Liquid phase selective oxidation of veratryl alcohol to veratraldehyde using pure and Mg-doped copper chromite catalysts.

Mg-doped copper chromite (CuCr2O4) nanocomposites were synthesised through conventional technique. The pure and doped CuCr2-xMgx O4 (x = 0.00-0.1, 0.2 and 0.3%) nanocomposites were characterized in terms of their morphology, crystal structure, surface area and catalytic performance. The chemical composition of CuCr2-xMgx O4 was confirmed via FT-IR. The formation of pure and doped catalysts was validated by XRD results. TEM/SEM confirmed the formation of CuCr2-xMgxO4 nanoparticles. Mg-doped samples possess a high specific surface area compared to pure CuCr2O4. Thus, the effects of temperature, solvent, time, oxidant and the amount of catalyst on the oxidation of veratryl alcohol were reported. Furthermore, detailed mechanisms of the catalytic oxidation of veratryl alcohol as well as the reusability and stability of the nanomaterial were investigated. The resulting composites were shown to be effective heterogeneous catalysts for the oxidation of veratryl alcohol.

Use of nanotechnology-based nanomaterial as a substitute for antibiotics in monogastric animals.

Nanotechnology has emerged as a promising solution for tackling antibiotic resistance in monogastric animals, providing innovative methods to enhance animal health and well-being. This review explores the novel use of nanotechnology-based nanomaterials as substitutes for antibiotics in monogastric animals. With growing global concerns about antibiotic resistance and the need for sustainable practices in animal husbandry, nanotechnology offers a compelling avenue to address these challenges. The objectives of this review are to find out the potential of nanomaterials in improving animal health while reducing reliance on conventional antibiotics. We examine various forms of nanomaterials and their roles in promoting gut health and also emphasize fresh perspectives brought by integrating nanotechnology into animal healthcare. Additionally, we delve into the mechanisms underlying the antibacterial properties of nanomaterials and their effectiveness in combating microbial resistance. By shedding light on the transformative role of nanotechnology in animal production systems. This review contributes to our understanding of how nanotechnology can provide safer and more sustainable alternatives to antibiotics.

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran).

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to -25.3%, -29.6% and -57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has -204.9 value for AIC which has grown by 5.23% (-194.7) compared to the state without any pre-processing method (ANN_Relu_25).

Development and evaluation of a pH-responsive Mimosa pudica seed mucilage/ß- cyclodextrin-co-poly(methacrylate) hydrogel for controlled drug delivery: In vitro and in vivo assessment.

In this comprehensive investigation, a novel pH-responsive hydrogel system comprising mimosa seed mucilage (MSM), ß-cyclodextrin (ß-CD), and methacrylic acid (MAA) was developed via free radical polymerization technique to promote controlled drug delivery. The hydrogel synthesis involved strategic variations in polymer, monomer, and crosslinker content in fine-tuning its drug-release properties. The resultant hydrogel exhibited remarkable pH sensitivity, selectively liberating the model drug (Capecitabine = CAP) under basic conditions while significantly reducing release in an acidic environment. Morphological, thermal, and structural analyses proved that CAP has a porous texture, high stability, and an amorphous nature. In vitro drug release experiments showcased a sustained and controlled release profile. Optimum release (85.33 %) results were recorded over 24 h at pH 7.4 in the case of MMB9. Pharmacokinetic evaluation in healthy male rabbits confirmed bioavailability enhancement and sustained release capabilities. Furthermore, rigorous toxicity evaluations and histopathological analyses ensured the safety and biocompatibility of the hydrogel. This pH-triggered drug delivery system can be a promising carrier system for drugs involving frequent administrations.

Safeguarding drinking water: A brief insight on characteristics, treatments and risk assessment of contamination.

Water pollution stands as a critical worldwide concern, bearing extensive repercussions that extend to human health and the natural ecosystem. The sources of water pollution can be diverse, arising from natural processes and human activities and the pollutants may range from chemical and biological agents to physical and radiological contaminants. The contamination of water disrupts the natural functioning of the system, leading to both immediate and prolonged health problems. Various technologies and procedures, ranging from conventional to advanced, have been developed to eliminate water impurities, with the choice depending on the type and level of contamination. Assessing risks is a crucial element in guaranteeing the safety of drinking water. Till now, research is continuing the removal of contaminates for the sake of supplying safe drinking water. The study examined physical, inorganic, organic, biological and radiological contaminants in drinking water. It looked at where these contaminants come from, their characteristics, the impact they have and successful methods used in real-world situations to clean the contaminated water. Risk assessment methodologies associated with the use of unsafe drinking water as future directives are also taken into consideration in the present study for the benefit of public concern. The manuscript introduces a comprehensive study on water pollution, focusing on assessing and mitigating risks associated with physical, inorganic, organic, biological and radiological contaminants in drinking water, with a novel emphasis on future directives and sustainable solutions for public safety.

Chitosan-based intelligent polymeric networks for site-specific colon medication delivery: A comprehensive study on controlled release of diloxanide furoate and network formation dynamics.

Oral medications are prone to gastric degradation and enzymatic inactivation, diminishing their efficacy. This study investigates a solution by developing intelligent polymeric networks, incorporating chitosan, methacrylic acid, N, N, methylene bisacrylamide, and montmorillonite clay, to enable the controlled release of Diloxanide Furoate (DF), an anti-protozoal drug. Employing a swelling-assisted diffusion technique, drug loading percentages varied from 63.96 % to 76.82 % among different formulations. Increased chitosan and methacrylic acid content enhanced drug loading, while N, N, methylene bisacrylamide and montmorillonite clay demonstrated an inverse relationship affecting diffusion and swelling. Equilibrium swelling studies unveiled formulation-dependent behaviors, with chitosan reducing swelling and methacrylic acid promoting it. Higher N, N, methylene bisacrylamide concentrations decreased swelling, indicating a denser cross-linked structure, while montmorillonite clay reduced hydrophilicity and swelling capacity. Further analyses confirmed successful gel formation, particularly in formulations with higher chitosan, methacrylic acid, and N, N, methylene bisacrylamide content, while montmorillonite clay limited gel fraction due to restricted polymer chain mobility. Techniques such as Fourier transform infrared spectroscopy, Differential scanning calorimetry, and thermal gravimetric analyses supported network development, enhancing thermal stability and cross-linking density. This research underscores the flexibility of polymeric networks for precise drug delivery, offering potential advancements in targeted therapies for various medical conditions.

A review on fluoride contamination in groundwater and human health implications and its remediation: A sustainable approaches.

Contamination of drinking water due to fluoride (F-) is a major concern worldwide. Although fluoride is an essential trace element required for humans, it has severe human health implications if levels exceed 1.5 mg. L-1 in groundwater. Several treatment technologies have been adopted to remove fluoride and reduce the exposure risk. The present article highlights the source, geochemistry, spatial distribution, and health implications of high fluoride in groundwater. Also, it discusses the underlying mechanisms and controlling factors of fluoride contamination. The problem of fluoride-contaminated water is more severe in India's arid and semiarid regions than in other Asian countries. Treatment technologies like adsorption, ion exchange, precipitation, electrolysis, electrocoagulation, nanofiltration, coagulation-precipitation, and bioremediation have been summarized along with case studies to look for suitable technology for fluoride exposure reduction. Although present technologies are efficient enough to remove fluoride, they have specific limitations regarding cost, labour intensity, and regeneration requirements.

The Impact of Mental Well-Being, Stress, and Coping Strategies on Resilience among Staff Nurses during COVID-19 in Saudi Arabia: A Structural Equational Model.

Previous studies have revealed various factors related to nurses' resilience and predictors of resilience. However, there are no studies analysing the association of three variables-mental well-being, coping strategies, and stress-with resilience. This study aims to assess the impact of mental well-being, levels of stress, and coping strategies on resilience using path analysis. This study used a cross-sectional approach that involved 763 nurses from 16 major hospitals in the eastern and northern regions of Saudi Arabia during the COVID-19 pandemic. The data gathering was conducted from August to November 2022. The staff nurses possessed positive mental wellness (3.75 ± 1.08), moderate stress levels (3.06 ± 1.21), adequate coping skills (3.33 ± 1.23), and a low level of resilience (2.90 ± 1.040). Age had a small effect on resilience (ß = 0.040; p < 0.001) but work experience (ß = -0.019; p > 0.139) and marital status (ß = 0.019; p > 0.072) were not significant. Conversely, mental well-being (ß = 0.043; p < 0.001) and stress (ß = -0.089; p < 0.001) had a small effect on resilience, but coping strategies (ß = 0.561; p < 0.001) had a large effect on resilience. Therefore, coping strategies must be reinforced at all times to assist nurses and other healthcare professionals in identifying contributing elements that maintain these workers' resilience in the face of unforeseen and protracted pandemics and other life events.

The Mediating Role of Spiritual Intelligence on Well-Being and Life Satisfaction among Nurses in the Context of the COVID-19 Pandemic: A Path Analysis.

Introduction: As they are satisfied with life, nurses who demonstrate spiritual intelligence and well-being at work are tremendous assets to an organisation. This study aimed to determine the mediating effect of spiritual intelligence on the well-being and life satisfaction of nurses in the context of COVID-19. Methods: This research employed a cross-sectional study design. It was conducted in the Hail region of Saudi Arabia. The participants were government hospital nurses who were chosen using a multi-stage sampling method. A total of 1121 (75% response rate) nurses participated in the study. Data gathering was conducted from July to September 2022. Results: The life satisfaction level of the participants was deemed positive. Their well-being was better (10/15), and they rated high in spiritual intelligence (90/120). Life satisfaction had a strong association with well-being (r = 0.640, p < 0.001), but a weak association with spiritual intelligence (r = 0.391, p < 0.001). In comparison, well-being had a moderate association with spiritual intelligence (r = 0.551, p < 0.001). The direct effect of well-being on spiritual intelligence was positive and significant (ß = 0.7817, p < 0.0001), and that of spiritual intelligence on life satisfaction was positive and significant (ß = 0.1082, p = 0.0257). The direct effect of well-being on life satisfaction was also positive and significant (ß = 1.5985, p < 0.0001). Conversely, well-being had an indirect impact on life satisfaction (ß = 0.0846), and this effect was significant. Overall, the impact of well-being on life satisfaction was positive and significant (ß = 1.6831, p < 0.0001). Conclusion: The nurses in this study were deemed satisfied, to have better well-being, and better spiritual intelligence. Life satisfaction has a strong association with well-being but a weak one with spiritual intelligence, while well-being has a moderate association with spiritual intelligence. Overall, spiritual intelligence was found to have a mediating effect on the relationship between well-being and life satisfaction. These findings suggest that an increase in spiritual growth can serve as the foundation for people to live better and more integrated lives.

Resilience and Emotional Intelligence of Staff Nurses during the COVID-19 Pandemic.

Although numerous scholars have studied resilience during the COVID-19 pandemic, research exploring its relationship with emotional intelligence is scarce. The aim of this study was to determine the relationship between the resilience and emotional intelligence (EI) of staff nurses during the COVID-19 pandemic. Data for this quantitative correlational study were gathered from the staff nurses of hospitals in the city of Hail, Saudi Arabia. The researchers employed simple random sampling, which yielded 261 staff nurses. Nationality (t = 6.422; p < 0.001) was found to have a significant relationship with resilience. Sex (t = 5.22; p < 0.001), ward assignment (t = 5.22; p < 0.001), age (F = 6.67; p < 0.001), and years of experience (F = 6.67; p < 0.001) revealed significant relationships with emotional intelligence. Resilience had a moderate positive relationship with EI (r = 0.55; p < 0.023), a weak positive relationship with self-emotion (r = 0.21; p < 0.003), and a very strong relationship with emotional appraisal (r = 0.85; p < 0.001). Improving emotional-intelligence skills is critical for assisting nurses during pandemic outbreaks. This can increase their individual and social resilience, while also improving their professional and life outcomes. These research findings suggest that emotional intelligence should be integrated into clinical practice and that EI data should be integrated into decision-making.

Laboratory Biomarkers Associated with Severity and Mortality of COVID-19.

BACKGROUND: Corona Virus Disease 2019 (COVID-19) emerged late 2019 and has become a global pandemic. There is an urgent need for identification of biomarkers to predict severity of the disease for early treatment and to avoid mortality especially in high-risk population. Therefore, the aim of this study is to investigate laboratory results in COVID-19 patients in Saudi Arabia to identify potential biomarkers correlated with disease severity. METHODS: Clinical records of 200 patients diagnosed with COVID-19 from July to August 2020 at Jeddah East Hospital were retrospectively analyzed. Laboratory tests including coagulation parameters, D-dimer, kidney, cardiac, and liver enzymes were statistically investigated in patients admitted to wards and intensive care units (ICU). RESULTS: The majority of patients admitted to ward (156/200) were young (mean 47 years old) compared to those admitted to ICU (mean 60 years old), 14/44 passed away in the ICU. Magnesium was significantly (p < 0.05) elevated in the ICU group while blood urea nitrogen and creatinine level was significantly higher in deceased patients (p < 0.05). Lactate dehydrogenase results were high among all groups, compared to normal range, although its level significantly increased (p > 0.05) in ICU and death groups. CONCLUSIONS: Elevated lactate dehydrogenase, blood urea nitrogen and creatinine levels may increase the risk of ICU admission and death from COVID-19, which can be used as potential biomarkers for disease severity. Using these markers could help physicians choose the optimal therapeutical option and provide patients with better treatment.

Prevalence of anemia and associated risk factors among pregnant women in an urban community at the North of Saudi Arabia.

BACKGROUND: Anemia in pregnancy is common public health problem with poor outcome for both mother and child. This study, aimed to determine the prevalence of anemia and its associated factors among pregnant women in an urban community at the north of Saudi Arabia. MATERIALS AND METHODS: A cross-sectional study was carried out among 390 pregnant attending for antenatal care in one of eight Primary Health Care (PHC) centers in Hail city, Saudi Arabia. RESULTS: Among participants, 133 (34.1%) were anemic (hemoglobin level < 11 g/dl). Out of which, 24.9% were mildly anemic, 9.2% were moderately anemic and none of the participant was found to be severely anemic. The prevalence of anemia increased significantly with low income (p = 0.026), bigger family size (p = 0.020), higher parity (p = 0.023), longer menstrual cycle > 5 days (p = 0.042), bleeding during pregnancy (p = 0.028), infrequent intake of meat (p = 0.020), the habit of drinking tea just after meals (p = 0.019), past history of anemia (p < 0.001), clinical anemia (pallor) (p < 0.001). On the other hand, increased BMI (p = 0.002) and frequent intake of food from restaurants (p = 0.008) were found to be negatively associated with anemia among pregnant women. CONCLUSION: "In urban Hail region, Saudi Arabia, anemia among pregnant women was a moderate public health problem. Low income, bigger family size, higher parity, longer menstrual cycle > 5 days, bleeding during pregnancy, infrequent intake of meat, the habit of drinking tea just after meals, past history of anemia, and the sign of clinical anemia (pallor), were found to be significantly associated with anemia. These findings give insight to healthcare providers about the importance of early detection and management of anemia in early pregnancy. Further research utilizing prospective cohort design to study risk factors of anemia including rural areas, should be considered to support and extend the present study findings".