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Control of a Twin Rotor Multi-input Multi-output System (TRMS) is not a simple work. Because it has complex nonlinear dynamics, cross-coupling, uncertainties, and instability. This paper provides a practical method for control of a TRMS, named Quantitative Feedback Theory (QFT) as one of the robust approaches. Firstly, the TRMS set and modeling procedure are introduced. Secondly, the nonlinear and linear equations of electrical and mechanical parts in both vertical and horizontal planes are presented. Next, using the QFT method, a controller is designed for motion in each plane. Finally, the robustness of the control strategy is illustrated by simulations of vertical and horizontal motions, including controller and pre-filter in the presence of uncertainties. The results demonstrate that the proposed robust controller can guarantee the system stabilization, as well as pitch and yaw tracking of TRMS. [ABSTRACT FROM AUTHOR]

Background: Since vascular endothelial growth factor (VEGF) is a significant regulator of cancer angiogenesis, it is essential to develop a technology for its sensitive detection. Herein, we sensitized a chemiluminescence (CL) immunoassay through the combination of H2O2-sensitive TGA-CdTe quantum dot (QD) as signal transduction, dextran as a cross-linker to prepare enzyme-labeled antigen and the ultrahigh bioactivity of catalase (CAT) as reporter enzyme. Results: Under the optimized experimental conditions, the chemiluminescence enzyme-linked immunosorbent assay (CL-ELISA) method can detect VEGF in the excellent linear range of 2–35,000 pg mL−1, with a detection limit (S/N = 3) of 0.5 pg mL−1 which was approximately ten times lower than the commercial colorimetric immunoassay. This proposed method has been successfully applied to the clinical determination of VEGF in the human serum samples, and the results illustrated an excellent correlation with the conventional ELISA method (R2 = 0.997). The suitable recovery rate of the method in the serum ranged from 97 to 107%, with a relative standard deviation of 1.2% to 13.4%. Conclusions: The novel immunoassay proposes a highly sensitive, specific, and stable method for very low levels detection of VEGF that can be used in the primary diagnosis of tumors. With the well-designed sensing platform, this approach has a broad potential to be applied for quantitative analysis of numerous disease-related protein biomarkers for which antibodies are available. [ABSTRACT FROM AUTHOR]

We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing such data is prohibitively expensive. Therefore, we develop a framework for synthesizing the training data. First, we learn representative human poses from a large corpus of real motion captured human skeleton data. Next, we fit synthetic 3D humans with different body shapes to each pose and render each from 180 camera viewpoints while randomly varying the clothing textures, background and lighting. Generative Adversarial Networks are employed to minimize the gap between synthetic and real image distributions. CNN models are then learned that transfer human poses to a shared high-level invariant space. The learned CNN models are then used as invariant feature extractors from real RGB and depth frames of human action videos and the temporal variations are modelled by Fourier Temporal Pyramid. Finally, linear SVM is used for classification. Experiments on three benchmark human action datasets show that our algorithm outperforms existing methods by significant margins for RGB only and RGB-D action recognition. [ABSTRACT FROM AUTHOR]

Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a set of non-linear transformations that connects the views. The R-NKTM is learned from 2D projections of dense trajectories of synthetic 3D human models fitted to real motion capture data and generalizes to real videos of human actions. The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to incorporate new action classes. R-NKTM is learned with dummy labels and does not require knowledge of the camera viewpoint at any stage. Experiments on three benchmark cross-view human action datasets show that our method outperforms existing state-of-the-art. [ABSTRACT FROM AUTHOR]

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the histogram of oriented principal components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of spatio-temporal keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The experimental results show that our techniques provide significant improvement over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Point-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. Previous studies have shown that incorporating contextual information such as geographical, temporal, social, and categorical substantially improves the quality of POI recommendations. However, fewer works have studied in-depth the multi-aspect benefits of context fusion on POI recommendation, in particular on beyond-accuracy , fairness , and interpretability of recommendations. In this work, we propose a linear regression-based fusion of POI contexts that effectively finds the best combination of contexts for each (i) user, or (ii) group of users from their historical interactions. The results of large-scale experiments on two popular datasets Gowalla and Yelp reveal several interesting findings. First, the proposed approach does not present significant loss in accuracy and unfairness of popularity bias as with classical collaborative baselines, and yet improves the beyond-accuracy of recommendation compared with existing context-aware (CA) approaches using heuristic context fusions; for instance, the proposed approach improves the accuracy and beyond-accuracy compare to best baseline model by 25% and 30%, respectively. Second, our proposed approach is interpretable, allowing to explain to the user why she has been recommended specific POIs, based on the learned context weights from user past check-ins; for example, if you are in Rome and our method recommends you a historical place like 'Colosseum', it can also provide an explanation why this item is recommended to you based on your personal preference on context (e.g., you were recommended to visit 'Colosseum' because in the past your visited historical places). Third, by analyzing the fairness of recommendation with respect to users (based on their activity levels) and items (based on the popularity of items), we found that a model which is recommend fairly on one dataset can recommend unfair on another dataset. Overall, our study suggests that appropriate context fusion is an essential element of an accurate, fair, and transparent POI recommendation system. We highlight that while we have tested the efficacy of our context-fusion methods on two popular CA recommendation models in the POI domain, namely GeoSoCa and LORE, our system can be flexibly utilized to extend the capability of other CA algorithms. • The paper investigates the role of context fusion in POI recommendations. • Evaluations of the accuracy, beyond-accuracy, and fairness of POI recommendations. • We provide better recommendations in terms of beyond accuracy and accuracy metrics. • The study pushes the research forward by choosing several evaluation objectives. • The results of fairness show that contextual models provide better fairness results. [ABSTRACT FROM AUTHOR]

In this study, a solution for anisotropic conductive heat transfer in a composite cylindrical shell is computed by an exact analytical method. The fibers are considered to be winded around the cylinder at any arbitrary angle. A general linear boundary condition is applied on both circular bases of the cylindrical shell to account for various combinations of thermal conditions. The solution considers the effects of convection of the ambient flow motion and various external radiative heat fluxes. The analytical solution describes the temperature distribution in the axial and circumferential directions. In principle, the heat conduction equation should involve a dual second-order derivation, which precludes solving the equation by the direct application of common exact methods. Therefore, an appropriate canonical mapping is selected as a solution to cancel the dual derivation of temperature in the mapped equations. The separation of variables method is then applied to the mapped equations, which are conducted to obtain an exact form of the solution. Finally, an analytical technique based on the Fourier series concept is proposed to determine unknown coefficients in the final solution. The obtained analytical results correspond well with the solved numerical results. The capability of the current solution is examined by application to a few relevant industrial cases. [ABSTRACT FROM AUTHOR]

Graphene oxide (GO) was functionalized using three different diamines, namely ethylenediamine (EDA), 4,4′-diaminodiphenyl sulfone (DDS) and p -phenylenediamine (PPD) to reinforce an epoxy adhesive, with the aim of improving the bonding strength of carbon fiber/epoxy composite. The chemical structure of the functionalized GO (FGO) nanosheets was characterized by elemental analysis, FT-IR and XRD. Hand lay-up, as a simple method, was applied for 3-ply composite fabrication. In the sample preparation, the fiber-to-resin ratio of 40:60 (w:w) and fiber orientations of 0°, 90°, and 0° were used. The GO and FGO nanoparticles were first dispersed in the epoxy resin, and then the GO and FGO reinforced epoxy (GO- or FGO-epoxy) were directly introduced into the carbon fiber layers to improve the mechanical properties. The GO and FGO contents varied in the range of 0.1–0.5 wt%. Results showed that the mechanical properties, in terms of tensile and flexural properties, were mainly dependent on the type of GO functionalization followed by the percentage of modified GO. As a result, both the tensile and flexural strengths are effectively enhanced by the FGOs addition. The tensile and flexural moduli are also increased by the FGO filling in the epoxy resin due to the excellent elastic modulus of FGO. The optimal FGO content for effectively improving the overall composite mechanical performance was found to be 0.3 wt%. Scanning electron microscopy (SEM) revealed that the failure mechanism of carbon fibers pulled out from the epoxy matrix contributed to the enhancement of the mechanical performance of the epoxy. These results show that diamine FGOs can strengthen the interfacial bonding between the carbon fibers and the epoxy adhesive. [ABSTRACT FROM AUTHOR]

Based on the super twisting concept, this article develops an integral sliding mode controller with nonlinear disturbance observer for position control and extending the traveling range of an initially curved micro-beam. The nonlinear damped model of the curved micro-beam is modeled based on the non-classical continuum theory. The single-mode assumption is used to transform the nonlinear governing PDE of the system into a nonlinear state-space form. The robust controller is designed to overcome the position control problem in the presence of non-parametric uncertainties and unknown non-symmetric input saturation due to the existing constraints in the electrostatic actuation. The effects of the uncertainties are considered as a compound disturbance term which can be estimated in finite time. Furthermore, due to the estimation properties of the observer, knowledge about the bounds of the uncertainties is not required. Extensive simulation results clearly verified the effectiveness of the controller in position tracking and extending the traveling range of the initially curved micro-beam to the unstable zones under smooth control effort. [ABSTRACT FROM AUTHOR]

Abstract: Disk scheduling is an operating system process to service disk requests. It has an important role in QOS guarantee of soft real-time environments such as video-on-demand and multimedia servers. Since now, some disk scheduling algorithms have been proposed to schedule disk requests in an optimized manner. Most of these methods try to minimize makespan by decreasing the number of disk head seeks as one of the slowest operations in modern computers and crucial for system performance because it usually takes some milli-seconds. In this paper, we propose a new disk scheduling method based on genetic algorithm that considers makespan and number of missed tasks simultaneously. In the proposed method, a new coding scheme is presented which employs simple GA procedures such as crossover and mutation and a penalty function in fitness. To get the best performance of the proposed method, its parameters such as number of chromosomes in initial population, mutation, and crossover probabilities, etc have been adjusted by applying it on some sample problems. The algorithm has been tested on several problems and its results were compared with well-known related methods. Experimental results showed that the proposed method worked very well and excelled most related works in terms of miss ratio and average seeks. [Copyright &y& Elsevier]

The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus. [ABSTRACT FROM AUTHOR]

Regularization is an essential aspect in the context of deep learning as it mitigates the risk of overfitting in deep neural networks. This study presents a novel deep learning regularization method, referred to DL-Reg, which effectively reduces the nonlinearity of deep networks by enforcing linearity to a certain extent. The proposed method is based on the incorporation of a linear constraint into the objective function of deep neural networks, which is defined as the error of a linear mapping from the inputs to the outputs of the model. Specifically, DL-Reg imposes a linear constraint on the network, which is further adjusted by a regularization factor, thereby preventing the network from overfitting. The effectiveness of DL-Reg is evaluated by training state-of-the-art deep network models on several benchmark datasets. The results of the experiments demonstrate that the proposed regularization method provides significant improvements over existing regularization techniques and enhances the performance of deep neural networks, particularly when dealing with small-sized training datasets. The main code of DL-Reg written in PyTorch is also available here: https://github.com/m2dgithub/DL-Reg.git. • Regularizing deep networks using a linear regression model. • Allowing supervised deep networks to be trained using the semi-supervised learning. • Increasing the performance of deep networks on the small-sized dataset. • Conducting extensive experiments to certify the significance of the proposed method. [ABSTRACT FROM AUTHOR]

Mustard Gas is one of chemical warfare agent that used in Iraq-Iran War. The first effects of SM are on the epithelial tissues of the eye, the respiratory system and the skin but in chronic phase patients suffer from systemic inflammation due to this exposure. Some of this patient suffer from cardiovascular disease and need to Coronary artery bypass grafting (CABG). Cardiac surgery evocates generalized inflammatory response in all patients and in this patient monitoring inflammatory before surgery is necessary because this mediator in this patients is more higher than other patients and preoperative inflammatory help us to better predict of this patients outcome and better manage on pump CABG. [ABSTRACT FROM AUTHOR]

Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term global information that is complementary to the local action information of the partial sequences and helps improve action prediction. We show that action prediction can be improved by combining the latent global information with the local action information. We test the proposed method on three challenging skeleton datasets and report state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Background and aims: Pain management is one of the most important responsibilities of nurses in an intensive care unit (ICU). It is difficult to perform pain assessment appropriately in patients who are unable to report their pain. This study is aimed to determine the impact of implementing the critical care pain observation tool (CPOT) on the amount and frequency of analgesics' administration in ICUs. Materials and methods: This interventional study was conducted in 2014. Sixty nurses and 240 patients were studied. This study was carried out in three phases: first the data about amount and frequency of analgesic administration were extracted from patients' medical files. Then the CPOT was implemented into the nursing assessment process and finally, nurses' performance regarding the amount and frequency of analgesic administration was recorded. This data obtained before and after intervention were analyzed using chi-square and independent t-test p values less than 0.05 were considered significant. Results: In this interventional study, we found that there was no difference in the demography and cause of ICU admission before and after implementation of CPOT (age p = 0.937, gender p = 0.996, and the cause of admission p = 0.996). We found that after implementing the CPOT into the nursing assessment process, the amount of analgesics administered (7.95 ± 8.77 mg vs. 11.01 ± 11.04 mg, p = 0.018) and the frequency of administration (2.91 ± 1.38 vs. 4.16 ± 0.99, p <0.001) increased significantly. Moreover, there was a significant increase in the frequency of pain assessment per patient per day in nursing practice after implementation of CPOT as compared to the practice before (7.2 ± 2.48 vs. 1.03 ± 1.63, p <0.001). The mean pain scores before and after the intervention (5.5 ± 1.08 vs.2.2 ± 0.48) were also significantly different. Conclusion: Applying CPOT, as an objective mean of pain assessment, was effective in improving the performance of ICU nurses in assessment and management of patients' pain. It increased the amount and frequency of analgesic administration. We can recommend that COPT is a useful tool for assessment and management of pain in ICU patients and should be implemented in all ICUs. [ABSTRACT FROM AUTHOR]

Sulfur mustard (SM) is a toxic chemical agent that belongs to a class of vesicant compounds. In the 1980s it was used by the Iraqi army against Iranian forces. Sulfur mustard severely irritates the skin, eyes and lungs. The highest side effects seen in patients affected by this gas are pulmonary complications including different types of lung diseases such as bronchiolitis. It has also led to a certain type of chronic obstructive pulmonary disease called mustard lung. Similar extra-pulmonary, molecular and hormonal effects can be observed in these patients and patients with chronic obstructive pulmonary disease. Here cardiovascular complications may be one of the most dangerous visible effects. And atherosclerosis is probable following the direct effects or consequential long-term effects of SM. The development of atherosclerosis in these patients is associated with an increased risk of cardiovascular and coronary artery disease. Coronary artery bypass grafting surgery is the treatment of coronary artery disease. Doing this surgery by bypass pump has its own morbidity and due to local and systemic inflammation changes in patients with SM pulmonary disorders it may have more side effects. Therefore, detailed knowledge of inflammatory diseases as well as the serum level or even the local lung fluid of the inflammatory factors in these patients before surgery are needed so that it would be possible to reduce the rate of morbidity and mortality by normalizing the inflammatory conditions of the patients before cardiac surgery. [ABSTRACT FROM AUTHOR]

Graphene oxide (GO) was functionalized using three different diamines, namely ethylenediamine (EDA), 4,4′-diaminodiphenyl sulfone (DDS) and p -phenylenediamine (PPD) to reinforce an epoxy/glass fiber (EP/GF) composite laminate, with the aim of improving the overall composite mechanical performance. Different mechanical characterization techniques were used to determine the mechanical performance, including: tensile stress strain, double cantilever beam (DCB) mode-I fracture toughness and dynamic mechanical thermal analysis (DMTA). Scanning electron microscopy (SEM) was used to support the results and conclusions. The results demonstrated remarkable enhancements in the mechanical performance of EP/GF composite laminates by incorporation of functionalized graphene oxide (FGO) nanofiller, whilst the mechanical performance of the GO reinforced composite only improved marginally. Finally, the mechanical performance of the EP/GF/FGO multi-scale composites was found to be dependent on the type of FGO functional groups; of which EDA exhibited the highest performance. These investigations confirmed that the EDA-FGO-reinforced EP/GF composites possess excellent potential to be used as multifunctional engineering materials in industrial applications. [ABSTRACT FROM AUTHOR]

The knuckle creases present on the dorsal side of the human hand can play significant role in identifying the offenders of serious crime, especially when evidence images of more recognizable biometric traits, such as the face, are not available. These knuckle creases, if localized appropriately, can result in improved identification ability. This is attributed to ambient inclusion of the creases and minimal effect of background, which lead to quality and discerning feature extraction. This paper presents an ensemble approach, utilizing multiple object detector frameworks, to localize the knuckle regions in a functionally appropriate way. The approach leverages from the individual capabilities of the popular object detectors and provide a more comprehensive knuckle region localization. The investigations are completed with two large-scale public hand databases which consist of hand-dorsal images with varying backgrounds and finger positioning. In addition to that, effectiveness of the proposed approach is also tested with a novel proprietary unconstrained multi-ethnic hand dorsal dataset to evaluate its generalizability. Several novel performance metrics are tailored to evaluate the efficacy of the proposed knuckle localization approach. These metrics aim to measure the veracity of the detected knuckle regions in terms of their relation with the ground truth. The comparison of the proposed approach with individual object detectors and a state-of-the-art hand keypoint detector clearly establishes the outperforming nature of the proposed approach. The generalization of the proposed approach is also corroborated through the cross-dataset framework. [ABSTRACT FROM AUTHOR]

Sulphur mustard (SM) is a well-known chemical warfare agent. It was first used during World War I and more recently in the Iran/Iraq conflict causing more than 100,000 Iranian casualties. Previous study showed that this gas can raised inflammatory marker but in this current case that suffered from cancer. After examine current case for candidate CABG, we found that Hs-CRP and IL-6 was very low than the other and this result show that this patients have bad inflammatory status and need to more tests. Result of the inflammatory marker in this patient was very abnormal and showed unknown effect of Sulphur mustard on patient and need to more research on mustard gas mechanism on inflammatory markers. [ABSTRACT FROM AUTHOR]

• Heat conduction in heterogeneous composite conical shells is solved analytically. • Fiber angle varies from zero to 90 degrees. • Heat conduction coefficients are temperature-depended. • Heat convection and external radiation are modeled. In present paper, problem of anisotropic heat conduction in heterogeneous composite conical shells is solved analytically. Arbitrary values for fiber angle (ranging from zero to 90 degrees) cause anisotropy for the heat conduction problem in composite conical shells. In our analysis, heat convection between conical shell and ambient flow is taken into account. In addition, an external source of radiative heat transfer is modeled. Herein, the heat conduction problem is assumed to be heterogeneous which is due to dependence of the conductivity coefficient on temperature. Kirchhoff transformation followed by an integral transform method are used to solve present heterogeneous heat conduction problem. Green's functions are applied to find the solution of final ordinary differential equations. Verification of the present analytical solution is obtained through comparing the analytical results with those of a second order finite difference method. Good agreement between the analytical and numerical results has been found. In order to evaluate the capability of our analytical solution in solving real industrial problems, the temperature distributions in a typical pressure vessel and a pin fin are calculated. [ABSTRACT FROM AUTHOR]

Background and aim: Spiritual care is an important part of nursing practice that seems to provide comprehensive care to patients. Nursing manager's attention to the spiritual dimension is one of the most important aspects affecting human health. Therefore, the aim of this study was to determine nursing managers' attitude to spirituality and spiritual care in hospitals in Khorasan Razavi Province in 2016. Methods: In this cross-sectional study, 110 management nurses (8 matrons, 37 supervisors and 65 head nurses) of hospitals in Khorasan Razavi Province (2016) were recruited for the study. Data were collected using a Spirituality & Spiritual Care Rating Scale (SSCRS) questionnaire in two parts, demographic information and questions related to the research objectives. Data were collected and analyzed using SPSS 16 software and doing descriptive (frequency, mean, standard deviation) and inferential statistics (independent-samples t-test, ANOVA) and considering levels of significance (p<0.05). Results: The results showed mean and standard deviation in management nursing attitude to spirituality (4.01±0.35) and spiritual care (4.03±0.78). The result showed significant differences between the attitude to spirituality and spiritual care and gender, age and work experience in nursing management (p<0.05). Conclusions: Management nursing has high level attitude to spirituality and spiritual care. Therefore, providing the perfect platform to provide this care, and for nurses to implement it in hospitals is recommended. [ABSTRACT FROM AUTHOR]

Introduction: The chronic systemic inflammation is a result of releasing inflammatory cytokines from the cells relating to the body immunity system and chronic activation of the innate immunity system. Aim: To evaluate the relationship among serum levels of interleukin 6 (IL-6), interleukin 8 (IL-8), C-reactive protein (CRP) with forced expiratory volume in 1st s (FEV1) in patients with mustard lung (ML) and chronic obstructive pulmonary diseases (COPD). Material and methods: A published literature search was performed through SID, web of science, ISI, Science Direct, Scopus, Medline, and PubMed databases for articles published in English. The correlation coefficient (r) and 95% confidence intervals (95% CIs) were calculated using random or fixed effects models. Heterogeneity was assessed using χ² and I² statistics. Results: In total, 4 published studies were included in the final analysis. Using the random-effect model, metaanalysis showed that the r was -0.052 (95% CI: -0.14-0.049, p = 0.28) at serum level of IL-8, serum levels of CRP and FEV1 in these results were r = -0.13, p = 0.012, serum levels of tumor necrosis factor (TNF) and FEV1 levels were r = -0.39, p = 0.03 in the conducted studies on mustard lung patients. The IL-6 serum level was explored in COPD patients. The results of the given studies in these patients are r = -0.006, 95% CI: -0.37-0.15, and p = 0.44. Conclusions: In this meta-analysis, there was evidence that serum levels of CRP and TNF have been significantly increased in chronic obstructive pulmonary diseases compared to the healthy control group, which signifies the presence of systemic inflammation in ML and COPD patients. [ABSTRACT FROM AUTHOR]

Maltogenic amylases (MAases, EC 3.2.1.133) have been gotten much attention due to their various applications in industry and commercial processes. MAases belong to subfamily 20 of glycoside hydrolase family 13 (NPase or CDases subfamily) and they have important differences with other members of the family. This enzyme consists of two subunits which form two active sites in the dimer form by binding of the central domain of each subunit to the N domain of the next one (domain-swapping dimeric structure). Allosterism is a possible way of regulating enzymatic activity and no evidence has been found regarding to the cooperativity and correlation between MAases subunits, therefore in this study the allosteric behavior of MAases from a native strain ( Geobacillus sp. Gh6) was investigated. Unlike other members of α-amylase family, MAases showed positive cooperativity between their subunits and the enzyme exhibited sigmoidal nature towards all three cyclodextrin (CD) substrates with a Hill constant ( nH ) value equal to 2, 1.6 and 1.1 for α-CD, β-CD and γ-CD, respectively. On further analysis, the effect of glucose and maltose as MAases allosteric effectors in the presence of β-CD substrate showed that these two effectors had a biphasic effect; while they stimulated the enzyme activity at low concentrations (with a decrease in Hill constant), these metabolites acted as allosteric inhibitors at higher concentrations. Due to the key role of MAases in carbohydrate metabolization, an efficient regulating system for this enzyme is required. In this experiment, for the first time the allosteric properties of MAases were observed and investigated. [ABSTRACT FROM AUTHOR]

Chemotherapy Induced Nausea and Vomiting (CINV) is among the most intensive side effects and critical concerns for patients with cancer. Most of these patients experience nausea and vomiting after chemotherapy. Sometimes, this is so annoying that it may prevent them from continuing the therapy. With the recent advances, a variety of therapeutic methods are innovated and applied to control CINV. Among them, the main methods include medicinal therapy, relaxation, and herbal therapy. Yet, using dexamethasone together with massage therapy and ginger is identified as the most effective method. [ABSTRACT FROM AUTHOR]

Introduction: Childhood asthma is the main public health problem in world, and dramatically has increased in developed as well as developing countries. Aim: To assess prevalence and risk factors for asthma based on ISAAC questionnaire. Material and methods: It was a cross-sectional school population-based study which was carried out on 1803 school children aged 6-7 and 13-14 in Ahvaz city, Khuzestan, south west of Iran, in 2011. The International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire was used as a tool to identify prevalence of asthma. Chi-square (c2) test was then used to identify possible potential risk factors associated with asthma. Logistic regression analysis was then applied to determine the various potential risk factors associated with asthma. Data were entered and analyzed using SPSS (version 17) statistical analysis software. The significant level in this study was considered 0.05. Results: The overall prevalence of asthma among school children was 4.9%, but in males it was 5.5% and in females - 4.3%. Also findings have demonstrated that prevalence of asthma was higher among 13-14 years age group (6.1%) as compared to children aged 6-7 (3.7%). The prevalence of wheeze in the last 12 months, wheeze after exercise and playing was 18.1%, 13.9%, and 8.4% respectively. Prevalence of dry cough at night was reported in 17.6%. Prevalence of asthma was highest in winter (2.6%). Conclusions: Prevalence of asthma in children was higher in males than females and prevalence of allergic symptoms will be increased with high air pollution. Family history of asthma was a strong risk factor for childhood asthma. [ABSTRACT FROM AUTHOR]

Pharmaceutical development of biosimilars is primarily focused on meeting the regulatory requirements for analytical comparability of the product's critical quality attributes (CQAs), concerning safety and efficacy, to those of the originator drug of interest. To this end, the early adoption of a systematic science-based approach, as guided by quality-by-design (QbD) principles, is crucial due to the blind starting point where the same insights of an originator developer into the challenges of a given biopharmaceutical and its manufacturing process are lacking. In this study, we devised a pharmaceutical QbD-guided approach to undertake the biosimilar development of Pembrolizumab (Keytruda®), the ace of therapeutic monoclonal antibodies (mAbs) in terms of approved indications and market sales, and its manufacturing process development. Quality target product profile (QTPP) for Pembrolizumab biosimilar product was assembled using publicly available information on Keytruda®. Upon preliminary analyses of four different lots of Keytruda®, the product CQAs and their acceptable ranges of specification were determined via risk assessment based on the relevant pharmaceutical development quality guidelines, particularly those of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). The development and clone selection of Chinese Hamster Ovary (CHO) DG44 cell line was performed using DHFR expression vectors and Methotrexate (MTX) selective pressure. The CHO clone stably expressing relatively higher mAb titer (∼1200 mg/l) in small-scale shake-flask cultures, with the highest similarity of the CQAs charge variants contents (CVCs), N-glycan profile, and biological potency to those of Keytruda® reference standard was selected as the lead clone and the produced Pembrolizumab candidate was named PSG-024. The upstream process (USP) and downstream process (DSP) developments for production were started with the process evaluation screening experiments for the identification of critical process parameters (CPPs) founded upon the prior knowledge on different process stages, input process parameters (iPPs), output process parameters (oPPs), and their impacts on product CQAs. Thereby, screening experiments of USP fed-batch cell culture in 5-liter bioreactor resulted in improvement of PSG-024 expression titer to 2060 ± 70 mg/l and selection of the iPPs feed amount (A), glucose setpoint (B), culture temperature (C), and agitation rate (D) for the optimization design of experiments (DoEs) mainly focused on the CQA acidic CVC and the oPPs mAb expression yield. The USP optimization DoEs using response surface methodology (RSM) yielded valid prediction models and optimal conditions of A = 35%, B = 4.5 g/l, C = 37 °C, and D = 160–220 rpm, which resulted in the final PSG-024 expression titer of 3170 ± 40 mg/l without an excessive rise in acidic CVC. The DSP screening experiments led to achieving the mAb recovery rates of 94% ± 3% and 71.5% ± 3.5% for affinity (capture) and cation-exchange (polishing) chromatography stages, respectively. The capture eluate buffer and viral inactivation conditions were optimized to prevent mAb eluate turbidity and protein aggregation. Moreover, the polishing stage optimization DoEs via one-factor-at-a-time method focused on wash and elution steps for control of the acidic CVC CQA and achieving >80% mAb recovery rate. By shifting to Step elution from the primary salt gradient method and considering an additional intermediate wash step, the maximum mAb recovery of 87% ± 1.5% was achievable while maintaining the CQA acidic CVC within the acceptable range. The consistency of final analytical comparability of PSG-024 demonstrated the effectiveness of the adopted pharmaceutical QbD approach for Pembrolizumab biosimilar development, paving the way for the technology transfer to the client to proceed further development. [Display omitted] [ABSTRACT FROM AUTHOR]