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Competencies of nurses to participate in safe medication management practices for biologics: A scoping review

Aim To review the existing literature relating to nurse competence in safe medication management practices for biologics, identify evidence, and develop a competency framework to clarify the role of nurses in these practices. Background With the widespread use of biological agents in disease treatment, ensuring the safe and economical use of high-cost medicines is particularly important. Even though nurses are essential in patient care, detailed knowledge regarding their competence and role in the safe administration of biologics is lacking. Design and methods A scoping review was performed following the methodology of Arksey and O’Malley and the PRISMA ScR guidelines. Electronic databases, including PubMed, CINAHL, Embase, Scopus, and Web of Science, were searched using accepted keywords, and relevant articles were identified using inclusion and exclusion criteria. Results A total of 3,422 studies were retrieved, 24 of which were eligible for inclusion. The required competencies for nurses were summarized into six areas: clinical specialized knowledge, critical thinking and problem-solving skills, safe medication skills, health education skills, communication and coordination skills, and technological literacy. Conclusion We provide insights into the competencies of nurses involved in the safe medication management of biologics. These competencies can be used to assess the actual competency level of nurses and facilitate the maximization of biological treatment goals and outcomes. This plays a vital role in optimizing the use of healthcare resources and demonstrating outcomes.

Determinants of future anxiety across individual, household, and regional levels in South Korea using a social ecological model

The link between the atherogenic index of plasma and the risk of hypertension: Analysis from NHANES 2017–2020

Background The atherogenic index of plasma (AIP) is a newly identified metabolic marker for atherosclerosis. However, there are inconsistent conclusions regarding the relationship between AIP and hypertension. Methods The study subjects were sourced from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2020. Logistic regression analyses were employed to explore the correlation between AIP and hypertension. The value of AIP in predicting hypertension was assessed using ROC curves, and their nonlinear relationship was described using restricted cubic splines (RCS). Subgroup analyses, interactions, and sensitivity analyses were also conducted. Results The study included 7,067 participants who were sourced from the NHANES database. There were 2723 participants diagnosed hypertension. We observed a notable correlation between AIP and hypertension (OR:1.89, 95%CI: 1.11–3.22, P = 0.019). ROC curve showed AIP had a good predictive value for the onset of hypertension, with the AUC of 0.652 (95% CI:0.639–0.664, p<0.001). RCS found that there existed a nonlinear association between AIP and the incidence of hypertension (p<0.001). Even after excluding individuals under the age of 40 years old, the results still indicate a strong association between AIP and hypertension. Conclusions AIP may serve as an early biological marker for identifying hypertension, facilitating early screening of susceptible populations.

Patients with neurological or psychiatric complications of COVID-19 have worse long-term functional outcomes: COVID-CNS—A multicentre case–control study

Abstract It is established that patients hospitalised with COVID-19 often have ongoing morbidity affecting activity of daily living (ADL), employment, and mental health. However, little is known about the relative outcomes in patients with COVID-19 neurological or psychiatric complications. We conducted a UK multicentre case–control study of patients hospitalised with COVID-19 (controls) and those who developed COVID-19 associated acute neurological or psychiatric complications (cases). Among the 651 patients, [362 (55%) cases and 289 (45%) controls], a higher proportion of cases had impairment in ADLs (199 [68.9%] vs 101 [51.8%], OR 2.06, p < 0.0002) and reported symptoms impacting employment (159 [58.2%] vs 69 [35.6%] OR 2.53, p < 0.0001). There was no significant difference in the proportion with depression or anxiety between case and control groups overall. For cases, impairment of ADLs was associated with increased risk in female sex, age > 50 years and hypertension (OR 5.43, p < 0.003, 3.11, p = 0.02, 3.66, p = 0.04). Those receiving either statins or angiotensin converting enzyme (ACE) inhibitors had a lower risk of impairment in ADLs (OR 0.09, p = 0.0006, 0.17, p = 0.03). Patients with neurological or psychiatric complications of COVID-19 had worse functional outcomes than those with respiratory COVID-19 alone in terms of ADLs and employment. Female sex, age > 50 years, and hypertension were associated with worse outcomes, and statins or ACE inhibitors with better outcomes.

Alzheimer’s disease image classification based on enhanced residual attention network

With the increasing number of patients with Alzheimer’s Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer’s disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer’s disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates. To address these issues, this study proposes a deep learning model to detect Alzheimer’s disease; it is called Enhanced Residual Attention Network (ERAN) that can classify medical images. By combining residual learning, attention mechanism, and soft thresholding, the feature representation ability and classification accuracy of the model have been improved. The accuracy of the model in detecting Alzheimer’s disease has reached 99.36%, with a loss rate of only 0.0264. The experimental results indicate that the Enhanced Residual Attention Network has achieved excellent performance on the Alzheimer’s disease test dataset, providing strong support for the early diagnosis and treatment of Alzheimer’s disease.

Improved equivalent optical turbulence method for anisotropic compressible and atmospheric turbulence under different beam transmission distances

Effects of treadmill running on anxiety- and craniofacial pain-like behaviors with histone H3 acetylation in the brain of mice subjected to social defeat stress

This study examined the effects of treadmill running (TR) regimens on craniofacial pain- and anxiety-like behaviors, as well as their effects on neural changes in specific brain regions of male mice subjected to repeated social defeat stress (SDS) for 10 days. Behavioral and immunohistochemical experiments were conducted to evaluate the impact of TR regimens on SDS-related those behaviors, as well as epigenetic and neural activity markers in the anterior cingulate cortex (ACC), insular cortex (IC), rostral ventromedial medulla (RVM), and cervical spinal dorsal horn (C2). Behavioral responses were quantified using multiple tests, while immunohistochemistry measured histone H3 acetylation, histone deacetylases (HDAC1, HDAC2), and neural activity markers (FosB and phosphorylated cAMP response element-binding protein (pCREB). The effects of both short-term TR (2 days, TR2) and long-term TR (10 days, TR10) regimens were conducted. TR10 significantly reduced anxiety- and formalin-evoked craniofacial pain-like behaviors in SDS mice. It normalized SDS-induced increases in histone H3 acetylation in both the anterior and posterior portions of the ACC, as well as the anterior portion of the IC. These inhibitory effects were also observed in SDS-related increases in HDAC1, FosB, and pCREB expression. Additionally, TR10 normalized increased histone H3 acetylation in the RVM and C2 regions, with specific effects on FosB and pCREB expression observed in the C2 region. In contrast, TR2 showed limited effects on craniofacial pain-like behaviors but reduced anxiety-like behaviors in SDS mice. Under sham conditions, TR2 had minimal impact on histone H3 acetylation. Paradoxically, TR2 increased formalin-evoked craniofacial pain-like behaviors during the early phase despite not altering acetylated histone H3 expression. In conclusion, the TR10 regimen is effective in attenuating SDS-induced craniofacial pain- and anxiety-like behaviors, likely by normalizing epigenetic modifications and neural activity in key brain regions.

Experimental study on the simultaneous effect of smart water and clay particles on the stability of asphaltene molecule and emulsion phase

Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model

To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors. The MNN, by simulating the memory effect of biological neurons, effectively captures the complex nonlinear relationships within vulnerability data, thereby enhancing the data insight capabilities of the network security management system. Subsequently, structural optimization and parameter adjustments are made to the TCNN model, incorporating residual connections and attention mechanisms to improve its classification performance, making it more adaptable to the dynamically changing network security environment. Through data preprocessing, feature extraction, and model training, this study conducts experimental validation on a public vulnerability dataset. The experimental results indicate that: The MNN model demonstrates excellent performance across evaluation metrics such as Accuracy (ACC), Precision (P), Recall (R), and F1 Score, achieving an ACC of 89.5%, P of 90.2%, R of 88.7%, and F1 of 89.4%. The improved TCNN model shows even more outstanding performance on the aforementioned evaluation metrics. After structural optimization and parameter adjustments, the TCNN model’s ACC increases to 93.8%, significantly higher than the MNN model. The P value also improves, reaching 91.5%, indicating enhanced capability in reducing false positives and improving vulnerability identification accuracy. The integrated classification system, leveraging the strengths of both the MNN and improved TCNN models, achieves an ACC of 95.2%. This improvement not only demonstrates the system’s superior capability in accurately classifying vulnerability data but also proves the synergistic effect of MNN and TCNN models in addressing complex network security environments. The comprehensive classification system proposed in this study significantly enhances the classification performance of computer vulnerabilities, providing robust technical support for network security management. The system exhibits higher accuracy and stability in handling complex vulnerability datasets, making it highly valuable for practical applications and research.

Effects of copper/graphene oxide core-shell nanoparticles on Rhipicephalus ticks and their detoxification enzymes

Abstract Nanopesticides have been recently introduced as novel pesticides to overcome the drawbacks of using traditional synthetic pesticides. The present study evaluated the acaricidal activity of Copper/Graphene oxide core-shell nanoparticles against two tick species, Rhipicephalus rutilus and Rhipicephalus turanicus. The Copper/Graphene oxide core-shell nanoparticles were synthetized through the solution plasma (SP) method under different conditions. The nanoparticles synthesized at 180 W and 45 min were highly toxic to Rh. rutilus and Rh. turanicus, with 50% lethal concentration (LC50) values of 248.1 and 195.7 mg ml−1, respectively, followed by those which were synthesized at 120 W/30 mins (LC50 = 581.5 and 526.5 mg ml−1), 120 W/15 mins (LC50 = 606.9 and 686.7 mg ml−1), and 100/45 mins (LC50 = 792.9 and 710.7 mg ml−1), after 24 h of application. The enzyme assays revealed that 180 W/45 min treatment significantly inhibited the activity of acetylcholinesterase (115 ± 0.81 and 123 ± 0.33 U/ mg protein/min) and superoxide dismutase (290 ± 0.18 and 310 ± 0.92 U/ mg protein/min) in Rh. rutilus and Rh. turanicus, respectively, as compared with the negative control. The results also revealed a significantly increased catalase activity (895 ± 0.37 and 870 ± 0.31 U/ mg protein/min) in Rh. rutilus and Rh. turanicus, respectively. The above results indicated that Copper/Graphene oxide core-shell nanoparticles could be a promising alternatives for the management of ticks.

Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma

Background Ras-GTPase-activating protein (GAP)-binding protein 1 (G3BP1) emerges as a pivotal oncogenic gene across various malignancies, notably including nasopharyngeal carcinoma (NPC). The use of automated image analysis tools for immunohistochemical (IHC) staining of particular proteins is highly beneficial, as it could reduce the burden on pathologists. Interestingly, there have been no prior studies that have examined G3BP1 IHC staining using digital pathology. Methods Whole-slide images (WSIs) were meticulously collected and annotated by experienced pathologists. A model was intricately designed and rigorously tested to yield the quantitative data regarding staining intensity and extent. The collective output data was subjected multiplicative analysis, exploring its correlation with the prognosis. Results The G3BP1 molecular marker scoring model was successfully established utilizing deep learning methodologies, with a calculated threshold staining scores of 1.5. Notably, patients with NPC exhibiting higher expression levels of G3BP1 proteins displayed significantly lower for overall survival rates (OS). Multivariate analysis further validated that positive expression of G3BP1 stood as an independent poorer prognostic factors, indicating a poorer prognosis for NPC patients. Conclusion Computational pathology emerges as a transformative tool capable of substantially reducing the burden on pathologists while concurrently enhancing and diagnostic sensitivity and specificity. The positive expression of G3BP1 protein serves as valuable, independent biomarker, offering predictive insights into a poor prognosis for patients with NPC.

Tunable optical nonreciprocity in double-cavity optomechanical system with nonreciprocal coupling

Ecological filters shape arbuscular mycorrhizal fungal communities in the rhizosphere of secondary vegetation species in a temperate forest

The community assembly of arbuscular mycorrhizal fungi (AMF) in the rhizosphere results from the recruitment and selection of different AMF species with different functional traits. The aim of this study was to analyze the relationship between biotic and abiotic factors and the AMF community assembly in the rhizosphere of four secondary vegetation (SV) plant species in a temperate forest. We selected four sites at two altitudes, and we marked five individuals per plant species at each site. Soil rhizosphere samples were collected from each SV plant species, during the rainy and dry seasons. Soil samples from the rhizosphere of each plant species were analyzed for AMF spores, organic matter (OM), pH, soil moisture, and available phosphorus, and nitrogen. Three ecological filters influenced the AMF community assembly: host plant identity, abiotic factors, and AMF species co-occurrence. This assembly consisted of 61 AMF species, with different β-diversity values among plant species across seasons and altitudes. Canonical correspondence analysis revealed that AMF community composition is linked to OM and available P and N, with only a few AMF species co-occurring, while most do not. Our study highlights how ecological filters shape AMF structure, which is essential for understanding how soil and environmental factors affect AMF in SV plant species across seasons and altitudes.

Geospatial and econometric approaches or older driver safety: Analysis of crash injury severity of regional highways

This study tried to focus on the older drivers’ group and explore the impact factors of injury severity involving older drivers from geo-spatial analysis. To reach the goal, a spatial analysis was proposed employing geographic information systems (GIS) with a case study application to two counties in Nevada. First, crash clusters were explored using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach to investigate the spatial crash pattern for older drivers, and determine high risk locations of injury severity. Next, Bayesian spatial binary probit model was presented in order to determine the significant impact factors of injury severity involving older drivers. It was found that at-fault driver condition and vehicle condition, not-at-fault vehicle action and road factors were significant factors for injury severity of older drivers. Results revealed that DBSCAN provides a solid option for hotspot identification of injury severity and Bayesian spatial binary probit model addresses the factor determinants spatially. The GIS-based spatial analysis can benefit more reliable older driver-concentrated evaluation and injury severity analysis.

“I just felt that everything came tumbling down around me”—Barriers in cancer care for patients with severe mental illness: A qualitative study

Background Patients with severe mental illness experience serious inequity when facing cancer treatment. They are less likely to be referred for cancer treatment following recommended guidelines and have poorer cancer survival than patients without mental illness. Relevant specialties such as psychiatry and general practice are rarely involved, and the patient perspective is rarely represented in research in the field. The present study investigated how patients with severe mental illness experience barriers to and facilitators of patient-centred cancer treatment and care. Methods In this qualitative case study, field observations, semi-structured interviews, and patient file analysis were performed with five patients with cancer in an adult psychiatric setting, included through purposeful sampling. Results Our analysis showed one major theme, “Complexity on many levels”, and four subthemes: “How the mental illness is affected by the cancer trajectory”, “The complexity of patient vulnerability”, “Fragmented healthcare system and lack of structure”, and “The role of the relationship between patient and health professional.” Barriers included the cancer trajectory leading to severe worsening of the mental illness, as well as fragmentation of the healthcare system and a lack of a systematic approach to the patient group. Facilitators included the health professionals acknowledging the patient’s own resources and approaching the patient as a person rather than a disease. Conclusion This study highlights critical focal points to improve care for patients with cancer who also struggle with severe mental illness. By addressing these target areas, healthcare providers can better tailor their approach to meet the unique needs of this population.

Effect of intra- and inter-specific plant interactions on the rhizosphere microbiome of a single target plant at different densities

Root and rhizosphere studies often focus on analyzing single-plant microbiomes, with the literature containing minimum empirical information about the shared rhizosphere microbiome of multiple plants. Here, the rhizosphere of individual plants was analyzed in a microcosm study containing different combinations and densities (1–3 plants, 24 plants, and 48 plants) of cover crops: Medicago sativa, Brassica sp., and Fescue sp. Rhizobacterial beta diversity was reduced by increasing plant density for all plant mixtures. Interestingly, plant density had a significant influence over beta diversity while plant diversity was found to be a less important factor since it did not have a significant change. Regardless of plant neighbor identity or density, a low number of rhizobacteria were strongly associated with each target species. Nonetheless, a few bacterial taxa were shown to have conditional associations such as being enriched within only high plant densities, which may alleviate plant competition between these species. Also, we found evidence of bacterial sharing of nitrogen fixers from alfalfa to fescue. Although rhizosphere bacterial networks had overlapping bacterial modules, the modules showing the largest percentage of the network changed depending on plant neighbor. In summary, this study found that for the most part plants maintained their rhizosphere microbiome despite escalating plant-plant competition.

Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions

As people’s material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy. However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. To this end, a new garbage image classification model is proposed, with the ResNet-50 network as the core architecture. Specifically, first, a redundancy-weighted feature fusion module is proposed, enabling the model to fully leverage valuable feature information, thereby improving its performance. At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. Second, the standard 3×3 convolutions in ResNet-50 are replaced with depth-separable convolutions, significantly improving the model’s computational efficiency while preserving the feature extraction capability of the original convolutional structure. Finally, to address the issue of class imbalance, a weighting factor is added to the Focal Loss, aiming to mitigate the negative impact of class imbalance on model performance and enhance the model’s robustness. Experimental results on the TrashNet dataset show that the proposed model effectively reduces the number of parameters, improves detection speed, and achieves an accuracy of 94.13%, surpassing the vast majority of existing deep learning-based waste image classification models, demonstrating its solid practical value.

Antiretroviral therapies and status of people living with HIV in Japan: An update from hospital survey and national database

No updated data on people living with HIV (PLHIV) in Japan have been available since 2015, leaving a critical gap in understanding the current status of care and treatment. Therefore, this study aimed to conduct a nationwide evaluation of the second and third goals of the “90-90-90 target” defined by UNAIDS between 2016 and 2020. The study utilized data from approximately 360 core hospitals through structured questionnaires and the National Database of Health Insurance Claims and Specific Health Checkups (NDB). Key findings revealed that over 95% of diagnosed outpatients were retained in care (second 90), and more than 99% achieved successful viral suppression (third 90). A significant transition to single-tablet regimens and newer, highly effective antiretroviral drugs was observed, optimizing treatment adherence and outcomes. These results underscore the efficacy of Japan’s universal health insurance system in ensuring consistent access to HIV care and treatment, supporting both individual patient outcomes and national surveillance efforts.

Factors associated with receiving a Functional Disorder diagnostic label: A systematic review

Objectives Functional Disorders (FD) are highly prevalent conditions that are diagnosed based on the presence of specific patterns of somatic symptoms. Examples of FDs include Fibromyalgia and Irritable Bowel Syndrome. Many patients who meet the criteria do not receive a formal diagnostic label. This systematic review aims to assess factors associated with receiving an FD diagnostic label. Methods A systematic search of PubMed, PsycINFO, and Embase was performed following the PRISMA guidelines. All research methodologies and languages were included with a focus on experiences and impacts of receiving/having an FD diagnostic label. Excluded studies were those not mentioning diagnostic labels, only involving single pain symptoms, and studies solely focusing on functional neurological symptoms. Screening, data extraction and quality ratings (using the QuADS instrument) were performed by two independent reviewers. Results 15 Studies were identified (10 quantitative and 5 qualitative). Our results show that female patients were more likely to receive an FD diagnostic label for their symptoms; other associations were less consistent and only found for specific labels or research designs. In general, quality of life and healthcare use did not seem to differ between patients with and without an FD diagnostic label. From the healthcare professional’s perspective there was doubt about giving an FD diagnostic label, mainly due to concerns of harm for patients. Quality of included studies was rated low to moderate. Conclusion Better understanding of factors associated with receiving or having an FD diagnostic label, independently from symptom development can help healthcare professionals make evidence-based decisions in labelling or not; however, high quality studies on this topic are urgently needed.

Age-related change in inhibitory processes when controlling working memory capacity and processing speed: A confirmatory factor analysis

The main purpose of this study was to examine the age-related changes in inhibitory control of 450 children at the ages of 7–8, 11–12, and 14–16 when controlling for working memory capacity (WMC) and processing speed to determine whether inhibition is an independent factor far beyond its possible reliance on the other two factors. This examination is important for several reasons. First, empirical evidence about age-related changes of inhibitory control is controversial. Second, there are no studies that explore the organization of inhibitory functions by controlling for the influence of processing speed and WMC in these age groups. Third, the construct of inhibition has been questioned in recent research. Multigroup confirmatory analyses suggested that inhibition can be organized as a one-dimension factor in which processing speed and WMC modulate the variability of some inhibition tasks. The partial reliance of inhibitory processes on processing speed and WMC demonstrates that the inhibition factor partially explains the variance of inhibitory tasks even when WMC and processing speed are controlled and some methodological concerns are addressed.