Browse Articles

Hepatitis B virus X protein (HBx)-mediated immune modulation and prognostic model development in hepatocellular carcinoma

Hepatitis B virus (HBV) X protein (HBx) is critical in hepatocellular carcinoma (HCC) development, but its influence on tumor immunity and the tumor microenvironment (TME) remains unclear. This study aimed to construct a prognostic model based on HBx-related genes and explore their relationship with immune infiltration and immunotherapy response. Through transcriptome sequencing of our HBx-expressing HepG2 cells and analysis of HCC patient data from the cancer genome atlas (TCGA) and genotype-tissue expression (GTEx), we identified seven HBx-related genes, nuclear VCP-like (NVL), WD repeat domain 75 (WDR75), NOP58 nucleolar protein (NOP58), Brix domain-containing protein 1 (BRIX1), deoxynucleotidyltransferase terminal interacting protein 2 (DNTTIP2), MKI67 FHA domain interacting nucleolar phosphoprotein (NIFK), and ribosome production factor 2 (RPF2), associated with poor prognosis. LASSO Cox regression narrowed these to four key genes (BRIX1, RPF2, DNTTIP2, and WDR75), which were used to develop a prognostic riskscore signature. High-risk patients exhibited lower survival rates, decreased infiltration of anti-tumor immune cells, poorer responses to immunotherapy, and increased immune evasion. Among the four genes, DNTTIP2 showed higher expression in single-cell data, was linked to migration inhibitory factor (MIF) signaling, and may play a pivotal role in shaping an immunosuppressive TME. Elevated DNTTIP2 expression was confirmed in HBx-expressing HepG2 cells and HBV-infected HCC samples. This study highlights a novel HBx-related four-gene prognostic model that predicts clinical outcomes, immune infiltration, and immunotherapy response, offering insights into HCC progression and potential therapeutic targets.

Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma

Objective Accurate prediction of glioblastoma (GBM) progression is essential for improving therapeutic interventions and outcomes. This study aimed to develop and validate an integrated clinical-radiomics model to predict overall survival (OS) and evaluate the risk of disease progression in patients with isocitrate dehydrogenase-wildtype GBM (IDH-wildtype GBM). Materials and Methods The data of 423 IDH-wildtype GBM patients were retrospectively analyzed. Radiomic features were extracted from preoperatively acquired MR images. Least absolute shrinkage and selection operator-Cox proportional hazards (LASSO-Cox) regression was used to identify radiomic features significantly associated with OS and calculate a risk score and construct a radiomic signature for each patient. Kaplan‒Meier survival analysis and the log-rank test were used to compare survival between the high-risk and low-risk groups. A clinical‒radiomic model and a nomogram were developed on the basis of the results of multivariable Cox proportional hazards regression and were evaluated with the concordance index (C-index). Results Radiomics models were developed on the basis of feature extracted from the three sub-regions individually, and a multiregional radiomics model was established by aggregating 16 features selected from these subregions. Kaplan-Meier survival analysis indicated that the high-risk group exhibited significantly worse outcomes than the low-risk group did (p < 0.05). The C-index of the multiregional radiomics model was the highest. Univariable Cox regression analysis revealed that the risk score, age, and extent of gross total resection (GTR) were significant prognostic factors for OS in GBM patients. According to the C-index, the combined clinical‒radiomic model outperformed the standalone radiomic and clinical models. The multifactor nomogram showed high accuracy in predicting the OS rates of preclinical GBM patients at 3 months, 6 months, 1 year, and 3 years in both the training and test cohorts. Conclusions The integrated model combining clinicopathological data with a radiomic signature achieves good risk stratification and survival prediction in GBM and thus could be an important tool in clinical practice.

Data-driven mechanisms for network freight platforms: An evolutionary game perspective

This paper examines the impact of data-driven mechanisms in network freight platforms. The main objective is to understand how these mechanisms can improve operational efficiency and encourage cooperation among key stakeholders, including a risk-neutral shipper, a loss-averse carrier and network freight platform. The study uses evolutionary game theory to model the interactions between these parties. Numerical simulations are conducted to evaluate the effects of initial conditions and important parameters on cooperation. The results show that consistent implementation of data-driven mechanisms fosters stable and honest cooperation. Key factors, such as subsidies, penalties for dishonest behavior, the likelihood of detecting dishonesty, and incentives for honest actions, motivate carriers and shippers to participate in fair transactions. Additionally, specific costs are identified as deterrents to dishonest practices. These findings contribute to our understanding of digital transformation and provide valuable insights for enhancing resilience and collaboration within network freight platforms. The risk appetite can obviously influence the decision of the three parties. The study also highlights important implications for policymakers and industry practitioners, emphasizing the importance of effective data governance and the strategic use of information to shape the future of freight logistics.

Using smartphone step counts to monitor patients with total hip arthroplasty: The impact of patients’ living arrangements and residential location

Background Smartphone step counts may capture real-world activities in patients’ daily lives, and the self-monitoring of step counts may introduce healthier behavioral changes. We investigated the association between living arrangements (solitude/cohabiting) and residential location (urban/suburban) of patients and their preoperative and postoperative smartphone step counts following total hip arthroplasty (THA). Materials and methods Patients scheduled for THA at a university hospital from September 10, 2021, to December 5, 2023 were enrolled into the study from August 4, 2021, to November 10, 2023. We remotely monitored the patients’ daily step counts from smartphone application until up to 365 days after THA. We used the moving average method and latent growth curve modeling to analyze the time-series data of the step count. Results Overall, 85 patients were included in this study. Comparing the 37 solitary and 48 cohabiting patients, the percentage of men was higher in the solitary group (27% vs. 8%, P = 0.037). There were no notable differences in the demographics between the 44 urban and 41 suburban patients. Urban patients experienced a 483-step per 2-week greater preoperative increase than suburban patients (P = 0.027) after adjusting for confounding factors. From postoperative 2 weeks to preoperative 12 weeks, the intercept was larger for urban patients than for suburban patients by 893 steps (P = 0.040). From postoperative 20 weeks to preoperative 50 weeks, the intercept was larger for solitary patients than for cohabiting patients by a difference of 1,360 steps (P = 0.027). Conclusions Living arrangements and residential locations were associated with daily step counts before and after THA. Patients in urban areas had higher step counts after the initiation of step-count monitoring and during the early postoperative period. Solitary patients walked more than cohabiting patients. Our findings underscore the utility of smartphone step counts as objective outcome measures for patient assessment and encouraging healthier behavioral changes.

Effects of DA-9701 (motilitone®) on gastric emptying, glycemic control, and oxidative stress in diabetic rats

Background/aims Diabetes is associated with various gastrointestinal disorders, including altered gastric emptying, which may be rapid, slow, or transient. These alterations can significantly influence gastrointestinal symptoms and directly influence blood glucose levels. DA-9701 (motilitone), a prokinetic agent derived from Corydalis tuber and Pharbitis seed, is employed in Korea to manage functional dyspepsia due to its anti-inflammatory and gastrointestinal motility-enhancing properties. This study aims to investigate the potential of DA-9701 in addressing altered gastric emptying and glycemic control in diabetic rats, thereby validating its broader clinical utility. Materials/methods Diabetes mellitus was induced in rats by streptozocin injection (65 mg/kg, i.p.). Following the onset of diabetes, rats received daily oral administration of DA-9701 for 2 weeks. Gastric emptying rates for liquid and solid meals were measured using plasma acetaminophen levels and residual food mass, respectively. Oral glucose tolerance tests (OGTT), insulin levels, and oxidative stress markers (malondialdehyde [MDA], Ogg1, Gpx, Cat) were assessed. Western blotting and qPCR were used to evaluate the expression of ERK1/2, c-Kit, and proliferating cell nuclear antigen (PCNA) in gastric tissue. Results Diabetic rats exhibited significantly accelerated gastric emptying (liquid GE AUC: + 45.2%; solid GE: + 23.1%, p < 0.01) and elevated blood glucose (327.4 ± 22.8 mg/dL vs. 96.2 ± 10.1 mg/dL in controls, p < 0.001), accompanied by increased oxidative stress markers and expression of c-Kit, ERK1/2, and PCNA. DA-9701 treatment normalized gastric emptying rates (solid GE restored to 55.8%, p < 0.05), reduced MDA, Ogg1, Gpx, and Cat expression, and significantly downregulated ERK1/2, c-Kit, and PCNA. Moreover, insulin secretion increased 2.1-fold in DA-9701-treated diabetic rats (p < 0.05), resulting in improved glucose tolerance (OGTT AUC reduction: −24.6%, p < 0.01). Conclusion DA-9701 normalized gastric emptying and glycemic control while reducing the expression of ERK1/2, c-Kit, and PCNA, which are elevated in diabetic gastric tissues. These findings highlight the dual therapeutic potential of DA-9701 in regulating both gastrointestinal motility and glycemic variability in diabetes, warranting further investigation in clinical settings.

Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle

Lameness is one of the major production diseases affecting dairy cattle. It is associated with negative welfare in affected cattle, economic losses at the farm level, and adverse effects on sustainability. Prompt identification of lameness is necessary to facilitate early treatment, enhance animal welfare, and mitigate short and long-term production impacts associated with the disease. In recent years, automated detection systems have emerged as a potential solution for identifying early signs of lameness. Among these systems, accelerometers have been widely adopted, as they continuously capture data on animal movement. Analyzing accelerometer data is challenging due to its wide, high-dimensional structure as it has many features and typically much fewer animals or samples, reducing the utility of many machine learning (ML) models and increasing the risk of overfitting. To handle this, researchers often summarize accelerometer data into indices like step counts, which simplifies analysis but may sacrifice important details needed for accurate prediction of lameness. Dimension reduction techniques, such as principal component analysis (PCA) and functional principal component analysis (fPCA), offer solutions by reducing the dimensionality of the data while retaining key information and allowing for the application of a broader set of ML approaches. Using data containing 20 thousand recordings from 383 dairy cows in 11 dairy herds, this study evaluated the effectiveness of ML methods in detecting foot lesions in dairy cows using accelerometer data, with a focus on dimensionality reduction approaches and cross-validation strategies. Our study offers practical insights for the dairy industry by highlighting the potential benefits of combining dimensionality reduction with cross-validation strategies to improve the performance of ML methods applied to wide accelerometer data. In addition, our study highlights the impact and importance of using data from independent farms. A by-farm approach to cross-validation will likely give a more robust, realistic estimate of general model performance.

Integrating network pharmacology and experimental verification to explore the protective effects of Evodia rutaecarpa in ischemic stroke

Background Evodia rutaecarpa is a traditional Chinese herbal medicine known for its potential benefits in the treatment of cardiovascular and cerebrovascular diseases. Despite its recognized effects, the effects of Evodia rutaecarpa on ischemic stroke (IS), along with the primary active compounds and precise mechanisms of action, require elucidation. Methods Network pharmacology analyses and molecular docking were performed to integrate information related to Evodia rutaecarpa and IS. Cell oxygen–glucose deprivation (OGD) and rat middle cerebral artery occlusion (MCAO) models were established to simulate cerebral ischemic injury. The effects of rutaecarpine on these models were evaluated to assess its effect on IS. Results Network pharmacological analysis indicated that rutaecarpine from Evodia rutaecarpa showed therapeutic effects against IS. The mechanism underlying these effects mainly involved the mitogen-activated protein kinase (MAPK), and targets such as matrix metalloproteinase (MMP)-9, caspase 3 and MMP-2 may be activated to exert these effects. In vitro studies showed that rutaecarpine significantly improved the mitochondrial membrane potential of HT22 cells, reduced the production of reactive oxygen species, and reversed OGD-induced cytotoxicity. In the MCAO rat model, pretreatment with rutaecarpine significantly reduced neuronal death, decreased infarct volume, and improved neurological functional deficits. In addition, rutaecarpine alleviated damage to the blood–brain barrier in the brain tissue. These effects may be related to the regulation of the MAPK-mediated MMPs pathway. Conclusion This study revealed the neuroprotective effects and molecular mechanisms of rutaecarpine on IS, providing a new theoretical basis for the clinical application of Evodia rutaecarpa.

The development of numerical mapping in preschool children

The developmental order of numerical mapping and the symmetry of the mapping direction in preschool children remain controversial. In light of this context, this study investigated the developmental characteristics of numerical mapping among preschool children in China. In Experiment 1, 128 children aged 2–4 years were recruited to participate in six numerical mapping tasks with small and large numbers. The results showed no reliable differences between the mapping directions. Notably, the developmental orders of small- and large-number mapping differed, potentially confirming two systems of number representation. Furthermore, previous studies have found that counting or not could affect children’s performance on numerical tasks. Therefore, in Experiment 2, we introduced the condition of “reminding children to count” and recruited 97 children aged 3–4 years to explore its influence on numerical mapping. Our findings revealed that reminding children to count could improve their performance on numerical mappings, resulting in alterations in the symmetry of the mapping direction and developmental order of mapping paths. In conclusion, number size and counting reminders would affect researchers’ assessments of the developmental order of numerical mapping in preschool children, which serves as a guide for nurturing and evaluating the numerical mapping ability of preschool children.

Correction: ChatGPT versus human in generating medical graduate exam multiple choice questions—A multinational prospective study (Hong Kong S.A.R., Singapore, Ireland, and the United Kingdom)

Analysing fast food consumption behaviour patterns: The case of Jordan

This study aimed to examine the factors influencing fast food consumption among a Jordanian population by utilizing the extended theory of planned behavior as its theoretical framework. The research employed theory-related scales, income, BMI, ability to consume (intention), indirect intention, and a retrospective measure of fast-food consumption within a cross-sectional sample of 408 university students aged 18–23 years. Structural equation modeling was used to analyze the determinants of consumption. The findings indicated that the self-reported intention measure failed to mediate the attitude-behavior relationship. However, the use of projective intention (indirect) measures revealed that personal factors such as attitude, subjective norms, and self-identification significantly explained over 50% of the variation in the intention to consume fast food, suggesting that participants often underestimated their intention to consume fast food. Furthermore, the study identified available personal funds, perceived and actual behavioral control, and BMI as significant external predictors of fast-food consumption. The originality of this study lies in its contribution to understanding the growing preference for fast food in the Middle East, particularly in Jordan. It introduces a theoretical model that employs projection-based assessments to implicitly gauge intentions and explores the relationship between income, BMI, and consumption behavior. The practical implications of these findings underscore the importance of key psychosocial elements in developing and implementing preventive strategies aimed at promoting healthy eating behaviors among university students.

Biased echoes: Large language models reinforce investment biases and increase portfolio risks of private investors

Large language models are increasingly used by private investors seeking financial advice. The current paper examines the potential of these models to perpetuate investment biases and affect the economic security of individuals at scale. We provide a systematic assessment of how large language models used for investment advice shape the portfolio risks of private investors. We offer a comprehensive model of large language model investment advice risk, examining five key dimensions of portfolio risks (geographical cluster risk, sector cluster risk, trend chasing risk, active investment allocation risk, and total expense risk). We demonstrate across four studies that large language models used for investment advice induce increased portfolio risks across all five risk dimensions, and that a range of debiasing interventions only partially mitigate these risks. Our findings show that large language models exhibit similar “cognitive” biases as human investors, reinforcing existing investment biases inherent in their training data. These findings have important implications for private investors, policymakers, artificial intelligence developers, financial institutions, and the responsible development of large language models in the financial sector.

Subjective assessment of sensory function and oral function decline in older adults

Sensory decline in older adults significantly affects quality of life and contributes to cognitive decline, depression, falls, and injuries. Although several studies exist in this area, most were focused on individual senses, with few being conducted on comprehensive assessments of all five senses. The aim of this study was to investigate the relationship between subjective sensory assessment and oral function, to developing health strategies. This study was conducted as part of the Mima-SONGS Study for examining relationships between oral, cognitive, and physical functions, social factors, nutrition, and health, in older adults living in a mountainous region of Japan. The cohort included 62 participants (40 women and 22 men; mean age: 80.8 yrs.) as of December 2023. Participants completed a questionnaire assessing sensory perception and eating enjoyment rated on a four-point scale. Oral health was evaluated based on the conditions of remaining teeth, tongue coating, oral dryness, occlusal force, oral diadochokinesis, and repetitive salivary swallow test. Sensory assessments indicated minimal overall issues, with auditory problems scoring the highest and taste/tactile issues scoring the lowest. Males scored higher in hearing and maximum occlusal force. Eating enjoyment was generally high and negatively correlated with olfactory and taste problems. Subjective sensory issues were less strongly associated with oral function and age. Most older adults were not subjectively aware of sensory problems, especially olfaction, taste, and tactile problems. Subjective sensory problems showed a moderate but meaningful association with oral health conditions and age. The findings might be valuable data developing future support measures.

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A cautionary tale

Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advancements in neonatal care, the prediction of sPTB remains elusive, in part due to complex etiologies and heterogeneous patient populations. This study aimed to validate and extend information on gene expression biomarkers previously described for predicting sPTB using maternal whole blood from the All Our Families pregnancy cohort study based in Calgary, Canada. The results of this study are two-fold: first, using additional replicates of maternal blood samples from the All Our Families cohort, we were unable to repeat the findings of a 2016 study which identified top maternal gene expression predictors for sPTB. Second, we conducted a secondary analysis of the original gene expression dataset from the 2016 study using five modelling approaches (random forest, elastic net regression, unregularized logistic regression, L2-regularized logistic regression, and multilayer perceptron neural network) followed by external validation using a pregnancy cohort based in Detroit, USA. The top performing model (random forest classification) suggested promising performance (area under the receiver operating curve, AUROC 0.99 in the training set), but performance was significantly degraded on the test set (AUROC 0.54) and further degraded in external validation (AUROC 0.50), suggesting poor generalizability, likely due to overfitting exacerbated by a low feature-to-noise ratio. Similar performance was observed in the other four learning models. Prediction was not improved when using higher complexity machine learning (e.g., neural network) approaches over traditional statistical learning (e.g., logistic regression). These findings underscore the challenges in translating biomarker discovery into clinically useful predictive models for sPTB. This study highlights the critical need for rigorous methodological safeguards and external validation in biomarker research. It also emphasizes the impact of data noise and overfitting on model performance, particularly in high-dimensional omics datasets. Future research should prioritize robust validation strategies and explore mechanistic insights to improve our understanding and prediction of sPTB.

B-spline curve fitting based on dynamic adjustment of knot vector using feature points

An essential challenge in B-spline curve fitting is how to produce a B-spline curve that satisfies the accuracy requirement with a minimal number of knots and control points. This paper suggests a better algorithm based on feature points method. During the curve approximation process, the projection points of data points and their parameters are calculated, and the data point parameters are corrected to achieve dynamic adjustment of the knot vector. At the same time, traditional methods are improved in terms of initial feature point selection and new feature points determination. The experimental results indicate that the B-spline curve produced using the method in this work has higher fitting accuracy, fewer control points, and shorter fitting time.

Genome-wide landscape of miRNA-mRNA-lncRNA-circRNA ceRNA network in Nanos2 deficient mice

Nanos2 plays a key role in self-renewing spermatogenic stem cells (SSCs) and maintains the stem cell state during spermatogenesis. Alleles of the Nanos2 gene knockout showed germline ablated but otherwise structurally normal. To identify the probable ceRNA regulator involved in the process of spermatogenesis by Nanos2, whole transcriptome sequencing was performed in the testes between Nanos2 knock out mice and wild type mice. Finally, a total of 8644 Differentially expressed (DE) mRNAs,180 DE miRNAs, 9538 DE lncRNA and 481 DE circRNAs were identified. Three of each RNAs were selected randomly and identified by real-time PCR to verify the accuracy of sequencing. GO and KEGG functional enrichment analyses revealed similar result of DE mRNAs and target of DE miRNAs/lncRNAs/ circRNAs, mainly involved in the generation, composition, and activity of sperm cells. Furthermore, the regulatory ceRNA network of miRNA(up)-circRNA-lncRNA-mRNA and miRNA(down)-circRNA-lncRNA-mRNA were constructed based on the common targeted miRNA.The results enable us to better understand the interaction of coding RNA and non coding RNA in regulating the generation of spermatogenic stem cells through Nanos2 pathway, and also provided novel insights into molecular mechanism of spermatogenesis.

Neutrophil extracellular traposis in cancer patients with acute ischemic stroke

Background Patients with cancer exhibit an increased risk of acute ischemic stroke (AIS), and neutrophil extracellular traposis (NETosis) has been proposed as a mechanism underlying cancer-associated hypercoagulability. However, studies validating these findings in independent cohorts are limited. Objective We sought to explore whether NETosis-associated markers (plasma DNA and nucleosomes) are increased in patients with active cancer and AIS, and whether these increases correlate with coagulopathy markers in cancer patients. Methods We analyzed NETosis-associated markers in cancer patients with and without AIS and healthy controls, and assessed the correlation between these markers and coagulopathy markers. Additionally, we compared the levels of Netosis-associated markers between cancer patients with conventional stroke mechanisms (CSM) and those with embolic stroke of undetermined source (ESUS). Results Plasma DNA and nucleosome levels were significantly higher in cancer patients with AIS than in cancer controls and healthy controls (p < 0.001, respectively). Both markers correlated with D-dimer levels in cancer patients with AIS. In a sub-analysis, cancer patients with ESUS showed higher levels of NETosis-associated markers compared to those with CSM, whereas vascular risk factors were more frequently observed in cancer patients with CSM. Conclusion These findings suggest that NETosis may contribute to hypercoagulability in patients with active cancer and AIS, particularly in those with ESUS. These results provide additional evidence supporting the establishment of pathophysiology-based therapeutic approaches.

An empirical study on the teaching mode of cultural translation in college English based on the Production Oriented Approach (POA)

Under the circumstances of “Curriculum ideological and political”, in order to adapt to the reform of college English teaching and the needs of Education for Sustainable Development (ESD), as well as optimize the A-level distinguishable teaching mode of college English stratified teaching, this paper takes the fifth stage of “Production Oriented Approach” (POA) as the theoretical basis and completes a one-semester mixed teaching experiment for the Chinese culture translation section in New Horizon College English. Taking the paragraph translation of “Tai Chi” as an example, a detailed teaching design and implementation process were presented, attempting to construct a sustainable blended teaching model for the cultural translation section of college English. Quantitative and qualitative data statistics and analysis were used to evaluate and reflect on the effectiveness of teaching practice. The research results indicate that compared to traditional college English teaching, blended teaching model based on POA can enhance the English language and cultural output ability of A-level students. The mode is of sustainable value, which is beneficial to students understanding of Chinese culture, thereby enhancing their cultural confidence. In addition, this model can effectively enhance teachers’ ability to apply theoretical knowledge in blended teaching design, which has certain significance for the college English curriculum reform and the practice of “curriculum-based political and ideological education”.

Exploring perceptions of health literacy, healthcare access, and utilisation among higher education students in Alentejo, Southern Portugal: A qualitative study

Introduction Health Literacy (HL), defined as the ability to access, understand, appraise and apply health information to make informed decisions, is essential for individual and public health. However, in rural regions such as Alentejo, Portugal, higher education students often encounter specific challenges that hinder the development and effective use of these skills. This study aimed to explore the perceptions of higher education students in Alentejo, Portugal, with regard to health literacy, access to and utilisation of healthcare, and to identify barriers, facilitators and interventions to inform strategies that promote equity, build student capacity and sustainably improve health outcomes. Methodology A qualitative, exploratory, and descriptive study was conducted using three focus groups held on 11th, 13th, and 18th December 2023, with a purposive non-probability sample of 29 participants across first-year, intermediate, and final-year levels. The participants shared their experiences, challenges, and resources related to health literacy, healthcare access, and utilisation. The focus group discussions were audio-recorded, transcribed verbatim, and analysed through a systematic process of open, axial, and selective coding. Codes were organised into categories, and thematic identification was facilitated using a constant comparative method. Content analysis was conducted with MAXQDA® 22 software. Ethical approval was obtained from the Ethics Committee of the University of Évora, Portugal, and informed consent was secured from all participants. Results The students’ perception was that health literacy, access to and utilisation of healthcare are influenced by a dynamic interaction of barriers and facilitators. The identified barriers encompassed the presence of misinformation, financial and logistical challenges, and emotional stigma, particularly in the context of mental and sexual health. Conversely, facilitators were identified as encompassing access to digital tools, a family and peer support network, professional guidance and institutional resources such as libraries and workshops. The students recommended that interventions to improve health literacy be integrated into the higher education curriculum, that navigation support systems be improved and that structural inequalities be explored in order to improve access to and participation in healthcare. Conclusion Digital resources have the potential to improve access to health information; however, persistent challenges such as misinformation, financial constraints and stigma limit their impact. This study underscores the significance of specific interventions to enhance health literacy and healthcare navigation skills. These interventions include the integration of health literacy into academic curricula, the provision of specialised workshops, and the facilitation of access to healthcare services. The study identifies nursing interventions and institutional support as key strategies to promote equitable access to healthcare and improve student wellbeing.

Isotopic niche plasticity of American alligators within the southern Everglades

Hydrologic alterations within the Everglades have degraded American alligator (Alligator mississippiensis) habitat, reduced prey base, and increased physiological stress. Alligator body condition declined across many management areas from 2000 through 2014, prompting us to investigate the relationship between their intraspecific isotopic niche dynamics and body condition. Alligators within the estuary had a larger niche driven by a wider range in stable carbon isotope ratios than those sampled in freshwater habitats. Spatially, model predictability was higher at the smaller scale, reflecting the variability in basal sources and biochemistry among capture sites. Male niches were often larger than those of females, driven by wider ranges of δ13C values, suggesting that they differ in their proportional use of habitats and or resources. However, the similar ranges of δ15N values indicated both sexes foraged within the same trophic level. Furthermore, while not significantly different, large alligators often had a larger niche with elevated δ15N values compared to medium-sized alligators. Although alligators utilize similar stable carbon and nitrogen isotope pools through time, there was considerable temporal variability. These temporal variations in alligators’ isotopic niche were likely influenced by seasonal hydrologic fluctuations within each site, with their niches often being larger in the spring captures than the fall captures. Alligators’ body condition estimates were correlated with intraspecific niche characteristics, including the mean centroid distance between sexes and the interaction between male and female niche size and overlap, within a site, capture period, and year. The variability in intraspecific niche dynamics, landscape heterogeneity, and dynamic hydrology are considerations for designing sustainable management strategies to conserve and enhance alligator populations within the Everglades landscape.

Reinsurance–investment game between two α-maxmin mean–variance insurers

This paper examines a non-zero-sum stochastic differential reinsurance-investment game between two competitive insurers under the α-maximin mean-variance criterion. Both insurers can purchase proportional reinsurance and invest in a financial market consisting of one risk-free asset and one risky asset, and each insurer is concerned with its terminal surplus and relative performance compared to its competitor. The insurers aim to maximize the α-maximin mean-variance utility, which allows them to exhibit different attitudes towards model ambiguity. By solving the extended Hamilton-Jacobi-Bellman (HJB) equations for both insurers, we derive the α-robust equilibrium reinsurance and investment strategies. Finally, several numerical examples are provided to illustrate the impact of some model parameters on the equilibrium strategies.