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This study investigated the adsorption of methyl orange (MO) from aqueous solutions using a novel MnFe₂O₄/GO nanocomposite coated with cetyltrimethylammonium bromide (CTAB). The dual-layer surfactant modification facilitates both electrostatic and lipophilic interactions. This enhancement significantly improves dye removal efficiency. The adsorption process was monitored using spectrophotometry at 464 nm, with various characterization techniques confirming the structural and magnetic properties of the nanocomposite. The optimized parameters for maximum adsorption include a 2-minute ultrasonic dispersion, pH 6.8, and a surfactant-to-adsorbent ratio of 1, achieving a maximum adsorption capacity of 285.7 mg/g. The kinetic data followed a pseudo-second-order model, whereas the adsorption isotherm aligned with the Freundlich model, indicating multilayer adsorption. Thermodynamic analysis revealed the spontaneous and exothermic nature of the process. Additionally, the magnetic properties of the nanocomposite enabled efficient separation and reusability over three cycles without significant loss of performance. This study demonstrates the potential of MnFe₂O₄/GO coated with CTAB for rapid, efficient, and reusable removal of anionic dyes from wastewater through a combined mechanism of electrostatic and hydrophobic interactions.

In this work, norepinephrine (NE) was determined by an electrochemical sensor represented by a carbon paste electrode boosted using nitrogen-doped porous carbon (NDPC) derived from Spirulina Platensis microalga anchored CoFe 2 O 4 @NiO and 1-Ethyl-3-methylimidazolium acetate (EMIM Ac) ionic liquid. The morphological characteristics of the catalyst were recorded by field emission scanning electron microscope (FE-SEM) images. Moreover, the electrochemical behavior of norepinephrine on the fabricated electrode was checked using various voltammetric methods. All tests were done at pH 7.0 as the optimized condition in phosphate buffer solution. The results from linear sweep voltammetry revealed that the electro-oxidation of norepinephrine was diffusion, and the diffusion coefficient value was obtained by chronoamperometry (D⁓6.195 × 10 –4 ). The linear concentration of the modified electrode was obtained from 10 to 500 μM with a limit of detection of 2.26 μM using the square wave voltammetry (SWV) method. The sensor selectivity was investigated using various species, and the results from stability and reproducibility tests showed acceptable values. The sensor's efficiency was tested in urine and pharmaceutical as real samples with recovery percentages between 97.1% and 102.82%.

In this report, Azithromycin (Azi) antibiotic was measured by carbon paste electrode (CPE) improved by graphene nanoribbon–CoFe2O4@NiO nanocomposite and 1-hexyl-3 methylimidazolium hexafluorophosphate (HMIM PF6) as an ionic liquid binder. The electrochemical behavior of Azi on the graphene nanoribbon–CoFe2O4@NiO/HMIM PF6/CPE is investigated by voltammetric methods, and the results showed that the modifiers improve the conductivity and electrochemical activity of the CPE. According to obtained data, the electrochemical behavior of Azi is related to pH. under optimum conditions, the sensor has linear ranges from 10 µM to 2 mM with a LOD of 0.66 µM. The effect of scan rate and chronoamperometry were studied, which showed that the Azi electro-oxidation is diffusion controlled with the diffusion coefficient of 9.22 × 10–6 cm2/s. The reproducibility (3.15%), repeatability (2.5%), selectivity, and stability (for 30 days) tests were investigated, which results were acceptable. The actual sample analysis confirmed that the proposed sensor is an appropriate electrochemical tool for Azi determination in urine and Azi capsule.

Nowadays, atropine has been highlighted because of its anticholinergic effect and contamination in foodstuffs, and therefore, using an accurate and sensitive method for its determination is crucial in human health and food safety. In this study, a novel spectrophotometric method was suggested for the swift quantification of atropine. The proposed method was based on the formation of red ion-pair complexes between the drugs and the cyanidin reagent extracted from red cabbage (RC). In this regard, the effect of pH, time, and temperature was explored and optimized. According to the results, atropine determining was shown the best performance in pH 2 at room temperature in 30 min. In addition, this method revealed linear responses from 10 nM to 1 µM of atropine with limit of detection (LOD) value of 0.0019 µM. Also, the selectivity value of this method was investigated in the presence of some drugs with the same structure and some common species as interferences. The results verified no interference in atropine determination, as well as, the results obtained from repeatability (RSD ⁓ 2.56) of this method were acceptable. Moreover, the applicability of this method was tested in buckwheat and atropine sulfate as food and pharmaceutical real sample, respectively. Real sample analysis was carried out with the standard addition method and the recovery percentages (96.54–104.87%) witnessed the high capability of this method in atropine determination.

Purpose To propose an evidence based diagnostic algorithm using mass characteristics to determine malignancy in patients with adrenal incidentaloma by CTscan. Methods A systematic review in Medline, Scopus, relevant reference books and desk searching was performed up to January 2016 with relevant reference checking. The summery estimates of sensitivity, specificity, positive and negative likelihood ratio of different characteristics were calculated in two groups of the articles investigating the cases without previous malignancy and the articles investigating the oncologic cases. Results Thirty six articles were included in this study. In the first group with no history of malignancy a positive and negative LR of 3.1 and 0.13 in 4 cm threshold and positive and negative LR of 2.85 and 0 in 10HU density were found. In the second group with history of malignancy positive and negative LR of 2.3 and 0.27 in 3 cm threshold and positive and negative LR of 3.6 and 0.08 in 20HU density were resulted. Conclusion The results retrieved in this study considering the limitations show that adrenal incidentaloma with a size less than 4 cm or a mass larger than 4 cm with density less than 10HU in the first group can be managed with imaging follow up. For masses larger than 4 cm with density more than 10HU another diagnostic procedure should be performed. In the second group an adrenal mass larger than 3 cm or less than 3 cm with density more than 20HU should go under operation. But masses smaller than 3 cm with less than 20HU density can be followed by imaging.

Background Eating behavior is an essential aspect of life that can have long-term effects on health outcomes. Nutrition literacy is crucial for better health and well-being. It empowers individuals to make informed decisions about their nutrition and take control of their eating habits. Objectives This study aimed to assess the relationship between nutritional literacy and eating behavior among nursing students at the nursing faculties of Ardabil University of medical sciences. Methods A cross-sectional correlational study was conducted in Ardabil province, northwest Iran. The study collected data through simple random sampling at nursing schools in Ardabil province, with 224 nursing students participating. The study collected data from a demographic information form, the nutritional literacy self-assessment questionnaire for students (NL-SF12), and the adult eating behavior questionnaire (AEBQ). The data were analyzed using SPSS version 14.0 software. Results Based on the results, nutritional literacy explains 44% of the variance in eating behavior and shows significant explanatory power in two sub-scales of eating behavior. The adjusted R 2 values for food approach and food avoidance scales were 0.33 and 0.27, respectively. Conclusion Given the significant relationship between nutritional literacy and eating behaviors among nursing students, nursing faculty managers and health policymakers should develop new public health strategies to increase nutritional literacy among nursing students.

Using cationic and anionic substitution reactions to precipitate different metal oxides to increase the capacitance can be a simple, fast, cheap, and very effective method. In this research, Graphite coin (GC) electrodes based on commercial graphite and Zn-metal powders were used as novel and inexpensive substrates to replace strontium and cobalt metals. This electrode offers several benefits, including reusability, cost effectiveness, reduced energy consumption, high capacitance, and high cyclic stability. The surface morphological investigation (FE-SEM) results showed that Co and Sr nanostructures with special honeycomb and cauliflower-like morphology are deposited on the GC electrode through substitution reactions without energy consumption. Also, the BET, XRD, and EDX analysis results confirmed the successful growth of Co and Sr nanostructure onto porous GC electrodes from substitution reactions. The results of the electrochemical investigation showed that the fabricated Co/Anodize Sr-PGC electrodes have an excellent capacitance of 6721.62 mF cm −2 at 0.5 mA cm −2 in 1M NaOH. Finally, the results of examining the capacitive behavior of the fabricated symmetric solid-state supercapacitor device with Co/Anodize Sr-PGC electrode showed that the constructed supercapacitor device has an excellent specific capacitive capacity of 177.5 mF/cm 2 , power, and energy density of 3035.25 mW/cm 2 and 84.31 mWh/cm 2 at 0.4 mA/cm 2 .

The outcome of the immune response depends on the content and magnitude of inflammatory mediators, the right time to start, and the duration of inflammatory responses. Patients with coronavirus disease 2019 (COVID-19) represent diverse disease severity. Understanding differences in immune responses in individuals with different disease severity levels can help elucidate disease mechanisms. Here, we serially analyzed the cytokine profiles of 809 patients with mild to critical COVID-19. The cytokine profile revealed an overall increase in IL-1β, IL-1Ra, TNF-α, IL-6, IL-2, IL-8, and IL-18 and impaired production of IFN-α and -β. Only an early rise in IL-1Ra, IL-6, and IL-2 levels was linked to worse disease outcomes. On the other hand, long-term rises in IL-1β, IL-1Ra, TNF-α, IL-6, IL-2, IL-8, and IL-18 levels were linked to worse disease outcomes. Principal component analysis identified a component, including IL-1β, TNF-α, IFN-α, and IL-12, that was associated with disease severity. Spearman analysis revealed that the correlation of IL-1β and IFN-α was entirely different between mild and critical patients. Therefore, the ratio of IL-1β to IFN-α seemed to be a suitable criterion for distinguishing critical patients from mild ones. The higher levels of the IL-1β to IFN-α ratio correlated with improved outcomes. These data point to an imbalance of IL-1β/IFNα, contributing to hyperinflammation in COVID-19.

Background: When nurses feel that the learning processes in their organization are fair and just, they are more likely to feel confident about sharing their knowledge, expressing their concerns, and contributing to the learning process. Conversely, suppose employees perceive a lack of organizational justice. In that case, they may be less likely to speak up and share their valuable input due to concerns about unfair treatment or possible negative consequences. Objective: Nurses’ silence and organizational learning may have a connection yet to be thoroughly investigated. We are exploring whether organizational justice mediates this relationship by improving nurses’ perception of it and reducing silence among them. Methods: A study was conducted in Ardabil, Iran, to analyze the correlation between organizational learning, organizational justice, and organizational silence among 319 healthcare professionals from five hospitals. The study utilized three assessment tools: the organizational learning questionnaire, the organizational justice scale, and the organizational silence scale. The collected data were analyzed using IBM SPSS Statistics, and a structural equation model (SEM) was developed using the bootstrap method in AMOS 24.0 to test the proposed model. Results: Our study found a strong positive relationship between organizational learning and organizational justice and a significant negative correlation between organizational learning and silence. Also, there was a significant negative relationship between organizational justice and silence. SEM showed that organizational learning indirectly affects organizational silence through organizational justice as a mediator, explaining 72.3% of all variance in organizational silence. Conclusion: Our findings indicated that organizational learning is positively associated with justice but negatively associated with silence. When nurses experience organizational justice, they are less likely to remain silent. Encouraging nurses to share their opinions and concerns reduces silence and improves working conditions, morale, and patient care. Further research is needed to understand the complex interplay between organizational learning, justice, and silence in nursing settings.

Background Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. Methods Using a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score. Result The study comprised 980 patients in total. The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%. Conclusion ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.