Exercise as a treatment modality for depression: A narrative review

Depression is a major health burden associated with poor quality of life and impaired functioning. Depression is a leading cause of disability worldwide and is associated with profound economic costs. Depression is usually treated with antidepressant medications and psychological therapy or combination of both. However, there are lot of limitations associated with these therapies and as a result, a number of alternative or adjunctive therapies have been sought for. Exercise is one such option with a lot of substantial supportive research. The objective of the article was to review the beneficial effects of exercise in depression. An electronic search of literature from inception till 06/2017 highlighting the effects of exercise on depression and the possible mechanistic pathways involved was conducted using PubMed/ Medline, Google scholar and Scopus and relevant articles were utilized for this review. The results of this review confirmed the beneficial role of exercise in depression as evidenced by the improvement in the outcomes and the various psychobiological parameters measured. Thus exercise can be considered as a treatment option for the management of depression.Keywords: Depression, Exercise, Physical activity, Mechanistic pathways, BDN

COVID-19 Outbreak: Effect of an Educational Intervention Based on Health Belief Model on Nursing Students' Awareness and Health Beliefs at Najran University, Kingdom of Saudi Arabia

This study aimed to investigate the effect of an educational intervention based on the health belief model (HBM) about COVID-19 on nursing  students' awareness and health beliefs. A true-experimental research design was conducted at nursing college, Najran University, KSA. A  comprehensive sampling was followed to include all female students at the colleges (164 students). The sample was divided randomly into  intervention (82) and control group (82). The educational intervention was designed and conducted based on the HBM through four sequential phases: assessment, planning, implementation, and evaluation. The current study results indicated no statistically significant differences between intervention and control groups concerning their demographic characteristic, awareness, and health beliefs before intervention. After intervention, significant differences (p< 0.05) were observed between intervention and control groups in their awareness and all HBM constructs regarding COVID19. There were positive, statistically significant correlations (P< 0.05) between participants' total HBM score and their total awareness score. This study concluded that HBM is effective in increasing nursing students' awareness regarding COVID-19. It also increases their perceived  susceptibility, severity, and benefits. Besides, it may increase their self-efficacy to overcome perceived barriers to practice protective and preventive actions while dealing with COVID-19. Keywords: Awareness, COVID-19, Health belief model, Nursing students, Saudi Arabia   Cette étude visait à étudier l'effet d'une intervention éducative basée sur le modèle de croyance en santé (HBM) à propos du COVID-19 sur la sensibilisation des étudiants en soins infirmiers et leurs croyances en matière de santé. Une conception de recherche véritablement expérimentale a été menée au collège d'infirmières de l'Université de Najran, en Arabie Saoudite. Un échantillonnage complet a été suivi pour inclure toutes les étudiantes des collèges (164 étudiantes). L'échantillon a été divisé au hasard en groupe d'intervention (82) et groupe témoin (82). L'intervention éducative a été conçue et menée sur la base du HBM à travers quatre phases séquentielles: évaluation, planification, mise en oeuvre et évaluation. Les résultats de l'étude actuelle n'ont indiqué aucune différence statistiquement significative entre les groupes d'intervention et de contrôle concernant leurs caractéristiques démographiques, leur sensibilisation et leurs croyances en matière de santé avant l'intervention. Aprèsl'intervention, des  différences significatives (p <0,05) ont été observées entre les groupes d'intervention et de contrôle dans leur connaissance et tutes les constructions HBM concernant COVID19. Il y avait des corrélations positives et statistiquement significatives (P <0,05) entre le score HBM total des participants et leur score total de sensibilisation. Cette étude a conclu que HBM est efficace pour sensibiliser les étudiants en sciences infirmières au COVID-19. Cela augmente également leur sensibilité, leur gravité et leurs avantages perçus. En outre, cela peut augmenter leur efficacité personnelle pour surmonter les obstacles perçus à la pratique d'actions de protection et de prévention tout en traitant le COVID-19. Mots-clés: Sensibilisation, COVID-19, modèle de croyance en matière de santé, étudiants en sciences infirmières, Arabie saoudit

In vitro studies on CNGRC-CPG2 fusion proteins for ligand-directed enzyme prodrug therapy for targeted cancer therapy

The sequence asparagine-glycine arginine (NGR), flanked by Cysteine (Cys) residues so as to form a disulfide-bridge (CNGRC), has previously been found to target and bind specifically to aminopeptidase N (APN), which is highly expressed on the surface of tumor cells. The goal of this study was to develop and evaluate the potential of fusion proteins carrying the CNGRC sequence linked to the enzyme carboxypeptidase G2 (CPG2) for targeted cancer therapy. We refer to this strategy as ligand-directed enzyme prodrug therapy (LDEPT). We constructed two forms of the CNGRC-CPG2 fusions, containing one or two copies of the cyclic NGR motif and designated CNGRC-CPG2 (X-CPG2) and CNGRC-CPG2-CNGRC (X-CPG2-X), respectively. binding assays of the purified constructs showed that both X-CPG2 and X-CPG2-X bound with high affinity to cancer cells expressing high levels of APN, compared to their binding to cells expressing low levels of APN. Further studies of the constructs to assess the therapeutic potential of LDEPT were carried out using cells expressing high and low levels of APN. Using methotrexate, it was demonstrated that cancer cell survival was significantly higher in the presence of the fusion proteins, due to the hydrolysis of this cytotoxic drug by CPG2. Conversely, when the prodrug ZD2767P was used, cancer cell killing was higher in the presence of the fused CPG2 constructs than in their absence, which is consistent with CPG2-mediated release of the cytotoxic drug from the prodrug. Furthermore, the doubly-fused CPG2 construct (X-CPG2-X) was significantly more effective than the singly-fused construct (X-CPG2)

Preparation and Preliminary Dielectric Characterization of Structured C\u3csub\u3e60\u3c/sub\u3e-Thiol-Ene Polymer Nanocomposites Assembled Using the Thiol-Ene Click Reaction

Fullerene-containing materials have the ability to store and release electrical energy. Therefore, fullerenes may ultimately find use in high-voltage equipment devices or as super capacitors for high electric energy storage due to this ease of manipulating their excellent dielectric properties and their high volume resistivity. A series of structured fullerene (C60) polymer nanocomposites were assembled using the thiol-ene click reaction, between alkyl thiols and allyl functionalized C60 derivatives. The resulting high-density C60-urethane-thiol-ene (C60-Thiol-Ene) networks possessed excellent mechanical properties. These novel networks were characterized using standard techniques, including infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and thermal gravimetric analysis (TGA). The dielectric spectra for the prepared samples were determined over a broad frequency range at room temperature using a broadband dielectric spectrometer and a semiconductor characterization system. The changes in thermo-mechanical and electrical properties of these novel fullerene-thiol-ene composite films were measured as a function of the C60 content, and samples characterized by high dielectric permittivity and low dielectric loss were produced. In this process, variations in chemical composition of the networks were correlated to performance characteristics

Prevalence of anelloviruses (TTV, TTMDV, and TTMV) in healthy blood donors and in patients infected with HBV or HCV in Qatar

Background Anelloviruses (TTV, TTMV, and TTMDV) have been associated with non A-G hepatitis. The goal of the current study was to estimate the prevalence of these anelloviruses in Qatar. Methods A total of 607 blood samples (500 healthy donors, and 53 HBV-and 54 HCV-positive patients) representing different nationalities were tested for the presence of TTV, TTMV, and TTMDV DNA by nested PCR. Results Prevalence rates for the three viruses were high in all studied groups, and exceeding 95% in the HBV group (for TTV and TTMDV). Infection with more than one type of viruses was common and significant in most of the positive patients (p  0.05) albeit the detection of higher infection rates among females and Qatari subjects. Conclusion This was the first published study to look at prevalence of Anellowviruses in the Middle East. High prevalence rates of the three viruses in all studied groups was noted. Further studies are needed to explore and compare the different genotypes of these viruses in the region.This work was made possible by UREP grant # (UREP 15-015-3-006) from the Qatar National Research Fund (a member of Qatar Foundation)

Correction:Isolation and molecular characterization of novel glucarpidases: Enzymes to improve the antibody directed enzyme pro-drug therapy for cancer treatment

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Production of "biobetter" variants of glucarpidase with enhanced enzyme activity

Glucarpidase, also known as carboxypeptidase G(2), is a Food and Drug Administration-approved enzyme used in targeted cancer strategies such as antibody-directed enzyme prodrug therapy (ADEPT). It is also used in drug detoxification when cancer patients have excessive levels of the anti-cancer agent methotrexate. The application of glucarpidase is limited by its potential immunogenicity and limited catalytic efficiency. To overcome these pitfalls, mutagenesis was applied to the glucarpidase gene of Pseudomonas sp. strain RS-16 to isolate three novels "biobetter" variants with higher specific enzyme activity. DNA sequence analysis of the genes for the variants showed that each had a single point mutation, resulting in the amino acid substitutions: I100 T, G123S and T239 A. K-m, V-max and K-cat measurements confirmed that each variant had increased catalytic efficiency relative to wild type glucarpidase. Additionally, circular dichroism studies indicated that they had a higher alpha-helical content relative to the wild type enzyme. However, three different software packages predicted that they had reduced protein stability, which is consistent with having higher activities as a tradeoff. The novel glucarpidase variants presented in this work could pave the way for more efficient drug detoxification and might allow dose escalation during chemotherapy. They also have the potential to increase the efficiency of ADEPT and to reduce the number of treatment cycles, thereby reducing the risk that patients will develop antibodies to glucarpidase

Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent

Optimizing the Gamma Ray-Based Detection System to Measure the Scale Thickness in Three-Phase Flow through Oil and Petrochemical Pipelines in View of Stratified Regime

As the oil and petrochemical products pass through the oil pipeline, the sediment scale settles, which can cause many problems in the oil fields. Timely detection of the scale inside the pipes and taking action to solve it prevents problems such as a decrease in the efficiency of oil equipment, the wastage of energy, and the increase in repair costs. In this research, an accurate detection system of the scale thickness has been introduced, which its performance is based on the attenuation of gamma rays. The detection system consists of a dual-energy gamma source ( 241 Am and 133 Ba radioisotopes) and a sodium iodide detector. This detection system is placed on both sides of a test pipe, which is used to simulate a three-phase flow in the stratified regime. The three-phase flow includes water, gas, and oil, which have been investigated in different volume percentages. An asymmetrical scale inside the pipe, made of barium sulfate, is simulated in different thicknesses. After irradiating the gamma-ray to the test pipe and receiving the intensity of the photons by the detector, time characteristics with the names of sample SSR, sample mean, sample skewness, and sample kurtosis were extracted from the received signal, and they were introduced as the inputs of a GMDH neural network. The neural network was able to predict the scale thickness value with an RMSE of less than 0.2, which is a very low error compared to previous research. In addition, the feature extraction technique made it possible to predict the scale value with high accuracy using only one detector