The growing use of generative artificial intelligence (AI) has coincided with more timely manuscript submissions and improved efficiency in scientific writing. While AI tools help authors produce clear and well-structured work, excessive or undisclosed reliance raises concerns about originality, authenticity, and the integrity of scholarly publications. Editors increasingly recognize AI-assisted writing and face the challenge of preserving rigorous standards. Emerging guidelines emphasize transparency in AI use, alongside the need to retain individual voice and diversity in scientific expression. This editorial highlights the balance between leveraging AI’s benefits and safeguarding ethical publication practices, while acknowledging contributors to the current issue.

Occupational allergies and respiratory health issues are an increasing concern across various industries, significantly impacting worker productivity, well-being and long-term health outcomes. These health problems are primarily triggered by exposure to airborne allergens such as pollen, mold and chemical irritants commonly found in workplace environments. Climate change exacerbates these issues by altering environmental conditions that influence allergen distribution and intensity. Rising temperatures, increased humidity, worsening air pollution and extreme weather events contribute to prolonged pollen seasons, greater mold proliferation, increased volatile organic compound (VOC) emissions and heightened respiratory health risks among workers. VOCs are chemical pollutants emitted from a range of indoor and outdoor sources, further aggravating air quality in workplace environments.

An emerging concern is the relationship between climate change and thunderstorm asthma, a phenomenon where allergens are dispersed during thunderstorms, leading to acute asthma events. Ongoing research continues to examine how environmental changes contribute to the frequency and intensity of asthma-related health issues, particularly in occupational settings.

Both outdoor and indoor workers face distinct challenges due to climate-driven changes in allergen exposure. Agricultural, forestry and construction workers are especially vulnerable to longer pollen seasons and higher concentrations of air pollutants. In contrast, office, healthcare and industrial workers face increased exposure to indoor allergens due to poor ventilation, rising VOC concentrations and other environmental stressors. Low-income workers, older adults and those in developing countries face disproportionate risks due to limited access to healthcare and protective measures.

This viewpoint article explores the relationship between climate change and occupational respiratory allergies, emphasizing the need for effective workplace adaptation strategies such as improving ventilation systems, using air filtration technology and creating allergen-free zones. It also examines the socioeconomic barriers to implementing these measures, mental health impacts of occupational allergies (including stress, anxiety and absenteeism) and the emergence of new allergens due to changing environmental conditions. Furthermore, technological solutions such as AI-driven air quality monitoring systems and wearable sensors hold promise in mitigating allergen exposure by providing real-time data, enabling proactive health measures and supporting policy development to protect worker health. Addressing these challenges is critical to ensuring workers’ health, well-being and productivity in the face of ongoing climate change.

OBJECTIVES

This study explored the interplay of three Filipino cultural values—hiya (modesty/self-restraint), pakikisama (conflict-avoidance dimension of social harmony), and pakikipag-ugnayan (professional engagement, characterized by vigor, dedication, and absorption)—among Generation Z nurses in Quezon City. Specifically, it assessed their levels, examined interrelationships through correlation and path analysis, and developed a framework to illustrate their influence on nursing practice. In the Filipino context, hiya encompasses both positive dimensions, such as conscientiousness, moral accountability, and social responsibility, and constraining dimensions, including excessive self-restraint, fear of criticism, and inhibition. Pakikisama, while fostering harmony, may discourage assertiveness and limit open communication. Pakikipag-ugnayan, conceptualized here as engagement, reflects dedication, vigor, and absorption, which are vital for performance, patient safety, and workforce retention.

A descriptive–correlational design was utilized to assess the relationships among hiya, pakikisama, and pakikipag-ugnayan. Data were gathered through a structured, self-administered questionnaire comprising validated scales for each construct, distributed in online formats to accommodate participants’ availability and work schedules. The purposive sample consisted of 115 Filipino Generation Z nurses engaged in direct patient care in various healthcare facilities in Quezon City. Data analysis involved descriptive statistics to summarize variable scores, Spearman’s rho to examine bivariate correlations, and path analysis to determine the direct and indirect effects among the variables. The results of the path analysis were used to develop a proposed framework illustrating the interplay of these cultural values in nursing practice.

Path analysis revealed distinct patterns in the interplay of hiya, pakikisama, and pakikipag-ugnayan among Generation Z nurses in Quezon City. Very high hiya scores (mean = 5.32) reflected both moral accountability and restraint-oriented tendencies. Despite these positive indicators, hiya showed a negative correlation with pakikipag-ugnayan, suggesting that its inhibiting side—marked by fear of criticism and avoidance of mistakes—may overshadow its constructive moral aspects. In contrast, the path analysis identified a moderate positive direct effect between hiya and pakikipag-ugnayan (β = 0.400, pCONCLUSION

Among Generation Z nurses, hiya demonstrates a dual role: while its ethical and conscientious aspects can strengthen pakikipag-ugnayan, its inhibiting side may suppress pakikipag-ugnayan. Pakikisama, operationalized as conflict avoidance and withholding dissent, emerged as a barrier to pakikipag-ugnayan by discouraging open communication. These findings informed the development of the Hiya–Pakikisama– Pakikipag-ugnayan Cultural Dynamics Framework, which illustrates how cultural values can simultaneously enable and constrain professional engagement. The framework has potential applications in nursing education and organizational practice, particularly in designing reflective learning, mentorship, and communication strategies that balance cultural sensitivity with assertiveness. Further validation across generations and healthcare settings is recommended to refine its applicability.

BACKGROUND

Internationalization in medical education aims to align programs with global standards, foster intercultural competence, and expand academic collaboration. In private medical schools in Northern Luzon, clarifying how internationalization is conceptualized and enacted can enhance the medical curriculum.

To explore how private medical schools in Northern Luzon conceptualize and implement internationalization, identify associated challenges, and propose curriculum inputs to enhance delivery of the medical curriculum.

The qualitative study utilized a multiple case study design. The study was approved by the Saint Louis University Research Ethics Committee. Using purposive sampling, 45 participants from private institutions in Northern Luzon Philippines consented to engage in focus group discussions and in-depth interviews from February to April 2025. Data were analyzed through inductive content and thematic techniques.

Participants conceptualized internationalization as (1) alignment with international quality and practice standards; (2) global engagement and exchange encompassing knowledge, skills, culture, institutional networking, and collaboration; (3) inclusivity and multicultural responsiveness; and (4) pursuit of global recognition and accreditation. Implementation occurred via “internationalization at home” (embedding global perspectives in local learning environments), “internationalization abroad” (student/faculty mobility and external exposure), and alignment of philosophical and curricular frameworks. Reported challenges included institutional and curricular constraints, language and cultural barriers, sociocultural and financial limitations, and external/contextual pressures.

Addressing the identified barriers and scaling the documented practices can enhance the delivery of the medical curriculum. The study offers actionable curricular revisions that private institutions may adopt to strengthen internationalization in medical education.

Tujia ethnic group medicine Xuetong is derived from Kadsura heteroclita, the stem of which has the medicinal value for anti-rheumatoid arthritis, liver protection, anti-tumor, anti-oxidation effects, and has been widely used in Hunan and Guangdong in China. The selection of reliable and stable reference genes is the basis for subsequent molecular research on K. heteroclita. In this study, GAPDH, TUA, Actin, UBQ, EF-1α, 18S-rRNA, CYP, UBC, TUB, H2A, and RPL were selected as candidate reference genes in Kadsura heteroclita. The gene expression levels of the 11 candidate reference genes of K. heteroclita in its 6 different parts(stem-inside of the cambium, stem-outside of the cambium, fruit, flower, root, and leaf) and under different intervention conditions [drought stress, salt stress, and methyl jasmonate(MeJA) treatment] were detected by quantitative real-time polymerase chain reaction(qRT-PCR). The expression stability of the 11 candidate reference genes was comprehensively analyzed and evaluated by geNorm, NormFinder, ΔCT algorithm, and RefFinder software. The results showed that the expression of UBC and RPL was relatively stable in 6 different parts, and UBC and GAPDH genes were relatively stable under different intervention conditions. To verify the reliability of reference genes for K. heteroclita, this study further examined the relative expression levels of KhFPS, KhIDI, KhCAS, KhSQE, KhSQS, KhSQS-2, KhHMGS, KhHMGR, KhMVD, KhMVK, KhDXR, KhDXS, KhPMVK, and KhGGPS in different parts and under different intervention conditions, which might relate to the synthesis of the main component(Xuetongsu) of K. heteroclita. The results showed that with UBC and RPL or UBC and GAPDH as the reference genes, the expression trends of these 14 genes were basically consistent in different parts or under different intervention conditions for K. heteroclita. In conclusion, UBC can be used as a reference gene of K. heteroclita for its different parts and different intervention conditions, which lays a foundation for further research on the biosynthetic pathway of main components in K. heteroclita.
Real-Time Polymerase Chain Reaction/methods* ; Reference Standards ; Gene Expression Regulation, Plant ; Gene Expression Profiling ; Plant Proteins/metabolism* ; Drugs, Chinese Herbal

Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model-calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
Humans ; Electroencephalography/methods* ; Meditation ; Calibration ; Neural Networks, Computer ; Brain/physiology* ; Rest/physiology* ; Deep Learning ; Signal Processing, Computer-Assisted

In animal navigation, head direction is encoded by head direction cells within the olfactory-hippocampal structures of the brain. Even in darkness or unfamiliar environments, animals can estimate their head direction by integrating self-motion cues, though this process accumulates errors over time and undermines navigational accuracy. Traditional strategies rely on visual input to correct head direction, but visual scenes combined with self-motion information offer only partially accurate estimates. This study proposed an innovative calibration mechanism that dynamically adjusts the association between visual scenes and head direction based on the historical firing rates of head direction cells, without relying on specific landmarks. It also introduced a method to fine-tune error correction by modulating the strength of self-motion input to control the movement speed of the head direction cell activity bump. Experimental results showed that this approach effectively reduced the accumulation of self-motion-related errors and significantly enhanced the accuracy and robustness of the navigation system. These findings offer a new perspective for biologically inspired robotic navigation systems and underscore the potential of neural mechanisms in enabling efficient and reliable autonomous navigation.
Animals ; Neural Networks, Computer ; Calibration ; Spatial Navigation/physiology* ; Head Movements/physiology* ; Neurons/physiology* ; Models, Neurological ; Head/physiology* ; Action Potentials/physiology*

Medical robotic arm often encounters multi-source and nonlinear errors during the calibration process, making it difficult for traditional mathematical modeling methods to fully characterize system error features, thereby limiting further improvement in calibration accuracy. In this study, a robotic arm parameter error identification model is established, and a calibration method based on an adaptive long short-term memory (ALSTM) neural network is proposed. The method incorporates a particle swarm optimization (PSO) algorithm to optimize the weights of each layer of the LSTM neural network, enabling more effective fitting of robotic arm kinematic errors and ultimately yielding more accurate Denavit-Hartenberg (D-H) parameters. To validate the proposed approach, 110 sets of experimental data are collected using the HSR-JR680 robotic arm calibration system. Experimental results demonstrate that the ALSTM model reduces the root mean square error (RMSE) by 23.07%-80.39% compared to traditional calibration methods, and shortens the convergence time by 32.44% compared to a standard LSTM model. The optimized D-H parameters obtained meet the high-precision calibration requirements of medical robotic arm, confirming the effectiveness of the proposed method.
Calibration ; Neural Networks, Computer ; Algorithms ; Robotics ; Robotic Surgical Procedures ; Models, Theoretical

Objective:To establish normal reference value ranges for dynamic balance function parameters in healthy Chinese adults aged 20-69 years. Methods:A total of 100 healthy subjects were selected and evenly divided into five age groups: 20-29, 30-39, 40-49, 50-59, and 60-69 years, with equal gender distribution in each group. Balance function was assessed using the EquiTest system （NeuroCom）, with following tests performed Sensory Organization Test （SOT）, Motor Control Test （MCT）, Adaptation Test （ADT）, and Limits of Stability （LOS） test. All parameters were statistically analyzed and expressed as ±S. Results:The normal reference ranges for SOT, MCT, ADT, and LOS parameters were established for each age group. Multiple balance function parameters demonstrated a gradual decline with advancing age, with more pronounced deterioration observed after the age of 60. Specific findings included decreased vestibular ratio and reduced visual preference in SOT, as well as prolonged reaction time, impaired directional control, and reduced maximum excursion in the backward direction during LOS testing. Conclusion:This study is the first to establish age-specific reference ranges for dynamic balance function parameters in a healthy Chinese population aged 20-69 years, providing localized and objective criteria for the assessment of balance function and supporting clinical diagnosis of balance-related disorders in China.
Humans ; Middle Aged ; Adult ; Postural Balance/physiology* ; Reference Values ; Aged ; Male ; Female ; Young Adult ; Healthy Volunteers

OBJECTIVES: We propose a new melanoma segmentation model, SG-UNet, to enhance the precision of melanoma segmentation in dermascopy images to facilitate early melanoma detection. METHODS: We utilized a U-shaped convolutional neural network, UNet, and made improvements to its backbone, skip connections, and downsampling pooling sections. In the backbone, with reference to the structure of VGG, we increased the number of convolutions from 10 to 13 in the downsampling part of UNet to achieve a deepened network hierarchy that allowed capture of more refined feature representations. To further enhance feature extraction and detail recognition, we replaced the traditional convolution the backbone section with self-calibrated convolution to enhance the model's ability to capture both spatial and channel dimensional features. In the pooling part, the original pooling layer was replaced by Haar wavelet downsampling to achieve more effective multi-scale feature fusion and reduce the spatial resolution of the feature map. The global attention mechanism was then incorporated into the skip connections at each layer to enhance the understanding of contextual information of the image. RESULTS: The experimental results showed that the SG-UNet model achieved significantly improved segmentation accuracy on ISIC 2017 and ISIC 2018 datasets as compared with other current state-of-the-art segmentation models, with Dice reached 92.41% and 86.62% and IoU reaching 92.31% and 86.48% on the two datasets, respectively. CONCLUSIONS The proposed model is capable of effective and accurate segmentation of melanoma from dermoscopy images.
Melanoma/diagnosis* ; Humans ; Neural Networks, Computer ; Dermoscopy/methods* ; Skin Neoplasms ; Image Processing, Computer-Assisted/methods* ; Calibration ; Algorithms