethylene sorbitan oleate modified hollow gold nanoparticle system to escape macrophage phagocytosis designed for triple combination lung cancer therapy via LDL-R mediated endocytosis.

Presently, a combination of chemotherapy, radiotherapy, thermotherapy, and other treatments has become a hot topic of research for the treatment of cancer, especially lung cancer. In this study, novel hollow gold nanoparticles (HGNPs) were used as drug carriers, and in order to improve the targeting ability of HGNPs to a lung tumor site, polyoxyethylene sorbitol oleate (PSO) was chosen here as a target ligand since it can be specifically recognized by the low-density lipoprotein (LDL) receptor which is usually over expressed on A549 lung cancer cells. In this way, a PSO-modified doxorubicin-loaded HGNP drug delivery system (PSO-HGNPs-DOX) was constructed and its physicochemical properties, photothermal conversion ability, and drug release of PSO-HGNPs-DOX was investigated. Further, the effects of triple combination therapy, the intracellular uptake, and the ability to escape macrophage phagocytosis of PSO-HGNPs-DOX were also studied using A549 cells in vitro. In addition, an in vivo mouse model was also used to study the targeting of PSO-HGNPs-DOX to lung cancer. PSO-HGNPs-DOX demonstrated a good triple therapeutic effect for lung cancer (A549 cell viability was only 10% at 500 µM) by LDL receptor mediated endocytosis and was able to escape macrophage phagocytosis to enhance its accumulation at the target site. Therefore, PSO-HGNPs-DOX is a novel, safe, promising, and targeted drug carrier designed for triple combination lung cancer therapy which should be further studied for such applications.

Delay of gCJD aggravation in sick TgMHu2ME199K mice by combining NPC transplantation and Nano-PSO administration.

gCJD is a fatal late-onset neurodegenerative disease linked to mutations in the PRNP gene. We have previously shown that transplantation of neural precursor cells (NPCs), or administration of a nanoformulation of pomegranate seed oil (Nano-PSO, GranaGard), into newborn asymptomatic TgMHu2ME199K mice modeling for E200K gCJD significantly delayed the advance of clinical disease. In the present study, we tested the individual and combined effects of both treatments in older and sick TgMHu2ME199K mice. We show that while transplantation of NPCs at both initial (140 days) and advance clinical states (230 days) arrested disease progression for about 30 days, after which scores rapidly climbed to those of untreated Tgs, administration of Nano-PSO to transplanted TgMHu2ME199K mice resulted in detention of disease advance for 60-80 days, followed by a slower disease progression thereafter. Pathological examinations demonstrated the combined treatment extended the survival of the transplanted NPCs, and also increased the generation of endogenous stem cells. Our results suggest that administration of Nano-PSO may increase the beneficial effects of NPCs transplantation.

Preparation and Evaluation Pumpkin Seed Oil-based Vitamin E Cream Formulations for Topical Application.

Plant seed oil is often incorporated into the cream emulsions to provide multifunctional effects on the skin. In the current study, pumpkin seed oil (PSO) was used to develop a stable oil-in-water emulsion. The study aimed to optimise PSO cream formulation and determine the synergistic effect of the PSO with vitamin E oil added. The physical properties, antioxidant activities and storage stability of the formulations were analysed. Besides, the synergistic effect of the best formulation was analysed based on α-tocopherol content using ultra-high performance liquid chromatography (UHPLC). The storage stability test was assessed upon storing at 25 ± 2°C and 40 ± 2°C for 12 weeks. The best formulation (20% PSO, vitamin E oil and beeswax) selected showed physically and microbiologically stable. The incorporation of vitamin E oil into the formulation produced with PSO was found to be compatible, as it showed a synergistic effect in the amount of α-tocopherol content (combination index (CI) = 0.98). Thus, PSO had shown its potency to be incorporated into the topical products with a promising potential in delivering additional properties that can nourish the skin.

Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems.

Most inverse problems in the industry (and particularly in geophysical exploration) are highly underdetermined because the number of model parameters too high to achieve accurate data predictions and because the sampling of the data space is scarce and incomplete; it is always affected by different kinds of noise. Additionally, the physics of the forward problem is a simplification of the reality. All these facts result in that the inverse problem solution is not unique; that is, there are different inverse solutions (called equivalent), compatible with the prior information that fits the observed data within similar error bounds. In the case of nonlinear inverse problems, these equivalent models are located in disconnected flat curvilinear valleys of the cost-function topography. The uncertainty analysis consists of obtaining a representation of this complex topography via different sampling methodologies. In this paper, we focus on the use of a particle swarm optimization (PSO) algorithm to sample the region of equivalence in nonlinear inverse problems. Although this methodology has a general purpose, we show its application for the uncertainty assessment of the solution of a geophysical problem concerning gravity inversion in sedimentary basins, showing that it is possible to efficiently perform this task in a sampling-while-optimizing mode. Particularly, we explain how to use and analyze the geophysical models sampled by exploratory PSO family members to infer different descriptors of nonlinear uncertainty.

Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach.

Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes and deterioration of water quality and all its uses in general, when the production of algae and other aquatic vegetations are increased. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on support vector machines (SVM) approach in combination with the particle swarm optimization (PSO) technique, for predicting the eutrophication from biological and physical-chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the SVM training procedure, which significantly influences the regression accuracy. The results of the present study are twofold. In the first place, the significance of each biological and physical-chemical variables on the eutrophication is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.90 for the Total phosphorus estimation and 0.92 for the Chlorophyll concentration were obtained when this hybrid PSO-SVM-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.

Continues administration of Nano-PSO significantly increased survival of genetic CJD mice.

We have shown previously that Nano-PSO, a nanodroplet formulation of pomegranate seed oil, delayed progression of neurodegeneration signs when administered for a designated period of time to TgMHu2ME199K mice, modeling for genetic prion disease. In the present work, we treated these mice with a self-emulsion formulation of Nano-PSO or a parallel Soybean oil formulation from their day of birth until a terminal disease stage. We found that long term Nano-PSO administration resulted in increased survival of TgMHu2ME199K lines by several months. Interestingly, initiation of treatment at day 1 had no clinical advantage over initiation at day 70, however cessation of treatment at 9months of age resulted in the rapid loss of the beneficial clinical effect. Pathological studies revealed that treatment with Nano-PSO resulted in the reduction of GAG accumulation and lipid oxidation, indicating a strong neuroprotective effect. Contrarily, the clinical effect of Nano-PSO did not correlate with reduction in the levels of disease related PrP, the main prion marker. We conclude that long term administration of Nano-PSO is safe and may be effective in the prevention/delay of onset of neurodegenerative conditions such as genetic CJD.

Intervention of pumpkin seed oil on metabolic disease revealed by metabonomics and transcript profile.

BACKGROUND: Understanding the metabolic and transcription basis of pumpkin seed oil (PSO) intervention on metabolic disease (MD) is essential to daily nutrition and health. RESULTS: This study analyzed the liver metabolic variations of Wistar rats fed normal diet (CON), high-fat diet (HFD) and high-fat plus PSO diet (PSO) to establish the relationship between the liver metabolite composition/transcript profile and the effects of PSO on MD. By using proton nuclear magnetic resonance spectroscopy together with multivariate data analysis, it was found that, compared with CON rats, HFD rats showed clear dysfunctions of choline metabolism, glucose metabolism and nucleotide and amino acid metabolism. Using quantitative real-time polymerase chain reaction (qPCR), it was found that, compared with HFD rats, PSO rats showed alleviated endoplasmic reticulum stress accompanied by lowered unfolded protein response. CONCLUSION: These findings provide useful information to understand the metabolic alterations triggered by MD and to evaluate the effects of PSO intervention. © 2016 Society of Chemical Industry.

Design Optimization and Fabrication of High-Sensitivity SOI Pressure Sensors with High Signal-to-Noise Ratios Based on Silicon Nanowire Piezoresistors.

In order to meet the requirement of high sensitivity and signal-to-noise ratios (SNR), this study develops and optimizes a piezoresistive pressure sensor by using double silicon nanowire (SiNW) as the piezoresistive sensing element. First of all, ANSYS finite element method and voltage noise models are adopted to optimize the sensor size and the sensor output (such as sensitivity, voltage noise and SNR). As a result, the sensor of the released double SiNW has 1.2 times more sensitivity than that of single SiNW sensor, which is consistent with the experimental result. Our result also displays that both the sensitivity and SNR are closely related to the geometry parameters of SiNW and its doping concentration. To achieve high performance, a p-type implantation of 5 × 10¹8 cm-3 and geometry of 10 µm long SiNW piezoresistor of 1400 nm × 100 nm cross area and 6 µm thick diaphragm of 200 µm × 200 µm are required. Then, the proposed SiNW pressure sensor is fabricated by using the standard complementary metal-oxide-semiconductor (CMOS) lithography process as well as wet-etch release process. This SiNW pressure sensor produces a change in the voltage output when the external pressure is applied. The involved experimental results show that the pressure sensor has a high sensitivity of 495 mV/V·MPa in the range of 0⁻100 kPa. Nevertheless, the performance of the pressure sensor is influenced by the temperature drift. Finally, for the sake of obtaining accurate and complete information over wide temperature and pressure ranges, the data fusion technique is proposed based on the back-propagation (BP) neural network, which is improved by the particle swarm optimization (PSO) algorithm. The particle swarm optimization⁻back-propagation (PSO⁻BP) model is implemented in hardware using a 32-bit STMicroelectronics (STM32) microcontroller. The results of calibration and test experiments clearly prove that the PSO⁻BP neural network can be effectively applied to minimize sensor errors derived from temperature drift.

Treatment of a multiple sclerosis animal model by a novel nanodrop formulation of a natural antioxidant.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system and is associated with demyelination, neurodegeneration, and sensitivity to oxidative stress. In this work, we administered a nanodroplet formulation of pomegranate seed oil (PSO), denominated Nano-PSO, to mice induced for experimental autoimmune encephalomyelitis (EAE), an established model of MS. PSO comprises high levels of punicic acid, a unique polyunsaturated fatty acid considered as one of the strongest natural antioxidants. We show here that while EAE-induced mice treated with natural PSO presented some reduction in disease burden, this beneficial effect increased significantly when EAE mice were treated with Nano-PSO of specific size nanodroplets at much lower concentrations of the oil. Pathological examinations revealed that Nano-PSO administration dramatically reduced demyelination and oxidation of lipids in the brains of the affected animals, which are hallmarks of this severe neurological disease. We propose that novel formulations of natural antioxidants such as Nano-PSO may be considered for the treatment of patients suffering from demyelinating diseases. On the mechanistic side, our results demonstrate that lipid oxidation may be a seminal feature in both demyelination and neurodegeneration.

Pomegranate seed oil nanoemulsions for the prevention and treatment of neurodegenerative diseases: the case of genetic CJD.

Neurodegenerative diseases generate the accumulation of specific misfolded proteins, such as PrP(Sc) prions or A-beta in Alzheimer's diseases, and share common pathological features, like neuronal death and oxidative damage. To test whether reduced oxidation alters disease manifestation, we treated TgMHu2ME199K mice, modeling for genetic prion disease, with Nano-PSO, a nanodroplet formulation of pomegranate seed oil (PSO). PSO comprises large concentrations of a unique polyunsaturated fatty acid, Punicic acid, among the strongest natural antioxidants. Nano-PSO significantly delayed disease presentation when administered to asymptomatic TgMHu2ME199K mice and postponed disease aggravation in already sick mice. Analysis of brain samples revealed that Nano-PSO treatment did not decrease PrP(Sc) accumulation, but rather reduced lipid oxidation and neuronal loss, indicating a strong neuroprotective effect. We propose that Nano-PSO and alike formulations may be both beneficial and safe enough to be administered for long years to subjects at risk or to those already affected by neurodegenerative conditions. FROM THE CLINICAL EDITOR: This team of authors report that a nanoformulation of pomegranade seed oil, containing high levels of a strong antioxidant, can delay disease onset in a mouse model of genetic prion diseases, and the formulation also indicates a direct neuroprotective effect.

Evaluation of exopolysaccharide producing Weissella cibaria MG1 strain for the production of sourdough from various flours.

This study determined exopolysaccharide (EPS) production by Weissella cibaria MG1 in sourdoughs prepared from gluten-free flours (buckwheat, oat, quinoa and teff), as well as wheat flour. Sourdoughs (SD) were fermented without sucrose, or by replacing 10% flour with sucrose to support EPS production. The amount of EPS depended on the substrate: high amounts of EPS corresponding to low amounts of oligosaccharides were found in buckwheat (4.2 g EPS/kg SD) and quinoa sourdoughs (3.2 g EPS/kg SD); in contrast, no EPS but panose-series oligosaccharides (PSO) were detected in wheat sourdoughs. Organic acid production, carbohydrates and rheological changes during fermentation were compared to the EPS negative control without added sucrose. Corresponding to the higher mineral content of the flours, sourdoughs from quinoa, teff and buckwheat had higher buffering capacity than wheat. Fermentable carbohydrates in buckwheat, teff and quinoa flours promoted W. cibaria growth; indicating why W. cibaria failed to grow in oat sourdoughs. Endogenous proteolytic activity was highest in quinoa flour; α-amylase activity was highest in wheat and teff flours. Protein degradation during fermentation was most extensive in quinoa and teff SD reducing protein peaks 18-29, 30-41 and 43-55 kDa extensively. Rheological analyses revealed decreased dough strength (AF) after fermentation, especially in sucrose-supplemented buckwheat sourdoughs correlating with amounts of EPS. High EPS production correlated with high protein, fermentable sugars (glucose, maltose, fructose), and mineral contents in quinoa flour. In conclusion, W. cibaria MG1 is a suitable starter culture for sourdough fermentation of buckwheat, quinoa and teff flour.

Gravity inversion of a fault by Particle swarm optimization (PSO).

Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. In this paper we introduce and use this method in gravity inverse problem. We discuss the solution for the inverse problem of determining the shape of a fault whose gravity anomaly is known. Application of the proposed algorithm to this problem has proven its capability to deal with difficult optimization problems. The technique proved to work efficiently when tested to a number of models.