Tag: pubmed

Sci Rep. 2025 Aug 7;15(1):28949. doi: 10.1038/s41598-025-14320-5.

ABSTRACT

With the growing demand for English language teaching, the efficient and accurate evaluation of students’ writing ability has become a key focus in English education. This study introduces a Hybrid Feature-based Cross-Prompt Automated Essay Scoring (HFC-AES) model that leverages deep learning for intelligent text analysis. Building on traditional deep neural networks (DNNs), the model incorporates text structure features and attention mechanisms, while adversarial training is employed to optimize feature extraction and enhance cross-prompt adaptability. In the topic-independent stage, statistical methods and DNNs extract shared features for preliminary scoring. In the topic-specific stage, topic information is integrated into a hierarchical neural network to improve semantic understanding and topic alignment. Compared with existing Transformer-based scoring models, HFC-AES demonstrates superior robustness and semantic modeling capabilities. Experimental results show that HFC-AES achieves strong cross-prompt scoring performance, with an average Quadratic Weighted Kappa (QWK) of 0.856, outperforming mainstream models. Ablation studies further highlight the critical role of text structure features and attention mechanisms, particularly in improving argumentative writing assessment. Overall, HFC-AES offers effective technical support for automated essay grading, contributing to more reliable and efficient evaluation in English language teaching.

PMID:40775439 | DOI:10.1038/s41598-025-14320-5

Sci Rep. 2025 Aug 7;15(1):28990. doi: 10.1038/s41598-025-14276-6.

ABSTRACT

Straightlining (uniform responses across items), poses a risk in surveys. Among adolescents, previous studies have investigated the prevalence and impact of straightlining in shorter questionnaires within larger surveys. A typical finding is that straightlining is more common among younger respondents, and particularly among boys. A better understanding of straightlining is important for improving data quality. The present study aims to estimate the prevalence of straightlining among adolescents filling out a survey covering different aspects of social media use across 64 items. Additionally, it seeks to assess the impact of straightlining on internal consistency and the associations between six domains of social media use and symptoms of anxiety and depression. Data from the «LifeOnSoMe»-study (N = 3,285), collected from adolescents (aged 16+) in Bergen, Norway. Descriptive and inferential statistics. In total, 5.4% of participants were straightliners, (8.6% of the boys vs. 2.9% of the girls (p < 0.001)). There were no differences in age between straightliners and the remainder of the sample. Overall, the prevalence and impact of straightlining was limited in the present sample. However, there were large discrepancies in terms of both internal consistency, correlations between domains of social media use, and associations with symptoms of anxiety and depression between straightliners and the remainder of the sample. Straightlining behavior had minimal effects on this sample’s analytical epidemiological conclusions. While boys were more prone to straightlining than girls, overall prevalence was low. However, significant discrepancies between straightliners and other respondents suggest potential risks in samples with higher straightlining prevalence.

PMID:40775438 | DOI:10.1038/s41598-025-14276-6

Sci Rep. 2025 Aug 7;15(1):28969. doi: 10.1038/s41598-025-13240-8.

ABSTRACT

This study assesses the practical implications of ChatGPT’s ability to identify hotspots by comparing its performance to Geographical Information System (GIS) software in detecting CO₂ sources and sinks observed by the Orbiting Carbon Observatory-3 (OCO-3) satellite. ChatGPT exhibited performance comparable to ArcGIS in both z-score statistics and spatial distribution patterns of XCO₂ hot and cold spots. The results generated by ChatGPT showed a strong correlation with ArcGIS-generated hotspots, demonstrating a z-score correlation coefficient of R²=0.82 and a cosine similarity score of 0.90. As multimodal artificial intelligence becomes more prevalent in earth monitoring, ChatGPT is expected to be a valuable tool for identifying CO₂ emission patterns, particularly for users who lack specialized GIS expertise. These findings establish a significant benchmark for ChatGPT’s potential in this field, offering a novel approach to identifying area-wide spatial patterns of CO₂ emissions compared to conventional GIS software.

PMID:40775428 | DOI:10.1038/s41598-025-13240-8

Sci Rep. 2025 Aug 7;15(1):28925. doi: 10.1038/s41598-025-13380-x.

ABSTRACT

This study used data from a large dam site to model changes in groundwater quality variables. Several indicators were investigated to check the quality of water sources for the site for different purposes. The factor analysis results displayed that four factors control 87.58% of water quality changes. The primary factor responsible for approximately half of the impact on water quality, accounting for 55.12% of the total variance, includes the EC, Ca²⁺, SAR, SO₄^–, Na⁺, CO₃^–, %Na, Cl^–, and TDS parameters. These parameters are directly related to water quality and are influenced by the natural characteristics of the region. Considering that the main control factor for water quality is the first factor mentioned, these factors were used in multivariate analysis and intelligent modeling. Therefore, Na⁺, Cl⁺, Na%, CO₃^–, and SO₄^2- were used as input variables (independent variables), and EC, TDS, and SAR were used as output variables (dependent variables). Support vector machine (SVM) with various kernel functions, multilayer perceptron artificial neural network (MLP-ANN) with various training algorithms, random forest algorithm (RFA), Gaussian process regression (GPR), and statistical analysis methods were used for modeling. Among the kernel functions used in SVM, the radial basis function (RBF) kernel provided the most accurate results. On the other hand, among the learning algorithms used in neural networks, the Levenberg-Marquardt algorithm demonstrated the highest level of accuracy. Modeling results based on error value, Wilmot agreement index, A₂₀ index, determination coefficient, and violin diagrams showed that the SVM (R² > 0.99, RMSE < 0.04, A₂₀ = 1.00, WAI = 1.00) achieved better than the other models. The results of Kruskal-Wallis’s test disclosed that there is no substantial difference between the water quality parameters obtained from the models and the measured values.

PMID:40775421 | DOI:10.1038/s41598-025-13380-x

Neural Netw. 2025 Aug 5;192:107925. doi: 10.1016/j.neunet.2025.107925. Online ahead of print.

ABSTRACT

The development of efficient segmentation strategies for medical images has evolved from its initial dependence on Convolutional Neural Networks (CNNs) to the current investigation of hybrid models that combine CNNs with Vision Transformers (ViTs). There is an increasing focus on developing architectures that are both high-performing and computationally efficient, capable of being deployed on remote systems with limited resources. Although transformers can capture global dependencies in the input space, they face challenges from the corresponding high computational and storage expenses involved. The objective of this research is to propose that Vision Extended Long Short-Term Memory (Vision-xLSTM) forms an appropriate backbone for medical image segmentation, offering excellent performance with reduced computational costs. This study investigates the integration of CNNs with Vision-xLSTM by introducing the novel U-VixLSTM. The Vision-xLSTM blocks capture the temporal and global relationships within the patches extracted from the CNN feature maps. The convolutional feature reconstruction path upsamples the output volume from the Vision-xLSTM blocks to produce the segmentation output. The U-VixLSTM exhibits superior performance compared to the state-of-the-art networks in the publicly available Synapse, ISIC and ACDC datasets. The findings suggest that U-VixLSTM is a promising alternative to ViTs for medical image segmentation, delivering effective performance without substantial computational burden. This makes it feasible for deployment in healthcare environments with limited resources for faster diagnosis. Code provided: https://github.com/duttapallabi2907/U-VixLSTM.

PMID:40773779 | DOI:10.1016/j.neunet.2025.107925

JMIR Public Health Surveill. 2025 Aug 7;11:e72064. doi: 10.2196/72064.

ABSTRACT

BACKGROUND: Violence against women (VAW) is a major public health and human rights concern with profound mental health consequences. However, the association between specific VAW forms and mental health, particularly among left-behind women in rural China, remains unclear.

OBJECTIVE: This study aimed to identify the associations of VAW with depression, anxiety, and comorbid symptoms and to explore the potential roles of resilience and social support.

METHODS: The cross-sectional survey was conducted in Y City, Henan Province, China, in July 2023. A multistage stratified random sampling method was used to recruit left-behind women, resulting in a final sample of 1503 participants. Data on participants and their VAW were collected through a face-to-face questionnaire survey. The forms of VAW assessed were nonpartner violence (NPV) and intimate partner violence (IPV; including remote IPV). Depressive symptoms were evaluated using the 10-item Center for Epidemiological Studies Depression Scale, while anxiety symptoms were assessed with the Generalized Anxiety Disorder-7. The comorbid symptoms of depression and anxiety (CDA) were ascertained as the simultaneous presence of depressive and anxiety symptoms. A multivariable logistic regression model was used to estimate the odds ratio and 95% CIs. A 4-way decomposition analysis was conducted to test the mediation roles and interactions of resilience and social support between VAW and mental health outcomes. Population attributable fractions and pathway-specific population attributable fractions were calculated to estimate the burden of mental health outcomes attributable to VAW.

RESULTS: Lifetime VAW (adjusted odds ratio [aOR] 1.84, 95% CI 1.32-2.54) was associated with an increased risk of CDA. Women who were exposed to lifetime IPV (aOR 1.84, 95% CI 1.32-2.56), remote IPV (aOR 2.79, 95% CI 1.60-4.74), and NPV (aOR 2.63, 95% CI 1.58-4.26) had an increased likelihood of reporting CDA. Similar associations could also be found for depressive symptoms and anxiety symptoms. In the 4-way decomposition analysis for VAW and CDA, mediation effects of low resilience and social support were statistically significant (P<.05), whereas none of the interactions reached significance (P>.05). The pure mediation proportion was 28.2% for the low resilience and 18.6% for the social support between VAW and CDA. A total of 20.8% of CDA cases, 15.1% of depressive symptoms cases, and 22.7% of anxiety symptoms cases were attributable to VAW. Among these, low resilience accounted for 7.2% and low social support accounted for 4.7% of CDA cases as mediators.

CONCLUSIONS: Lifetime VAW, including IPV (and remote IPV) and NPV, shows significant associations with CDA and depressive and anxiety symptoms among rural left-behind women in China. The associations are partly mediated by low resilience and social support. Targeted strategies, including efforts to reduce violence against rural left-behind women, enhance their resilience and strengthen their social support networks, are urgently needed.

PMID:40773765 | DOI:10.2196/72064

Obstet Gynecol. 2025 Aug 7. doi: 10.1097/AOG.0000000000006028. Online ahead of print.

ABSTRACT

Healthy People 2030 aims to decrease low-risk cesarean delivery rates to 23.6% in the United States. In 2023, the national rate was 26.6%, though rates vary widely by state and hospital. This suggests a need for localized geographic estimates to identify places with higher burden. We modeled 2023 low-risk cesarean delivery rates by county of birth using birth certificate data and hierarchical Bayesian models that spatially smooth unstable estimates. We found considerable variation in rates, with county rates ranging from 5.8% to 53.4%. Counties in the West had lower rates than those in the Midwest, South, and Northeast. County rates increased with urbanicity. Only 47.7% (985) of counties had rates meeting the Healthy People 2030 target.

PMID:40773757 | DOI:10.1097/AOG.0000000000006028

JMIR Form Res. 2025 Aug 7;9:e53080. doi: 10.2196/53080.

ABSTRACT

BACKGROUND: Fatigue is a common symptom occurring in individuals with visual impairment (VI). Feeling fatigued has a strong impact on an individual’s well-being, with profound consequences. Cognitive and emotional functioning, social roles, and participation are negatively affected in severely fatigued individuals with VI. Therefore, we developed E-nergEYEze, a blended vision-specific eHealth intervention based on cognitive behavioral therapy and self-management to reduce fatigue severity in adults with VI.

OBJECTIVE: We aimed to report the experience of patients and professionals with E-nergEYEze. To complement cost-effectiveness outcomes, the user experiences from both perspectives were considered relevant for a better understanding of the intervention uptake.

METHODS: E-nergEYEze was studied in a randomized controlled trial. User experiences of participants with VI and severe fatigue (51/98, 52%; median age 58.0, IQR 53.0-65.0 years; female participants: 32/51, 63%), who were randomized to the intervention group, and professionals (n=11), who provided blended support, were evaluated. The Dutch Mental Health Care Thermometer questionnaire and a therapist evaluation were used and analyzed using mixed methods. A focus group meeting with social workers (4/7, 57%), a computer trainer (1/7, 14%), and psychologists (2/7, 29%) was held for more in-depth information. The eHealth platform provided data on user engagement from both perspectives.

RESULTS: E-nergEYEze was completed by 63% (32/51) of patients for more than 80% of the module steps. Overall, results on user engagement showed that a median 89% (IQR 45%-100%) of all assigned module steps were completed, with all modules being completed by at least 50% (37/51) of the patients. Completion of the intervention was related to the presence of digital proficiency; having the appropriate expectations; content that matches personal preferences and life context; and the absence of impeding personal circumstances, mental health issues, or other concurrent rehabilitation programs. The intervention was given a median grade of 7.0 out of 10.0 (IQR 6.0-8.0), and 87% (39/45) of the patients reported that they would recommend E-nergEYEze to others. However, improvements in the frequency and quality of guidance were considered highly relevant. Professionals reported that E-nergEYEze required patients’ self-efficacy, motivation, and digital skills; therefore, preselection was seen as essential. Professionals’ affinity with eHealth was considered important to provide appropriate remote support.

CONCLUSIONS: eHealth provides treatment opportunities for individuals with VI for which guidance is considered highly relevant. During participation in E-nergEYEze, patients were engaged, internalized personally relevant topics, and made use of the benefits of eHealth. More attention to the suitability of patients and training of professionals for providing remote support is considered essential. These user experiences underlined the potential of E-nergEYEze to reduce fatigue severity in adults with VI and provided valuable insights to learn from and optimize E-nergEYEze.

TRIAL REGISTRATION: International Clinical Trials Registry Platform (ICTRP) NL7764; https://tinyurl.com/32b3xt74.

PMID:40773749 | DOI:10.2196/53080

JMIR Hum Factors. 2025 Aug 7;12:e70181. doi: 10.2196/70181.

ABSTRACT

BACKGROUND: Cognitive bias modification for interpretation (CBM-I) is a technique to modify interpretation and is used to reduce unhelpful negative biases. CBM-I has been extensively studied in anxiety disorders where interpretation bias has been shown to play a causal role in maintaining the condition. Successful Treatment of Paranoia (STOP) is a CBM-I smartphone app targeting interpretation bias in paranoia. It has been developed following research on the feasibility and acceptability of a computerized version. This qualitative study extended that research by investigating the acceptability of STOP in individuals with paranoia. The study design and implementation were informed by the Evidence Standards Framework for Digital Health Technologies (DHTs) published by the UK National Institute for Health and Care Excellence (NICE).

OBJECTIVE: The aim of the study was to involve service users in the design, development, and testing of STOP and understand the degree of satisfaction with the product. We aimed to establish the extent to which STOP met the NICE minimum and best practice standards for DHTs, specifically its acceptability to intended end users.

METHODS: In total, 12 participants experiencing mild to moderate levels of paranoia were recruited to complete 6 weekly sessions of STOP before being invited to a feedback interview to share their experiences. Interview questions revolved around the acceptability of the app, its perceived usefulness, and barriers to the intervention, as well as practicality and views on the use of a digital intervention in principle. Interviews were coded and analyzed using the framework analysis method, combining both deductive and inductive approaches.

RESULTS: Framework analysis yielded 6 themes: independent use and personal fit; digital versus traditional approaches; user reactions and emotional impact; impact on thinking, awareness, and well-being; design, engagement, and usability; and intervention relevance and practical fit.

CONCLUSIONS: STOP was found to be a broadly acceptable intervention and was positively received by most participants. The study findings are in line with the NICE Evidence Standards Framework for DHTs, as intended end users were involved in the development, design, and testing of STOP and were mostly satisfied with it. These findings will contribute to the further iterative development of this intervention that targets interpretation bias in paranoia.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-024-08570-3.

PMID:40773747 | DOI:10.2196/70181

JMIR Form Res. 2025 Aug 7;9:e77113. doi: 10.2196/77113.

ABSTRACT

BACKGROUND: There is an increasingly diverse range of mobile apps and digital health devices available to help patients manage their health. Despite evidence for the effectiveness of such technologies, their potential has not been fully realized because adoption remains low. Such limited uptake can have direct implications for the intended benefits of these technologies.

OBJECTIVE: This study aimed to understand what matters most to US military veterans when deciding whether to use digital health technologies (DHTs) such as mobile health apps or devices to manage their health and compare these factors between veterans with and without prevalent chronic physical and mental health conditions.

METHODS: We conducted a cross-sectional analysis of survey data collected from a national sample of veterans who receive care from the Veterans Health Administration (VHA), which was predominantly gathered as part of the last wave of a larger longitudinal data collection effort.

RESULTS: Among respondents (n=857), 86.7% (736/849) reported currently using or having previously used ≥1 devices to manage their health, and 78.4% (639/815) also reported using either VHA or non-VHA health apps. Considerations most frequently endorsed as “very important” by veterans when deciding whether to use DHTs included receiving secure messages from their health care team about DHTs, knowing data from DHTs would be used to inform their care, and receiving recommendations from providers to use DHTs. Conversely, considerations most frequently endorsed as “not at all important” included seeing information about DHTs on social media, having community support to use DHTs, and receiving encouragement from peers to use DHTs. Considerations did not significantly differ between veterans with or without prevalent chronic health conditions; however, a greater proportion of veterans with prevalent mental health conditions reported the following considerations to be “very important:” seeing information about DHTs on social media, having community support to use DHTs, having other veterans encourage DHT use, and having help from family, friends, or other important people to use DHTs.

CONCLUSIONS: Understanding what matters most to patients when they are deciding to adopt a technology for their health can, and should, inform implementation strategies and other approaches to enhance health-related technology use. Our results suggest that, for veterans, recommendations from health care team members and knowing that the data from DHTs will be used in clinical care are more important than information from social media, community sources, or peers when deciding to use DHTs, although perceptions of importance regarding the latter may differ among patients with different conditions. Our findings suggest that communication from health care team members to patients, perhaps either in-person or electronically, could help encourage DHT adoption and use.

PMID:40773745 | DOI:10.2196/77113