Search Results (220)

Introduction: Drug–drug interactions (DDI) are a major, preventable cause of patient harm, a challenge amplified in Jordan by rising polypharmacy and documented high rates of medication errors. To date, no study in Jordan has systematically compared hospital and community pharmacists. This study aimed to conduct the first national, comparative assessment of DDI management among these two cadres. Materials and Methods: A national, cross-sectional study was conducted with 380 licensed pharmacists (175 hospitals, 205 community) recruited via proportionate stratified random sampling. A validated online questionnaire assessed demographics, objective DDI knowledge, professional attitudes, practices, and barriers. Multivariable logistic regression was used to identify independent predictors of high knowledge and optimal practice. All collected data were coded, cleaned, and analyzed using the Statistical Package for the Social Sciences (SPSS V28.0). Results: Hospital pharmacists achieved significantly higher mean objective knowledge scores than community pharmacists (10.3 vs. 8.1 out of 15, p < 0.001), a gap particularly wide for interactions involving high-risk OTC medications. The primary barrier for community pharmacists was a lack of access to patient data (85.4%), contrasting with high workload and physician resistance in hospitals. Optimal practice was independently predicted by higher knowledge (AOR = 1.25), a hospital practice setting (AOR = 3.65), and was inhibited by perceived physician resistance (AOR = 0.45). Conclusions: Jordanian hospital and community pharmacists operate in distinct worlds of knowledge and practice. A tailored, dual-pronged national strategy is essential. For hospitals, interventions should target interprofessional dynamics. For community pharmacies, health policy reform to provide access to integrated patient data is the most urgent priority. These findings highlight a globally relevant challenge of practice-setting disparities, offering a model for other nations to develop tailored, context-specific interventions to improve medication safety. Full article

Non-thermal plasma has attracted strong interest in medicine and agriculture due to its ability to generate reactive oxygen and nitrogen species (RONS). These species can stimulate wound healing and seed germination, but at higher levels they induce DNA damage—useful in cancer therapy but harmful when healthy cells must be preserved. Direct study of DNA damage in cells is difficult because of repair processes and protective barriers. To address this, we applied a dual-model system combining plasmid DNA and human lymphocytes exposed to plasma from the RPS40 device. Using selective scavengers, we identified hydroxyl radicals, ozone, and reactive nitrogen species as key mediators of DNA strand breaks and structural changes. Our results support a mechanistic model in which long-lived plasma-derived species (NOx, ozone, acids) dissolve in water and subsequently generate short-lived radicals such as hydroxyl radicals and peroxynitrite. These reactive molecules then directly attack DNA. This integrated approach—linking plasmid and cellular assays with scavenger-based identification of RONS—offers a novel and cost-effective method for dissecting plasma–DNA interactions. The findings provide mechanistic insight into how plasma-activated water damages DNA, guiding the safer and more effective application of plasma technologies in biomedical and agricultural contexts. Full article

The Internet of Medical Things (IoMT) is a rapidly expanding network of medical devices, sensors, and software that exchange patient health data. While IoMT supports personalized care and operational efficiency, it also introduces significant privacy risks, especially when handling sensitive health information. Data Identification and Classification (DIC) are therefore critical for distinguishing which data attributes require stronger safeguards. Effective DIC contributes to privacy preservation, regulatory compliance, and more efficient data management. This study introduces SDAIPA (SDAIA-HIPAA), a standardized hybrid IoMT data classification framework that integrates principles from HIPAA and SDAIA with a dual risk perspective—uniqueness and harm potential—to systematically classify IoMT health data. The framework’s contribution lies in aligning regulatory guidance with a structured classification process, validated by domain experts, to provide a practical reference for sensitivity-aware IoMT data management. In practice, SDAIPA can assist healthcare providers in allocating encryption resources more effectively, ensuring stronger protection for high-risk attributes such as genomic or location data while minimizing overhead for lower-risk information. Policymakers may use the standardized IoMT data list as a reference point for refining privacy regulations and compliance requirements. Likewise, AI developers can leverage the framework to guide privacy-preserving training, selecting encryption parameters that balance security with performance. Collectively, these applications demonstrate how SDAIPA can support proportionate and regulation-aligned protection of health data in smart healthcare systems. Full article

Background and Clinical Significance: Bladder neoplasms often present with coexisting thrombi and hematuria, appearing as complex intraluminal masses on imaging, and posing a key diagnostic challenge in distinguishing neoplastic tissue from thrombus, to prevent harmful overstaging. Case Presentation: An 82-year-old man with recurrent gross hematuria and urinary disturbances was evaluated by ultrasound, which identified a large endoluminal lesion in the anterior bladder wall. The patient subsequently underwent contrast-enhanced CT using a second-generation dual-layer spectral CT system, which utilizes a dual-layer detector to simultaneously acquire high- and low-energy X-ray data. Conventional CT images confirmed a multifocal, bulky hyperdense lesion along the bladder wall, protruding into the lumen and raising suspicion for a heterogeneous mass, though further characterization was not possible. Spectral imaging enabled the reconstruction of additional maps—such as iodine density, effective atomic number (Z-effective), and electron density—which were used to further characterize these findings. The combination of these techniques clearly demonstrated differences in iodine uptake and tissue composition within the parietal lesions, allowing for a reliable differentiation between neoplastic tissue and intraluminal thrombus. Conclusions: The integration of conventional CT imaging with spectral-derived maps generated in post-processing allowed for accurate and reliable tissue differentiation between bladder neoplasm and thrombus. Spectral imaging holds the potential to prevent tumor overstaging, thereby supporting more appropriate clinical management. The dual-layer technology enables the generation of these maps from every acquisition without altering the scan protocol, thereby having minimal impact on the daily clinical workflow. Full article

Background: Drug–drug interactions (DDIs) are frequent and potentially harmful in pediatric cancer patients due to polypharmacy and complex chemotherapy regimens. However, data on DDIs in hospitalized pediatric oncology patients remain limited, particularly in Middle Eastern settings. Methods: In this prospective study, we analyzed prescriptions for hospitalized pediatric oncology patients in Iran to assess the prevalence, severity, and nature of potential DDIs (PDDIs). Chemotherapy and supportive medications were analyzed using two validated databases (Lexi-Interact™ and Drugs.com™) between November 2019 and June 2020. Results: Of 80 patients (median age 8.9 years), 21.2% had at least one documented PDDI. We identified 197 total PDDIs involving 42 unique drug pairs. The most common DDIs included acetaminophen and granisetron (severity rating: moderate). Methotrexate and vincristine were the most frequent antineoplastic DDI pair. Methotrexate alone accounted for 156 interactions. Conclusions: This is the first prospective study from Iran—and the largest in the region—investigating PDDIs in pediatric oncology. The dual-database screening approach improved PDDI detection. Clinical teams should routinely evaluate medication profiles in pediatric cancer patients to minimize avoidable harms from DDIs. Full article

Triclosan (TCS), a persistent antimicrobial and endocrine-disrupting compound, is commonly found in surface and groundwater due to incomplete removal by conventional wastewater treatment. This study evaluated its fate in authentic rainwater runoff collected from a state road using rubber tiles made from recycled tires that were either uncoated (RRT) or coated with TiO₂ via the sol–gel method (SGT). Pollutants were analyzed by a high-resolution liquid chromatography–quadrupole time-of-flight mass spectrometry system (LC/MS QTOF) before and after treatment in a flat-plate cascade reactor under UV-A irradiation. After 120 min SGT achieved >50% TCS removal, while RRT achieved ~44%. Further analysis identified degradation products (chlorocatechole, quinone, and transient dioxin-like species). ECOSAR predictions indicated moderate to high toxicity for some degradation products, but their transient and low-abundance detection suggests that photocatalysis suppresses accumulation, ultimately yielding less harmful products such as benzoic acid. These findings highlight the dual role of TiO₂-coated rubber tiles: improving material durability while enabling photocatalytic degradation. Full article

The intricate crosstalk between intestinal microbiota and host defense peptides (HDPs) in aquaculture has emerged as a cornerstone for advancing sustainable disease management and reducing reliance on antibiotics. This review synthesizes current insights into the bidirectional interactions shaping aquatic animal health, where HDPs, multifunctional immune molecules, directly neutralize pathogens while selectively modulating intestinal microbial communities to favor beneficial taxa (including Lactobacillus, Bacillus, Cetobacterium, Lactococcus, and so on) and suppress harmful species. Conversely, intestinal microbiota regulate HDP expression through microbial-derived signals, such as lipopolysaccharides and metabolites, which activate host immune pathways like Toll-like receptors (TLRs) to amplify innate defenses. This dynamic interplay underpins critical physiological functions, including nutrient absorption, intestinal barrier integrity, and systemic immune homeostasis, offering a dual mechanism to enhance disease resistance and growth performance. Practical applications, such as HDP-enriched feeds and probiotic–HDP synergies, have demonstrated efficacy in reducing mortality and improving productivity across species like shrimp, salmon, and carp. However, challenges such as HDP instability, species-specific variability in peptide efficacy, and the complexity of microbiota–HDP networks hinder broad implementation. Future research must prioritize innovative strategies, including engineered microbial systems for scalable HDP production, multi-omics approaches to unravel interaction mechanisms, and eco-friendly combinatorial therapies integrating HDPs, probiotics, and plant-derived compounds. By bridging immunology, microbiology, and aquaculture science, this field can transition toward antibiotic-free practices, ensuring ecological sustainability and global food security in the face of rising aquatic disease threats and environmental pressures. Full article

A critical challenge in engine research lies in minimizing harmful emissions while optimizing the efficiency of internal combustion engines. Dual-fuel engines, operating with methanol and diesel, offer a promising alternative, but their combustion modeling remains complex due to the intricate thermochemical interactions involved. This study proposes a predictive framework that combines validated CFD simulations with deep learning techniques to estimate key combustion and emission parameters in a methanol–diesel dual-fuel engine. A three-dimensional CFD model was developed to simulate turbulent combustion, methanol injection, and pollutant formation, using the RNG k-ε turbulence model. A temporal dataset consisting of 1370 samples was generated, covering the compression, combustion, and early expansion phases—critical regions influencing both emissions and in-cylinder pressure dynamics. The optimal configuration identified involved a 63° spray injection angle and a 25% methanol proportion. A Gated Recurrent Unit (GRU) neural network, consisting of 256 neurons, a Tanh activation function, and a dropout rate of 0.2, was trained on this dataset. The model accurately predicted in-cylinder pressure, temperature, NOx emissions, and impact-related parameters, achieving a Pearson correlation coefficient of ρ = 0.997. This approach highlights the potential of combining CFD and deep learning for rapid and reliable prediction of engine behavior. It contributes to the development of more efficient, cleaner, and robust design strategies for future dual-fuel combustion systems. Full article

Diaporthe eres is a harmful pathogen affecting Hongyang kiwifruit (Actinidia chinensis) after harvest, yet the antioxidant defense strategies are not well understood. This research thoroughly examines the dynamics of the antioxidant response during the infection process. Significant findings indicate an initial 3-day latent period (0–3 dpi) that allowed for pathogen establishment, followed by irreversible tissue breakdown characterized by water-soaked lesions at 4 dpi. The study identified a biphasic activation pattern of superoxide dismutase (SOD) with dual activity peaks (1 dpi and 4 dpi), orchestrated by mitochondrial hub gene CEY00_Acc02790 that coordinates peroxidase (POD) networks, while peroxidase (POD) activity exhibited a synchronized but temporary increase, peaking at 4 dpi. Further bioinformatic analysis revealed the possible functional specialization of POD isoforms: α-helix-rich extracellular variants drove cell wall reinforcement through lignification, while random coil-dominant intracellular variants formed to mitigate cytoplasmic reactive oxygen species (ROS) damage, establishing dual physicochemical barriers. Malondialdehyde (MDA) levels rose significantly by 3 dpi, indicating permanent membrane damage. Collectively, these findings elucidate the mechanistic foundation of the Actinidia–Diaporthe pathosystem, identifying the bimodal SOD response and POD specialization as prime targets for developing resistant cultivars and precision postharvest interventions, ultimately reducing losses through biochemical interception of pathogenesis. Full article

Mental disorders (MDs) constitute significant risk factors for self-harm and suicide. The incidence of MDs has been increasing annually, primarily due to inadequate diagnosis and intervention. Early identification and timely intervention can effectively slow the progression of MDs and enhance the quality of life. However, the high cost and complexity of in-hospital screening exacerbate the psychological burden on patients. Moreover, existing studies primarily focus on the identification of individual subcategories and lack attention to model explainability. These approaches fail to adequately address the complexity of clinical demands. Early screening of MDs using EEG signals and deep learning techniques has demonstrated simplicity and effectiveness. To this end, we constructed a Dual-Branch Network (DBN) leveraging resting-state Quantitative Electroencephalogram (QEEG) features. The DBN is designed to enable the detection of multiple categories of MDs. Firstly, a dual-branch feature extraction strategy was designed to capture multi-dimensional latent features. Further, we propose a Multi-Head Attention Mechanism (MHAM) that integrates dynamic routing. This architecture assigns greater weights to key elements and enhances information transmission efficiency. Finally, the diagnosis is derived from a fully connected layer. In addition, we incorporate SHAP analysis to facilitate feature attribution. This technique elucidates the contribution of significant features to MD detection and improves the transparency of model predictions. Experimental results demonstrate the effectiveness of DBN in detecting various MD categories. The performance of DBN surpasses that of traditional machine learning models. Ablation studies further validate the architectural soundness of DBN. The DBN effectively reduces screening complexity and demonstrates significant potential for clinical applications. Full article

Environmental emissions from the maritime sector, including CO₂, NO_x, and SO_x, contribute significantly to global air pollution and climate change. The International Maritime Organization (IMO) has set a target to reduce greenhouse gas emissions from international shipping to reach zero GHG by 2050 compared to 2008 levels. To meet these goals, the IMO strongly encourages the transition to alternative fuels, such as hydrogen, ammonia, and biofuels, as part of a broader decarbonization strategy. This study presents a comparative analysis of converting conventional diesel engines to dual-fuel systems utilizing alternative fuels such as methanol or natural gas. The methodology of this research is based on theoretical calculations to estimate various types of emissions produced by conventional marine fuels. These results are then compared with the emissions generated when using methanol and natural gas in dual-fuel engines. The analysis is conducted using the EVER ALOT container ship as a case study. The evaluation focuses on both environmental and economic aspects of engines operating in natural gas–diesel and methanol–diesel dual-fuel modes. The results show that using 89% natural gas in a dual fuel engine reduces nitrogen oxides (NO_x), sulfur oxides (SO_x), carbon dioxide (CO₂), particulate matter (PM), and carbon monoxide (CO) pollutions by 77.69%, 89.00%, 18.17%, 89.00%, and 30.51%, respectively, while the emissions percentage will be 77.78%, 91.00%, 54.67%, 91.00%, and 55.90%, in order, when using methanol as a dual fuel with percentage 91.00% Methanol. This study is significant as it highlights the potential of natural gas and methanol as viable alternative fuels for reducing harmful emissions in the maritime sector. The shift toward these cleaner fuels could play a crucial role in supporting the maritime industry’s transition to low-emission operations, aligning with global environmental regulations and sustainability goals. Full article

The role of nitric oxide (NO) in the onset and pathogenesis of hearing loss remains a matter of debate. To address this, we conducted a narrative review of the literature on the subject. We performed a literature search of SCOPUS, PubMed, Cochrane Library, EMBASE, and Google Scholar databases on the production and role of NO in hearing loss using the search terms/strategy “nitric oxide” AND “hearing loss” to ensure a comprehensive review of available studies. Results: Of 186 papers initially retrieved, 166 were unrelated to hearing loss and NO and were excluded. Of the 23 papers ultimately reviewed, 58% (12 articles) reported that NO caused or worsened hearing loss, 26% (5 articles) reported a beneficial effect of NO in the treatment of and/or defense against hearing loss, and 16% (3 articles) reached no firm conclusion on whether NO played a positive or negative role. This review highlights the dual role of NO in auditory health, where it is essential for normal cochlear function through regulation of blood flow and neurotransmission. However, excessive or dysregulated NO production, particularly via inducible nitric oxide synthase (iNOS), can lead to oxidative stress and hearing loss. Conversely, NO also exhibits protective effects in certain contexts, such as reducing noise-induced hearing damage through its antioxidant properties. These findings underscore the potential of NO modulation as a therapeutic strategy for hearing loss, emphasizing the need for further research to optimize its application and understand the conditions under which it is beneficial or harmful. Full article

To address carbon emission challenges in the transportation sector, intermodal transport—which enhances both economic and environmental benefits—is becoming ever more crucial. Governments often implement policies like subsidies or carbon taxes to steer intermodal transport towards sustainable development. This paper constructs a Stackelberg game model involving an eco-conscious shipper, an intermodal transport operator, and a carrier to analyze the combined economic and environmental impacts of carbon taxes, subsidies, and their dual-policy implementation on the intermodal transport system. The results of the study were as follows: (1) While either carbon taxes or subsidies alone enhance emission reduction and freight volume, their dual implementation generates synergistic effects, achieving superior emission reduction and freight growth; the study also challenges conventional wisdom by demonstrating that “reducing subsidies for intermodal transport may promote carbon reduction in transportation, while increasing taxes does not necessarily disadvantage logistics companies.” (2) Governments can achieve a win–win outcome for the economy and the environment by first prioritizing the increase of carbon taxes to effective levels, and guiding carriers to bear higher emissions reduction costs, before increasing subsidies. (3) Continuously enhancing shippers’ environmental awareness can effectively reduce total emissions. However, its impact on profits depends on the decision-making mode (decentralized vs. centralized) and the cost sharing among logistics companies. (4) There exists an optimal value for the intermodal operator’s share of emission reduction costs. Values that are too low can weaken the incentives for emission reduction, whereas values that are too high may harm profits. This research quantifies the complex interactions among policy combinations, consumer preferences, and enterprise cooperation modes. It offers valuable guidance for governments to design precise emission-reduction policies and helps upstream–downstream enterprises in intermodal transport systems optimize their operational strategies. Full article

Meeting the International Maritime Organization’s (IMO) 2050 targets for reducing greenhouse gas (GHG) emissions requires cost-effective solutions that minimize wind resistance without compromising safety, particularly for medium-sized multipurpose vessels (MPVs), which have been underrepresented in prior research. This study numerically evaluates 20 bow-mounted NaviScreen configurations using a coupled high-fidelity computational fluid dynamics (CFD) and finite element analysis (FEA) approach. Key design variables—including contact angle (35–50°), lower-edge height (1.2–2.0 m), and horn position (3.2–5.3 m)—were systematically varied. The sloped Type-15 shield reduced aerodynamic resistance by 17.1% in headwinds and 24.5% at a 30° yaw, lowering total hull resistance by up to 8.9%. Nonlinear FEA under combined dead weight, wind loads, and Korean Register (KR) green-water pressure revealed local buckling risks, which were mitigated by adding carling stiffeners and increasing plate thickness from 6 mm to 8 mm. The reinforced design satisfied KR yield limits, ABS buckling factors (>1.0), and NORSOK displacement criteria (L/100), confirming structural robustness. This dual-framework approach demonstrates the viability of NaviScreens as passive aerodynamic devices that enhance fuel efficiency and reduce GHG emissions, aligning with global efforts to address climate change by targeting not only CO2 but also other harmful emissions (e.g., NOx, SOx) regulated under MARPOL. The study delivers a validated CFD-FEA workflow to optimize performance and safety, offering shipbuilders a scalable solution for MPVs and related vessel classes to meet IMO’s GHG reduction goals. Full article

There is a growing interest in using essential oils with phytoprotectant properties instead of synthetic pesticides to mitigate the risks of insect pesticide resistance, environmental harm, and adverse effects on non-target organisms and human health. This study focused on the effects of Coridothymus capitatus essential oil on host selection, settling behaviour, and survival of Macrosiphoniella sanborni in dual-choice and no-choice tests. The essential oil and methanol extract of C. capitatus were analyzed using Gas Chromatography–Mass Spectrometry (GC-MS) and Liquid Chromatography–Mass Spectrometry (LTQ-LC-MS Orbitrap), respectively. The antioxidant activity was also tested through the radical scavenging assay. The settling inhibitory activity in the dual-choice test increased dose-dependently from 60% to 72% for essential oil concentrations of 0.1 to 0.3% (v/v) for up to 120 min exposure, but decreased thereafter. However, under no-choice conditions, the inhibitory effect after 60 min of exposure was inversely proportional to the concentration but became proportional by the end of the experiment (72 h). After 72 h, both assays produced a mortality rate of 15% to 17%. C. capitatus was classified as a Carvacrol chemotype. Fifteen phenolic compounds were identified in the MeOH extract, and both the extract and essential oil exhibited substantial antioxidant activity. In conclusion, our findings indicate that C. capitatus essential oil affects the behaviour and survival of M. sanborni. Full article