Surface Modification of Magnetoactive Elastomers by Laser Micromachining

It has been recently demonstrated that laser micromachining of magnetoactive elastomers is a very convenient method for fabricating dynamic surface microstructures with magnetically tunable properties, such as wettability and surface reflectivity. In this study, we investigate the impact of the micromachining process on the fabricated material's structural properties and its chemical composition. By employing scanning electron microscopy, we investigate changes in size distribution and spatial arrangement of carbonyl iron microparticles dispersed in the polydimethylsiloxane (PDMS) matrix as a function of laser irradiation. Based on the images obtained by a low vacuum secondary electron detector, we analyze modifications of the surface topography. The results show that most profound modifications occur during the low-exposure (8 J/cm2) treatment of the surface with the laser beam. Our findings provide important insights for developing theoretical models of functional properties of laser-sculptured microstructures from magnetoactive elastomers.

Sex- and age-related differences in outcomes of patients with acute myocardial infarction: MINOCA vs. MIOCA

AIMS

The aim of the study is to evaluate the impact of sex on acute myocardial infarction (AMI) patients' clinical presentation and outcomes, comparing those with non-obstructive and obstructive coronary arteries (MINOCA vs. MIOCA).

METHODS AND RESULTS

We enrolled 2455 patients with AMI undergoing coronary angiography from January 2017 to September 2021. Patients were divided according to the type of AMI and sex: male (n = 1593) and female (n = 607) in MIOCA and male (n = 87) and female (n = 168) in MINOCA. Each cohort was further stratified based on age (≤/> 70 years). The primary endpoint (MAE) was a composite of all-cause death, recurrent AMI, and hospitalization for heart failure (HF) at follow-up. Secondary outcomes included all-cause and cardiovascular death, recurrent AMI, HF re-hospitalization, and stroke. MINOCA patients were more likely to be females compared with MIOCA ones (P < 0.001). The median follow-up was 28 (15-41) months. The unadjusted incidence of MAE was significantly higher in females compared with males, both in MINOCA [45 (26.8%) vs. 12 (13.8%); P = 0.018] and MIOCA cohorts [203 (33.4%) vs. 428 (26.9%); P = 0.002]. Age was an independent predictor of MAE in both cohorts. Among MINOCA patients, females ≤70 years old had a higher incidence of MAE [18 (23.7%) vs. 4 (5.9%); P = 0.003] compared with male peers, mainly driven by a higher rate of re-hospitalization for HF (P = 0.045) and recurrence of AMI (P = 0.006). Only in this sub-group of MINOCA patients, female sex was an independent predictor of MAE (hazard ratio = 3.09; 95% confidence interval: 1.02-9.59; P = 0.040). MINOCA females ≤70 years old had worse outcomes than MIOCA female peers.

CONCLUSION

MINOCA females ≤70 years old had a significantly higher incidence of MAE, compared with males and MIOCA female peers, likely due to the different pathophysiology of the ischaemic event.

TRIAL REGISTRATION

Data were part of the ongoing observational study 'AMIPE: Acute Myocardial Infarction, Prognostic and Therapeutic Evaluation' (ClinicalTrials.gov Identifier: NCT03883711).

Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread

BACKGROUND

Bayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important.

METHODS

In this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons.

RESULTS

A particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall.

DISCUSSION

The models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall 'best' model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality.

CONCLUSIONS

From a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance. In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.

Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga Cladostephus spongiosus

Cladostephus spongiosus was harvested once a month during its growing season (from May to August) from the Adriatic Sea. Algal volatile organic compounds (VOCs) were obtained by headspace solid-phase microextraction (HS-SPME) and hydrodistillation (HD) and analysed by gas chromatography and mass spectrometry (GC-MS). The effects of air drying and growing season on VOCs were determined. Two different extraction methods (ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE)) were used to obtain ethanolic extracts of C. spongiosus. In addition, the seasonal antioxidant potential of the extracts was determined, and non-volatile compounds were identified from the most potent antioxidant extract. Aliphatic compounds (e.g., pentadecane) were predominantly found by HS-SPME/GC-MS. Hydrocarbons were more than twice as abundant in the dry samples (except in May). Aliphatic alcohols (e.g., hexan-1-ol, octan-1-ol, and oct-1-en-3-ol) were present in high percentages and were more abundant in the fresh samples. Hexanal, heptanal, nonanal, and tridecanal were also found. Aliphatic ketones (octan-3-one, 6-methylhept-5-en-2-one, and (E,Z)-octa-3,5-dien-2-one) were more abundant in the fresh samples. Benzene derivatives (e.g., benzyl alcohol and benzaldehyde) were dominant in the fresh samples from May and August. (E)-Verbenol and p-cymen-8-ol were the most abundant in dry samples in May. HD revealed aliphatic compounds (e.g., heptadecane, pentadecanal, (E)-heptadec-8-ene, (Z)-heptadec-3-ene), sesquiterpenes (germacrene D, epi-bicyclosesquiphellandrene, gleenol), diterpenes (phytol, pachydictyol A, (E)-geranyl geraniol, cembra-4,7,11,15-tetraen-3-ol), and others. Among them, terpenes were the most abundant (except for July). Seasonal variations in the antioxidant activity of the ethanolic extracts were evaluated via different assays. MAE extracts showed higher peroxyl radical inhibition activity from 55.1 to 74.2 µM TE (Trolox equivalents). The highest reducing activity (293.8 µM TE) was observed for the May sample. Therefore, the May MAE extract was analysed via high-performance liquid chromatography with high-resolution mass spectrometry and electrospray ionisation (UHPLC-ESI-HRMS). In total, 17 fatty acid derivatives, 9 pigments and derivatives, and 2 steroid derivatives were found. The highest content of pheophorbide a and fucoxanthin, as well as the presence of other pigment derivatives, could be related to the observed antioxidant activity.

Swin MAE: Masked autoencoders for small datasets

The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets. Unsupervised learning does not require labels and is more suitable for solving medical image analysis problems. However, most unsupervised learning methods must be applied to large datasets. To make unsupervised learning applicable to small datasets, we proposed Swin MAE, a masked autoencoder with Swin Transformer as its backbone. Even on a dataset of only a few thousand medical images, Swin MAE can still learn useful semantic features purely from images without using any pre-trained models. It can equal or even slightly outperform the supervised model obtained by Swin Transformer trained on ImageNet in the transfer learning results of downstream tasks. Compared to MAE, Swin MAE brought a performance improvement of twice and five times for downstream tasks on BTCV and our parotid dataset, respectively. The code is publicly available at https://github.com/Zian-Xu/Swin-MAE.

Comparative Study on Assisted Solvent Extraction Techniques for the Extraction of Biologically Active Compounds from Sideritis raeseri and Sideritis scardica

The plants in the Sideritis genus are postulated to exhibit several important medicinal properties due to their unique chemical composition. To isolate the targeted phytochemical compounds, the selection of a suitable extraction method is of primary importance. In this work, a comparative study on the phytochemical profiles of various Sideritis raeseri and Sideritis scardica extracts has been carried out. An untargeted metabolomics approach based on ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry was applied to investigate the metabolic differences between extracts obtained by conventional extraction and extractions assisted by microwaves, ultrasounds and high pressure. Additionally, the influence of extraction solvents on HPLC antioxidant profiles obtained following the derivatization of analytes with ABTS reagent was evaluated. A total of 102 metabolites have been putatively identified. The major secondary metabolites groups were classified as flavonoids, terpenoids, phenylethanoid glycosides and phenolic acids. The main antioxidants in the extracts were isoscutellarein and hypolaetin derivatives as well as verbascoside and chlorogenic acid. The results showed that 70% ethanol was the most effective extractant for different classes of phytochemicals including antioxidants. In addition, extraction supported with microwaves, ultrasounds or high pressure improved the overall recovery of metabolites by about 3 times compared to the conventional extraction method.

Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms

ECG provides critical information in a waveform about the heart's condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians.

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease

According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. However, delayed treatment of this disease may lead to several other health problems, such as fertility issues and obesity. Therefore, early treatment is essential for patient survival. The proposed technology could be used for the prediction of hypothyroid disease and its severity during its early stages. Though several classification and regression algorithms are available for the prediction of hypothyroid using clinical information, there exists a gap in knowledge as to whether predicted outcomes may reach a higher accuracy or not. Therefore, the objective of this research is to predict the existence of hypothyroidism with higher accuracy by optimizing the estimator list of the pycaret classifier model. With this overview, a blunge calibration intelligent feature classification model that supports the assessment of the presence of hypothyroidism with high accuracy is proposed. A hypothyroidism dataset containing 3163 patient details with 23 independent and one dependent feature from the University of California Irvine (UCI) machine-learning repository was used for this work. We undertook dataset preprocessing and determined its incomplete values. Exploratory data analysis was performed to analyze all the clinical parameters and the extent to which each feature supports the prediction of hypothyroidism. ANOVA was used to verify the F-statistic values of all attributes that might highly influence the target. Then, hypothyroidism was predicted using various classifier algorithms, and the performance metrics were analyzed. The original dataset was subjected to dimensionality reduction by using regressor and classifier feature-selection algorithms to determine the best subset components for predicting hypothyroidism. The feature-selected subset of the clinical parameters was subjected to various classifier algorithms, and its performance was analyzed. The system was implemented with python in the Spyder editor of Anaconda Navigator IDE. Investigational results show that the Gaussian naive Bayes, AdaBoost classifier, and Ridge classifier maintained the accuracy of 89.5% for the regressor feature-selection methods. The blunge calibration regression model (BCRM) was designed with naive Bayes, AdaBoost, and Ridge as the estimators with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCRM showed 99.5% accuracy in predicting hypothyroidism. The implementation results show that the Kernel SVM, KNeighbor, and Ridge classifier maintained an accuracy of 87.5% for the classifier feature-selection methods. The blunge calibration classifier model (BCCM) was developed with Kernel SVM, KNeighbor, and Ridge as the estimators, with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCCM showed 99.7% accuracy in predicting hypothyroidism. The main contribution of this research is the design of BCCM and BCRM models that were built with accuracy optimization with soft blending based on the sum of predicted probabilities of classifiers. The BCRM and BCCM models uniqueness's are achieved by updating the estimators list with the effective classifiers and regressors that suit the application at runtime.

Searching for the urine osmolality surrogate: an automated machine learning approach

OBJECTIVES

Automated machine learning (AutoML) tools can help clinical laboratory professionals to develop machine learning models. The objective of this study was to develop a novel formula for the estimation of urine osmolality using an AutoML tool and to determine the efficiency of AutoML tools in a clinical laboratory setting.

METHODS

Three hundred routine urinalysis samples were used for reference osmolality and urine clinical chemistry analysis. The H2O AutoML engine completed the machine learning development steps with minimum human intervention. Four feature groups were created, which include different urinalysis measurements according to the Boruta feature selection algorithm. Method comparison statistics including Spearman correlation, Passing-Bablok regression analysis were performed, and Bland Altman plots were created to compare model predictions with the reference method. The minimum allowable bias (24.17%) from biological variation data was used as the limit of agreement.

RESULTS

The AutoML engine developed a total of 183 ML models. Conductivity and specific gravity had the highest variable importance. Models that include conductivity, specific gravity, and other urinalysis parameters had the highest R² (0.70-0.83), and 70-84% of results were within the limit of agreement.

CONCLUSIONS

Combining urinary conductivity with other urinalysis parameters using validated machine learning models can yield a promising surrogate. Additionally, AutoML tools facilitate the machine learning development cycle and should be considered for developing ML models in clinical laboratories.

Comparison of Extraction Techniques for the Determination of Volatile Organic Compounds in Liverwort Samples

This article focuses on the comparison of four popular techniques for the extraction of volatile organic compounds (VOCs) from liverworts of the Calypogeia azurea species. Since extraction is the most important step in the sample analysis of ingredients present in botanical preparations, their strengths, and weaknesses are discussed. In order to determine the VOCs present in plants, selecting the appropriate one is a key step of the extraction technique. Extraction should ensure the isolation of all components present in the oily bodies of Calypogeia azurea without the formation of any artifacts during treatment. The best extraction method should yield the determined compounds in detectable amounts. Hydrodistillation (HD), applying Deryng apparatus and solid-liquid extraction (SLE), microwave-assisted extraction (MAE), and headspace solid-phase microextraction (HS-SPME) were used for volatile extraction. The extracts obtained were analysed by gas chromatography coupled to mass spectrometry (GC-MS) to determine the compounds.

Polyphenols Recovery from Thymus serpyllum Industrial Waste Using Microwave-Assisted Extraction-Comparative RSM and ANN Approach for Process Optimization

The aim of this study was to valorize Thymus serpyllum L. herbal dust, the particular fraction distinguished as industrial waste from filter-tea production. This work demonstrated comparable analysis considering model fitting, influence analysis and optimization of microwave-assisted extraction (MAE) of bioactive compounds from the aforementioned herbal dust using face-centered central composite experimental design within the response surface methodology (RSM), as well as artificial neural networks (ANN). In order to increase yield and amount of compounds of interest and minimize solvent, time and energy consumption, the ethanol concentration (45, 60 and 75%), extraction time (5, 12.5 and 20 min), liquid–solid ratio (10, 20 and 30 mL/g) and irradiation power (400, 600 and 800 W) were used as independent variables. Total extraction yield (Y), total phenols yield (TP), as well as antioxidant activity parameters obtained by DPPH and ABTS assays, were selected as responses. It could be concluded that the MAE technique is an efficient approach for the extraction of biologically active compounds from T. serpyllum herbal dust, which represents a high-value source of natural antioxidants with great potential for further use in various forms within different branches of industry.

Modeling and prediction of KSE - 100 index closing based on news sentiments: an applications of machine learning model and ARMA (p, q) model

The main financial markets of every country are stock exchange and consider as an imperative cause for the corporations to increase capital. The novelty of this study to explore machine learning techniques when applied to financial stock market data, and to understand how machine learning algorithms can be applied and compare the result with time series analysis to real lifetime series data and helpful for any investor. Investors are constantly reviewing past pricing history and using it to influence their future investment decisions. The another novelty of this study, using news sentiments, the values will be processed into lists displaying and representing the stock and predicting the future rates to describe the market, and to compare investments, which will help to avoid uncertainty amongst the investors regarding the stock index. Using artificial neural network technique for prediction for KSE 100 index data on closing day. In this regard, six months' data cycle trained the data and apply the statistical interference using a ARMA (p, q) model to calculate numerical result. The novelty of this study to find the relation between them either they are strongly correlated or not, using machine learning techniques and ARMA (p, q) process to forecast the behavior KSE 100 index cycles. The adequacy of model describes via least values Akaike information criterion (AIC), Bayesian Schwarz information criterion (SIC) and Hannan Quinn information criterion (HIC). Durbin- Watson (DW) test is also applied. DW values (< 2) shows that all cycles are strongly correlated. Most of the KSE-100 index cycles expresses that the appropriate model is ARMA (2,1). Cycle's 2nd,3rd,4th and 5th shows that ARMA (3,1) is best fitted. Cycle 8th is shows ARMA (1,1) best fit and cycle 12th shows that the most appropriate model is ARMA (4,1). Diagnostic checking tests like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Theil's U-Statistics are used to predict KSE-100 index cycles. Theil's U-Statistics demonstrate that each cycle is strongly correlated to previous one.

Rapid green microwave assisted extraction of lutein from Chlorella sorokiniana (NIOT-2) - Process optimization

Chloropycean microalgae are looked up as a prospective alternate source for the production of xanthophyll carotenoid lutein. Despite, the market significance and multitude of nutraceutical applications of lutein commercial production from microalgae still remains a challenge due to the prohibitive downstream cost. This necessitates innovative less energy intensive, high lutein yielding green processes. The present work presents a comprehensive study on the rapid green microwave assisted extraction (MAE) of lutein from marine chlorophycean microalgae Chlorella sorokiniana (NIOT-2). The process parameters of microwave assisted alkali pre-treatment like exposure time (ET), alkali concentration (AC) and solid (biomass): liquid (aqueous Potassium hydroxide-KOH) ratio (S: L ratio) were optimized using single factor and response surface method (RSM) experiments. The optimized conditions for microwave assisted alkali pre-treatment (ET:1.47 min; AC: 8.16 M KOH and S:L ratio of 36.8:1 (mg/mL) augmented the lutein yield (20.69 ± 1.2 mg/g) 3.26 fold when compared to conventional extraction (6.35 ± 0.44 mg/g). Lutein extracted using optimized MAE conditions was purified and characterized. Visualization of the MAE extracted algal biomass using Scanning electron microscope confirmed the effective cell disruption. X-ray diffraction (XRD) analysis of microwave assisted alkali treated biomass (83.85%) revealed a significantly higher crystallinity index when compared to untreated control (17.28%). MAE pre-treatment can thus be propounded as a suitable process for lutein extraction from marine microalgae due to its amalgamated rapidity, homogenous heating, less energy intensiveness and high extraction yield.

Microwave-assisted extraction and gas chromatographic determination of thirty priority micropollutants in biowaste fraction derived from municipal solid waste for material recovery in the circular-economy approach

European and national waste directives prioritize recycling of wastes, as well as material and energy recovery from wastes themselves. Bio-waste fraction can be converted into new resources whose quality is strictly dependent upon that of waste feedstock. Methods to evaluate the contamination from organic micropollutants in bio-waste are rarely investigated. The aim of this work was to develop an innovative analytical method for the extraction and quantification of 16 polycyclic aromatic hydrocarbons (PAHs) and 14 polychlorinated biphenyls (PCBs, including dioxin-like compounds) in bio-waste. Through a full-factorial experimental design, a microwave-assisted extraction technique was optimized to extract the thirty targeted micropollutants, studying the effect of cyclohexane and dichloromethane as extraction solvents with or without acetone, and of extraction temperature. Purification of the extract was obtained by a silica-based solid-phase extraction cartridge, followed by a sulfuric acid treatment. The analysis was carried out by gas chromatography coupled with mass spectrometry. The optimized method, validated directly in the bio-waste matrix fortified with isotopically marked surrogates, is characterized by good extraction recoveries, included within 47 and 106% (relative standard deviations <10%), by satisfactory intra-day (<1.1%) and inter-day (<9.3%) precision, and by low matrix effect (<17%), despite the complexity of the matrix. The optimized procedure, applied to the analysis of PAHs and PCBs in a bio-waste sample collected from a local anaerobic digestion and composting plant, showed a total PAHs content of 562 μg/kg. As regards PCBs, the dioxin-like congener PCB 118 was the only compound quantified (25 ± 6 μg kg^-1).

Optimization of Commercial Microwave Assisted-Extraction Conditions for Recovery of Phenolics from Lemon-Scented Tee Tree (Leptospermum petersonii) and Comparison with Other Extraction Techniques

The lemon-scented tea tree (LSTT) is an Australian native herb and is a rich source of essential oil and phenolics. The ETHOS X extraction system is known as a commercial microwave-assisted extraction (MAE) system for extracting bioactive compounds from plant materials. This study investigated the influence of soaking time, radiation time, microwave power, and sample to solvent ratio on the extraction efficiency of polyphenols and antioxidant properties from lemon-scented tea tree leaves and optimized the extraction conditions using response surface methodology (RSM). The effectiveness of ETHOS X was further compared with ultrasound-assisted extraction (UAE) and shaking water bath (SWB) techniques. The results revealed that soaking time did not significantly affect the recovery of phenolics from the leaves (p > 0.05). Thus, soaking is not required for the ETHOS X extraction of polyphenols from LSTT leaves. RSM was successfully applied to explore the impact of ETHOS X extraction conditions and optimize the extraction conditions. Radiation time significantly affects the recovery yield of phenolics (p < 0.05) positively, whereas irradiation power and sample to solvent ratio adversely influenced the extraction yields of phenolics. The optimal ETHOS X extraction conditions were: radiation time of 60 min, irradiation power of 600 W, and sample to solvent ratio of 2 g/100 mL. Under these conditions, 119.21 ± 7.09 mg of phenolic, 85.31 ± 4.55 mg of flavonoids, and 137.51 ± 12.52 mg of proanthocyanidins can be extracted from a gram of dried LSTT leaves. In comparison with UAE and SWB, ETHOS X is not more effective for the extraction of phenolics than UAE and SWB. However, this technique can save half of the solvent volume compared to UAE and SWB techniques.

The application of rigid and flexible mediastinoscopy in esophagectomy: our experience and a new technology

Background

To avoid the inconvenience of triangulation among various rigid operating instruments in mediastinoscopy-assisted esophagectomy, we invented a new technique: used a flexible endoscope to mobilize thoracic esophagus and dissected mediastinal lymph nodes through the left cervical incision. This technology has not been reported so far. In this study, we introduce our long-term experience and demonstrate this new technique.

Methods

Twenty-nine patients with early esophageal cancer underwent mediastinoscopy-assisted esophagectomy in our hospital from June 2018 to September 2020. Among them, 12 patients used flexible mediastinoscopy, and 17 patients used conventional rigid mediastinoscopy and instruments to observe their therapeutic effect.

Results

There were no significant differences between the two groups in gender, average age, body mass index, incidence of adverse reactions, bleeding volume, and postoperative hospital stay. The operation time of flexible mediastinoscopy group was significantly shorter than that of rigid mediastinoscopy group (192.9 ± 13.0 vs 246.8 ± 6.9 min, p < 0.01). The number of lymph nodes removed by flexible endoscopy was significantly more than that of rigid endoscopy (8.5 ± 0.6 vs 6.0 ± 0.3, P < 0.01). Postoperative follow-up was completed for all patients, and the average follow-up time was 11.6 ± 7.2 months. During the follow-up period, no recurrence or death was observed.

Conclusions

Mediastinoscopy-assisted esophagectomy is an effective way to treat early esophageal cancer. The application of flexible mediastinoscopy provides more convenience and better stability. It can facilitate the operation of the surgeon and lymph node dissection, which proved to be a feasible technology.

Applying neural networks to predict HPC-I/O bandwidth over seismic data on lustre file system for ExSeisDat

HPC or super-computing clusters are designed for executing computationally intensive operations that typically involve large scale I/O operations. This most commonly involves using a standard MPI library implemented in C/C++. The MPI-I/O performance in HPC clusters tends to vary significantly over a range of configuration parameters that are generally not taken into account by the algorithm. It is commonly left to individual practitioners to optimise I/O on a case by case basis at code level. This can often lead to a range of unforeseen outcomes. The ExSeisDat utility is built on top of the native MPI-I/O library comprising of Parallel I/O and Workflow Libraries to process seismic data encapsulated in SEG-Y file format. The SEG-Y File data structure is complex in nature, due to the alternative arrangement of trace header and trace data. Its size scales to petabytes and the chances of I/O performance degradation are further increased by ExSeisDat. This research paper presents a novel study of the changing I/O performance in terms of bandwidth, with the use of parallel plots against various MPI-I/O, Lustre (Parallel) File System and SEG-Y File parameters. Another novel aspect of this research is the predictive modelling of MPI-I/O behaviour over SEG-Y File benchmarks using Artificial Neural Networks (ANNs). The accuracy ranges from 62.5% to 96.5% over the set of trained ANN models. The computed Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values further support the generalisation of the prediction models. This paper demonstrates that by using our ANNs prediction technique, the configurations can be tuned beforehand to avoid poor I/O performance.

Transition-metal adatoms on 2D-GaAs: a route to chiral magnetic 2D materials by design

Using relativistic density-functional calculations, we examine the magneto-crystalline anisotropy and exchange properties of transition-metal atoms adsorbed on 2D-GaAs. We show that single Mn and Mo atom (Co and Os) strongly bind on 2D-GaAs, and induce local out-of-plane (in-plane) magnetic anisotropy. When a pair of TM atoms is adsorbed on 2D-GaAs in a close range from each other, magnetisation properties change (become tunable) with respect to concentrations and ordering of the adatoms. In all cases, we reveal presence of strong Dzyaloshinskii-Moriya interaction. These results indicate novel pathways towards two-dimensional chiral magnetic materials by design, tailored for desired applications in magneto-electronics.

Evaluation of Performance of an LR and SVR models to predict COVID-19 Pandemic

Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.

Microwave-Assisted Extraction and HPLC-UV-CD Determination of (S)-usnic Acid in Cladonia foliacea

During the years, many usnic acid (UA) conjugates have been synthesized to obtain potent endowed with biological properties. Since (S)-UA is less abundant in nature than (R)-enantiomer, it is difficult to source, thus precluding a deeper investigation. Among the lichens producing UA, Cladonia foliacea is a valuable (S)-UA source. In the present work, we report on a rapid HPLC-UV/PAD-CD protocol suitable for the analysis and the identification of the main secondary metabolites present in C. foliacea extract. Best results were achieved using XBridge Phenyl column and acetonitrile and water, which were both added with formic acid as mobile phase in gradient elution. By combining analytical, spectroscopical, and chiroptical analysis, the most abundant analyte was unambiguously identified as (S)-UA. Accordingly, a versatile microwave-assisted extractive (MAE) protocol, assisted by a design of experiment (DoE), to quantitatively recover (S)-UA was set up. The best result in terms of UA extraction yield was obtained using ethanol and heating at 80 °C under microwave irradiation for 5 min. Starting from 100 g of dried C. foliacea, 420 mg of (S)-UA were achieved. Thus, our extraction method resulted in a suitable protocol to produce (S)-UA from C. foliacea for biological and pharmaceutical investigation or commercial purposes.

Characterization of stereoisomeric 5-(2-nitro-1-phenylethyl)furan-2(5H)-ones by computation of 1 H and 13 C NMR chemical shifts and electronic circular dichroism spectra

In the present work, we describe the preparation of two diastereomers from the enantioselective Michael addition of furan-2(5H)-one to (E)-(2-nitrovinyl)benzene catalyzed by a dinuclear Zn-complex. The relative configurations of the diastereomeric products were assigned by comparing nuclear magnetic resonance (NMR) experimental chemical shift data with those computed by density functional theory (DFT) methods. Corrected mean absolute error (CMAE) and CP3 analyses were used to compare the data sets. The absolute configuration of each diastereomer was initially assigned by analysis of electronic circular dichroism (ECD) data, which was consistent with that of the known X-ray crystallographic structure of the product of a related reaction, namely, (R)-5-((R)-1-(4-chlorophenyl)-2-nitroethyl)furan-2(5H)-one.

Bioactive compounds, antioxidant activity and antiproliferative effects in prostate cancer cells of green and roasted coffee extracts obtained by microwave-assisted extraction (MAE)

Coffee consumption has been investigated as a protective factor against prostate cancer. Coffee may be related to prostate cancer risk reduction due to its phytochemical compounds, such as caffeine, chlorogenic acids, and trigonelline. The roasting process affects the content of the phytochemicals and undesired compounds can be formed. Microwave-assisted extraction is an alternative to conventional extraction techniques since it preserves more bioactive compounds. Therefore, this study aimed to evaluate the phytochemical composition and the putative preventive effects in prostate cancer development of coffee beans submitted to four different coffee-roasting degrees extracted using microwave-assisted extraction. Coffea arabica green beans (1) were roasted into light (2), medium (3) and dark (4) and these four coffee samples were submitted to microwave-assisted extraction. The antioxidant capacity of these samples was evaluated by five different methods. Caffeine, chlorogenic acid and caffeic acid were measured through HPLC. Samples were tested against PC-3 and DU-145 metastatic prostate cancer cell lines regarding their effects on cell viability, cell cycle progression and apoptotic cell death. We found that green and light roasted coffee extracts had the highest antioxidant activity. Caffeine content was not affected by roasting, chlorogenic acid was degraded due to the temperature, and caffeic acid increased in light roasted and decreased in medium and dark roasted. Green and light roasted coffee extracts promoted higher inhibition of cell viability, caused greater cell cycle arrest in S and G₂/M and induced apoptosis more compared to medium and dark roasted coffee extracts and the control samples. Coffee extracts were more effective against DU-145 than in PC-3 cells. Our data provide initial evidence that among the four tested samples, the consumption of green and light coffee extracts contributes to inhibit prostate cancer tumor progression features, potentially preventing aspects related to advanced prostate cancer subtypes.

Sample pre-treatment and analytical methodology for the simultaneous determination of pharmaceuticals and personal care products in sewage sludge

This work describes the design, optimization and validation of an analytical method for the simultaneous determination of 14 pharmaceuticals and personal care products (PPCPs) in sewage sludge. A thorough optimization of the sample pre-treatment was carried out. As a result, microwave-assisted extraction (MAE) was combined with an in-situ clean-up stage and a filtration step. A combination of MilliQ® water/MeOH 95:5 (v/v) adjusted to pH 9 turned out to be the optimal solvent mixture for extraction. The instrumental part of the method presents a significant novelty based on a fully automated sample preparation for the analysis of PPCPs. It consisted of a direct immersion solid phase microextraction followed by on-fiber derivatization, online coupled to gas chromatography - mass spectrometry (DI-SPME-On-fiber derivatization - GC-MS). An isotope dilution approach was used for quantifying, which conferred high reliability to the method. This methodology was validated for 10 compounds with good analytical performance, limit of detection below 20 ng g^-1 and absolute recovery in the range of 30-70% for most of the compounds. It supposes an ecological analytical alternative for many routine analysis laboratories around the world. The developed method was applied to different real samples generated in both a pilot-scale thermal hydrolysis treatment plant and an anaerobic digester operated in mesophilic conditions. Salicylic acid and naproxen were found at concentrations above 1000 ng g^-1.

Synthesis and structural elucidation of a phthalide analog using NMR analysis and DFT calculations

Phtalides are secondary metabolites found in several fungi with a wide range of biological activities. A novel phthalide analog was synthesized by Diels-Alder reaction between cyclopentadiene and 3,4-dichlorofuran-2(5H)-one. Quantum mechanical calculations were used in conjunction with the spectrometric methods to determine the structure of the title compound. The calculated NMR chemical shifts for eight candidate pairs of enantiomers were compared with the experimental NMR chemical shifts applying the DP4 probability and mean absolute errors methodology. DP4 analysis using ¹ H and ¹³ C NMR chemical shifts without assignment of the signals presented 100% probability for the correct candidate structure 3d, proving the consistency of the method even without spectra interpretation. Results from theoretical calculation and NMR spectra interpretation were in agreement to the structure of rac-(3aR,4S,4aS,5R,8S,8aR,9R,9aS)-3a,9a-dichloro-3a,4,4a,5,8,8a,9,9a-octahydro-4,9:5,8-dimethanonaphtho[2,3-c]furan-1(3H)-one.

Recent Advances in Microwave Assisted Extraction of Bioactive Compounds from Complex Herbal Samples: A Review

Microwaves are utilized for extraction of Phytoconstituents from complex herbal sample as a result of incredible research. Conventional extraction strategies are tedious and need more solvents and are no more relevant for thermal sensitive plant components. This review emphasize on the working and significance of microwave extraction technology in herbal research and medical field. The extraction step must be more yielding; quick, particular, not more solvent consuming, ensuring stability of thermolabile components and these features are available with microwave extraction method. In this nonconventional technology heat is created utilizing microwave energy. The important parameters that influence extraction efficiency are solvent properties, volume, duration of exposure, microwave control, system attributes, temperature and application were discussed in this article. The microwave assisted extraction, as green technology is contrasted with other extraction technique. This review is intended to discuss this green extraction technique along with its critical parameters for extracting bioactive compounds from complex plant matrices.

Identification and Quantification of β-Sitosterol β-d-Glucoside of an Ethanolic Extract Obtained by Microwave-Assisted Extraction from Agave angustifolia Haw

β-sitosterol β-d-glucoside (BSSG) was extracted from “piña” of the Agave angustifolia Haw plant by microwave-assisted extraction (MAE) with a KOH solution such as a catalyst and a conventional maceration method to determine the best technique in terms of yield, extraction time, and recovery. The quantification and characterization of BSSG were done by high-performance thin layer chromatography (HPTLC), Fourier-transform infrared spectroscopy (FT-IR), and high-performance liquid chromatography−electrospray ionization−mass spectrometry (HPLC-ESI-MS). With an extraction time of 5 s by MAE, a higher amount of BSSG (124.76 mg of β-sitosterol β-d-glucoside/g dry weight of the extract) than those for MAE extraction times of 10 and 15 s (106.19 and 103.97 mg/g dry weight respectively) was shown. The quantification of BSSG in the extract obtained by 48 h of conventional maceration was about 4–5 times less (26.67 mg/g dry weight of the extract) than the yields reached by the MAE treatments. MAE achieved the highest amount of BSSG, in the shortest extraction time while preserving the integrity of the compound’s structure.

Usefulness of preschool and school versions of the Behavioral Rating Inventory of Executive Functions in the evaluation of the daily life executive function in myoclonic-atonic epilepsy

PURPOSE

Executive functions (EF) are high-order cognitive skills that have a major influence on quality of life, social skills, and school achievement. We aimed to screen EF daily life abilities in young patients with myoclonic-atonic epilepsy (MAE) using an ecological questionnaire and to correlate EF to epilepsy characteristics.

METHODS

Behavioral Rating Inventory of Executive Functions - Preschool (BRIEF-P) and BRIEF - for school-aged patients - parental questionnaires were proposed to patients with MAE and typically developing children (TDC) including Inhibit, Shift, Emotional control, Working memory (WM), Plan/Organize, Initiate, Organization of materials, and Monitor subscales. We included prospectively 12 patients with MAE and 44 TDC aged 3 to 5 years and seven patients with MAE and 21 TDC aged 6-7 years. We performed in addition for all patients an intellectual efficiency evaluation using WPPSI-IV (Wechsler intelligence scale for preschool children version IV) and collected demographics, age at onset of epilepsy, epilepsy duration, response to treatment, number and type of treatments including AEDs (antiepileptic drugs), and ketogenic diet.

RESULTS

Four out of 12 patients for BRIEF-P and 6/7 patients for BRIEF had pathological scores for at least one domain. Behavioral Rating Inventory of Executive Functions' questionnaires showed higher pathological scores for WM, Plan/Organize, Initiate, Monitor, and Metacognition Index in patients with MAE compared to TDC suggesting higher problems reported by parents. Working memory scores were higher in the group with MAE than TDC for both BRIEF-P and BRIEF. Response to treatment is a predictor of multiple BRIEF-P domains. Epilepsy duration predicts Shift and WM domains while age at onset predicts WM domain on BRIEF in this syndrome.

CONCLUSIONS

This study is the first to assess prospectively EF in young patients with MAE. We show everyday deficits in EF reported by parents. Metacognition and more specifically WM, appear to be a core deficit. Early evaluation of EF using both questionnaires and standardized tools is necessary for early detection of EF deficit and initiating tailored rehabilitation. Given the normal development before seizure onset and the absence of cerebral lesion in MAE, these results are in favor of the impact of epilepsy on EF.

To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques

Objective:

The main objective of this paper is to easily identify thyroid symptom for treatment.

Methods:

In this paper two main techniques are proposed for mining the hidden pattern in the dataset. Ensemble-I and Ensemble-II both are machine learning techniques. Ensemble-I generated from decision tree, over fitting and neural network and Ensemble-II generated from combinations of Bagging and Boosting techniques. Finally proposed experiment is conducted by Ensemble-I vs. Ensemble-II.

Results:

In the entire experimental setup find an ensemble –II generated model is the higher compare to other ensemble-I model. In each experiment observe and compare the value of all the performance of ROC, MAE, RMSE, RAE and RRSE. Stacking (ensemble-I) ensemble model estimate the weights for input with output model by thyroid dataset. After the measurement find out the results ROC=(98.80), MAE= (0.89), 6RMSE=(0.21), RAE= (52.78), RRSE=(83.71)and in the ensemble-II observe thyroid dataset and measure all performance of the model ROC=(98.79), MAE= (0.31), RMSE=(0.05) and RAE= (35.89) and RRSE=(52.67). Finally concluded that (Bagging+ Boosting) ensemble-II model is the best compare to other.

Computation and structural elucidation of compounds formed via epoxide alcoholysis

Isobenzofuranones are known for their wide range of biological activities such as fungicide, insecticide, and anticancer. The search for novel bioactive compounds was performed by reaction of epoxide 2 with methanol, ethanol, propan-1-ol, propan-2-ol, and butan-1-ol. The mechanism for the stereoselective and stereospecific epoxide opening with methanol was reasoned by calculating the transition states for the two putative structures (rac)-3a and (rac)-3b. The compound (rac)-3a is the kinetic product as inferred from the lower energies of its transition state (TS1). The ¹ H and ¹³ C nuclear magnetic resonance (NMR) chemical shifts for these two candidate structures were calculated and compared with the experimental data using mean absolute error (MAE) and DP4 analyses. Therefore, the relative stereochemistry of (rac)-3a was established by the mechanism, MAE, and DP4 approaches. The hydroxyl group was acetylated to surpass the problem of signal overlapping of H5 and H6 in the ¹ H NMR. The relative stereochemistry of the corresponding ester determined by NMR interpretation was in agreement with the structure of (rac)-3a.

Optimization of microwave-assisted extraction of bioactive compounds from New Zealand and Chinese Asparagus officinalis L. roots

The extraction of total phenolics (TPC), total flavonoids content (TFC), total saponins content (TSC), and caffeic acid (AC) contents of asparagus roots extract (ARE) from New Zealand and Chinese AR cultivars was optimized following a microwave-assisted extraction combined with central composite design. The determination of AC was conducted by HPLC in samples extracted under the optimum extraction conditions. The optimal variables for ethanol extraction generated a maximum TPC, TFC and TSC of optimal results for 68.6 mg GAE/g, 11.9 mg RE/g and 0.7 mg SE/g as well as antioxidant power towards β-carotene bleaching assay (%β_sc) (57.2%), superoxide anion radical (%O_sc ^2-) scavenging capacity (20.1%) and ferric reducing antioxidant power assay (FRAP) (1.63 µmol/g). For methanol, optimum extraction conditions obtained maximum TPC (62.6 mg GAE/g) TFC (10.7 mg RE/g), TSC (0.68 mg SE/g) with %β_sc (53.9%), %O_sc ^2- (19.1%) and FRAP (0.63 µmol/g). The content of caffeic acid from ARE ranged from 0.46 to 2.89 mg/g with ethanol and from 0.41 to 2.64 mg/g with methanol.

Environmentally Friendly Valorization of Solieria filiformis (Gigartinales, Rhodophyta) from IMTA Using a Biorefinery Concept

Marine macroalgae (seaweed) are an excellent source of novel bioactive metabolites. The biorefinery concept applied to seaweed facilitates the extraction of many chemical constituents from the same biomass ensuring that the resource is used fully, generating few residues through a succession of extraction steps. In the present study, the biomass of the carragenophyte Solieria filiformis (Rhodophyta, Gigartinales) cultured in an integrated multi-trophic aquaculture (IMTA) system was evaluated to obtain valuable products by a biorefinery approach. Enzymatic-assisted extraction (EAE) and microwave-assisted extraction (MAE) were the eco-friendly technologies used to ensure an environmentally friendly valorization of the biomass. Three valuable products were successfully recovered: a water-soluble extract rich in proteins and sulfated polysaccharides suitable as a food supplement; a lipid fraction rich in polyunsaturated fatty acids (PUFAs) with potential to be used in the nutraceutical industry; and a pure ι-carrageenan with a powerful antiviral activity against Herpes simplex virus (EC₅₀ = 6.3 µg mL^-1) comparable to the commercial antiviral acyclovir (EC₅₀ = 3.2⁻5.4 µg mL^-1).

Atriplex mollis Desf. Aerial Parts: Extraction Procedures, Secondary Metabolites and Color Analysis

A method using high-performance liquid chromatography coupled with a photodiode array detector was proposed for the rapid characterization of different phenolic constituents from the extracts of Atriplex mollis aerial parts. Atriplex species are known for their multiple biological activities, but no information is available in the literature about A. mollis. With the aim to firstly characterize the main secondary metabolites of this plant, so as to orient better the biological evaluation, we applied three different extraction procedures and compared the chromatographic results. Microwave-assisted extraction gave the best yield and recovery of important compounds such as gallic acid, catechin, chlorogenic acid, p-OH benzoic acid, rutin, sinapinic acid, t-ferulic acid, naringenin and benzoic acid. These constituents belong to three important chemical classes: phenolic acids, flavonoids and monoterpenes. Color evaluation and analysis of chlorophylls (a and b) and carotenoids complete the preliminary profile of this plant. From these analyses, Atriplex mollis is a source of bioactive compounds (especially rutin, t-ferulic acid and gallic acid) and could be recommended as a plant of phyto-pharmaceutical relevance, opening new perspectives on this salt-tolerant plant.

Use of Innovative (Micro)Extraction Techniques to Characterise Harpagophytum procumbens Root and its Commercial Food Supplements

INTRODUCTION

For the determination of harpagoside and the wide phenolic pattern in Harpagophytum procumbens root and its commercial food supplements, dispersive liquid-liquid microextraction (DLLME), ultrasound-assisted DLLME (UA-DLLME), and sugaring-out liquid-liquid extraction (SULLE) were tested and compared.

OBJECTIVES

In order to optimise the extraction efficiency, DLLME and UA-DLLME were performed in different solvents (water and aqueous solutions of glucose, β-cyclodextrin, (2-hydroxypropyl)-β-cyclodextrin, sodium chloride, natural deep eutectic solvent, and ionic liquid).

MATERIAL AND METHODS

The plant material was ground and sieved to obtain a uniform granulometry before extraction. Commercial food supplements, containing H. procumbens are commercially available in Italy.

RESULTS

The most effective sodium chloride-aided-DLLME was then optimised and applied for analyses followed by HPLC-PDA. For comparison, microwave-assisted extraction was performed using the same solvents and the best results were obtained using 1% of β-cyclodextrin or 15% of sodium chloride.

CONCLUSION

All commercial samples respected the European Pharmacopoeia monograph for this plant material, showing a harpagoside content ≥ 1.2%. Copyright © 2017 John Wiley & Sons, Ltd.

Qualitative and Quantitative Phytochemical Analysis of Different Extracts from Thymus algeriensis Aerial Parts

This study was performed to evaluate the metabolite recovery from different extraction methods applied to Thymus algeriensis aerial parts. A high-performance liquid chromatographic method using photodiode array detector with gradient elution has been developed and validated for the simultaneous estimation of different phenolic compounds in the extracts and in their corresponding purified fractions. The experimental results show that microwave-assisted aqueous extraction for 15 min at 100 °C gave the most phenolics-enriched extract, reducing extraction time without degradation effects on bioactives. Sixteen compounds were identified in this extract, 11 phenolic compounds and five flavonoids, all known for their biological activities. Color analysis and determination of chlorophylls and carotenoids implemented the knowledge of the chemical profile of this plant.

Defining the phenotypic spectrum of SLC6A1 mutations

OBJECTIVE

Pathogenic SLC6A1 variants were recently described in patients with myoclonic atonic epilepsy (MAE) and intellectual disability (ID). We set out to define the phenotypic spectrum in a larger cohort of SCL6A1-mutated patients.

METHODS

We collected 24 SLC6A1 probands and 6 affected family members. Four previously published cases were included for further electroclinical description. In total, we reviewed the electroclinical data of 34 subjects.

RESULTS

Cognitive development was impaired in 33/34 (97%) subjects; 28/34 had mild to moderate ID, with language impairment being the most common feature. Epilepsy was diagnosed in 31/34 cases with mean onset at 3.7 years. Cognitive assessment before epilepsy onset was available in 24/31 subjects and was normal in 25% (6/24), and consistent with mild ID in 46% (11/24) or moderate ID in 17% (4/24). Two patients had speech delay only, and 1 had severe ID. After epilepsy onset, cognition deteriorated in 46% (11/24) of cases. The most common seizure types were absence, myoclonic, and atonic seizures. Sixteen cases fulfilled the diagnostic criteria for MAE. Seven further patients had different forms of generalized epilepsy and 2 had focal epilepsy. Twenty of 31 patients became seizure-free, with valproic acid being the most effective drug. There was no clear-cut correlation between seizure control and cognitive outcome. Electroencephalography (EEG) findings were available in 27/31 patients showing irregular bursts of diffuse 2.5-3.5 Hz spikes/polyspikes-and-slow waves in 25/31. Two patients developed an EEG pattern resembling electrical status epilepticus during sleep. Ataxia was observed in 7/34 cases. We describe 7 truncating and 18 missense variants, including 4 recurrent variants (Gly232Val, Ala288Val, Val342Met, and Gly362Arg).

SIGNIFICANCE

Most patients carrying pathogenic SLC6A1 variants have an MAE phenotype with language delay and mild/moderate ID before epilepsy onset. However, ID alone or associated with focal epilepsy can also be observed.

Characterization of novel isobenzofuranones by DFT calculations and 2D NMR analysis

Phthalides are frequently found in naturally occurring substances and exhibit a broad spectrum of biological activities. In the search for compounds with insecticidal activity, phthalides have been used as versatile building blocks for the syntheses of novel potential agrochemicals. In our work, the Diels-Alder reaction between furan-2(5H)-one and cyclopentadiene was used successfully to obtain (3aR,4S,7R,7aS)-3a,4,7,7a-tetrahydro-4,7-methanoisobenzofuran-1(3H)-one and (3aS,4R,7S,7aR)-3a,4,7,7a-tetrahydro-4,7-methanoisobenzofuran-1(3H)-one (2) and (3aS,4S,7R,7aR)-3a,4,7,7a-tetrahydro-4,7-methanoisobenzofuran-1(3H)-one and (3aR,4R,7S,7aS)-3a,4,7,7a-tetrahydro-4,7-methanoisobenzofuran-1(3H)-one (3). The endo adduct (2) was brominated to afford (3aR,4R,5R,7R,7aS,8R)-5,8-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one and (3aS,4S,5S,7S,7aR,8S)-5,8-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one (4) and (3aS,4R,5R,6S,7S,7aR)-5,6-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one and (3aR,4S,5S,6R,7R,7aS)-5,6-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one (5). Following the initial analysis of the NMR spectra and the proposed two novel unforeseen products, we have decided to fully analyze the classical and non-classical assay structures with the aid of computational calculations. Computation to predict the (13) C and (1) H chemical shifts for mean absolute error analyses have been carried out by gauge-including atomic orbital method at M06-2X/6-31+G(d,p) and B3LYP/6-311+G(2d,p) levels of theory for all viable conformers. Characterization of the novel unforeseen compounds (4) and (5) were not possible by employing only the experimental NMR data; however, a more conclusive structural identification was performed by comparing the experimental and theoretical (1) H and (13) C chemical shifts by mean absolute error and DP4 probability analyses. Copyright © 2016 John Wiley & Sons, Ltd.

Simultaneous determination of seven bioactive components in Guizhi Fuling capsule by microwave-assisted extraction combined with ultra performance liquid chromatography tandem mass spectrometry

A simple, rapid and reliable microwave-assisted extraction (MAE) combined with ultra performance liquid chromatography tandem mass spectrometry method was developed for simultaneous determination of the seven bioactive constituents in Guizhi Fuling capsule (GFC), namely gallic acid, amygdalin, albiflorin, paeoniflorin, paeonol, cinnamic acid and pachymic acid, respectively. The operation of MAE optimised through orthogonal array design experiment was performed at 80°C for 10 min with methanol-water (70:30, v/v) as the extracting solvent. The method was validated including intra- and inter-day precision, repeatability and stability, with relative standard deviation less than 3.9%, 3.3%, 4.4% and 3.1%, respectively. All analytes showed the good linearity (r >0.999), and their average recoveries varied between 98.2% and 101.2%. The results indicated that this method was simple, effective and suitable for the quality control of GFC.

A new spin on vection in depth

Previous research has shown that adding lateral viewpoint changes to visual displays simulating self-motion in depth can increase the strength of linear vection. We performed experiments to determine whether these vection increases are caused by reduced adaptation to retinal motion, rather than increased motion parallax in the visual display. In Experiment 1, we added increasing amplitudes of sinusoidal angular viewpoint oscillation around the viewing axis (up to 94.2°/s) to radial flow simulating self-motion in depth. We found that angular viewpoint oscillation systematically reduced the onset latencies and increased the overall strength of vection in depth, compared with pure radial flow. In Experiment 2, we compared vection strength between radial flow displays with either added angular oscillation or continuous spiral rotation of equivalent peak velocity around the viewing axis (62.8°/s), and found that angular viewpoint oscillation generated the strongest vection. In Experiment 3, we found that pure radial flow with or without continuous spiral rotation produced radial motion aftereffects that lasted longer than that produced by radial flow with angular viewpoint oscillation. These findings support the view that the way viewpoint oscillation increases vection does not critically depend on motion parallax, but rather, on a changing pattern of retinal motion that serves to reduce visual adaptation and sustain sensitivity to optic flow.

Comparisons between conventional, ultrasound-assisted and microwave-assisted methods for extraction of anthraquinones from Heterophyllaea pustulata Hook f. (Rubiaceae)

This work reports a comparative study about extraction methods used to obtain anthraquinones (AQs) from stems and leaves of Heterophyllae pustulata Hook (Rubiáceae). One of the conventional procedures used to extract these metabolites from a vegetable matrix is by successive Soxhlet extractions with solvents of increasing polarity: starting with hexane to eliminate chlorophylls and fatty components, following by benzene and finally ethyl acetate. However, this technique shows a low extraction yield of total AQs, and consumes large quantities of solvent and time. Ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) have been investigated as alternative methods to extract these compounds, using the same sequence of solvents. It was found that UAE increases the extraction yield of total AQs and reduces the time and amount of solvent used. Nevertheless, the combination UAE with benzene, plus MAE with ethyl acetate at a constant power of 900 W showed the best results. A higher yield of total AQs was obtained in less time and using the same amount of solvent that UAE. The optimal conditions for this latter procedure were UAE with benzene at 50 °C during 60 min, followed by MAE at 900 W during 15 min using ethyl acetate as extraction solvent.

Phytochemical analysis of Rosa hybrida cv. 'Jardin de Granville' by HPTLC, HPLC-DAD and HPLC-ESI-HRMS: polyphenolic fingerprints of six plant organs

The Rosa hybrida cultivar 'Jardin de Granville', a delicate clear pink flower, is here investigated through a progressive analytical strategy using complementary phytochemical screening methods in order to characterize the polyphenol content of several parts of the plant. The microwave hydro-ethanolic extract analysis of six different parts of the plant, carried out by High Performance Thin Layer Chromatography (HPTLC) and High Performance Liquid Chromatography coupled with a Diode Array Detector (HPLC-DAD) enabled initial identification of the polar molecular families present in each organ, namely tannins and flavonoids (quercetin and kaempferol derivatives). The HPLC fingerprints displayed different profiles for each organ, attesting to the original composition and potential valuation of the different plant parts. More detailed analyses of the extracts were then carried out by High Performance Liquid Chromatography coupled with electrospray ionization (ESI) mass spectrometry with a Q-TOF analyzer (ESI-HR-Q-TOF). Around 60 compounds were identified, mainly gallo-tannins, ellagi-tannins, catechin derivatives and glycoside derivatives of quercetin and kaempferol. Some compounds such as hyperoside or ellagic acid appeared to be ubiquitous and were found in abundance in each plant part. Woods were the richest organ in catechin and proanthocyanidin derivatives while kaempferol derivatives were more numerous and abundant in bud and flower parts.

Analysis and advanced oxidation treatment of a persistent pharmaceutical compound in wastewater and wastewater sludge-carbamazepine

Pharmaceutically active compounds (PhACs) are considered as emerging environmental problem due to their continuous input and persistence to the aquatic ecosystem even at low concentrations. Among them, carbamazepine (CBZ) has been detected at the highest frequency, which ends up in aquatic systems via wastewater treatment plants (WWTPs) among other sources. The identification and quantification of CBZ in wastewater (WW) and wastewater sludge (WWS) is of major interest to assess the toxicity of treated effluent discharged into the environment. Furthermore, WWS has been subjected for re-use either in agricultural application or for the production of value-added products through the route of bioconversion. However, this field application is disputable due to the presence of these organic compounds and in order to protect the ecosystem or end users, data concerning the concentration, fate, behavior as well as the perspective of simultaneous degradation of these compounds is urgently necessary. Many treatment technologies, including advanced oxidation processes (AOPs) have been developed in order to degrade CBZ in WW and WWS. AOPs are technologies based on the intermediacy of hydroxyl and other radicals to oxidize recalcitrant, toxic and non-biodegradable compounds to various by-products and eventually to inert end products. The purpose of this review is to provide information on persistent pharmaceutical compound, carbamazepine, its ecological effects and removal during various AOPs of WW and WWS. This review also reports the different analytical methods available for quantification of CBZ in different contaminated media including WW and WWS.

Longitudinal assessment of atrioventricular annulus excursion by grey-scale m-mode and colour tissue Doppler imaging in premature infants

BACKGROUND

There is a lack of standardized echocardiographic parameters to quantify ventricular function in newborn infants. Long-axis systolic ventricular shortening is a useful parameter of global ventricular function.

AIMS

Serial assessment of long-axis systolic atrioventricular annulus excursion in premature infants.

METHODS

Two-centre, prospective observational study. Fifty-seven premature infants (26 girls), median (range) gestational age 33(+5) (31(0)-34(+6)) weeks(+days), birth weight 1925 (1127-2836) grams were included. Echocardiographic examinations were performed at the first three days of life and at expected term.

OUTCOME MEASURES

Systolic annulus excursion by grey-scale m-mode and colour tissue Doppler imaging (cTDI).

RESULTS

A consistent longitudinal pattern was found for annulus excursion by grey-scale m-mode and cTDI. All parameters showed a pronounced increase at expected term (p < 0.001). After normalizing for ventricular size the increase at term was less pronounced. Results were lower by cTDI than by grey-scale m-mode for the left and right ventricular walls (p < 0.001). Intraobserver variability (range 1.5-9.8%) was lower than interobserver variability (5.5-18%).

CONCLUSIONS

Serial assessment of longitudinal ventricular shortening in premature infants by grey-scale m-mode and cTDI was feasible and the measurements displayed a consistent pattern. cTDI excursion measurements were lower and more dependent on image quality than m-mode measurements. Lower intraobserver variability suggests that repeated measurements should preferably be performed by the same observer. Annulus excursion varies with ventricular size and we suggest normalizing measurements by ventricular size.

In situ application of activated carbon and biochar to PCB-contaminated soil and the effects of mixing regime

The in situ use of carbon amendments such as activated carbon (AC) and biochar to minimize the bioavailability of organic contaminants is gaining in popularity. In the first in situ experiment conducted at a Canadian PCB-contaminated Brownfield site, GAC and two types of biochar were statistically equal at reducing PCB uptake into plants. PCB concentrations in Cucurbita pepo root tissue were reduced by 74%, 72% and 64%, with the addition of 2.8% GAC, Burt's biochar and BlueLeaf biochar, respectively. A complementary greenhouse study which included a bioaccumulation study of Eisenia fetida (earthworm), found mechanically mixing carbon amendments with PCB-contaminated soil (i.e. 24 h at 30 rpm) resulted in shoot, root and worm PCB concentrations 66%, 59% and 39% lower than in the manually mixed treatments (i.e. with a spade and bucket). Therefore, studies which mechanically mix carbon amendments with contaminated soil may over-estimate the short-term potential to reduce PCB bioavailability.

Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling

Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities.

The CURE-AF trial: a prospective, multicenter trial of irrigated radiofrequency ablation for the treatment of persistent atrial fibrillation during concomitant cardiac surgery

BACKGROUND

Ablation technology has been introduced to replace the surgical incisions of the Cox-Maze procedure in order to simplify the operation. However, the efficacy of these ablation devices has not been prospectively evaluated.

OBJECTIVE

The purpose of this study was to examine the efficacy and safety of irrigated unipolar and bipolar radiofrequency ablation for the treatment of persistent and long-standing persistent atrial fibrillation (AF) during concomitant cardiac surgical procedures.

METHODS

Between May 2007 and July 2011, 150 consecutive patients were enrolled at 15 U.S. centers. Patients were followed for 6 to 9 months, at which time a 24-hour Holter recording and echocardiogram were obtained. Recurrent AF was defined as any atrial tachyarrhythmia (ATA) lasting over 30 seconds on the Holter monitor. The safety end-point was the percent of patients who suffered a major adverse event within 30 days of surgery. All patients underwent a biatrial Cox-Maze lesion set.

RESULTS

Operative mortality was 4%, and there were 4 (3%) 30-day major adverse events. Overall freedom from ATAs was 66%, with 53% of patients free from ATAs and also off antiarrhythmic drugs at 6 to 9 months. Increased left atrial diameter, shorter total ablation time, and an increasing number of concomitant procedures were associated with recurrent AF (P <.05).

CONCLUSION

Irrigated radiofrequency ablation for treatment of AF during cardiac surgery was associated with a low complication rate. No device-related complications occurred. The Cox-Maze lesion set was effective at restoring sinus rhythm and had higher success rates in patients with smaller left atrial diameters and longer ablation times.

Extraction, chemical characterization and biological activity determination of broccoli health promoting compounds

Broccoli (Brassica oleracea L. var. Italica) contains substantial amount of health-promoting compounds such as vitamins, glucosinolates, phenolic compounds, and dietary essential minerals; thus, it benefits health beyond providing just basic nutrition, and consumption of broccoli has been increasing over the years. This review gives an overview on the extraction and separation techniques, as well as the biological activity of some of the above mentioned compounds which have been published in the period January 2008 to January 2013. The work has been distributed according to the different families of health promoting compounds discussing the extraction procedures and the analytical techniques employed for their characterization. Finally, information about the different biological activities of these compounds has been also provided.

Sample preparation for arsenic speciation in terrestrial plants--a review

Arsenic is an element widely present in nature. Additionally, it may be found as different species in several matrices and therefore it is one of the target elements in chemical speciation. Although the number of studies in terrestrial plants is low, compared to matrices such as fish or urine, this number is raising due to the fact that this type of matrix are closely related to the human food chain. In speciation analysis, sample preparation is a critical step and several extraction procedures present drawbacks. In this review, papers dealing with extraction procedures, analytical methods, and studies of species conservation in plants cultivated in terrestrial environment are critically discussed. Analytical procedures based on extractions using water or diluted acid solutions associated with HPLC-ICP-MS are good alternatives, owing to their versatility and sensitivity, even though less expensive strategies are shown as feasible choices.