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Utility and clinical significance of endoscopic ultrasound-guided tissue acquisition for diagnosing lymphadenopathies in biliary tract cancer

The Depression, Anxiety, Stress Scales-21: Principal component analysis and evaluation of abbreviated versions in young adults with temporomandibular disorders

Background The Depression, Anxiety, Stress Scales-21 (DASS-21) contain three subscales measuring depression, anxiety, and stress. Several abbreviated DASS-21 versions have been developed, demonstrating better clinical utility and measurement properties than the original instrument. This study explored the factor structure of various abbreviated DASS-21 versions and identified/validated the optimal one for assessing young adults with temporomandibular disorders (TMDs). Methods A total of 974 university-attending young adults were recruited in two waves (wave 1: 519; wave 2: 455). Demographic information, the DASS-21, and quintessence five TMD symptoms (5Ts) of the Diagnostic Criteria for TMDs were collected. Principal component analysis (PCA) was employed to condense the DASS-21 (wave 1 data), while confirmatory factor analysis (CFA) was used to determine maximum likelihood estimates and compare different abbreviated DASS-21 versions (wave 2 data). Known-group, concurrent (criterion) validity and reliability were subsequently evaluated. Results The mean age of the study participants was 21 (SD = 0.1) years and 80.4% were women. Twelve DASS-21 items were identified from the PCA. However, the Korean DASS-12 provided the best-fit model (χ2/df = 2.07, CFI = 0.975, TLI = 0.960, RMSEA = 0.049, SRMR = 0.033) among the seven abbreviated versions in the CFA. The Korean DASS-12 showed good known-group and concurrent (rs = 0.959) validity and reliability when contrasted to the DASS-21. Conclusion The Korean DASS-12 possessed a good fit, known-group, as well as concurrent (criterion) validity and reliability, and was the best abbreviated DASS-21 version for screening young adults with TMD symptoms for psychological distress.

Characteristics and paleoclimate significance of authigenic ferrimagnetic minerals in the Xuancheng red earth, southern China

Oral health-related conditions in Ecuador: A temporal trend analysis of hospital discharges from 2000–2023

Background Monitoring hospitalization rates associated with oral health conditions is an important part of epidemiological surveillance, especially when these conditions have increased significantly in low—and middle-income countries. This study aimed to evaluate the temporal trends in hospital discharges associated with oral health-related conditions in Ecuador from 2000 to 2023 and identify the leading diagnoses groups. Methods An ecological time-series study was conducted based on annual data from the National Institute of Statistics and Censuses of Ecuador. We identified oral conditions using hospital discharge records, which were classified according to the International Classification of Diseases. We estimated crude and age-standardized rates per 100,000 inhabitants for the entire population and crude rates by sex, age, region, and the diagnosis. Joinpoint analysis was used to identify national trends in hospital discharges. Results A total of 93652 hospitalizations were identified. Malignant tumors of the head, face, and neck were the most common diagnosis attributed to hospital discharges (16.5%), followed by cleft palate (14.5%) and cleft lip (7.8%) and other diseases of the jaws (5.4%). The crude rate of OHRC increased from 17.94 to 28.81 hospitalizations per 100,000 population between 2000 and 2023. Based on joinpoint analysis, hospital discharges increased by 2.2% annually during the study period. However, three temporal trends were identified: from 2000 to 2017, hospital discharges increased annually by 4.3% (p<0.05); from 2017 to 2020 decreased by 17.2%; and from 2020 to 2023 increased annually by 12.9%. Average hospitalization rates were higher among those aged 0–9 and > 64 years. Conclusions Hospitalization rates associated with oral health-related conditions in Ecuador have increased significantly over the past twenty-four years, except during the COVID-19 pandemic, where cases dropped considerably. Many of the oral conditions identified in the study were malignancies, which are caused by a complex relationship between genetic, environmental, and behavioral factors. Conducting early detection analysis is essential to reduce their occurrence.

The role of homogenization cycles and Poloxamer 188 on the quality of mitochondria isolated for use in mitochondrial transplantation therapy

Synbiotics effects of d-tagatose and Lactobacillus rhamnosus GG on the inflammation and oxidative stress reaction of Gallus gallus based on the genus of cecal bacteria and their metabolites

Backgrounds Abuse of feed supplement can cause oxidative stress and inflammatory responses in Gallus gallus. Synbiotics are composed of prebiotics and probiotics and it possess huge application potentials in the treatment of animal diseases. Methods This study examined the effect of d-tagatose on the probiotic properties of L. rhamnosus GG, L. paracasei, and S. lactis so as to screen the best synbiotic combinations. Treat Gallus gallus exhibiting oxidative stress and immune response caused by aflatoxin b1 with optimal synbiotics for 14 days, detect the changes of inflammatory markers and oxidative stress markers of Gallus gallus using qRT-PCR, and identified the intestinal bacteria genera and their metabolites in the cecum of Gallus gallus using gut microbiota and metabolomics analysis. Results and conclusion The results indicated that oxidative stress and immune response factor expressions quantity in Gallus gallus decreased significantly after 14 days of treatment, compared with model group, the low-dose treatment group’s SOD1, SOD3, GPX1, GPX2, GSR, H6DP, and HO-1 genes in liver were downregulated by 36.03%, 40.01%, 45.86%, 40.79%, 37.68%, 25.04%, and 29.89%, the IL-1, IL-2, IL-4, IL-6, IgA, IgM, and IgG genes in blood and spleen were downregulated by 26.59%, 34.19%, 21.19%, 28.18%, 35.93%, 12.67%, 21.81 and 35.93%, 22.85%, 21.19%, 28.78%, 35.93%, 15.36%, 29.73%. The intestinal bacteria genera and metabolomics analysis results indicated that the abundance of beneficial bacteria genus was up-regulated, and the proportion of pathogenic bacteria genera decreased. The amount of beneficial metabolites associated with antioxidant and anti-inflammatory effects was upregulated. The synbiotic composed of d-tagatose and L. rhamnosus GG can treat oxidative stress and immune response by altering the structure of intestinal bacteria genera and the production of metabolites.

Modeling future sediment retention service in the Bagh-e-Shadi Forest protected area using InVEST and the ACCESS-ESM1-5 climate model

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.