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In this study, copper oxide (Cu2O) particles were synthesized by octahedral shape and then were loaded with palladium (Pd). Photocatalysis, which was prepared by XRD, SEM/EDS, BET/BJH, TEM and DRS methods, was identified. The photocatalytic degradation of Direct Red (DR) 278 was investigated by Cu2O and, Pd-Cu2O octahedral. The photocatalytic activity of Cu2O was improved by the loaded Pd. The effect of parameters such as pH, time, mass of catalyst and concentration was evaluated by central composite design (CCD). Among the factors, pH, mass of catalyst, concentration and time have been affected the degradation process, respectively. The highest efficiency of dye degradation was obtained at optimum conditions with pH 2.95, mass of catalyst 0.11 (g), concentration 177.56 (mg/L) and time 11.02 (min), 99.99%. Based on the results, Pd-Cu2O octahedral is a very effective catalyst for the destruction of textile effluents. [ABSTRACT FROM AUTHOR]

In the present investigation, we synthesized copper (I) oxide nanoparticles (NPs) by the coprecipitation method. The obtained materials were characterized by X-ray diffraction (XRD), energy-dispersive X-ray analysis (EDXA), field emission scanning electron microscopy (FESEM), Transmission Electron Microscopy (TEM), and the Brunauer-Emmett-Teller (BET)/Barrett-Joyner-Halenda (BJH) Method. Surface areas and the average particle size were evaluated to be around 4.20 ± 0.04 m2 g−1 and 28 nm, respectively. Then, Ag/Cu2O NPs were synthesized by the same process, examined by X-ray diffraction, and the average particle size obtained was around 118 nm. The photocatalytic degradation of [1,3-Amino phenyl [4-Sulphonic acid][2,6-Dis azo phenyl] 4,4′sulphato ethyl [6′sulpho] ester of Sulphonic acid] (COG-423) was investigated with Cu2O and TiO2 NPs, Cu2O Microparticles (Micro-Ps) and Ag/Cu2O NPs under UV-C irradiation in the presence of hydrogen peroxide as auxiliary oxidant with three parameters including dopant concentration, intensity, and time, as the obtained experimental results showed a good agreement with theoretical values and succeeded to calculate the optimal conditions. Degradation efficiency with Cu2O Micro/NPs under UV-C irradiation (32 W), for 30 min. were determined to be 20.0% and 91.4% respectively, while for the synthesized TiO2 and Ag/Cu2O, NPs were 99.9%. The photocatalytic activity order was of the following nature: Ag/Cu2O ∼ TiO2 NPs > Cu2O NPs > Cu2O Micro-Ps. [ABSTRACT FROM AUTHOR]

Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample. Unlike conventional regression, the GP approach automatically determines the structure and combinations of a mathematical model. Given that mean square error (MSE) of 0.0001, root mean square error (RMSE) of 0.01, and correlation coefficient of 0.999, the proposed approach outperformed other approaches and proved effective in terms of efficiency and applicability in resource-constrained environments.

In this study, the potential of aluminum nitride (h-AlN), boron nitride (h-BN) and silicon carbide (h-SiC) nanosheets as the drug delivery systems (DDS) of isoniazid (INH) was scrutinized through density functional theory (DFT) and molecular dynamic (MD) simulations. We performed DFT periodic calculations on the geometry and electronic features of nanosheets adsorbed with INH by the DFT functional (DZP/GGA-PBE) employed in the SIESTA code. In the energetically favorable model, an oxygen atom of the C-O group of the INH molecule interacts with a Si atom of the h-SiC at 2.077 Å with an interaction energy of -1.361 eV. Charge transfer (CT) calculation by employing the Mulliken, Hirshfeld and Voronoi approaches reveals that the monolayers and drug molecules act as donors and acceptors, respectively. The density of states (DOS) calculations indicate that the HOMO-LUMO energy gap (HLG) of the h-SiC nanosheet declines significantly from 2.543 to 1.492 eV upon the adsorption of the INH molecule, which causes an electrical conductivity increase and then produces an electrical signal. The signal is linked to the existence of INH, demonstrating that h-SiC may be an appropriate sensor for INH sensing. The decrease in HLG for the interaction of INH and h-SiC is the uppermost (up to 41%) representing the uppermost sensitivity, whereas the sensitivity trend is σ (h-SiC) > σ (h-AlN) > σ (h-BN). Quantum theory of atoms in molecules (QTAIM) investigations is employed to scrutinize the nature of the INH/nanosheet interactions. The QTAIM analysis reveals that the interaction of the INH molecule and h-SiC has a partially covalent nature, while INH/h-AlN model electrostatic interaction occurs in the system and noncovalent and electrostatic interaction for the INH/h-BN model. Finally, the state-of-the-art DFT-MD simulations utilized in this study can mimic ambient conditions. The results obtained from the MD simulation show that it takes more time to bond the INH drug and h-SiC, and the INH/h-SiC system becomes stable. The results of the current research demonstrate the potential of h-SiC as a suitable sensor and drug delivery platform for INH drugs to remedy tuberculosis.

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythmia. In pursuit of increasing the accuracy of automated methods, many studies have been conducted. However, in none of the previous articles, the relationship and structure between the heart leads have not been included in the model. It seems that the structure of ECG data can help develop the accuracy of arrhythmia detection. Therefore, in this study, a new structure of Electrocardiogram (ECG) data was introduced, and the Graph Convolution Network (GCN), which has the possibility of learning the structure, was used to develop the accuracy of cardiac arrhythmia diagnosis. Considering the relationship between the heart leads and clusters based on different ECG poles, a new structure was introduced. In this structure, the Mutual Information(MI) index was used to evaluate the relationship between the leads, and weight was given based on the poles of the leads. Weighted Mutual Information (WMI) matrices (new structure) were formed by R software. Finally, the 15-layer GCN network was adjusted by this structure and the arrhythmia of people was detected and classified by it. To evaluate the performance of the proposed new network, sensitivity, precision, specificity, accuracy, and confusion matrix indices were used. Also, the accuracy of GCN networks was compared by three different structures, including WMI, MI, and Identity. Chapman's 12-lead ECG Dataset was used in this study. The results showed that the values of sensitivity, precision, specificity, and accuracy of the GCN-WMI network with 15 intermediate layers were equal to 98.74%, 99.08%, 99.97% & 99.82%, respectively. This new proposed network was more accurate than the Graph Convolution Network-Mutual Information (GCN-MI) with an accuracy equal to 99.71% and GCN-Id with an accuracy equal to 92.68%. Therefore, utilizing this network, the types of arrhythmia were recognized and classified. Also, the new network proposed by the Graph Convolution Network-Weighted Mutual Information (GCN-WMI) was more accurate than those conducted in other studies on the same data set (Chapman). Based on the obtained results, the structure proposed in this study increased the accuracy of cardiac arrhythmia diagnosis and classification on the Chapman data set. Achieving such accuracy for arrhythmia diagnosis is a great achievement in clinical sciences., (© 2024. The Author(s).)

Wearable computers can be used in different domains including healthcare. However, due to suffering from challenges such as faults their applications may be limited in real practice. So, in designing wearable devices, designer must take into account fault tolerance techniques. This study aims to investigate the challenging issues of fault tolerance in wearable computing. For this purpose, different aspects of fault tolerance in wearable computing namely hardware, software, energy, and communication are studied; and state of the art research regarding each category is analysed. In this analysis, the performed works using the fault tolerance techniques are included in the form of 25 components and referred to as "fault tolerance plan". Using this fault tolerance plan and the appropriate profile, the fault tolerance of any wearable system can be evaluated. In this article, fault tolerances of several of the most prominent works conducted in the field of wearable computing were evaluated. The obtained results, with the medical profile, showed that only one wearable system had a fault tolerance of 91%, with the other systems having a fault tolerance of 24% or less. Also, the results obtained from evaluating these works, with the military profile, showed that only one wearable system had a fault tolerance of 76%, with the other systems having a fault tolerance of 19% or less. These mean that few studies have been conducted on the fault tolerance of wearable computing.

Sinus Valsalva aneurysms (SVA) are rare asymptomatic cardiac anomalies, which can rupture and cause heart failure, myocardial infarction and also, they can be a potential source for embolic strokes. We report the first case of a patient with acute ischemic stroke associated with a ruptured SVA, who was treated with intravenous thrombolysis (tPA) without further complications., Competing Interests: Declaration of Competing Interest The authors declare they have no conflict of interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The deep learning models such as AlexNet, VGG, and ResNet achieved a good performance in classifying the breast cancer histopathological images in BreakHis dataset. However, these models are not practically appropriate due to their computational complexity and too many parameters; as a result, they are rarely utilized on devices with limited computational resources. This paper develops a lightweight learning model based on knowledge distillation to classify the histopathological images of breast cancer in BreakHis. This method employs two teacher models based on VGG and ResNext to train two student models, which are similar to the teacher models in development but have fewer deep layers. In the proposed method, the adaptive joint learning approach is adopted to transfer the knowledge in the final-layer output of a teacher model along with the feature maps of its middle layers as the dark knowledge to a student model. According to the experimental results, the student model designed by ResNeXt architecture obtained the recognition rate 97.09% for all histopathological images. In addition, this model has ∼69.40 million fewer parameters, ∼0.93 G less GPU memory use, and 268.17 times greater compression rate than its teacher model. While in the student model the recognition rate merely dropped down to 1.75%. The comparisons indicated that the student model had a rather acceptable outputs compared with state-of-the-art methods in classifying the images of breast cancer in BreakHis., (Copyright © 2022. Published by Elsevier Ltd.)

Introduction: Breast cancer remains the most frequent and lethal cancer among women worldwide. This study aimed to investigate the characteristics, prognostic factors and outcome of breast cancer patient treated and followed-up in Shiraz, southern Iran from 2000 to 2005., Methods: In this retrospective study, 1,024 patients with breast cancer who were treated in Namazi hospital of Shiraz University of Medical Sciences between 2000 and 2005 were included. Survival analysis was performed to determine potential factors influencing disease free-and overall survival in these patients., Results: Median age of the patients at diagnosis was 47 (range 19-83) years. Median follow-up for surviving patients was 68 months. The majority of patients presented at stage II (42%) and stage III (41%). Additionally, most (61%) patients had positive axillary node. The 5-, 10- and 15-year overall survival rates were 83.8%, 61.5% and 56.6% respectively. On multivariate analysis for overall survival, histologic type, Tumor stage, node stage, disease stage, M stage, hormone receptor status, adjuvant radiotherapy, and chemotherapy regimen remained independent prognostic factor for overall survival., Conclusion: in this study, the poor long-term oncologic outcome of the patients with breast cancer may be due to the higher rate of locally advanced disease; as well as the lack of modern systemic therapies in the study period.

Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively.

Magnification-independent (MI) classification is considered a promising method for detecting the histopathological images of breast cancer. However, it has too many parameters for real implementation due to dependence on input images in different magnification factors. In addition, magnification-dependent (MD) classification usually performs poorly on unseen samples, although it has lower input image sizes and fewer parameters. This paper proposes a novel method based on knowledge distillation (KD) to overcome the limitation of dissociation between MI classification and MD classification of breast cancer in histopathological images. The proposed KD method includes a pre-trained MI teacher model that is responsible for training an unprepared MD student model developed through only one magnification factor. In the proposed method, the decomposed feature maps of a teacher's intermediate layers are transferred as dark knowledge to a student. According to the experimental results, the student model developed through 40X images yielded accuracy rates of 99.41%, 99.26%, 99.14%, and 99.09% in response to unseen samples of 40X, 100X, 200X, and 400X images, respectively. Moreover, comparison results indicated the competitive performance of the proposed student model as opposed to the state-of-the-art method based on deep learning on BreakHis., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Myelitis, including longitudinally extensive transverse myelitis (LTEM), is reported in more than forty patients after coronavirus disease 2019 (COVID-19). Among COVID-19 vaccines, only Oxford-AstraZeneca COVID-19 vaccine (AZD1222) has been associated with few cases of myelitis (1 LTEM). We report the first case of myelitis/LTEM after BBIBP-CorV/Sinopharm vaccine, interestingly presented as a hemicord syndrome. A 71-year-old male presented with left-side diplegia, right-side hemihyposthesis with facial sparing and impaired position sensation in left foot after vaccination with BBIBP-CorV. MRI revealed a longitudinal signal in left cervical hemicord. This is the first reported myelitis and LTEM with COVID-19 vaccines other than AZD1222., (© 2021 The Authors. Published by Elsevier Inc. on behalf of University of Washington.)

The excessive presence of nitrite and nitrate in environmental matrixes has raised concerns among the scientific communities due to their negative impacts on human health and living organisms. Considering the necessity of regular monitoring and rapid evaluation of nitrite and nitrate, it is of great interest to develop methods capable of on-site detection of these compounds. This study presents a non-aggregation colorimetric method based on etching gold nanorods (AuNRs) for visual detection of nitrite and nitrate. Instead of temperature, we propose using thiourea as a sulfur-containing compound to accelerate the rate of AuNR etching. Thiourea forms stable cationic species with Au + ions and consequently speeds up the etching process by reducing the redox potential of Au + /Au. In the presence of thiourea, the AuNRs are etched by nitrite resulting in wide obvious color changes from brown to light brown, green, blue, purple, pink, and colorless. In addition to nitrite, the developed method is capable of nitrate determination by reducing nitrate to nitrite through acid-washed zinc powder and is the first report of colorful detection of nitrate. Under optimized conditions, a good linear relationship was found between nitrite/nitrate concentration and the colorimetric response in the range of 8.0 to 100 μmol L -1 and 0.5 to 3.0 mmol L -1 with a limit of detection (LOD) as low as 1.3 μmol L -1 and 173.3 μmol L -1 for nitrite and nitrate, respectively. Furthermore, the practical application of our developed probe was confirmed by accurate determination of nitrite and nitrate in various complex media including water samples, soil extracts, and food products such as salami and sausage.

Competing Interests: Conflict of interest The Authors declare that there is no conflict of interest.

Objectives: Cutaneous leishmania sis (CL) is considered as one of the most critical infections worldwide, in which the protozoa of the genus Leishmania infects a person. Today, the common and selective drugs for the treatment of CL are antimonial compounds present some limitations to their usage. The objective of this study is to investigate the cytotoxic and antileishmanial effects of various extracts of Capparis spinosa L. on the in vitro model., Materials and Methods: The primary phytochemical analysis of the C. spinosa extracts was performed to assess the presence of tannins, alkaloids, saponins, flavonoids, terpenoids, and glycosides. Furthermore, the in vitro cytotoxic and antileishmanial effects of C. spinosa extracts on Leishmania tropica promastigote were evaluated. Additionally, these effects on the J774-A1 macrophage cells by colorimetric cell viability 3-(4,5-dimethylthiazol- 2-yl)-2,5-diphenyl tetrazolium bromide assay were also assessed., Results: In this study, the findings of primary phytochemical screening of the C. spinosa extracts demonstrated the existence of flavonoids, tannins, terpenoids, glycosides, and alkaloids in this plant. Importantly, the findings indicated that the aqueous and methanolic extracts of C. spinosa exhibit a high potency to inhibit the growth of L. tropica promastigotes with inhibitory concentration 50 values of aqueous and methanolic extracts being 28.5 and 44.6 μg/mL, respectively. Based on the obtained results, C. spinosa extracts did not display a considerable cytotoxicity on the J774-A1 macrophage cells., Conclusion: The obtained findings exhibited remarkable antileishmanial effects of C. spinosa extracts on L. tropica , thereby indicating the ability of C. spinosa as a herbal product to be developed as a new antileishmanial drug. Nevertheless, supplementary investigations will be obligatory to achieve these findings, especially in human subjects.

Irregular and unknowingly use of chemical compounds is a serious threat to the environment, human health, and other living organisms attributable and intensified by the growing population and increasing demand for food. Nitrite and nitrate are among those compounds that are widely used in agricultural and industrial products. Therefore on-site, rapid, simple, and accurate monitoring of nitrite/nitrate is highly desirable. In this review, while emphasizing the importance of nitrite and nitrate in food chain safety and health of living organisms, their measurement methods, in particular, nanoplasmonic colorimetric sensors are comprehensively discussed based on the researches in this field. Nanoplasmonic-based sensors have proved to be successful in comparison with traditional methods due to their low cost, biocompatibility, high sensitivity and selectivity, and most importantly, the ability to visually detect and be used on-site to measure nitrite and nitrate. The design principle of nanoplasmonic sensors will be presented into two categories of aggregation- and etching-based detection followed by their applications in nitrite detection. The nitrate measurement will be discussed based on either direct detection of nitrate or indirect strategy in which nitrate is reduced to nitrite by enzymes or metals. Finally, the remaining challenges and prospects in this topic will be described and outlined., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Since there is no potential, effective vaccine available, treatment is the only controlling option against hydatid cyst or cystic echinococcosis (CE). This study was designed to systematically review the in vitro, in vivo, and ex vivo effects of nanoparticles against hydatid cyst. The study was carried out based on the 06- PRISMA guideline and registered in the CAMARADES-NC3Rs Preclinical Systematic Review and Meta-analysis Facility (SyRF) database. The search was performed in five English databases, including Scopus, PubMed, Web of Science, EMBASE, and Google Scholar without time limitation for publications around the world about the protoscolicdal effects of all the organic and inorganic nanoparticles without date limitation in order to identify all the published articles (in vitro, in vivo, and ex vivo). The searched words and terms were: "nanoparticles", "hydatid cyst", "protoscoleces", "cystic echinococcosis", "metal nanoparticles", "organic nanoparticles", "inorganic nanoparticles, "in vitro", ex vivo", "in vivo". Out of 925 papers, 29 papers including 15 in vitro (51.7%), 6 in vivo (20.7%), ex vivo 2 (6.9%), and 6 in vitro/in vivo (20.7%) up to 2020 met the inclusion criteria for discussion in this systematic review. The results demonstrated the most widely used nanoparticles in the studies were metal nanoparticles such as selenium, silver, gold, zinc, copper, iron nanoparticles ( n = 8, 28.6%), and metal oxide nanoparticles such as zinc oxide, titanium dioxide, cerium oxide, zirconium dioxide, and silicon dioxide ( n = 8, 28.6%), followed by polymeric nanoparticles such as chitosan and chitosan-based nanoparticles ( n = 7, 25.0%). The results of this review showed the high efficacy of a wide range of organic and inorganic NPs against CE, indicating that nanoparticles could be considered as an alternative and complementary resource for CE treatment. The results demonstrated that the most widely used nanoparticles for hydatid cyst treatment were metal nanoparticles and metal oxide nanoparticles, followed by polymeric nanoparticles. We found that the most compatible drugs with nanoparticles were albendazole, followed by praziquantel and flubendazole, indicating a deeper understanding about the synergistic effects of nanoparticles and the present anti-parasitic drugs for treating hydatid cysts. The important point about using these nanoparticles is their toxicity; therefore, cytotoxicity as well as acute and chronic toxicities of these nanoparticles should be considered in particular. As a limitation, in the present study, although most of the studies have been performed in vitro, more studies are needed to confirm the effect of these nanoparticles as well as their exact mechanisms in the hydatid cyst treatment, especially in animal models and clinical settings.

Leishmaniasis is a widespread disease that causes 20,000 to 30,000 deaths annually, making it a major health problem in endemic areas. Because of low-performance medications, drug delivery poses a great challenge for better treatment of leishmaniasis. The present study's purpose was to review the application of nanoparticles as a new method in leishmaniasis treatment . To identify all relevant literature, we searched Web of Sciences, Scopus, PubMed, NCBI, Scielo, and Google Scholar, and profiled studies published between 1986 and 2019. In the present study, we tried to identify different research efforts in different conditions that examined the influence of various nanoparticles on different forms of leishmaniasis. In this way, we could compare their results and obtain a reliable conclusion from the most recent studies on this subject. Our review's results indicate that incorporating nanoparticles with chemical drugs improves the quality, efficiency, and sustainability of drugs and reduces their costs. Finally, considering the use of nanoparticles in the destruction of parasites, their inhibitory effect (making drugs more effective and less harmful), and their utility in making effective vaccines to prevent and fight against parasites, further research on this issue is highly recommended., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Published by Elsevier Ltd on behalf of World Federation of Parasitologists.)

Background: The present work aimed to evaluate the chemical composition of Curcuma zadoaria essential oil and to investigate its efficacy and safety against hydatid cyst protoscoleces., Methods: Collected protoscoleces from liver fertile hydatid cysts of infected sheep were exposed to different concentrations of the essential oil (75, 150, 300 μl/mL) for 5-30 min in vitro and ex vivo. Then, by using the eosin exclusion assay, the viability of protoscoleces was studied. In the next step, 24 male NMRI mice were examined to assess the toxicity of C. zadoaria essential oil by measuring the biochemical and hematological parameters., Results: Based on the obtained results, the LD50 value of intraperitoneal injection of the C. zadoaria essential oil was 1.76 mL/kg of body weight and the maximum non-fatal dose was 0.96 mL/kg of body weight. C. zadoaria essential oil had a strong proto scolicidal activity in vitro so that at the 300 and 150 μl/ml entirely eliminates the parasite after 5 and 10 minutes; whereas, weak proto scolicidal activity was observed at lower doses. Ex vivo assay, no similar effect with in vitro was observed, therefore, more time is required to show a potent proto scolicidal activity. C. zadoaria essential oil at the concentrations of 300 and 150 μl/mL after an exposure time of 7 and 12 min, killed 100% of protoscoleces within the hydatid cyst, respectively. After intraperitoneal injection of the C. zadoaria essential oil for 2 weeks, no significant difference (p > 0.05) was observed in the clinical chemistry and hematologic parameters at the doses of 0.15, 0.3, 0.6 mL/kg., Conclusion: The obtained results in vitro and ex vivo exhibited that C. zadoaria essential oil had a favorable proto scolicidal activity on hydatid cyst protoscoleces. However, more supplementary works are required to verify these findings by assessing clinical subjects., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Introduction. Nanoparticles (NPs) have numerous biological benefits due to their large surface-volume ratio and convenient entry into cells compared to other particles. Previous research has shown the antimicrobial properties of biogenic selenium NPs (SeNps) and their effects on cellular immunomodulatory cytokines that play a key role in controlling infections. Aim. This study aimed to evaluate the therapeutic effects of SeNPs against chronic toxoplasmosis in mice. Methodology. Infected mice with Toxoplasma gondii (Tehran strain) were orally treated with SeNPs at doses of 2.5, 5 and 10 mg kg -1 once a day for 14 days. On the fifthteenth day, the mean number of brain-tissue cysts and the mRNA levels of TNF-α, IL-12, IL-10, IFN-γ and inducible nitric oxide synthase (iNOS) in the mice of each group were recorded. Moreover, serum clinical chemistry factors in the treated mice were examined to determine the safety of SeNPs. Results. The mean number of tissue cysts was significantly ( P <0.001) decreased in mice treated with SeNPs in a dose-dependent manner compared with the control group. The mRNA levels of inflammatory cytokines were significantly increased in mice treated with SeNPs at a dose of 10 mg kg -1 compared with the control subgroup ( P <0.05). No significant variation ( P >0.05) observed in clinical chemistry parameters among the mice in the control subgroup compared with those treated with SeNPs. Conclusion. The findings demonstrated the therapeutic effects of SeNPs with no considerable toxicity against latent toxoplasmosis in the mouse model. Nevertheless, further studies are obligatory to reveal the exact anti-Toxoplasma mechanisms of SeNPs.

Background and Aim: As a common complaint of patients with traumatic injuries, pain remains undermanaged in emergency departments (EDs). Our aim was to evaluate the effects of a nurse-initiated pain management protocol in patients with musculoskeletal injuries in an ED in Iran., Material and Methods: This pre-post intervention design study was conducted on 240 patients with orthopedic injuries selected through sequential sampling over two phases. The intervention consisted of case study sessions and the implementation of the nurse-initiated pain management protocol. The outcomes were assessed based on the Numeric Rating Scale (NRS) pain scores, the pain management satisfaction questionnaire, the nursing performance checklist and the waiting time evaluation form., Results: The mean pain intensity 30 and 60 min after triage and at discharge decreased significantly in the post-intervention group (p < .001). The patients' satisfaction with pain management (p < .01) and the nurses' performance (p < .001) improved in the post-intervention group. Waiting time: there was a significant reduction in the post-intervention group from the end of triage by the nurse to the visit by the physician, and from patient's arrival in ED to discharge or transferring, and also the time to initial analgesic., Conclusions: Education based on case study and the implementation of the nurse-initiated pain management protocol resulted in a significant increase in multimodal analgesia administration and a reduction in pain intensity, an increase in patient satisfaction, an improvement in the triage nurses' performance and the reduction of potential delays in pain management while maintaining the safety of patients with musculoskeletal trauma., (Copyright © 2019 American Society for Pain Management Nursing. Published by Elsevier Inc. All rights reserved.)

Breast cancer (BC) is the most frequently occurring malignancy in women worldwide. Despite the substantial advancement in understanding the molecular mechanisms and management of BC, it remains the leading cause of cancer death in women. One of the main reasons for this obstacle is that we have not been able to find the Achilles heel for the BC as a highly heterogeneous disease. Accumulating evidence has revealed that noncoding RNAs (ncRNAs), play key roles in the development of BC; however, the involving of complex regulatory interactions between the different varieties of ncRNAs in the development of this cancer has been poorly understood. In the recent years, the newly discovered mechanism in the RNA world is "competing endogenous RNA (ceRNA)" which proposes regulatory dialogues between different RNAs, including long ncRNAs (lncRNAs), microRNAs (miRNAs), transcribed pseudogenes, and circular RNAs (circRNAs). In the latest BC research, various studies have revealed that dysregulation of several ceRNA networks (ceRNETs) between these ncRNAs has fundamental roles in establishing the hallmarks of BC development. And it is thought that such a discovery could open a new window for a better understanding of the hidden aspects of breast tumors. Besides, it probably can provide new biomarkers and potential efficient therapeutic targets for BC. This review will discuss the existing body of knowledge regarding the key functions of ceRNETs and then highlights the emerging roles of some recently discovered ceRNETs in several hallmarks of BC. Moreover, we propose for the first time the "ceRnome" as a new term in the present article for RNA research., (© 2018 Wiley Periodicals, Inc.)

Background: Nowadays, surgery is considered as one of the most important treatments for hydatidosis. Due to laceration, the cyst and spread of the content within it (protoscoleces) during the surgery that can put the patient at the risk of re-infection, anaphylaxis shock and even death, surgeons use some chemical drugs as protoscolicidal agents. The study is aimed to evaluate the scolicidal effects of olive ( Olea europaea L.) leaf extract on hydatid cyst protoscoleces in vitro and ex vivo ., Methods: After the collection of protoscoleces from sheep livers infected with fertile hydatid cysts, they were treated with various concentrations of olive leave extract (75-300 mg/mL) for 5-30 min in vitro and ex vivo . Finally, the mortality of protoscoleces was assessed by the eosin exclusion test (0.1% eosin staining)., Results: The mean of the mortality of protoscoleces was 100% after 10 min of incubation with the concentration of 300 mg/ml of O. europaea leaves extract. On the other hand, the mean of the mortality of protoscoleces after 20 min of incubation with the concentration of 150 mg/ml of O. europaea leaves extract was 100%. After injection of O. europaea leaves extract directly into the hydatid cyst ( ex vivo ), the mean of the mortality of protoscoleces was 100% after 12 and 25 min of incubation with the concentration of 300 and 150 mg/ml of O. europaea leaves extract, respectively; indicating that the extract requiring a further time to display a potent protoscolicidal effects., Conclusion: Based on the findings of the study, it can be concluded that the extract of olive leaf had a significant scolicidal activity on hydatid cyst protoscoleces. However, further research, especially in human and animal subjects, are required to reach this conclusion.

[Purpose] This study was conducted to investigate the immediate prophylactic effects of local vibration on quadriceps muscle fatigue in young non-athletic males. [Participants and Methods] Thirty healthy young males were randomly assigned to vibration and sham control groups. Participants in the local vibration group received a single session vibration (2 minutes, 30 Hz). They also in the control group received a 2-minute vibration, while the vibration system was off. MVC, RMS and median frequency of EMG findings and time to reach fatigue were measured. [Results] Time to reach fatigue and MVC in the local vibration group was significantly high than those in the sham control group. [Conclusion] It seems that the prior local muscle vibration may be useful to attenuate some signs and symptoms of muscle fatigue., Competing Interests: None.

The development of sensors with the microelectromechanical systems technology expedites the emergence of new tools for human-computer interaction, such as inertial pens. These pens, which are used as writing tools, do not depend on a specific embedded hardware, and thus, they are inexpensive. Most of the available inertial pen character recognition approaches use the low-level features of inertial signals. This paper introduces a Persian/Arabic handwriting character recognition system for inertial-sensor-equipped pens. First, the motion trajectory of the inertial pen is reconstructed to estimate the position signals by using the theory of inertial navigation systems. The position signals are then used to extract high-level geometrical features. A new metric learning technique is then adopted to enhance the accuracy of character classification. To this end, a characteristic function is calculated for each character using a genetic programming algorithm. These functions form a metric kernel classifying all the characters. The experimental results show that the performance of the proposed method is superior to that of one of the state-of-the-art works in terms of recognizing Persian/Arabic handwriting characters.