OBJECTIVES

The challenges faced by patients with diabetes in rural Philippine communities highlight
the need to look into how we can improve ways of communicating health education and self-management strategies. Patient education materials play a vital role in promoting regimen adherence, yet their acceptability and effectiveness in resource-limited settings remain insufficiently investigated. Therefore, this study aimed to explore participants’ experiences with and perceptions of a community-based diabetes learning module through qualitative inquiry.

Using purposive sampling, 13 participants who successfully completed the Blood Sugar Bantayan, Diabetes Malikayan pilot health program from January to July 2022 in a rural community in southern Philippines consented to engage in in-depth interviews. A semi-structured topic guide was developed, validated by experts, and pretested.
Interviews were conducted in the local language, audio-recorded, transcribed, translated, and analyzed using Braun and Clarke's reflexive thematic approach.

Five major themes emerged from the analysis: initial perceptions of the module, aspects of the module found useful, perceived impact on regimen adherence, hindrances to adherence, and overall feedback with likelihood of recommendation. Participants valued the module’s visual appeal, use of local language, and comprehensive coverage of diabetes management. The module served as a guide that enabled participants to gain better control over their condition through improved self-discipline and health practices, often motivated by family support. However, challenges including time constraints, resource limitations, and competing priorities affected consistent implementation of recommended practices. Despite these barriers, participants expressed strong satisfaction with the module and willingness to share it with others, though sharing decisions were often based on perceived relevance to others’ health status.

Community-based learning modules can serve as valuable educational tools for diabetes care in rural Filipino communities. Findings underpin the importance of developing culturally appropriate and responsive campaigns for diabetes education in remote
environments, but multi-modal strategies that cut across sectors are still imperative to address persisting structural factors that pervade health program efforts.

Artificial intelligence (AI) technology enhances the function of acupuncture clinical decision support system (CDSS) by promoting the accuracy of its diagnosis, assisting the formulation of personalized therapeutic regimen, and realizing the scientific and precise evaluation of its therapeutic effect. This paper deeply analyzes the unique advantages of AI-based acupuncture CDSS, including the intelligence and high efficiency. Besides, it points out the challenges of data security, the lack of model interpretation and the complexity of interdisciplinary cooperation in the development of acupuncture CDSS. With the continuous development and improvement of AI technology, acupuncture CDSS is expected to play a more important role in the fields of personalized medicine, telemedicine and disease prevention, and to further advance the efficiency and effect of acupuncture treatment, drive the modernization of acupuncture, and enhance its position and influence in the global healthcare system.
Humans ; Acupuncture Therapy ; Artificial Intelligence ; Decision Support Systems, Clinical

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.
Humans ; Emotions/physiology* ; Electroencephalography ; Support Vector Machine ; Electromyography ; Signal Processing, Computer-Assisted ; Galvanic Skin Response/physiology* ; Machine Learning ; Male

Existing emotion recognition research is typically limited to static laboratory settings and has not fully handle the changes in emotional states in dynamic scenarios. To address this problem, this paper proposes a method for dynamic continuous emotion recognition based on electroencephalography (EEG) and eye movement signals. Firstly, an experimental paradigm was designed to cover six dynamic emotion transition scenarios including happy to calm, calm to happy, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG and eye movement data were collected simultaneously from 20 subjects to fill the gap in current multimodal dynamic continuous emotion datasets. In the valence-arousal two-dimensional space, emotion ratings for stimulus videos were performed every five seconds on a scale of 1 to 9, and dynamic continuous emotion labels were normalized. Subsequently, frequency band features were extracted from the preprocessed EEG and eye movement data. A cascade feature fusion approach was used to effectively combine EEG and eye movement features, generating an information-rich multimodal feature vector. This feature vector was input into four regression models including support vector regression with radial basis function kernel, decision tree, random forest, and K-nearest neighbors, to develop the dynamic continuous emotion recognition model. The results showed that the proposed method achieved the lowest mean square error for valence and arousal across the six dynamic continuous emotions. This approach can accurately recognize various emotion transitions in dynamic situations, offering higher accuracy and robustness compared to using either EEG or eye movement signals alone, making it well-suited for practical applications.
Humans ; Electroencephalography/methods* ; Emotions/physiology* ; Eye Movements/physiology* ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Algorithms

In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabilitation training strategies, this paper constructs a multi-scale convolutional neural network (MSCNN) - convolutional block attention module (CBAM) - bidirectional long short-term memory network (BiLSTM) muscle strength prediction model to fully explore the spatial and temporal features of the data and simultaneously suppress useless features, and finally achieve the improvement of the accuracy of the muscle strength prediction model. To verify the effectiveness of the model proposed in this paper, the model in this paper is compared with traditional models such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), CNN - squeeze excitation network (SENet), MSCNN-CBAM and MSCNN-BiLSTM, and the effect of muscle strength prediction by each model is investigated when the hand force application changes from 40% of the maximum voluntary contraction force (MVC) to 60% of the MVC. The research results show that as the hand force application increases, the effect of the muscle strength prediction model becomes worse. Then the ablation experiment is used to analyze the influence degree of each module on the muscle strength prediction result, and it is found that the CBAM module plays a key role in the model. Therefore, by using the model in this article, the accuracy of muscle strength prediction can be effectively improved, and the characteristics and laws of hand muscle activities can be deeply understood, providing assistance for further exploring the mechanism of hand functions .
Neural Networks, Computer ; Humans ; Hand Strength/physiology* ; Support Vector Machine ; Muscle Strength/physiology* ; Hand/physiology* ; Convolutional Neural Networks

OBJECTIVES: To investigate the chain mediating role of family care and emotional management in the relationship between social support and anxiety among rural primary school students. METHODS: A questionnaire survey was conducted among students in grades 4 to 6 from four counties in Hunan Province. Data were collected using the Social Support Rating Scale, Family Care Index Scale, Emotional Intelligence Scale, and Generalized Anxiety Disorder -7. Logistic regression analysis was used to explore the influencing factors of anxiety symptoms. Mediation analysis was conducted to assess the chain mediating effects of family care and emotional management between social support and anxiety. RESULTS: A total of 4 141 questionnaires were distributed, with 3 874 valid responses (effective response rate: 93.55%). The prevalence rate of anxiety symptoms among these students was 9.32% (95%CI: 8.40%-10.23%). Significant differences were observed in the prevalence rates of anxiety symptoms among groups with different levels of social support, family functioning, and emotional management ability (P<0.05). The total indirect effect of social support on anxiety symptoms via family care and emotional management was significant (β=-0.137, 95%CI: -0.167 to -0.109), and the direct effect of social support on anxiety symptoms remained significant (P<0.05). Family care and emotional management served as significant chain mediators in the relationship between social support and anxiety symptoms (β=-0.025,95%CI:-0.032 to -0.018), accounting for 14.5% of the total effect. CONCLUSIONS Social support can directly affect anxiety symptoms among rural primary school students and can also indirectly influence anxiety symptoms through the chain mediating effects of family care and emotional management. These findings provide scientific evidence for the prevention of anxiety in primary school students from multiple perspectives.
Humans ; Female ; Male ; Social Support ; Anxiety/etiology* ; Child ; Students/psychology* ; Emotions ; Logistic Models

OBJECTIVE: The relationship between benign prostatic hyperplasia (BPH) and the oxidative balance score (OBS) will be discussed in this study. METHODS: The clinical data on 16 dimensions of diet and 4 dimensions of lifestyle from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2008 were used to calculate OBS. We considered BPH as the outcome and investigated the linear and nonlinear relationships between the two. Additionally, subgroup analyses and interaction tests were conducted as well. Furthermore, the methods of machine learning including XGBoost, support vector machine (SVM) and naive Bayes (NB) were used to establish a predictive model for BPH. RESULTS: Higher OBS was consistently associated with an increased prevalence of BPH, with Restricted Cubic Splines highlighting a significant positive nonlinear association (P=0.015). Subgroup analyses revealed differences and interactive relationships based on alcohol consumption. Among the seven machine learning models that we included the OBS score in, the XGBoost model emerged as the best, with an AUC value of 0.769. CONCLUSION There is a significant association between OBS and the prevalence of BPH in the American population, which provides a valuable insight for further diagnosis and research of the disease.
Humans ; Male ; Prostatic Hyperplasia/epidemiology* ; Machine Learning ; Bayes Theorem ; Nutrition Surveys ; Support Vector Machine ; Life Style ; Oxidative Stress ; Aged ; Diet ; Prevalence

Enterostomy is an important means of treating colorectal cancer disease, and the nutritional problems of colorectal cancer patients with enterostomy are getting more and more attention. Malnutrition not only prolongs the hospitalization time of the patients and increases their economic burden, but also increases the incidence of patients' complications and death rate. At present, the nutritional management of colorectal cancer patients with enterostomy in China has not yet formed a consensus. Launched by the Chinese Society for Oncological Nutrition, experts with relevant backgrounds from multiple disciplines in China were invited, based on relevant references, the latest evidence and experts' clinical experience, and after several rounds of expert correspondence and expert demonstration meetings, to write the expert consensus on the whole-course nutritional management of colorectal cancer patients with enterostomy. The expert consensus centers on the teamwork model for the whole-course management of colorectal cancer patients with enterostomy, nutritional tertiary diagnosis, principles of nutritional therapy, perioperative nutritional management, nutritional management of intestinal stoma complications, and post-discharge nutritional management, aiming to provide standardized guidance for the whole-course nutritional management of patients with intestinal stoma.
Humans ; Colorectal Neoplasms/therapy* ; Consensus ; Enterostomy ; Nutritional Support ; Malnutrition ; Nutrition Therapy