Search

The design and synthesis of biocompatible nanostructures for biomedical applications are considered vital challenges. Herein, a nanobiocomposite based on acacia hydrogel, natural silk fibroin protein, and synthetic protein fibers of polyvinyl alcohol was fabricated and magnetized with iron oxide nanoparticles (Fe3O4 MNPs). The structural properties of the hybrid nanobiocomposite were investigated by essential analyses such as Fourier Transform Infrared Spectrometer (FTIR), Field emission scanning electron microscopy (FE-SEM), and X-ray powder diffraction)XRD(analyses, Thermogravimetric and Differential thermogravimetric analysis (TGA-DTG), Vibrating-sample magnetometry (VSM), and Energy Dispersive X-Ray Analysis (EDX). The biological activities and functional properties of the prepared magnetic nanobiocomposite were studied. Results proved that this nanobiocomposite is non-toxic to the healthy HEK293T cell line. In addition, the synthesized nanobiocomposite showed an approximately 22 % reduction in cell viability of BT549 cells after 72 h. All results confirmed the anti-cancer properties of nanobiocomposite against breast cancer cell lines. Therefore, the prepared nanobiocomposite is an excellent material that can use for in-vivo application. Finally, the hyperthermia application was evaluated for this nanobiocomposite. The SAR was measured 93.08 (W/g) at 100 kHz.

A bimetallic cobalt/nickel-based metal-organic framework (MOF), [Co/Ni(μ3-tp)2(μ2-pyz)2], denoted as Co/Ni-MOF, has been successfully prepared by a hydrothermal method. The MOF was prepared by incorporating mixed O− and N− donor ligands, specifically terephthalic acid (tp) and pyrazine (pyz). The Mesoporous Co/Ni-MOF was comprehensively characterized using various analytical methods such as XRD, BET, FT-IR, TGA (23 % char yields), SEM, and EDS analyses. The synthesized mesoporous Co/Ni-MOF was then used to absorb Co (II) from aquatic areas efficiently. Several critical parameters, such as the beginning Co (II) concentration (25–150 mg/L), the effect of pH (2–10), the duration of time (5–30 min), and the amount of adsorbent (0.003–0.02 g), were systematically investigated. Remarkably, the Mesoporous Co/Ni MOF displayed a significant adsorption capacity of 372.66 mg g−1 in the optimum conditions, including pH = 6, amount of adsorbent = 0.003 g, duration of time = 25 min, and beginning Co (II) concentration = 150 mg/L. Adsorption data from the experimental studies of the mesoporous Co/Ni MOF are matched based on the non-linear pseudo-first-order (PSO) kinetic model (R2 = 0.9999), and a chemical process is suggested for chemisorption. Furthermore, the adsorption isotherms of Co (II) heavy metal ions (HMIs) are an excellent fit with the non-linear Temkin, indicating that they explain the sorbent/sorbate interactions concerning the heat of adsorption. It is evident from the thermodynamic parameters that adsorption is a spontaneous and favorable exothermic process. These results highlight the promising adsorption performance and potential applications of the mesoporous Co/Ni-MOF as an effective adsorbent for Co (II) elimination from aquatic areas. Four-cycle regeneration studies were the most effective for the Co (II) under study.

Occupancy rate refers to the level of usage and presence of individuals within a building or a specific space. This factor can have a significant impact on building energy consumption. When the occupancy rate in a building is high, naturally, energy consumption also increases. This correlation might be due to the increased use of lighting, heating, and cooling, higher numbers of electrical and electronic devices, and similar factors associated with the presence of people in the building. One of the modern methods in the energy field involves empirically utilizing occupancy monitoring tools in buildings and analyzing the relationship between such utilization and building energy consumption through artificial neural network tools. In this research, a camera sensitive to entry and exit was installed at the entrance of an office building in Tehran, Iran. By doing so, the rate of entry and exit was accurately monitored. In the next stage, by investigating the impact of this entry and exit rate on the building's energy consumption, the energy consumption amount was predicted using an artificial neural network and a statistical method (moving average). The results indicate errors of 9.8 and 4.5 for the respective methods, highlighting that the artificial neural network yields the most accurate outcomes. Moreover, the study's findings suggest a direct correlation: as occupancy rates increase, the predicted energy consumption values also rise.

In microbiome studies, the diversity and types of microbes have been extensively explored; however, the significance of microbial ecology is equally paramount. The comprehension of metabolic interactions among the wide array of microorganisms in the lung microbiota is indispensable for understanding chronic pulmonary disease and for the development of potent treatments. In this investigation, metabolic networks were simulated, and ecological theory was employed to assess the diagnosis of COPD, subsequently suggesting innovative treatment strategies for COPD exacerbation. Lung sputum 16S rRNA paired-end data from 112 COPD patients were utilized, and a supervised machine-learning algorithm was applied to identify taxa associated with sex and mortality. Subsequently, an OTU table with Greengenes 99 % dataset was generated. Finally, the interactions between bacterial species were analyzed using a simulated metabolic network. A total of 1781 OTUs and 1740 bacteria at the genus level were identified. We employed an additional dataset to validate our analyses. Notably, among the more abundant genera, Pseudomonas was detected in females, while Lactobacillus was detected in males. Additionally, a decrease in bacterial diversity was observed during COPD exacerbation, and mortality was associated with the high abundance of the Staphylococcus and Pseudomonas genera. Moreover, an increase in Proteobacteria abundance was observed during COPD exacerbations. In contrast, COPD patients exhibited decreased levels of Firmicutes and Bacteroidetes. Significant connections between microbial ecology and bacterial diversity in COPD patients were discovered, highlighting the critical role of microbial ecology in the understanding of COPD. Through the simulation of metabolic interactions among bacteria, the observed dysbiosis in COPD was elucidated. Furthermore, the prominence of anaerobic bacteria in COPD patients was revealed to be influenced by parasitic relationships. These findings have the potential to contribute to improved clinical management strategies for COPD patients.

This research represents an innovative method for geometry generation of the squealer-tip in axial compressor rotors and its exploit in a numerical optimization process to obtain a better stage performance. For this purpose, the NASA Rotor-67 transonic compressor rotor blade is used as a test case to study the aerodynamic performance using computational fluid dynamics. The validation was performed for the characteristic map at the design speed and the comparison with the experimental results indicates excellent matching and high adaptability of the numerical method. An ingenious method of producing squealer tip for an axial compressor rotary blade is presented in this article, which is capable for locally shaping both suction and pressure surface geometry at a desired spanwise location simultaneously, while keeping the tip clearance at its value of the baseline NASA Rotor-67 geometry. In this method, control points are used to produce the starting spanwise location of the squealer, and modify the depth of the squealer geometry. The L-27 orthogonal array of the Taguchi method as the Design of Experiment (DOE) has been used to investigate the sensitivity of the aerodynamic results in three performance points of the choke, design and near stall regions, in relation to the design variables of the squealer. The generated database in the sensitivity analysis was used to train artificial neural networks to replace the CFD solutions with overwhelming run time. By coupling the genetic algorithm to the aforementioned neural networks and by applying penalties to maintain the minimum performance of the Rotor-67, enhancement of total pressure ratio, adiabatic efficiency, mass flow rate and even the surge margin was achieved. The main effect of the squealer is to modify the shape of blade tip vortices, and by more dissipation of energy in blade tip area and reduced equivalent flow area in this region, finally results in improved overall mass flow rate, total pressure ratio, adiabatic efficiency and surge margin by 0.58 %, 0.36 %, 0.19 % and 4.81 % respectively, at design point.

Background: In Northern Province, Rwanda, stunting is common among children aged under 5 years. However, previous studies on spatial analysis of childhood stunting in Rwanda did not assess its randomness and clustering, and none were conducted in Northern Province. We conducted a spatial-pattern analysis of childhood undernutrition to identify stunting clusters and hotspots for targeted interventions in Northern Province. Methods: Using a household population-based questionnaire survey of the characteristics and causes of undernutrition in households with biological mothers of children aged 1–36 months, we collected anthropometric measurements of the children and their mothers and captured the coordinates of the households. Descriptive statistics were computed for the sociodemographic characteristics and anthropometric measurements. Spatial patterns of childhood stunting were determined using global and local Moran's I and Getis-Ord Gi* statistics, and the corresponding maps were produced. Results: The z-scores of the three anthropometric measurements were normally distributed, but the z-scores of height-for-age were generally lower than those of weight-for-age and weight-for-height, prompting us to focus on height-for-age for the spatial analysis. The estimated incidence of stunting among 601 children aged 1–36 months was 27.1 %. The sample points were interpolated to the administrative level of the sector. The global Moran's I was positive and significant (Moran's I = 0.403, p

The use of recycled aggregates derived from construction and demolition wastes increased in the concrete construction industry. While the coarse fraction (i.e., from 5 to 20 mm) was successfully recycled, the fine fraction (i.e., from 0 to 5 mm) is hardly recyclable given its heterogenous nature and poor physical properties. This paper assesses the feasibility of recycled mortar aggregate (RMA) in pavement concrete applications requiring superior resistance to weathering and wearing effects. Three concrete strength grade categories prepared with different water-to-binder ratios (w/b) of 0.4, 0.5, and 0.6 are investigated; the natural sand replacement levels by RMA varied from 0 % to 60 % by volume, at 20 % increment rates. Test results showed that the detrimental RMA effect on durability depends on the concrete strength category, requiring proper adjustment of the maximum replacement rate to maintain acceptable losses in performance. Hence, the higher strength grade mixtures prepared with 0.4 w/b were found to yield significant drops in durability reaching 25 % in water sorptivity, 18 % in abrasion, and 22 % due to freeze/thaw (F/T) cycles. In contrast, the lower strength grade concrete made with 0.6 w/b exhibited marginal degradation in strength and durability, despite the incorporation of 60 % RMA. The resulting losses in sorptivity, abrasion, and F/T cycles were limited to 13 %, 8 %, and 12 %, respectively.

This work presents a novel, strong and efficient adsorbent (CS@TDI@EDTA@γ-AlO(OH)) prepared through the green process using three components, chitosan, BNPs and EDTA using amide and ester bridges. An eco-friendly and easy approach was used for the preparation of this novel adsorbent, the low cost, easy access to the used materials, and the simplicity of the preparation method are some of the interesting advantages of this work. Also, this prepared adsorbent was used as an adsorbent to remove diazinon organophosphate poison and tetracycline antibiotic from aqueous solutions. In order to confirm the prepared adsorbent structure, the CS@TDI@EDTA@γ-AlO(OH) composite was investigated by various analyses including FT-IR, EDX, XRD, FESEM and TGA. The adsorption behavior of the adsorbent prepared for the removal of tetracycline and diazinon was investigated under different conditions by varying the concentration, temperature, the adsorbent dose, pH and contact time. Based on various tests, the highest diazinon adsorption capacity was obtained for 0.12 g/L adsorbent at pH 7 and 60 °C with 40 mg/L initial concentration. Also, the maximum adsorption capacity of the tetracycline was obtained for 0.12 g/L adsorbent at pH 9 and 60 °C with 30 mg/L initial concentration. The equilibrium results for diazinon and for tetracycline were in good accordance with the Langmuir and Freundlich isotherm models, respectively. Also, the highest adsorption capacities for diazinon at pH 7 and tetracycline at pH 9 were 1428.5 and 555.5 mg/g, respectively. Also the kinetic investigations revealed that the correlation factor (R2) of pseudo-second-order model obtained for the adsorption of diazinon and tetracycline was 0.9986 and 0.9988, while the coefficient k (g/mg.min) was 0.000084 and 0.0033, respectively. These results indicate that the adsorption of diazinon and tetracycline is pseudo-second-order kinetics model. Formation of hydrogen bonds between adsorbate and adsorbent as well as the high specific surface area and porosity of the adsorbent are the main mechanisms that contribute to the adsorption process. In addition, thermodynamic studies indicated that the adsorption of diazinon and tetracycline is a spontaneous endothermic process. The adsorbent prepared in this work was expected to have wide range of applications in wastewater treatment thanks to its good reusability in water and strong removal of diazinon and tetracycline compared to other adsorbents.

Dry rangelands provide resources for half of the world's livestock, but degradation due to overgrazing is a major threat to system sustainability. Existing carrying capacity assessments are limited by low spatiotemporal resolution and high generalization, which hampers applied ecological management decisions. This paper provides an example for deriving the carrying capacity and utilization levels for cold drylands at a new level of detail by including major parts of the transhumance system. We combined field data on vegetation biomass and communities, forage quality, productivity, livestock species and quantities, grazing areas and their spatiotemporal variations with Sentinel-2 and MODIS snow cover satellite imagery to develop maps of forage requirements and availability. These products were used to calculate carrying capacity and grazing potential in the Pamir-Hindukush Mountains. Results showed high spatial variability of utilization rates between 5% and 77%. About 30% of the area showed unsustainable grazing above the carrying capacity. Utilization rates displayed strong spatial differences with unsustainable grazing in winter pastures and at lower elevations, and low rates at higher altitudes. The forage requirements of wild herbivores (ungulates and marmots) were estimated to be negligible compared to livestock, with one tenth of the biomass consumption and no increase in unsustainably grazed pastures due to the wider distribution of animals. The assessment was sensitive to model parameterization of forage requirements and demand, whereby conservative scenarios, i.e. lower fodder availability or higher fodder requirements of livestock due to climate and altitude effects, increased the area with unsustainable grazing practices to 50%. The presented approach enables an in-depth evaluation of the carrying capacity and corresponding management actions. It includes new variables relevant for transhumance systems, such as the combination of forage quantity and quality or accessibility restrictions due to snow, and shows utilization patterns at high spatial resolutions. Regional maps allow the identification of unsustainable utilization areas, such as winter pastures in this study.

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

As a special geographical unit on the earth, deserts have certain differences in planetary boundary layer (PBL) characteristics from other surface types. In order to find out the long-term evolution law of the Gurbantünggüt Desert, on the basis of evaluating the availability of reanalysis data, using the most effective reanalysis data and situ measured data in this area, the evolution law of the atmospheric boundary layer in the desert area was studied. The results show that among the ERA5, MERRA2, JRA-55 and NCEP-FNL reanalysis data, the ERA5 data has the smallest error with the measured data in the comparison of ground elements or high-altitude meteorology parameters, and can be used for the long-term evolution of the atmospheric boundary layer in desert areas. Based on the ERA5 dataset, the annual planetary boundary layer height (PBLH) of the desert fluctuated between 1979 and 1985, but showed a downward trend overall. From 1986 to 2019, the PBLH generally shows an upward trend, and by 2020, the PBLH decreases again. The PBLH in the summer of the desert was contrary to the inter-annual change trend of the PBLH throughout the year. The spatial distribution shows that the PBLH has the characteristics of north-south anisotropy. The characteristics of the ABL in the Gurbantünggüt Desert in different thermal states in summer vary greatly. Based on the sounding observational data, the average PBLH of the stable boundary layer in the Gurbantünggüt Desert in summer is 496 m, the average PBLH of the convective boundary layer is 1693 m, and the average PBLH of the neutral boundary layer is 1208 m. The ABL in desert areas from 02:00 to 08:00 and 23:00 is dominated by stable boundary layers, of which the proportion of stable boundary layers at 05:00 is as high as 67%. During the day, the boundary layer from 14:00 to 17:00 is mainly the convective boundary layer, accounting for more than 50%, and the boundary layer at 20:00 is mainly a neutral boundary layer, accounting for 55%.

The world badminton championship is a highly competitive arena. Successful athletes are those who have excellence, not only physically and technically but also mentally. The present study aims to identify the personality traits, environment, and career stages required by an athlete to achieve excellence. The research was conducted with a qualitative approach through in-depth interviews. As participants are 10 Indonesian badminton athletes who have achieved world-class achievements, such as the Olympics and world championships. They consist of 3 female athletes and 7 male athletes. The results showed that seven traits were effective in supporting their achievement, namely: achievement-oriented, hard work, persistent, autonomy, commitment, intelligence, and self-control. The family environment and sports environment influence the formation of traits and the emergence of high-achieving athletes. This research also found that athletes go through 5 phases in achieving achievement, namely exploration, specialization, investment, achievement, and maintenance.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620–0.686, 0.685–0.716, 0.632–0.727, 0.527–0.598, 0.548–0.655, 0.545–0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777–0.867, 0.795–0.848, 0.857–0.906, 0.788–0.875, 0.683–0.850, and 0.486–0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

In this research, turbulent flow numerical models in a wavy channel were investigated. The studied channel is simulated in two dimensions and symmetrically in the range of Reynolds numbers from Re=10,000 to 80,000. The significant cause of this research is to investigate and determine the appropriate method for estimating the behavior of turbulent flow in a wavy channel. In this research, the behavior of turbulent flow in a wavy channel will be simulated in 7 different ways, which are k-ω SST, k-ϵ RN, k-ϵ Realizable, k−ϵ Standard, k-ω Standard, Reynolds stress and Spalart-Allmaras. The findings of this research show that the impacts of the presence of flow viscosity (friction) and the presence of adverse pressure gradients are factors that strongly affect the velocity profiles in the upstream areas of the corrugated section. Among the studied models, due to better compatibility and guessing of flow and hydrodynamic properties, k-ω SST methods and Reynolds and Spalart-Allmaras stress are introduced as the best methods for such geometries. On the other hand, increasing the accuracy of other turbulence methods is related to the flow physics and geometric structure of each problem. In this research, the hydrodynamic parameters of the flow such as pressure drop, skin friction factor, and dynamic pressure drop coefficient and vortex contours, and pressure are plotted and described.

Nanobioconjugates are nanoscale drug delivery vehicles that have been conjugated to or decorated with biologically active targeting ligands. These targeting ligands can be antibodies, peptides, aptamers, or small molecules such as vitamins or hormones. Most research studies in this field have been devoted to targeting cancer. Moreover, the nanostructures can be designed with an additional level of targeting by being designed to be stimulus-responsive or “smart” by a judicious choice of materials to be incorporated into the hybrid nanostructures. This stimulus could be an acidic pH, raised temperature, enzyme, ultrasound, redox potential, an externally applied magnetic field, or laser irradiation. In this case, the smart capability can increase the accumulation at the tumor site or the on-demand drug release, while the ligand ensures selective binding to the tumor cells. The present review highlights some interesting studies classified according to the nanostructure material. These materials include natural substances (polysaccharides), multi-walled carbon nanotubes (and halloysite nanotubes), metal-organic frameworks and covalent-organic frameworks, metal nanoparticles (gold and silver), and polymeric micelles.

The novel coronavirus which first emerged in Wuhan, China has affected many sectors of the global economy particularly, the tourism sector. While prior studies have explored how pandemics and crisis in the tourism sector can be managed, only a few have tackled how the tourism industry, especially in developing countries can be restructured to withstand shocks and remain resilient in the face of crisis such as the COVID-19 pandemic in the future. This article sheds light on the socio-economic and ecological effects of the COVID-19 pandemic on the eco-tourism sector of Ghana's Savannah region while offering short and long term strategies for building resilience to withstand shocks. Between September and October 2020, data was collected in the tourism enclave of the Savannah region in Ghana using focus group discussions and interviews. Results show that the major socio-economic impact of the pandemic was the loss of livelihoods. Ecological impacts were however mixed – both positive and negative. The study concludes that socio-economic impacts have been severe because they are closely tied to tourism. The study therefore recommends that immediate measures including stimulus packages be offered to local enterprises affected by the pandemic. In the long term, efforts should be made to diversify the local economy and promote domestic tourism in the country.

Oxidative stress, the disrupted oxidation-reduction mechanism in our body, is caused by the excessive exposure of free radicals and the impaired antioxidant defenses that can accelerate skin aging. Antioxidants can be obtained from nature, which are available widely in therapeutic-rich plants, such as white saffron (Curcuma mangga Val., denoted as C. mangga). Although many pieces of evidence reveal that C. mangga contains an abundance of phenolic compounds and has antioxidative effects, its cosmeceutical potentials remain unclear. The present study aimed to disclose the unexplored antiaging potentials of C. mangga extract (CME) in oxidative stress-induced human BJ fibroblasts with a focus on collagen protection against pro-inflammatory mediators MMP1, MMP3, and MMP13. The oxidative stress-induced cells were treated with CME and curcumin at different doses. The results showed that treatment using CME (25 μg/mL) could maintain the collagen contents up to 18.45 ± 0.68 μg/mL in H2O2-treated fibroblasts (only ~26.63% reduction in collagen contents), while the figure for the negative control was the lowest (12.79 μg/mL), showing a significant reduction in collagen contents by 49.13%. In addition, the gene expression of pro-inflammatory MMPs arose significantly in BJ fibroblasts after oxidative stress induction using 200 μM H2O2, in which the expression for MMP1, MMP3, and MMP13 increased by 7.10, 38.96, and 2.69 times, respectively. Interestingly, CME treatment (100 μg/mL) could effectively inhibit MMP1, MMP3, and MMP13 gene expression by 3.65, 34.62, and 2.02 times, respectively. In conclusion, CME showed favorable antiaging activities in H2O2-treated human BJ fibroblasts as confirmed by the low levels of gene expression of MPP1, MMP3, and MMP13 after treatment with CME.

Pterocarpus erinaceus is a native tree species of the Guineo-Sudanian and Sudano-Sahelian zones where natural stands are under constant pressure and heavily exploited for timber, animal feeding and others uses. A part from the overexploitation, climate change could also become a serious threat to the species natural distribution. For that purposes, this study aims to assess the vulnerability of P. erinaceus potential niche to climate change within its natural distribution area in West Africa. Niche predictions are based on 6,981 natural occurrence of the species and 19 global bioclimatic variables available through WorldClim. The future niche of the species is predicted according to three concentration pathways (RCPs 2.6, 4.5 and 8.5) of BC model for 2050 and 2070, thanks to Maxent software. P. erinaceus is currently reported from Senegal to Cameroon. Its potential niche covers the Sudano-Sahelian zone and the Dahomey gap on approximately 17.42% of the total area of these countries. In general, the niche of the species is not sensitive to climate change, regardless of the climate scenario and the year. Compared to its initial niche, the niche of the species will increase from 22.33% to 43.61% in 2050 and from 27.12% to 53.61% in 2070. However, this ecological expansion observed mainly in the Gulf of Guinea, will be associated with a considerable decrease in the Sahel and central Nigeria. This study shows the importance of promoting the development of innovative silvicultural strategies for the extension and restoration of natural stands of P. erinaceus in order to meet sustainably the timber needs of the West African region. It helps also to strengthening the roles of natural forests in providing ecosystem services and mitigating climate change effects.

This research focused on studying the effects of orally administered pressure-blanched white saffron on the antioxidative properties and lipid profiles of wistar rats. White saffron was blanched in autoclave for 2.5, 5, 7.5, and 10 min at 100, 105, 110, 115, and 120 °C, which are equivalent to 14.71, 17.53, 20.79, 24.54, and 28.81 psia, respectively. A total of 30 male wistar rats aged four weeks were fed with a standard diet (N), oxidized peanut oil diet + unblanched white saffron (A), oxidized peanut oil diet + blanched white saffron (B), oxidized peanut oil diet + pressure-blanched white saffron (C), and oxidized peanut oil diet + aquadest (NC), for two weeks after pre-treatment with the standard diet for a week. In vivo study showed treatment with pressure-blanched white saffron could significantly improve SOD, Vitamin E, and HDL levels compared to the negative control (NC); 686.44 U/g Hb, 10.87 μg/mL, and 94.17 mg/dL versus 405.37 U/g Hb, 7.44 μg/mL, and 43.47 mg/dL, respectively. Meanwhile, treatment with pressure-blanched white saffron could significantly reduce MDA, total cholesterol, LDL, and triglyceride levels in the blood compared to the negative control (NC); 1.98 mmol/L, 108.74 mg/dL, 40.99 mg/dL, and 78.06 mg/dL versus 8.54 mmol/L, 232.46 mg/dL, 149.17 mg/dL, and 172.61 mg/dL, respectively. The results showed that pressurized blanching could significantly increase antioxidant levels of white saffron, and its dried form could improve antioxidative properties and lipid profiles in vivo.

Climate change increases the vulnerability of agrosystems to soil degradation and reduces the effectiveness of traditional soil restoration options. The implementation of some practices need to be readjusted due to steadily increasing temperature and lowering precipitation. For farmers, the best practice found, should have the potential to achieve maximum sustainable levels of soil productivity in the context of climate change. A study was conducted in South-West Niger to investigate the use of the suitable practice, through (i) a meta-analysis of case studies, (ii) using field survey and (iii) by using AquaCrop model. Results showed that the effects of the association zaï + mulch on crop yield was up to 2 times higher than control plots depending on climate projections scenario RCP 8.5 under which carbon dioxide (CO2) concentrations are projected to reach 936 ppm by 2100. The practice appeared to be an interesting option for enhancing crop productivity in a context of climate change. Concerning its ability, it offers the best prospects to reverse soil degradation in the study area. In addition, the simulation showed that this strategy was suitable for timely sowing and therefore confirmed scholars and farmers views. Furthermore, this practice is relatively more effective compared to the others practices. These results show that association zaï + mulch could be considered as the best practice that can participate to a successful adaptation to reduce risk from climate change at the same time by reducing the vulnerability of farmers in Southwest of Niger for now and even for the future.

Aim: To assess the relative efficacies of clozapine plus Electroconvulsive Therapy (ECT) compared against non-clozapine typical and atypical antipsychotics plus ECT for the treatment of “Treatment Resistant Schizophrenia” (TRS). Primarily to assess if clozapine delivers a significant improvement over other antipsychotics when combined with ECT. Design: Major electronic databases were searched between 1990 and March 2017 for trials measuring the effects of either clozapine augmented ECT, other antipsychotic-augmented ECT, or both. After the systematic review of the data, a random-effects meta-analysis was conducted measuring the relative effect sizes of the different treatment regimens. Subjects: 1179 patients in 23 studies reporting the usage of ECT augmentation with antipsychotics. A total of 95 patients were tested with clozapine, and ECT (9 studies) and 1084 patients were tested with non-clozapine antipsychotics (14 studies) such as flupenthixol, chlorpromazine, risperidone, sulpiride, olanzapine, and loxapine with concurrent ECT treatment considered for systematic review. Of these, 13 studies reported pre and post-treatment scores were included in the meta-analysis. Main outcome measures: The main outcome measure was the presence and degree of both positive and negative psychotic symptoms, as measured by either of two standardized clinician administered tests, the Brief Psychiatric Rating Scale (BPRS), and the Positive and Negative Symptom Scale (PANSS). Results: The comparison of the different antipsychotics established the supremacy of ECT-augmented clozapine treatment against other typical and atypical antipsychotics. The Forest Plot revealed that the overall standard mean difference was 0.891 for non-clozapine studies and 1.504 for clozapine studies, at a 95% interval. Furthermore, the heterogeneity plots showed that while clozapine studies showed no significant heterogeneity, non-clozapine studies showed an I2 statistic value at 42.19%, suggesting moderate heterogeneity. Lastly, publication bias showed asymmetrical plots and significant values of Kendal's tau and Egger's rank test. Conclusion: ECT augmentation technique was found to be effective in the reduction of psychometric scale scores, and the resulting improvement was significant. Clozapine maintained its stance as the most effective treatment for Treatment-Resistant Schizophrenia, followed by flupenthixol.

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research., 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., (© 2024 The Authors.)

This study presents a comprehensive framework to enhance Wikidata as an open and collaborative knowledge graph by integrating Open Biological and Biomedical Ontologies (OBO) and Medical Subject Headings (MeSH) keywords from PubMed publications. The primary data sources include OBO ontologies and MeSH keywords, which were collected and classified using SPARQL queries for RDF knowledge graphs. The semantic alignment between OBO ontologies and Wikidata was evaluated, revealing significant gaps and distorted representations that necessitate both automated and manual interventions for improvement. We employed pointwise mutual information to extract biomedical relations among the 5000 most common MeSH keywords in PubMed, achieving an accuracy of 89.40 % for superclass-based classification and 75.32 % for relation type-based classification. Additionally, Integrated Gradients were utilized to refine the classification by removing irrelevant MeSH qualifiers, enhancing overall efficiency. The framework also explored the use of MeSH keywords to identify PubMed reviews supporting unsupported Wikidata relations, finding that 45.8 % of these relations were not present in PubMed, indicating potential inconsistencies in Wikidata. The contributions of this study include improved methodologies for enriching Wikidata with biomedical information, validated semantic alignments, and efficient classification processes. This work enhances the interoperability and multilingual capabilities of biomedical ontologies and demonstrates the critical role of MeSH keywords in verifying semantic relations, thereby contributing to the robustness and accuracy of collaborative biomedical knowledge graphs., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Houcemeddine Turki reports financial support was provided by 10.13039/100011049Wikimedia Foundation Inc. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Flyrock represents a significant and fundamental challenge in surface mine blasting, carrying inherent risks to humans and the environment. Consequently, accurate prediction, minimization, and identification of the factors influencing flyrock distance are imperative for effective control and mitigation of its destructive consequences. Machine learning and artificial intelligence methodologies have emerged as viable means to predict and simulate in different scientific fields. This study employs Deep Neural Network in conjunction with three optimization algorithms including the JAYA Algorithm, Multi-Verse Optimization Algorithm, and Gravitational Search Algorithm to predict blasting flyrock distance. The developed model consists of a combination of seven input parameters, encompassing both blasting design parameters and rock geomechanical properties. The output of the Deep Neural Networks model is the flyrock distance. For the training and testing of the model, a dataset comprising of 245 blasting records, collected from Songun copper mine, Iran, was utilized. The DNN model yielded an R2 value of 0.96 and an MSE value of 34.11. These results demonstrate the high accuracy and predictive capability of the model. Furthermore, the application of three optimization algorithms resulted in similar optimized parameter values, which minimized flyrock distances., 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., (© 2024 The Authors.)

Conservation agriculture practices play a pivotal role in augmenting agricultural productivity while concurrently fostering biodiversity, mitigating soil erosion, and enhancing plant nutrition. This study aims to delve into the determinants influencing the adoption behavior of farmers regarding conservation agriculture practices, categorizing them into medium adoption, less adoption, and non-adoption groups. Primary data were meticulously gathered from three distinct regions within the coastal areas of Bangladesh, encompassing a total of 450 respondents. Descriptive statistics were employed to delineate the socio-economic status of farmers, while multinomial logistic regression was utilized to identify factors affecting conservation agriculture adoption. This study also employed to find out the severity of different climate problem faced by the farmers. Analysis of organizational communication patterns revealed that medium adopters maintained a more substantial connection with diverse organizations compared to other groups. Descriptive statistics elucidated that both medium and less adopters of conservation agriculture had undergone more training, possessed higher farm incomes, and exhibited greater levels of education compared to non-adopters. Further exploration through factor analysis unveiled that increased training significantly influenced farmers' decisions to embrace conservation agriculture practices, concurrently enhancing their knowledge of agricultural methodologies. The study identified positive relationships between crop income, cultivable land, different channel communication exposure, climate change perception and training duration with the adoption of conservation agriculture. Finally, the results underscore the importance of organizational monitoring systems and frequent farmer engagement, alongside an augmentation in training initiatives. These measures are posited to yield more favorable outcomes in promoting conservation agriculture practices among farmers. Alongside, Conservation agriculture play a key role for achieving sustainable land use and climate action., 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., (© 2024 The Authors.)

Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties., 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., (© 2024 The Authors.)

The co-doping of vanadium pentoxide (V 2 O 5 ) with rare-earth (RE) elements, namely 1.5 % holmium (Ho) and 1.5 % ytterbium (Yb) has been conducted using an eco-friendly, straightforward hydrothermal approach to assess the combined effects on structural, optical, and photocatalytic properties. The application of the density functional theory (DFT) approach effectively examined the impact of RE ions on the photocatalytic efficiency of co-doped V 2 O 5 . The stable orthorhombic crystal structure of co-doped V 2 O 5 has been confirmed using DFT and X-ray diffraction without a secondary phase. It appears that homogeneous nucleation occurs while heterogeneous nucleation slows down in co-doped samples, as evidenced by the larger crystallite sizes in co-doped samples compared to doped ones. It means a result, the co-doped samples exhibit photodegrades more quickly and have a higher rate constant than the doped samples. This is because they have less dislocation density (4.26 × 10 -3  nm -2 ) and internal micro-strain (4.93 × 10 -3 ). The bandgap and degradation efficiency are determined by the UV-vis spectroscopy and found to be 2.33 eV and 95 %, respectively, at the optimal pH of 7 in the visible range. The co-doped sample has a rate constant of 24 × 10 -3 min -1 , which is the highest in the RE-doped V 2 O 5 system. This is a good reason to think of co-doped V 2 O 5 as a possible catalyst., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Prof. Dr. Md Mohi Uddin reports writing assistance was provided by Department of Physics, University of Calcutta, West Bengal, India. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

Distributed control is an effective method to coordinate the microgrid with various components, and also in a smart microgrid, communication graph layouts are essential since changing the topology unexpectedly could disrupt the operation of the distributed controllers, and also an imbalance may occur between the production and load. Hence, reducing the exchanged data between units and system operator is essential in order to reduce the transmitted data volume and computational burden. For this purpose, an islanded microgrid with multiple agents which is using cloud-fog computing is proposed here, in order to reduce the computing burden on the central control unit as well as reducing data exchange among units. To balance the production power and loads in a smart island with a stable voltage/frequency, a hybrid backstepping sliding mode controller (BSMC) with disturbance observer (DO) is suggested to control voltage/frequency and current in the MG-based master-slave organization. Therefore, this paper proposes a DO-driven BSMC for controlling voltage/frequency, and power of energy sources within a Master-Slave organization; in addition, the study proposes a clod-fog computing for enhancing performance, reducing transferred data volume, and processing information on time. In the extensive simulations, the suggested controller shows a reduction in steady-state error, a fast response, and a lower total harmonic distortion (THD) for nonlinear and linear loads less than 0.33 %. The fog layer serves as a local processing level, so it reduces the exchanged data between cloud and fog nodes., 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., (© 2024 The Authors.)

In this study, a green and environmentally friendly catalyst (MgZnFe 2 O 4 @ZSC-Cu) was synthesized using a simple and clean method. The catalyst was prepared by combining MgZnFe 2 O 4 MNPs with Ziziphus spina-christi extract (ZSC) and immobilizing Cu ions on the resulting material. The catalyst was thoroughly characterized using various techniques including Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), Field emission-scanning electron microscopy (FE-SEM), Energy dispersive X-ray analyzer (EDS), Inductively coupled plasma-optical emission spectrometry (ICP-OES), Thermogravimetric (TGA), and Vibrating sample magnetometry (VSM) analyses. The catalytic activity of MgZnFe 2 O 4 @ZSC-Cu was evaluated in the Hantzsch reaction involving a four-component reaction with ethyl acetoacetate or dimedone/cyclohexane-1,3-dione, various aromatic aldehydes/di-aldehydes, and ammonium acetate. The structure of the newly formed bis compound and polyhydroquinolines was determined using FT-IR, 1 H and 13 C NMR spectroscopy. This catalyst provides numerous benefits, such as simple separation using a magnet, less reaction times, high product yield, solvent-free conditions, straightforward work-up, and the capability to reuse the catalyst for up to five cycles., 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., (© 2024 The Authors. Published by Elsevier Ltd.)

The prevalence of infectious diseases and rapid population expansion has increased the number of medical facilities. Due to the patients' limited mobility, these hospitals are more susceptible to fire disasters. Both Pathfinder and Fire Dynamic simulators were used to calculate the required safe evacuation time (RSET), heat release rate, visibility, temperature, CO, and oxygen effects on temperature, and available safe evacuation time (ASET). The safety egress of the medical hospital was then evaluated by comparing the available safe evacuation time (ASET) and required safe evacuation time (RSET). The simulation findings showed that the egress guides depend on delay time when delay time increases the egress guides drop. We also studied the importance of egress route decisions and suggested that at least 20 to 30 egress guides be on duty in the medical facility. The safety criteria for the medical facility have been proposed based on the delay time with the normalized egress guides. The high oxygen concentration in a hospital can put the medical staff and patients in danger and limit the required safe egress time to less than 150s. The proposed measures can be used to assess the evacuation safety of a typical medical hospital in use relatively quickly and efficiently., 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., (© 2024 The Authors. Published by Elsevier Ltd.)

Background: Environmental crises, on the one hand, and the impact of environmental protection education from childhood, on the other hand, have focused on the need for environmental education in early life, which tries to teach the concept that humans can live in harmony with nature. Therefore, this study aims to identify environmental education strategies for children with an emphasis on children under four years old in Iran., Methods: The study was conducted using a qualitative method and through interviews with 14 experts and practitioners with ability and knowledge in environmental education for children. The text of the interviews was analyzed using the content analysis technique and MAXQDA-2018 software., Results: Based on the study results, 216 codes were presented in the form of 15 subcategories and five main categories. The results showed that child empowerment, indirect education of the child, production of relevant content and reviewing existing content, environment-oriented socialization, and creating appropriate infrastructure are basic strategies for teaching the environment to children., Conclusion: it is necessary to revise policies, interventions, and educational programs. For education to be effective for children, it is better to provide suitable conditions in both the individual and structural dimensions and the education should be done indirectly. Additionally, promoting a positive attitude towards the environment through the design and implementation of community programs can be beneficial., 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., (© 2024 The Authors.)

Protein solubility prediction is useful for the careful selection of highly effective candidate proteins for drug development. In recombinant proteins synthesis, solubility prediction is valuable for optimizing key protein characteristics, including stability, functionality, and ease of purification. It contains valuable information about potential biomarkers or therapeutic targets and helps in early forecasting of neurodegenerative diseases, cancer, and cardiovascular disorders. Traditional wet-lab experimental protein solubility prediction approaches are error-prone, time-consuming, and costly. Researchers harnessed the competence of Artificial Intelligence approaches for replacing experimental approaches with computational predictors. These predictors inferred the solubility of proteins by analyzing amino acids distributions in raw protein sequences. There is still a lot of room for the development of robust computational predictors because existing predictors remain fail in extracting comprehensive discriminative distribution of amino acids. To more precisely discriminate soluble proteins from insoluble proteins, this paper presents ProSol-Multi predictor that makes use of a novel MLCDE encoder and Random Forest classifier. MLCDE encoder transforms protein sequences into informative statistical vectors by capturing amino acids multi-level correlation and discriminative distribution within raw protein sequences. The performance of proposed encoder is evaluated against 56 existing protein sequence encoding methods on a widely used protein solubility prediction benchmark dataset under two different experimental settings namely intrinsic and extrinsic. Intrinsic evaluation reveals that from all sequence encoders, proposed MLCDE encoder manages to generate non-overlapping clusters of soluble and insoluble classes. In extrinsic evaluation, 10 machine learning classifiers achieve better performance with proposed MLCDE encoder as compared to 56 existing protein sequence encoders. Moreover, across 4 public benchmark datasets, proposed ProSol-Multi predictor outshines 20 existing predictors by an average accuracy of 3%, MCC and AU-ROC of 2%. ProSol-Multi interactive web application is available at https://sds_genetic_analysis.opendfki.de/ProSol-Multi., 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., (© 2024 The Author(s).)

The species of the subfamily Polygonoideae is an essential component of temperate forests as well as the flora of the western Himalayan region. The aim of this research was to explore the taxonomic diversity, distribution patterns, and associated flora of Polygonoideae in relation to edaphic factors in various ecological zones in the Muzaffarabad division of the Kashmir Western Himalayan Region. We applied a random sampling approach for data collection from 10 different sites with a cumulative 780 quadrats to record the diversity of wild Polygonoideae species across the Muzaffarabad division between 2021 and 2022. This study revealed 279 plant species from 192 genera and 75 families associated with Polygonoideae, with the dominant families being Asteraceae, Poaceae, Lamiaceae, and Rosaceae. Herbs were predominant in the investigated area, with a proportion of 72.40 %, followed by shrubs (9.68 %) and pteridophytes (8.24 %). The flora was dominated by therophytes (37.35 %), whereas nanophylls (37.28 %) were the most dominant leaf form. Persicaria , Rumex , and Polygonum genera were observed and collected from various ecological zones, while Bistorta , Fagopyrum , Oxyria , and Rheum were only collected from a single zone, representing a restricted niche. A total of 28 taxa from 8 genera were studied in the investigative subfamily Polygonoideae, with the majority being therophytes (57.14 %), followed by hemicryptophytes (28.57 %), and leaf form dominated by microphylls (50 %) and nanophylls (17.85 %). The average values for Shannon and Simpson's diversity for the reported plant communities were 0.96 and 3.53, respectively, whereas species richness averaged 2.43 and species evenness 0.92. The vegetation exhibited a relatively lower (<50) maturity, averaging 32.08. Deforestation, overexploitation for medicinal purposes, soil erosion, overgrazing, forest fires, and the expansion of agricultural fields were identified as major threats to floral diversity. A significant correlation was observed between elevation and soil nutrient parameters, where moisture content, SOC, SOM, TK, and TN ratios showed a positive correlation, while pH and TP showed a negative correlation. Polygonum paronychioides and Rumex alpinus were the least observed of Polygonoideae taxa, whereas 39 species were found to be threatened, having low (<0.2) IVI values and seeking immediate conservation efforts. Climate change and anthropogenic pressure may lead to a change in the composition patterns and threaten the Polygonoidae species. We suggest community-based initiatives and sustainable conservation measures to safeguard the floral wealth of the Western Himalaya., 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., (© 2024 The Authors.)

One of the multifactorial worldwide health syndromes is diabetes mellitus which is increasing at a disturbing rate. The inhibition of α-glucosidase, an enzyme that catalyzes starch hydrolysis in the intestine, is one helpful therapeutic approach for controlling hyperglycemia related to type-2 diabetes. To discover α-glucosidase inhibitors, some 2-hydrazolyl-4-thiazolidinone hybrids ( 3a-e ) were synthesized from new one-pot reaction procedures. Next, their chemical structures were confirmed by 1 H NMR, 13 C NMR, and FT-IR spectra, and elemental analysis technique. Then, the α-glucosidase inhibitory activity of the titled compounds was evaluated. Among them, derivatives 3b and 3c revealed the highest activity against α-glucosidase compared to acarbose as a drug. Enzyme kinetic studies of the most active derivative ( 3b ) indicated a competitive inhibition. Finally, molecular modeling studies were accomplished to describe vital interactions of the most potent compounds ( 3b and 3c ) with the α-glucosidase enzyme., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Saghi Sepehri reports financial support was provided by 10.13039/501100006662Ardabil University of Medical Sciences. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: Trauma is commonly understood as a psychological and emotional response to distressing events. The subjective nature of trauma experiences has led to ongoing debates about the best theoretical frameworks for understanding and addressing trauma. This review aims to comprehensively critique traditional biomedical and psychological models and advocates for more inclusive and culturally sensitive frameworks., Methods: A narrative literature review was conducted, synthesizing data from 96 peer-reviewed journal articles, books, and authoritative reports from databases such as PubMed, PsycINFO, and Google Scholar. The review focused on studies related to trauma, post-traumatic stress disorder (PTSD), complex trauma, and related disorders, emphasizing both individual and socio-cultural aspects., Results: The review identifies several key criticisms of trauma models. For biological models, critiques include the oversimplification of trauma experiences, insufficient developmental considerations, failure to capture diverse trauma responses, limited cultural sensitivity, and inadequacy of the categorical approach. For psychological models, criticisms encompass an overemphasis on internal processes, neglect of developmental impacts, limited focus on symptom diversity, challenges in addressing socio-cultural contexts, and incomplete integration of emotional and relational aspects. These findings underscore the need for more comprehensive, culturally sensitive, and developmentally informed trauma frameworks., Conclusion: The findings emphasize the importance of adopting a holistic perspective in trauma research and treatment. By integrating individual, interpersonal, and socio-cultural dimensions, future research and interventions can better support trauma survivors. This approach necessitates ongoing interdisciplinary collaboration and the inclusion of diverse voices, including those of trauma survivors, to refine current methodologies and enhance therapeutic outcomes., 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., (© 2024 The Authors. Published by Elsevier Ltd.)

The worldwide health crisis triggered by the novel coronavirus (COVID-19) epidemic has resulted in an extensive variety of symptoms in people who have been infected, the most prevalent disorders of which are loss of smell and taste senses. In some patients, these disorders might occasionally last for several months and can strongly affect patients' quality of life. The COVID-19-related loss of taste and smell does not presently have a particular therapy. However, with the help of an early prediction of these disorders, healthcare providers can direct the patients to control these symptoms and prevent complications by following special procedures. The purpose of this research is to develop a machine learning (ML) model that can predict the occurrence and persistence of post-COVID-19-related loss of smell and taste abnormalities. In this study, we used our dataset to describe the symptoms, functioning, and disability of 413 verified COVID-19 patients. In order to prepare accurate classification models, we combined several ML algorithms, including logistic regression, k-nearest neighbors, support vector machine, random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The accuracy of the loss of taste model was 91.5 % with an area-under-cure (AUC) of 0.94, and the accuracy of the loss of smell model was 95 % with an AUC of 0.97. Our proposed modelling framework can be utilized by hospitals experts to assess these post-COVID-19 disorders in the early stages, which supports the development of treatment strategies., 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., (© 2024 The Author(s).)

Drug hypersensitivities are common reactions due to immunologic responses. They are of utmost importance because they may generate severe and fatal outcomes. Some drugs may cause Adverse Drug Reactions (ADRs), such as drug hypersensitivity reactions (DHRs), which can occur due to the interaction of intact drugs or their metabolites with Human Leukocyte Antigens (HLAs) and T cell receptors (TCRs). This type develops over a period of 24-72 h after exposure and is classified as type IV of DHRs. Acute generalized exanthematic pustulosis (AGEP), Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) and drug reaction with eosinophilia and systemic symptoms (DRESS) are types of Severe Cutaneous Adverse Reactions (SCARs). In this review, we aim to discuss the types of ADRs, the mechanisms involved in their development, and the role of immunogenetic factors, such as HLAs in type IV DHRs, single-nucleotide polymorphisms (SNPs), and some epigenetic modifications, e.g., DNA/histone methylation in a variety of genes and their promoters which may predispose subjects to DHRs. In conclusion, development of promising novel in vitro or in vivo diagnostic and prognostic markers is essential for identifying susceptible subjects or providing treatment protocols to work up patients with drug allergies as personalized medicine., 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., (© 2024 Published by Elsevier Ltd.)

In this study, we investigated buoyancy-induced convection in a permeable square hollow containing four embedded cylinders and subjected to a magnetic field using numerical methods. The finite element approach was used to solve the governing equations of the system as well as the initial and boundary conditions. We analyzed the effects of the emerging non-dimensional quantities on the flow pattern and thermal field, as well as entropy production, in relation to the thermophysical properties of the obstacles. In the limiting case, we compared our results with already published work and found outstanding concurrence. Our simulations revealed that increasing cylinder spacing leads to higher thermal entropy generation, while fluid friction irreversibility has the opposite effect. Additionally, the imposed magnetic field significantly suppressed temperature distribution and flow field, resulting in low thermal transmission within the cavity., 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., (© 2024 The Authors.)

Modern machining requires reduction in energy usage, surface roughness, and burr width to produce finished or near-finished parts. To ensure high surface quality in machining processes, it is crucial to minimize surface finish and minimize burr width, which are considered as significant parameters as specific cutting energy. The objective of this study was to identify the optimal machining parameters for milling in order to minimize surface roughness, burr width, and specific cutting energy. To achieve this, the research investigated the impact of feed per tooth, cutting speed, depth of cut, and number of inserts on the responses across three intervals using Taguchi L9 array. Observing the responses by varying these parameters, underlined the need for multi objective optimisation. Machining conditions of 0.14 mm/tooth f z , 350 m/min V c and 2 mm ap using 1 cutting insert (exp no 9) was identified as the best machining run using grey relational analysis owing to its highest grey relational grade of 0.936. ANOVA examination identified cutting speed as the leading factor impacting the grey relational grade with 31.07 % contribution ratio, with the number of inserts, depth of cut, and feed per tooth also making notable contributions. Conclusively, machining parameters identified through response surface optimisation resulted in 21.69 % improvement in surface finish, 11.39 % reduction in specific energy consumption, and 6.2 % decrease in burr width on the down milling side albeit with an increase of 9 % in burr width on the up-milling side., 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., (© 2024 The Authors.)

Integrating ceramic and metallic properties in MAX phases makes them highly desirable for diverse technological applications. In this study, through first-principles density functional theory (DFT), we investigated the physical properties of two new 312 MAX compounds, M 3 GaB 2 (M = Ti, Hf). Chemical stability is confirmed via formation energy assessment, while mechanical stability is established by determining elastic stiffness constants. A thorough analysis of mechanical behaviors includes bulk modulus, shear modulus, Young's modulus, and hardness parameters. M 3 GaB 2 demonstrates elastic constants and moduli closely aligned with other 312 carbides. Understanding the electronic band structure and density of states (DOS) sheds light on metallic properties, with anisotropy in electrical conductivity clarified through energy dispersion analysis. Investigation of photon interaction with titled compounds, including dielectric constants (real and imaginary parts), refractive index, absorption coefficient, photoconductivity, reflectivity, and energy loss function, has been carried out. The potential of M 3 GaB 2 borides as a coating to reduce solar is evaluated based on the reflectivity spectra. These findings deepen our understanding of material properties and suggest diverse applications for M 3 GaB 2 in various technological domains., 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., (© 2024 The Authors.)

Background: The rising prevalence of familial multiple sclerosis (MS) in Iran has spurred interest in the potential impact of parental consanguinity on the risk of developing the disease. This study aims to aggregate current knowledge on parental consanguinity and its possible effect on MS risk, particularly among familial MS patients from various regions and ethnicities in Iran. The objective is to enhance the understanding of MS genetics and encourage further research in this field., Materials and Methods: A cross-sectional study was conducted on clinically definite familial MS (FMS) patients registered in the nationwide MS registry of Iran (NMSRI). Data were extracted and supplemented with structured telephone follow-ups to gather detailed histories of MS in relatives and the familial relationships of the patients' parents. A family penetration score was proposed. Descriptive statistics and inferential statistical tests were used to analyze the data at a significance level of 0.05, adhering to ethical guidelines., Results: Out of 19,911 individuals registered in the NMSRI, 2307 FMS patients across 13 provinces were included in the final analysis. Among these, 385 (19.3 %) reported parental consanguinity, with 283 (14.2 %) having parents who were cousins and 102 (5.1 %) having parents who were distant relatives. The data showed no significant association between parental kinship and variables such as MS phenotype, number of affected relatives with MS, hospitalization rates, and expanded disability status scale score. Similarly, MS severity did not differ based on parental consanguinity ( P -value >0.05). While the rate of consanguineous marriage was higher among patients with an onset age less than 18 years, there was no statistically significant difference in disease onset age based on parental consanguinity status., Conclusion: Our study highlights the complexity of factors influencing MS development, including genetic and environmental components. These results highlight the need for further research to achieve a more comprehensive understanding of MS etiology., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Sharareh Eskandarieh reports financial support was provided by 10.13039/501100004484Tehran University of Medical Sciences. Sharareh Eskandarieh reports a relationship with 10.13039/501100004484Tehran University of Medical Sciences that includes: consulting or advisory, employment, equity or stocks, and funding grants. Sharareh Eskandarieh has patent licensed to NMSRI. None If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Support for the Reported Work: Tehran University of Medical Sciences. Relevant support: advisory positions, consulting fees. Intellectual Property: National multiple sclerosis registry system of Iran (NMSRI). Other Activities: None. The entire data of the study has been sent to the journal in Excel file format. If needed, it will be provided by contacting the website https://nmsri.ir/or by email., (© 2024 The Authors.)

A comprehensive study of fruits and leaves extracts of Citrus medica var. sarcodactylis Swingle and Limonia acidissima L. family Rutaceae was accomplished to investigate their antiviral activity along with their zinc oxide nanoparticles formulation (ZnONPs) against the avian influenza H5N1 virus. A thorough comparative phytochemical investigation of C. medica and L.acidissima leaves and fruits was performed using UPLC-QTOF-MS-MS. Antiviral effects further aided by molecular docking proved the highly significant potential of using C. medica and L.acidissima extracts as medicinal agents. Antiviral potency is ascendingly arranged as L. acidissima leaves (LAL) >  L. acidissima fruits (LAF) >  C. medica leaves (CML) at 160 μg. Nano formulation of LAF has the most splendid antiviral upshot. The metabolomic profiling of CMF and LAL revealed the detection of 48 & 74 chromatographic peaks respectively. Docking simulation against five essential proteins in survival and replication of the influenza virus revealed that flavonoid di-glycosides (hesperidin, kaempferol-3- O -rutinoside, and kaempferol-7-neohesperidoside) have shown great affinity toward the five investigated proteins and achieved docking scores which approached or even exceeded that achieved by the native ligands. Hesperidin has demonstrated the best binding affinity toward neuraminidase (NA), haemagglutinin (HA), and polymerase protein PB2 (-10.675, -8.131, and -10.046 kcal/mol respectively. We propose using prepared crude methanol extracts of both plants as an antiviral agent., 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., (© 2024 Published by Elsevier Ltd.)

The development of pragmatic competence is crucial for enhancing learners' ability to communicate more effectively in English in various cultural and social contexts. Numerous studies have explored the impact of implicit and/or explicit training on language learners' pragmatic development. However, the scarcity of online pragmatic training has motivated this research. To this end, a randomized experimental study was conducted to determine whether online pragmatic treatment could enhance students' pragmatic competence and to identify which procedure, explicit or implicit, is more effective. Sixty university students were randomly assigned to three groups of 20: two experimental groups (implicit and explicit) and one control group. 'Google Meet' was used for online training. All three groups were given a Discourse Completion Task (DCT) as a pre-test featuring 12 different scenarios, three for each of four speech acts. At the end of the treatment, the participants of the experimental and control groups received the same DCT as a post-test. The findings revealed that the participants of the experimental groups improved their pragmatic competence compared to the pre-test and post-test results of the control group. Although there is a difference between the implicit and explicit groups' post-test results, the difference is not statistically significant. Still, according to Cohen's d effect size, the effect size of the explicit group (1.5) is larger than that of the implicit group (1.1). According to the results of the study, it can be said that pragmatic training contributes to the effective use of the target language for communicative purposes., 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., (© 2024 The Authors. Published by Elsevier Ltd.)

Endophytic bacteria, living inside plants, are competent plant colonizers, capable of enhancing immune responses in plants and establishing a symbiotic relationship with them. Endophytic bacteria are able to control phytopathogenic fungi while exhibiting plant growth-promoting activity. Here, we discussed the mechanisms of phytopathogenic fungi control and plant growth-promoting actions discovered in some major groups of beneficial endophytic bacteria such as Bacillus , Paenibacillus, and Pseudomonas . Most of the studied strains in these genera were isolated from the rhizosphere and soils, and a more extensive study of these endophytic bacteria is needed. It is essential to understand the underlying biocontrol and plant growth-promoting mechanisms and to develop an effective screening approach for selecting potential endophytic bacteria for various applications. We have suggested a screening strategy to identify potentially useful endophytic bacteria based on mechanistic phenomena. The discovery of endophytic bacteria with useful biocontrol and plant growth-promoting characteristics is essential for developing sustainable agriculture., 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., (© 2024 The Authors. Published by Elsevier Ltd.)

The research aimed to enhance the nutritional value of gummy candies by incorporating pistachio green hull extract (PGHE), stevia, and starch into the formulations. The gummy candies formulations were optimized using PGHE (1-5 %), stevia (0.013-0.040 %) and gelatin-to-starch ratio (9:1, 2:8, and 3:7) by response surface methodology (RSM), central composite design (CCD), with six center points. The physicochemical and textural properties of the gummy candies were assessed. Three optimal formulations were determined, which were preferred by the majority of panelists. One of them was selected for testing total phenolic content (680.31 ± 0.6 mg GAE/100g gummy candy), antioxidant activity (IC 50  = 277 μg/mL), FTIR analysis, morphology examination, and storage stability. This study resulted in the development of gummy candies that not only offer a reduced-sugar product (50 %; equal to 12 % of sucrose) with high antioxidant activity but also eliminate the need for artificial flavors and synthetic colorants in the formulation., 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., (© 2024 The Authors.)

In Bangladesh, sweet potato holds the fourth position as a crucial carbohydrate source, trailing rice, wheat, and potato. However, locally grown sweet potato varieties often display limited stability and yield. To tackle this challenge, diverse selection methods and statistical models were utilized to pinpoint sweet potato genotypes showcasing both stability and superior yield and quality traits. In the initial two years, multiple selection methods were employed to narrow down the collections based on preferences for yield and its contributing traits. Subsequently, a multi-environment trial (MET) was conducted in the following year to pinpoint superior and stable genotypes with desirable yield and quality characteristics. An integrated approach involving the Multi-Trait Genotype Ideotype Distance Index (MGIDI), Factor Analysis and Ideotype-Design (FAI-BLUP), and Smith-Hazel Index (SH) led to the identification of 71 superior sweet potato genotypes out of a total of 351 in the initial growing season. In the subsequent season, the MGIDI selection index was applied to the 71 genotypes, resulting in the selection of 11 top-performing genotypes. This selection process was complemented by a detailed analysis of the strengths and weaknesses of the selected genotypes. In the MET, the mixed effect model, specifically the linear mixed model (LMM), identified significant genotypic and genotype-environment interaction (GEI) variances. This points to elevated heritability and selection accuracy, ultimately boosting the model's reliability. By combining the strengths of LMM and additive main effects and multiplicative interaction (AMMI), the best linear unbiased prediction (BLUP) index identified H20 as the top-performing genotype for marketable root yield (MRY), H37 for dry weight of root (DW), H8 for beta carotene (BC) and H41 for vitamin c (VC). These genotypes surpassed the overall average in the WAAS index. For simultaneous stability and high performance, the WAASBY index selected H37 for MRY, H6 for DW, H61 for BC, and H3 for VC. Finally, genotypes H3 and H20 were selected using multi-trait stability index (MTSI), as they possessed high performance and stability. Based on the selection sense, the objective has been achieved with regards to the trait MRW, which serves as a major criterion for a superior variety of sweet potato., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Zakaria Alam reports financial support, administrative support, and equipment, drugs, or supplies were provided by 10.13039/501100005867Bangladesh Agricultural Research Institute, Bangladesh. The International Potato Centre (CIP) in Lima, Peru, arranged the article processing charge (APC) for this manuscript., (© 2024 The Author(s).)

Water pollution represents one of the most important problems affecting the health of living organisms, so it was necessary to work on the formation of active materials to get rid of pollutants. In this study, Titanium dioxide (TiO 2 ) doping Zinc oxide (ZnO) nanocomposites were produced via simple sonication method at 500 Hz in ethanol medium. At different weight concentrations (2.5, 5, 7.5, and 10 %). The morphology, structure configuration, chemical bonding, crystalline phase, and surface properties of obtained nanocomposites were characterized via FESEM, BET, XRD, XPS, RAMAN and FTIR instrumentation. The nanocomposites were employed as an adsorbent to eliminate the methyl orange (MO) and Indigo Carmine (IC) dyes from an aqueous solution. Batch removal experiments revealed that the elimination of MO and IC dyes by the TiZnO surface was pH and doping Ti concentration-dependent, with maximum removal occurring at pH = 7 for MO and pH = 3 for IC contaminants at 10 % doping Ti concentration (Ti (10 %)@ZnO). Langmuir model fit the absorptive removal of MO and IC dyes into the Ti (10 %)@ZnO surface well. The maximal removal capacity of Ti (10 %)@ZnO nanocomposite was found to be 994.24 mg. g -1 for MO and 305.39 mg. g -1 for IC. The Ti (10 %)@ZnO nanocomposite showed remarkable high stability towards the removal of both dyes through consecutive four cycles., 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., (© 2024 The Authors.)

Purpose: To determine the accuracy of Tc-99 m sestamibi for the diagnosis of acute cholecystitis during a supply chain disruption of mebrofenin., Material and Methods: During a national shortage of Tc-99 m mebrofenin in 2019, our institution initiated sestamibi imaging for suspected cases of acute cholecystitis using a standard hepatobiliary imaging protocol. Forty-one patients underwent hepatobiliary imaging with sestamibi, 39 to assess for acute cholecystitis. The examinations were initially interpreted by one nuclear medicine physician and subsequently overread by 5 blinded nuclear medicine physicians (8-30 years' experience). SPECT/CT was obtained for 8 of these patients at the discretion of the primary interpreter. An additional 23 asymptomatic patients (6 with prior cholecystectomy) underwent abdominal scintigraphy as a negative control to determine the normal time to sestamibi accumulation in the gallbladder. A composite reference standard was used (chart review by 3 physicians). Sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated with and without SPECT/CT (mean ± 95%CI)., Results: Of 39 symptomatic patients, 17/39 had acute cholecystitis and 22 did not. The sensitivity, specificity, PPV and NPV for acute cholecystitis at planar imaging were 97.6 ± 4.6, 62.7 ± 5.2, 67.0 ± 3.6, and 97.3 ± 5.2 % (N = 39). The values changed to 95.7 ± 4.7, 77.9 ± 4.7, 72.1 ± 4.1, and 97.0 ± 3.3 % when control patients were included (N = 62). With SPECT/CT, these mildly improved to 98.8 ± 2.3 %, 69.1 ± 4.4 %, 71.3 ± 3.2 %, and 98.7 ± 2.6 % (N = 39), but not significantly different. On average, sestamibi activity was detected in the gallbladder in negative controls within 1 h., Conclusion: Tc-99 m sestamibi has excellent sensitivity and NPV for diagnosing acute cholecystitis and can serve as an alternative when mebrofenin is unavailable for evaluating cystic duct obstruction during shortages of standard agents., 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 paperThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Benjamin Viglianti reports administrative support and article publishing charges were provided by University of Michigan. Benjamin Viglianti reports a relationship with University of Michigan that includes:. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: Vaccination against Human papillomavirus (HPV) is recommended to avoid HPV infections and its associated diseases, including cervical cancer. However, there is no awareness study among Bangladeshi population. Hence, this nationwide study was conducted to explore HPV vaccine awareness and its determinants among parents of eligible adolescent girls., Methods: This study was conducted among the parents of daughters aged 9-15 years from 42 out of 64 randomly selected districts of Bangladesh between June 28 to August 2, 2023. A multistage sampling method was used to enroll 2151 study participants from all eight divisions of Bangladesh. A semi-structured questionnaire was used for face-to-face interviews in this study. The statistical software Stata (Version 17) was used for statistical analyses., Results: The average age of the participants was 38.18 (±5.86) years. Only 22.32 % of the participants were aware of the HPV vaccine. Every additional year of age increased the likelihood of being aware of the HPV vaccine by 3 % (AOR: 1.03; 95%CI: 1.00-1.06). Participants residing in the urban area had 3.56 times higher odds of awareness than rural and semi-urban people. Businessmen and housewives had 60 % (AOR: 0.40; 95 % CI: 0.22-0.69) and 77 % (AOR: 0.23; 95 % CI: 0.16-0.33) lower odds in comparison to job holders. The lower-income group exhibited significantly higher odds of awareness (AOR: 0.25, 95%CI: 0.16-0.39) compared to the middle and the higher-income group. Participants who never went through routine health check-ups had 77 % lower odds of being aware than those who availed of regular routine check-ups (AOR: 0.23; 95%CI: 0.16-0.34)., Conclusion: Awareness of the HPV vaccine among the general population of Bangladesh is very low. Age, residence, occupation, monthly income, and routine medical check-ups were associated with HPV vaccine awareness. A nationwide awareness campaign would increase this awareness level among the Bangladeshi population, especially among the parents of daughters., 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., (© 2024 The Authors.)

This research paper details the creation of innovative nanocomposites using the sol-gel technique, incorporating polyoxometalates SiW 9 Ba 3 to stabilize ceramic particles of strontium ferrite (SrFe 12 O 19 ) polymer and Chitosan (CS). The identification and confirmation of the nanocomposites obtained at each stage were carried out through the use of FT-IR, EDX, XRD, and FESEM analyses. To evaluate their ability to remove organic dyes, we analyzed the catalytic activity of these nanocomposites during photocatalytic detoxification procedures. With its exceptional photocatalytic properties, the nanocomposite (SiW 9 Ba 3 @SrFe 12 O 19 @Cs) was able to remove estamipride poison at an impressive rate of 85 % and xylene dye solution at an even higher rate of 98 %. In addition, an extensive examination was undertaken to explore the primary variables that influence process efficiency. The study suggests that ceramic nanocomposites incorporating heteropolyoxometalate may offer a viable approach to effectively eradicate pollutants from the environment., 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., (© 2024 The Authors.)