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Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99â¯% and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84â¯% and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.
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Investigating therapeutic miRNAs is a rewarding endeavour for pharmaceutical companies. Since its discovery in 1993, our understanding of miRNA biology has advanced significantly. Numerous studies have emphasised the disruption of miRNA expression in various diseases, making them appealing candidates for innovative therapeutic approaches. Hepatocellular carcinoma (HCC) is a significant malignancy that poses a severe threat to human health, accounting for approximately 70%-85% of all malignant tumours. Currently, the efficacy of several HCC therapies is limited. Alterations in various biomacromolecules during HCC progression and their underlying mechanisms provide a basis for the investigation of novel and effective therapeutic approaches. MicroRNAs, also known as miRNAs, have been identified in the last 20 years and significantly impact gene expression and protein translation. This atypical expression pattern is strongly associated with the onset and progression of various malignancies. Gene therapy, a novel form of biological therapy, is a prominent research area. Therefore, miRNAs have been used in the investigation of tumour gene therapy. This review examines the mechanisms of action of miRNAs, explores the correlation between miRNAs and HCC, and investigates the use of miRNAs in HCC gene therapy.
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This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.
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Globally, cardiovascular diseases (CVDs) constitute the leading cause of death at the moment. More effective treatments to combat CVDs are urgently required. Recent advances in nanotechnology have opened the door to new avenues for cardiovascular health treatment. Silver nanotechnology's inherent therapeutic powers and wide-ranging applications have made it the center of focus in recent years. This review aims to analyze the chemical, physical, and biological processes ofproducing AgNPs and determine their potential utility as theranostics. Despite significant advances, the precise mechanism by which AgNPs function in numerous biological systems remains a mystery. We hope that at the end of this review, you will better understand how AgNPs affect the cardiovascular system from the research done thus far. This endeavor thoroughly investigates the possible toxicological effects and risks associated with exposure to AgNPs. The findings shed light on novel applications of these versatile nanomaterials and point the way toward future research directions. Due to a shortage of relevant research, we will limit our attention to AgNPs as they pertain to CVDs. Future research can use this opportunity to investigate the many medical uses of AgNPs. Given their global prevalence, we fully endorse academics' efforts to prioritize nanotechnological techniques in pursuing risk factor targeting for cardiovascular diseases. The critical need for innovative solutions to this widespread health problem is underscored by the fact that this technique may help with the early diagnosis and treatment of CVDs.
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Enfermedades Cardiovasculares , Nanopartículas del Metal , Plata , Humanos , Plata/uso terapéutico , Fenómenos Fisiológicos Cardiovasculares/efectos de los fármacos , AnimalesRESUMEN
Titanium oxide nanopowder (TiO2 NPs) was synthesized via anodization in 0.7 M perchloric acid then annealed in nitrogen at 450 °C for 3 h to prepared the Titanium Oxide Nitrogen annealed nanoparticles (TiO2 NPs-N2) powder as catalytic support. Using a photodeposition process, gold was added with isopropanol as a sacrificial donor and H[AuCl4] acid, producing gold nanoparticles on nitrogen-annealed titanium oxide nanoparticles (Au-NPs on TiO2-NPs-N2). The mass loading of Au NPs was 2.86 × 10-4 (g/cm2). TEM images of Au NPs on TiO2-NPs-N2 suggest circular particles with a tendency to agglomerate. Cyclic voltammetry (CV) was used to investigate the electrocatalytic performance of the Au NPs/TiO2-NPs-N2 catalysts in ferrocyanide, KOH, and H2SO4, and the results were compared to those of a polycrystalline Au electrode that is readily accessible in the market. In KOH, H2SO4, and (2 M KOH + 0.1 M glycerol) solutions, the Au NPs/TiO2-NPs-N2 electrode displayed a startlingly high electrocatalytic performance. Using CV, the electrocatalytic oxygen reduction reaction (ORR) of Au NPs/TiO2-NPs-N2 and Au-NPs against glycerol oxidation in basic media was studied. The results indicated that Au NPs/TiO2-NPs-N2 is a promising support material for improving the electrocatalytic activity for acidic and basic oxidation. The electrode made of Au NPs/TiO2-NTs-N2 has steady electrocatalytic activity and may be reused repeatedly. TiO2 NPs and Au NPs/TiO2NPs-N2 showed satisfactory antibacterial activity against some human pathogenic bacteria using the disc diffusion method.
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Cardiovascular diseases (CVDs) are still leading to a significant number of deaths worldwide despite the remarkable advancements in medical technology and pharmacology. Managing patients with established CVDs is a challenge for healthcare providers as it requires reducing the chances of recurring cardiovascular events. On the other hand, changing one's way of life can also significantly impact this area, reducing the likelihood of cardiovascular disease and death through their unique advantages. Consequently, it is advisable for healthcare providers to regularly advise their patients with coronary issues to participate in organized physical exercise and improve their overall physical activity. Additionally, patients should adhere to a diet that promotes heart health, cease smoking, avoid exposure to secondhand smoke, and address any psychosocial stressors that may heighten the risk of cardiovascular problems. These lifestyle therapies, whether used alongside drug therapy or on their own in patients who may have difficulty tolerating medications, face financial barriers, or experience ineffectiveness, can substantially reduce cardiovascular mortality and the likelihood of recurring cardiac events. Despite the considerable advancements in creating interventions, it is still necessary to determine the optimal intensity, duration, and delivery method for these interventions. Furthermore, it is crucial to carry out further investigations incorporating extended monitoring and assessment of clinical outcomes to get a more comprehensive comprehension of the efficacy of these therapies. Presenting the findings within the framework of "lifestyle medicine," this review seeks to offer a thorough synopsis of the most recent scientific investigations into the potential of behavioral modifications to lower cardiovascular disease risk.
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Enfermedades Cardiovasculares , Humanos , Enfermedades Cardiovasculares/epidemiología , Enfermedades Cardiovasculares/etiología , Enfermedades Cardiovasculares/prevención & control , Estilo de Vida , Ejercicio Físico , DietaRESUMEN
Long non-coding RNAs (lncRNAs) are RNA molecules that regulate gene expression at several levels, including transcriptional, post-transcriptional, and translational. They have a length of more than 200 nucleotides and cannot code. Many human diseases have been linked to aberrant lncRNA expression, highlighting the need for a better knowledge of disease etiology to drive improvements in diagnostic, prognostic, and therapeutic methods. Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide. LncRNAs play an essential role in the complex process of heart formation, and their abnormalities have been associated with several CVDs. This Review article looks at the roles and relationships of long non-coding RNAs (lncRNAs) in a wide range of CVDs, such as heart failure, myocardial infarction, atherosclerosis, and cardiac hypertrophy. In addition, the review delves into the possible uses of lncRNAs in diagnostics, prognosis, and clinical treatments of cardiovascular diseases. Additionally, it considers the field's future prospects while examining how lncRNAs might be altered and its clinical applications.
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Enfermedades Cardiovasculares , Insuficiencia Cardíaca , Infarto del Miocardio , ARN Largo no Codificante , Humanos , Enfermedades Cardiovasculares/diagnóstico , Enfermedades Cardiovasculares/genética , Enfermedades Cardiovasculares/terapia , ARN Largo no Codificante/genética , ARN Largo no Codificante/metabolismo , PronósticoRESUMEN
Patients with preexisting cardiovascular disease or those at high risk for developing the condition are often offered exercise as a form of therapy. Patients with cancer who are at an increased risk for cardiovascular issues are increasingly encouraged to participate in exercise-based, interdisciplinary programs due to the positive correlation between these interventions and clinical outcomes following myocardial infarction. Diabetic cardiomyopathy (DC) is a cardiac disorder that arises due to disruptions in the homeostasis of individuals with diabetes. One of the primary reasons for mortality in individuals with diabetes is the presence of cardiac structural damage and functional abnormalities, which are the primary pathological features of DC. The aetiology of dilated cardiomyopathy is multifaceted and encompasses a range of processes, including metabolic abnormalities, impaired mitochondrial function, dysregulation of calcium ion homeostasis, excessive cardiomyocyte death, and fibrosis. In recent years, many empirical investigations have demonstrated that exercise training substantially impacts the prevention and management of diabetes. Exercise has been found to positively impact the recovery of diabetes and improve several metabolic problem characteristics associated with DC. One potential benefit of exercise is its ability to increase systolic activity, which can enhance cardiometabolic and facilitate the repair of structural damage to the heart caused by DC, leading to a direct improvement in cardiac health. In contrast, exercise has the potential to indirectly mitigate the pathological progression of DC through its ability to decrease circulating levels of sugar and fat while concurrently enhancing insulin sensitivity. A more comprehensive understanding of the molecular mechanism via exercise facilitates the restoration of DC disease must be understood. Our goal in this review was to provide helpful information and clues for developing new therapeutic techniques for motion alleviation DC by examining the molecular mechanisms involved.
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Diabetes Mellitus , Cardiomiopatías Diabéticas , Infarto del Miocardio , Humanos , Cardiomiopatías Diabéticas/etiología , Cardiomiopatías Diabéticas/prevención & control , Ejercicio FísicoRESUMEN
Polymeric films made from chitosan (CS) doped with metal oxide (MO = cobalt (II) oxide and strontium oxide) nanoparticles at different concentrations (5, 10, 15, and 20% wt. MO/CS) were fabricated with the solution cast method. FTIR, SEM, and XRD spectra were used to study the structural features of those nanocomposite films. The FTIR spectra of chitosan showed the main characteristic peaks that are usually present, but they were shifted considerably by the chemical interaction with metal oxides. FTIR analysis of the hybrid chitosan-CoO nanocomposite exhibited notable peaks at 558 and 681 cm-1. Conversely, the FTIR analysis of the chitosan-SrO composite displayed peaks at 733.23 cm-1, 810.10 cm-1, and 856.39 cm-1, which can be attributed to the bending vibrations of Co-O and Sr-O bonds, respectively. In addition, the SEM graphs showed a noticeable morphological change on the surface of chitosan, which may be due to surface adsorption with metal oxide nanoparticles. The XRD pattern also revealed a clear change in the crystallinity of chitosan when it is in contact with metal oxide nanoparticles. The presence of characteristic signals for cobalt (Co) and strontium (Sr) are clearly shown in the EDX examinations, providing convincing evidence for their incorporation into the chitosan matrix. Moreover, the stability of the nanoparticle-chitosan coordinated bonding was verified from the accurate and broadly parametrized semi-empirical tight-binding quantum chemistry calculation. This leads to the determination of the structures' chemical hardness as estimated from the frontier's orbital calculations. We characterized the dielectric properties in terms of the real and imaginary dielectric permittivity as a function of frequency. Dielectric findings reveal the existence of extensive interactions of CoO and SrO, more pronounced for SrO, with the functional groups of CS through coordination bonding. This induces the charge transfer of the complexes between CoO and SrO and the CS chains and a decrease in the amount of the crystalline phase, as verified from the XRD patterns.
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Globally, salinity and drought are severe abiotic stresses that presently threaten vegetable production. This study investigates the potential exogenously-applied glutathione (GSH) to relieve water deficits on Phaseolus vulgaris plants cultivated in saline soil conditions (6.22 dS m-1) by evaluating agronomic, stability index of membrane, water satatus, osmolytes, and antioxidant capacity responses. During two open field growing seasons (2017 and 2018), foliar spraying of glutathione (GSH) at 0.5 (GSH1) or 1.0 (GSH1) mM and three irrigation rates (I100 = 100%, I80 = 80% and I60 = 60% of the crop evapotranspiration) were applied to common bean plants. Water deficits significantly decreased common bean growth, green pods yield, integrity of the membranes, plant water status, SPAD chlorophyll index, and photosynthetic capacity (Fv/Fm, PI), while not improving the irrigation use efficiency (IUE) compared to full irrigation. Foliar-applied GSH markedly lessened drought-induced damages to bean plants, by enhancing the above variables. The integrative I80 + GSH1 or GSH2 and I60 + GSH1 or GSH2 elevated the IUE and exceeded the full irrigation without GSH application (I100) treatment by 38% and 37%, and 33% and 28%, respectively. Drought stress increased proline and total soluble sugars content while decreased the total free amino acids content. However, GSH-supplemented drought-stressed plants mediated further increases in all analyzed osmolytes contents. Exogenous GSH enhanced the common bean antioxidative machinery, being promoted the glutathione and ascorbic acid content as well as up-regulated the activity of superoxide dismutase, catalase, ascorbate peroxidase, and glutathione peroxidase. These findings demonstrate the efficacy of exogenous GSH in alleviating water deficit in bean plants cultivated in salty soil.
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Antioxidantes , Phaseolus , Antioxidantes/metabolismo , Phaseolus/metabolismo , Agua/metabolismo , Glutatión/metabolismo , SueloRESUMEN
This study investigated the effect of the silver nanoparticles (AgNPs) and platelet-rich fibrin (PRF) in the healing of the severed superficial digital flexor tendon in donkeys (SDFT). Twenty-seven adult donkeys were used in the study. The animals were divided into three equal groups. The first group (control group) in which the severed SDFT was sutured without the addition of any adjuvant. In the second group, there was a suture of severed SDFT with the addition of 1 ml of 1 mM silver nanoparticles (AgNPs group). The third group was subjected to the cutting of SDFT and then the addition of PRF after its suture. Each group of animals was divided into three equal subgroups that were examined after 1, 2, and 3 months. Each group of animals was clinically evaluated by assessing lameness. Gross and microscopic examinations of the healed tendons were performed after 1, 2, and 3 months of surgery. In comparison to the control group, the lameness degree decreased in the PRF and AgNPs groups, particularly in the third month after surgery. Furthermore, the lameness decreased significantly after the 3rd month relative to the 1st-month lameness in the AgNPs group. Interestingly, it was found that the PRF and AgNPs enhanced cell alignment and collagen deposition at the site of tendon injury, particularly among third-month subgroups. Therefore, it could be concluded that the PRF and AgNPs are effective materials for enhancing SDFT healing in donkeys.
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Nanopartículas del Metal , Fibrina Rica en Plaquetas , Animales , Plata/farmacología , Cojera Animal , Nanopartículas del Metal/uso terapéutico , Tendones/cirugíaRESUMEN
Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way - through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.
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Algoritmos , Diabetes Mellitus , Humanos , Teorema de Bayes , Diabetes Mellitus/diagnóstico , Minería de Datos , Pueblo PimaRESUMEN
In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results.
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This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment's borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm.
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The application of bio- and nanofertilizers are undoubtedly opening new sustainable approaches toward enhancing abiotic stress tolerance in crops. In this study, we evaluated the application of effective microorganisms (EMs) of five groups belonging to photosynthetic bacteria, lactic acid bacteria, yeast, actinobacteria, and fermenting fungi combined with magnesium oxide (MgO) nanoparticles (MgO-NP) on the growth and productivity of sweet potato plants grown in salt-affected soils. In two field experiments carried out in 2020 and 2021, we tested the impacts of EMs using two treatments (with vs. without EMs as soil drench) coupled with three foliar applications of MgO-NP (0, 50, and 100 µg ml-1 of MgO, representing MgO-NP0, MgO-NP50, and MgO-NP100, respectively). In our efforts to investigate the EMs:MgO-NP effects, the performance (growth and yield), nutrient acquisition, and physio-biochemical attributes of sweet potatoes grown in salt-affected soil (7.56 dS m-1) were assessed. Our results revealed that salinity stress significantly reduced the growth parameters, yield traits, photosynthetic pigment content (chlorophylls a and b, and carotenoids), cell membrane stability, relative water content, and nutrient acquisition of sweet potatoes. However, the EMs+ and/or MgO-NP-treated plants showed high tolerance to salt stress, specifically with a relatively superior increase when any of the biostimulants were combined. The application of EMs and/or MgO-NP improved osmotic stress tolerance by increasing the relative water content and membrane integrity. These positive responses owed to increase the osmolytes level (proline, free amino acids, and soluble sugars) and antioxidative compounds (non-enzymatic concentration, enzymatic activities, phenolic acid, and carotenoids). We also noticed that soil salinity significantly increased the Na+ content, whereas EMS+ and/or MgO-NP-treated plants exhibited lower Na+ concentration and increased K+ concentration and K+/Na+ ratio. These improvements contributed to increasing the photosynthetic pigments, growth, and yield under salinity stress. The integrative application of EMs and MgO-NP showed higher efficacy bypassing all single treatments. Our findings indicated the potential of coapplying EMs and MgO-NP for future use in attenuating salt-induced damage beneficially promoting crop performance.
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Given the current COVID-19 pandemic, medical research today focuses on epidemic diseases. Innovative technology is incorporated in most medical applications, emphasizing the automatic recognition of physical and emotional states. Most research is concerned with the automatic identification of symptoms displayed by patients through analyzing their body language. The development of technologies for recognizing and interpreting arm and leg gestures, facial features, and body postures is still in its early stage. More extensive research is needed using artificial intelligence (AI) techniques in disease detection. This paper presents a comprehensive survey of the research performed on body language processing. Upon defining and explaining the different types of body language, we justify the use of automatic recognition and its application in healthcare. We briefly describe the automatic recognition framework using AI to recognize various body language elements and discuss automatic gesture recognition approaches that help better identify the external symptoms of epidemic and pandemic diseases. From this study, we found that since there are studies that have proven that the body has a language called body language, it has proven that language can be analyzed and understood by machine learning (ML). Since diseases also show clear and different symptoms in the body, the body language here will be affected and have special features related to a particular disease. From this examination, we discovered that it is possible to specialize the features and language changes of each disease in the body. Hence, ML can understand and detect diseases such as pandemic and epidemic diseases and others.
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Antibodies play a crucial role in the immune response, in fighting off pathogens as well as helping create strong immunological memory. Antibody-dependent enhancement (ADE) occurs when non-neutralising antibodies recognise and bind to a pathogen, but are unable to prevent infection, and is widely known and is reported as occurring in infection caused by several viruses. This narrative review explores the ADE phenomenon, its occurrence in viral infections and evaluates its role in infection by SARS-CoV-2 virus, which causes coronavirus disease 2019 (COVID-19). As of yet, there is no clear evidence of ADE in SARS-CoV-2, though this area is still subject to further study.
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COVID-19 , SARS-CoV-2 , Anticuerpos Neutralizantes , Anticuerpos Antivirales/uso terapéutico , Acrecentamiento Dependiente de Anticuerpo , HumanosRESUMEN
This study is very promising for providing a renewable enrgy (H2 gas fuel) under the elctrochemical splitting of the wastwater (sewage water). This study has double benefits: hydrogen generation and contaminations removel. This study is carried out on sewage water, third stage treated, from Beni-Suef city, Egypt. Antimony tin oxide (ATO)/polyaniline (PANI)/PbI2 photoelectrode is prepared through the in situ oxidative polymerization of PANI on ATO, then PANI is used as an assistant for PbI2 deposition using the ionic adsorption deposition method. The chemical structural, morphological, electrical, and optical properties of the composite are confirmed using different analytical tools such as X-ray diffreaction (XRD), scanning electron microscope (SEM), transmision electron microscope (TEM), Fourier-transform infrared spectroscopy (FTIR), and UV-Vis spectroscopy. The prepared PbI2 inside the composite has a crystal size of 33 nm (according to the peak at 12.8°) through the XRD analyses device. SEM and TEM confirm the hexagonal PbI2 sheets embedded on the PANI nanopores surface. Moreover, the bandgap values are enhanced very much after the composite formation, in which the bandgap values for PANI and PANI/PbI2 are 3 and 2.51 eV, respectively. The application of ATO/PANI/PbI2 nanocomposite electrode for sewage splitting and H2 generation is carried out through a three-electrode cell. The measurements carreid out using the electrocehical worksattion under th Xenon lamp (100 mW.cm-2). The produced current density (Jph) is 0.095 mA.cm-2 at 100 mW.cm-2 light illumination. The photoelectrode has high reproducibility and stability, in which and the number of H2 moles is 6 µmole.h-1.cm-1. The photoelectrode response to different monochromatic light, in which the produced Jph decreases from 0.077 to 0.072 mA.cm-2 with decreasing of the wavelengths from 390 to 636 nm, respectively. These values confirms the high response of the ATO/PANI/PbI2 nanocomposite electrode for the light illuminaton and hydrogen genration under broad light region. The thermodynamic parameters: activation energy (Ea), enthalpy (ΔH*), and entropy (ΔS*) values are 7.33 kJ/mol, -4.7 kJ/mol, and 203.3 J/mol.K, respectively. The small values of ΔS* relted to the high sesnivity of the prepared elctrode for the water splitting and then the hydrogen gneration. Finally, a theoretical study was mentioned for calculation geometry, electrochemical, and thermochemistry properties of the polyaniline/PbI2 nanocomposite as compared with that for the polyaniline.
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Staphylococcus protein A (SpA) is found in the cell wall of Staphylococcus aureus bacteria. Its ability to bind to the constant Fc regions of antibodies means it is useful for antibody extraction, and further integration with inorganic materials can lead to the development of diagnostics and therapeutics. We have investigated the adsorption of SpA on inorganic surface models such as experimentally relevant negatively charged silica, as well as positively charged and neutral surfaces, by use of fully atomistic molecular dynamics simulations. We have found that SpA, which is itself negatively charged at pH7, is able to adsorb on all our surface models. However, adsorption on charged surfaces is more specific in terms of protein orientation compared to a neutral Au (111) surface, while the protein structure is generally well maintained in all cases. The results indicate that SpA adsorption is optimal on the siloxide-rich silica surface, which is negative at pH7 since this keeps the Fc binding regions free to interact with other species in solution. Due to the dominant role of electrostatics, the results are transferable to other inorganic materials and pave the way for new diagnostic and therapeutic designs where SpA might be used to conjugate antibodies to nanoparticles.
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Simulación de Dinámica Molecular , Proteína Estafilocócica A , Adsorción , Anticuerpos , Dióxido de Silicio/química , Staphylococcus , Propiedades de SuperficieRESUMEN
OBJECTIVE: In this study, the protective effect of sodium copper chlorophyllin and nebivolol was evaluated in a mice model of CCL4 induced hepatotoxicity. Silymarin was used as a traditional hepatoprotective drug. MATERIALS AND METHODS: Thirty (30) mice were used as they were divided into five groups: the first group was the control group which received distilled water + olive oil, the second group which received 1.5 ml/kg of CCl4 diluted in olive oil three times a week, the third group which received CCl4 + Silymarin 50 mg/kg/day, the fourth group which received CCl4 + nebivolol 4 mg/kg/day, and the fifth group which received 1.5 ml/kg of CCl4+ Cu-chlorophyllin 50 mg/kg/day. The drugs were given by intraperitoneal route for 5 weeks. The detection, quantification of CCl4 induced hepatotoxicity and possible protective effect of either silymarin, nebivolol, or sodium copper chlorophyllin were assessed using biochemical analysis of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total protein, lipid profile, an assay of oxidants and antioxidants, assay of interleukin 6 (IL6) and tumor necrosis factor-alpha (TNF-α), and histopathological examination. RESULTS: The administration of carbon tetrachloride (CCl4) produced pronounced liver impairment. It significantly increased ALT, AST, ALP, malondialdehyde, and serum nitric oxide levels compared to normal control group besides a decrease in total protein, serum catalase, tissue SOD, and GSH levels. IL-6 and TNF-α levels were significantly higher while total cholesterol was significantly lower in mice receiving CCL4 compared to the normal control group. CCL4 induced severe hyperemia and congestion inside the portal area with leukocytic infiltration, hepatic degeneration, and bridge fibrosis. CONCLUSIONS: Co-administration of either silymarin, nebivolol, or sodium copper chlorophyllin with CCl4 was able to ameliorate up to almost contradict CCl4 induced hepatic injury through their anti-inflammatory and antioxidant activities.