Shahid Bahonar University of Kerman

INTRODUCTION: Silver nanoparticles (AgNPs) are of great interest due to their unique and controllable characteristics. Different synthesis methods have been proposed to produce these nanoparticles, which often require elevated temperatures/pressures or toxic solvents. Thus, green synthesis could be a replacement option as a simple, economically viable and environmentally friendly alternative approach for the synthesis of silver nanoparticles. METHODS: Here, the potential of the walnut green husk was investigated in the production of silver nanoparticles. An aqueous solution extracted from walnut green husk was used as a reducing agent as well as a stabilizing agent. Then, the synthesized nanoparticles were characterized with respect of their anticancer, antioxidant, and antimicrobial properties. RESULTS: Results showed that the synthesized nanoparticles possessed an average size of 31.4 nm with a Zeta potential of -33.8 mV, indicating high stability. A significant improvement in the cytotoxicity and antioxidant characteristics of the green synthesized Ag nanoparticles against a cancerous cell line was observed in comparison with the walnut green husk extract and a commercial silver nanoparticle (CSN). This could be due to a synergistic effect of the synthesized silver nanoparticles and their biological coating. AgNPs and the extract exhibited 70% and 40% cytotoxicity against MCF-7 cancerous cells, respectively, while CSN caused 56% cell death (at the concentration of 60 µg/mL). It was observed that AgNPs were much less cytotoxic when tested against a noncancerous cell line (L-929) in comparison with the control material (CSN). The free radical scavenging analysis demonstrated profound anti-oxidant activity for the synthesized nanoparticles in comparison with the extract and CSN. It was also detected that the synthesized AgNPs possess antibacterial activity against nosocomial and standard strains of both Gram-positive and Gram-negative bacteria (minimum inhibitory concentration =5-30 µg/mL). CONCLUSION: These findings imply that the synthesized nanoparticles using green nanotechnology could be an ideal strategy to combat cancer and infectious diseases.

In the past few decades, polymeric nanocarriers have been recognized as promising tools and have gained attention from researchers for their potential to efficiently deliver bioactive compounds, including drugs, proteins, genes, nucleic acids, etc., in pharmaceutical and biomedical applications. Remarkably, these polymeric nanocarriers could be further modified as stimuli-responsive systems based on the mechanism of triggered release, i.e., response to a specific stimulus, either endogenous (pH, enzymes, temperature, redox values, hypoxia, glucose levels) or exogenous (light, magnetism, ultrasound, electrical pulses) for the effective biodistribution and controlled release of drugs or genes at specific sites. Various nanoparticles (NPs) have been functionalized and used as templates for imaging systems in the form of metallic NPs, dendrimers, polymeric NPs, quantum dots, and liposomes. The use of polymeric nanocarriers for imaging and to deliver active compounds has attracted considerable interest in various cancer therapy fields. So-called smart nanopolymer systems are built to respond to certain stimuli such as temperature, pH, light intensity and wavelength, and electrical, magnetic and ultrasonic fields. Many imaging techniques have been explored including optical imaging, magnetic resonance imaging (MRI), nuclear imaging, ultrasound, photoacoustic imaging (PAI), single photon emission computed tomography (SPECT), and positron emission tomography (PET). This review reports on the most recent developments in imaging methods by analyzing examples of smart nanopolymers that can be imaged using one or more imaging techniques. Unique features, including nontoxicity, water solubility, biocompatibility, and the presence of multiple functional groups, designate polymeric nanocues as attractive nanomedicine candidates. In this context, we summarize various classes of multifunctional, polymeric, nano-sized formulations such as liposomes, micelles, nanogels, and dendrimers.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

A novel modified carbon nanotube paste electrode of 2-(4-oxo-3-phenyl-3,4-dihydro-quinazolinyl)-N'-phenyl-hydrazinecarbothioamide (2PHC) was fabricated, and the electro-oxidation of epinephrine (EP), norepinephrine (NE), and their mixture has been studied using electrochemical methods. The modified electrode displayed strong catalytic function for the oxidation of EP and NE and resolved the overlap voltammetric response of EP and NE into two well-defined voltammetric peaks of about 240 mV with square wave voltammetry (SWV). A linear response in the range of (5 x 10(-8))-(5.5 x 10(-4)) M with a detection limit (S/N = 3) of 9.4 nM for EP was obtained.

Study 1 investigated the effect of mortality salience on support for martyrdom attacks among Iranian college students. Participants were randomly assigned to answer questions about either their own death or an aversive topic unrelated to death and then evaluated materials from fellow students who either supported or opposed martyrdom attacks against the United States. Whereas control participants preferred the student who opposed martyrdom, participants reminded of death preferred the student who supported martyrdom and indicated they were more likely to consider such activities themselves. Study 2 investigated the effect of mortality salience on American college students' support for extreme military interventions by American forces that could kill thousands of civilians. Mortality salience increased support for such measures among politically conservative but not politically liberal students. The roles of existential fear, cultural worldviews, and construing one's nation as pursing a heroic battle against evil in advocacy of violence were discussed.

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.

Landslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN-DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN-DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN-DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN-DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN-DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province.

Understanding the genetic changes underlying phenotypic variation in sheep (Ovis aries) may facilitate our efforts towards further improvement. Here, we report the deep resequencing of 248 sheep including the wild ancestor (O. orientalis), landraces, and improved breeds. We explored the sheep variome and selection signatures. We detected genomic regions harboring genes associated with distinct morphological and agronomic traits, which may be past and potential future targets of domestication, breeding, and selection. Furthermore, we found non-synonymous mutations in a set of plausible candidate genes and significant differences in their allele frequency distributions across breeds. We identified PDGFD as a likely causal gene for fat deposition in the tails of sheep through transcriptome, RT-PCR, qPCR, and Western blot analyses. Our results provide insights into the demographic history of sheep and a valuable genomic resource for future genetic studies and improved genome-assisted breeding of sheep and other domestic animals.

The serum concentrations of haptoglobin, serum amyloid A and alpha1 acid glycoprotein were determined in serum collected from healthy dairy cows and cows with clinical mastitis, graded as mild (clots in milk) or moderate (clots in milk and visible signs of inflammation in the mammary gland/s) to assess their relative diagnostic value in detecting the disease. The concentrations of haptoglobin and serum amyloid A were also measured in milk collected from infected and uninfected quarters. The concentrations of haptoglobin and serum amyloid A were higher in the serum and milk from the cows with mild or moderate mastitis. The diagnostic value of haptoglobin in differentiating between healthy animals and those with mastitis gave sensitivities and specificities of 82 per cent and 94 per cent respectively with serum and 86 per cent and 100 per cent with milk. The diagnostic value of serum amyloid A in differentiating between healthy animals and those with mastitis gave sensitivities and specificities of 83 per cent and 90 per cent with serum and 93 per cent and 100 per cent with milk. The diagnostic value of serum alpha1 acid glycoprotein in differentiating between healthy animals and those with mastitis gave sensitivities and specificities of 62 per cent and 91 per cent.

Credit card fraud is increasing considerably with the development of modern technology and the global superhighways of communication. Credit card fraud costs consumers and the financial company billions of dollars annually, and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for banks and financial institution, to minimize their losses. However, there is a lack of published literature on credit card fraud detection techniques, due to the unavailable credit card transactions dataset for researchers. The most commonly techniques used fraud detection methods are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor algorithms (KNN). These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. But amongst all existing method, ensemble learning methods are identified as popular and common method, not because of its quite straightforward implementation, but also due to its exceptional predictive performance on practical problems. In this paper we trained various data mining techniques used in credit card fraud detection and evaluate each methodology based on certain design criteria. After several trial and comparisons; we introduced the bagging classifier based on decision three, as the best classifier to construct the fraud detection model. The performance evaluation is performed on real life credit card transactions dataset to demonstrate the benefit of the bagging ensemble algorithm.

In the present study, effects of salinity stress were evaluated in the leaves and roots of two pistachio cultivars (Badami-Rize-Zarand (BZ) and Badami-e-Sefid (BS)). In overall, salinity negatively affects growth of both cultivars with more pronounced effects on BS. The physiological reason of the reduction could be attributed to some extent to more depletion of photosynthetic pigment in BS. In both cultivars, salinity increased proline content. Moderate and high salinities increased the soluble sugar contents in BZ. In both cultivars, Na+ content increased in plant organs with increasing Na+ in the media. Salinity treatment decreased the Fe and Pi contents in BS cultivar, while they remained unchanged in BZ. These results show that BZ cultivar exhibits more tolerance to salinity stress than BS cultivar possibly by better growth performance, accumulating more osmolytes, lesser accumulation of toxic sodium ion and lower Na+/K+ in the shoots as well as maintaining nutrient contents.

Vehicular Ad hoc Networks (VANETs) that are considered as a subset of Mobile Ad hoc Networks (MANETs) can be applied in the field of transportation especially in Intelligent Transportation Systems (ITS). The routing process in these networks is a challenging task due to rapid topology changes, high vehicle mobility and frequent disconnection of links. Therefore, developing an efficient routing protocol that satisfies restriction of delay and minimum overhead is faced with many difficulties and limitations. Also, the detection of malicious vehicles is a significant task in VANETs. To address these issues, using Unmanned Aerial Vehicles (UAVs) can be helpful to cope with these limitations. In this paper, operation of UAVs in ad hoc mode and their cooperation with vehicles in VANETs are studied to help in the process of routing and detection of malicious vehicles. A routing protocol named VRU is proposed that includes two distinct ways of routing of data: (1) delivering packets of data between vehicles with the help of UAVs using a protocol named VRU_vu, and (2) routing packet of data between UAVs using a protocol named VRU_u. The NS-2.35 simulator under Linux Ubuntu 12.04 is utilized in order to appraise the performance of VRU routing components in an urban scenario. Also, VanetMobiSim generator of mobility and MobiSim are used to produce the motions of vehicles and to produce the motions of UAVs, respectively. The performance analysis displays that VRU protocol can improve the packet delivery ratio by 16% and detection ratio by 7% compared to other reviewed routing protocol. Also, VRU protocol decreases end-to-end delay by an average of 13% and overhead by 40%.

The ongoing global spread of Tomato yellow leaf curl virus (TYLCV; Genus Begomovirus, Family Geminiviridae) represents a serious looming threat to tomato production in all temperate parts of the world. Whereas determining where and when TYLCV movements have occurred could help curtail its spread and prevent future movements of related viruses, determining the consequences of past TYLCV movements could reveal the ecological and economic risks associated with similar viral invasions. Towards this end we applied Bayesian phylogeographic inference and recombination analyses to available TYLCV sequences (including those of 15 new Iranian full TYLCV genomes) and reconstructed a plausible history of TYLCV's diversification and movements throughout the world. In agreement with historical accounts, our results suggest that the first TYLCVs most probably arose somewhere in the Middle East between the 1930s and 1950s (with 95% highest probability density intervals 1905-1972) and that the global spread of TYLCV only began in the 1980s after the evolution of the TYLCV-Mld and -IL strains. Despite the global distribution of TYLCV we found no convincing evidence anywhere other than the Middle East and the Western Mediterranean of epidemiologically relevant TYLCV variants arising through recombination. Although the region around Iran is both the center of present day TYLCV diversity and the site of the most intensive ongoing TYLCV evolution, the evidence indicates that the region is epidemiologically isolated, which suggests that novel TYLCV variants found there are probably not direct global threats. We instead identify the Mediterranean basin as the main launch-pad of global TYLCV movements.

Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach inspired by the SDSM method (statistical downscaling model) developed by Wilby, R.L., Dawson, C.W., Barrow, E.M. [2002. SDSM – a decision support tool for the assessment of regional climate change impacts, Environmental Modelling and Software 17, 147–159] is presented and assessed to reconstruct the observed climate in eastern Canada based extremes as well as mean state. In the ASD model, automatic predictor selection methods are based on backward stepwise regression and partial correlation coefficients. The ASD model also gives the possibility to use ridge regression to alleviate the effect of the non-orthogonality of predictor vectors. Outputs from the first generation Canadian Coupled Global Climate Model (CGCM1) and the third version of the coupled global Hadley Centre Climate Model (HadCM3) are used to test this approach over the current period (i.e. 1961–1990), and compare results with observed temperature and precipitation from 10 meteorological stations of Environment Canada located in eastern Canada. All ASD and SDSM models, as these two models are evaluated and inter-compared, are calibrated using NCEP (National Center for Environmental Prediction) reanalysis data before the use of GCMs atmospheric fields as input variables. The results underline certain limitations to downscale the precipitation regime and its strength to downscale the temperature regime. When modeling precipitation, the most commonly combination of predictor variables were relative and specific humidity at 500 hPa, surface airflow strength, 850 hPa zonal velocity and 500 hPa geopotential height. For modeling temperature, mean sea level pressure, surface vorticity and 850 hPa geopotential height were the most dominant variables. To evaluate the performance of the statistical downscaling approach, several climatic and statistical indices were developed. Results indicate that the agreement of simulations with observations depends on the GCMs atmospheric variables used as “predictors” in the regression-based approach, and the performance of the statistical downscaling model varies for different stations and seasons. The comparison of SDSM and ASD models indicated that neither could perform well for all seasons and months. However, using different statistical downscaling models and multi-sources GCMs data can provide a better range of uncertainty for climatic and statistical indices.

The suitable electrochemical performance of lithium-ion batteries (LIBs) led to an increase in demand and the use of LIBs in electrical and electronic equipment. Naturally, increased demand will be accompanied by an enhancement in the supply of metals from mineral resources. On the other hand, the lithium metal supply scenario will not be able to meet market demand over the next few years. Spent LIBs include valuable metals like lithium, cobalt, nickel, manganese, etc., which can be considered as a secondary source for the recovery of valuable metals. LIBs also contain toxic and organic compounds whose lack of attention, causes irreparable damage to the public health and the environment. Therefore, two important perspectives on the recycling of spent LIBs are the recovery of valuable metals as secondary sources and compliance with environmental regulations. Among the recycling process of spent lithium-ion batteries, hydrometallurgical processes are a suitable technique for recovery of valuable metals from spent lithium-ion batteries, due to their advantages such as the high recovery of metals with high purity, low energy consumption, and very low gas emissions. In this paper, the main aspects of spent LIBs recycling (environmental and economic) and hydrometallurgical processes (pre-treatment, leaching by organic and inorganic acids with various reducing agents, precipitation, solvent extraction, electrochemical methods, ion exchange resin, and regeneration) used to recover valuable metals, have been investigated.

This study suggests a carbon paste electrode modified with CuO nanoparticles and <italic>n</italic> -hexyl-3-methylimidazolium hexafluorophosphate (CPE/CuO-NPs/HMIPF ₆ ) as a powerful tool for the analysis of epinine for the first time.

Environmental pollution has been a known threat to our world due to the rapid urbanization, changing lifestyle of people, and modern industrialization.

Petroleum hydrocarbons are the most common environmental pollutants in the world and oil spills pose a great hazard to terrestrial and marine ecosystems. Oil pollution may arise either accidentally or operationally whenever oil is produced, transported, stored and processed or used at sea or on land. Oil spills are a major menace to the environment as they severely damage the surrounding ecosystems. To improve the survival and retention of the bioremediation agents in the contaminated sites, bacterial cells must be immobilized. Immobilized cells are widely tested for a variety of applications. There are many types of support and immobilization techniques that can be selected based on the sort of application. In this review article, we have discussed the potential of immobilized microbial cells to degrade petroleum hydrocarbons. In some studies, enhanced degradation with immobilized cells as compared to free living bacterial cells for the treatment of oil contaminated areas have been shown. It was demonstrated that immobilized cell to be effective and is better, faster, and can be occurred for a longer period.

, simplistic synthesis, acceptable environmental stability, beneficial optical, electronic, and mechanical features, researchers have largely considered them for diverse applications. Therefore, their capability of catalyzing several electrode reactions has been introduced as one of their significant features. A thin layer of the conducting polymer deposited on the substrate electrode surface can augment the electrode process kinetics of several solution species. Such electrocatalytic procedures with modified conducting polymer electrodes can create beneficial utilization in diverse fields of applied electrochemistry. This review article explores typical recent applications of conductive polymers (2016-2020) as active electrode materials for energy storage applications, electrochemical sensing, and conversion fields such as electrochemical supercapacitors, lithium-ion batteries, fuel cells, and solar cells.

Farm-animal species play crucial roles in satisfying demands for meat on a global scale, and they are genetically being developed to enhance the efficiency of meat production. In particular, one of the important breeders' aims is to increase skeletal muscle growth in farm animals. The enhancement of muscle development and growth is crucial to meet consumers' demands regarding meat quality. Fetal skeletal muscle development involves myogenesis (with myoblast proliferation, differentiation, and fusion), fibrogenesis, and adipogenesis. Typically, myogenesis is regulated by a convoluted network of intrinsic and extrinsic factors monitored by myogenic regulatory factor genes in two or three phases, as well as genes that code for kinases. Marker-assisted selection relies on candidate genes related positively or negatively to muscle development and can be a strong supplement to classical selection strategies in farm animals. This comprehensive review covers important (candidate) genes that regulate muscle development and growth in farm animals (cattle, sheep, chicken, and pig). The identification of these genes is an important step toward the goal of increasing meat yields and improves meat quality.