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This paper, the first in a three-part Series on work and health, provides a narrative review of research into work as a social determinant of health over the past 25 years, the key emerging challenges in this field, and the implications of these challenges for future research. By use of a conceptual framework for work as a social determinant of health, we identified six emerging challenges: (1) the influence of technology on the nature of work in high-income countries, culminating in the sudden shift to telework during the COVID-19 pandemic; (2) the intersectionality of work with gender, sexual orientation, age, race, ethnicity, migrant status, and socioeconomic status as codeterminants of health disparities; (3) the arrival in many Organisation for Economic Co-operation and Development countries of large migrant labour workforces, who are often subject to adverse working conditions and social exclusion; (4) the development of precarious employment as a feature of many national labour markets; (5) the phenomenon of working long and irregular hours with potential health consequences; and (6) the looming threat of climate change's effects on work. We conclude that profound changes in the nature and availability of work over the past few decades have led to widespread new psychosocial and physical exposures that are associated with adverse health outcomes and contribute to increasing disparities in health. These new exposures at work will require novel and creative methods of data collection for monitoring of their potential health impacts to protect the workforce, and for new research into better means of occupational health promotion and protection. There is also an urgent need for a better integration of occupational health within public health, medicine, the life sciences, and the social sciences, with the work environment explicitly conceptualised as a major social determinant of health.

One aspect of work sustainability pertains to workers' intention to remain in their current job until reaching retirement age. Various adverse working conditions are expected to diminish work sustainability among different social groups. This study aims to examine these associations across gender and age groups.OBJECTIVESOne aspect of work sustainability pertains to workers' intention to remain in their current job until reaching retirement age. Various adverse working conditions are expected to diminish work sustainability among different social groups. This study aims to examine these associations across gender and age groups.The study participants were 19,152 economically-active adults in a national survey conducted in Taiwan. Information concerning psychosocial working conditions were obtained through interviews, using the Job Content Questionnaire. Work sustainability was evaluated by one question that asked whether the participants felt they would be able to do their current job until the age of 60. The association between psychosocial work conditions and work sustainability was examined by logistic regression analysis. We further performed stratified analysis to explore age and gender-specific associations.METHODSThe study participants were 19,152 economically-active adults in a national survey conducted in Taiwan. Information concerning psychosocial working conditions were obtained through interviews, using the Job Content Questionnaire. Work sustainability was evaluated by one question that asked whether the participants felt they would be able to do their current job until the age of 60. The association between psychosocial work conditions and work sustainability was examined by logistic regression analysis. We further performed stratified analysis to explore age and gender-specific associations.We observed that 14.2% and 17.1% of male and female workers reported low work sustainability. Workers in the electronics industries and female workers in the healthcare and education sectors reported low work sustainability. Gender-specific analyses showed that low job control among men and shift work among women were significantly associated with low work sustainability. Age-specific analyses indicated that having poor health, shift work, and long working hours in younger workers, and having low job control in older workers were associated with low work sustainability.RESULTSWe observed that 14.2% and 17.1% of male and female workers reported low work sustainability. Workers in the electronics industries and female workers in the healthcare and education sectors reported low work sustainability. Gender-specific analyses showed that low job control among men and shift work among women were significantly associated with low work sustainability. Age-specific analyses indicated that having poor health, shift work, and long working hours in younger workers, and having low job control in older workers were associated with low work sustainability.To retain older workers in the labor market, policies should aim at the improvement of psychosocial work conditions, and gender- and age-specific issues should be taken into consideration.CONCLUSIONTo retain older workers in the labor market, policies should aim at the improvement of psychosocial work conditions, and gender- and age-specific issues should be taken into consideration.

Objectives: Innovative technology at work can lead to stress and has been linked with adverse work and health consequences. This study aimed to examine the association of techno-insecurity and techno-strain with mental well-being in different age and occupational groups.Methods: We used a nationally representative survey of the working population and restricted our analyses to 2814 employees who reported being engaged with new technology. Techno-insecurity and techno-strain were evaluated by a single question each. Mental health status was assessed by a 5-item scale, and burnout status was assessed by the Copenhagen Burnout Inventory. We used logistic regression analysis to examine the association of techno-insecurity and techno-strain with mental well-being, adjusting for job control, psychological demands, job insecurity, and workplace violence. We further stratified study participants by age and occupational group and examined the association in each group.Results: One-fifth of the study participants reported having techno-insecurity and techno-strain. Techno-insecurity was associated with a 1.8-fold increased risk of poor mental health and high burnout, whereas techno-strain was associated with a 2.2-fold increased risk of having poor mental health and high burnout. The associations between techno-insecurity or techno-strain and poor mental health were most profound among middle-aged workers. Among all occupational groups, the associations between techno-insecurity or techno-strain and burnout were most profound among manual workers.Conclusions: Techno-strain and techno-insecurity are emerging occupational mental health threats, particularly among middle-aged and manual workers. To promote mental health, resources provided by the organization are needed to help employees cope and work with technology.

Background While the impact of poor working conditions on workers' intention to leave the job is well-established, limited research has examined how the availability of pension benefits influences the association between adverse psychosocial work conditions and labor market exit for health reasons among middle-aged and older workers. This study explored the association of psychosocial and physical work conditions with labor market exit for health reasons among individuals with varying pension statuses. Methods This study utilized data from the Healthy Aging Longitudinal Study in Taiwan (HALST), which investigated reasons for labor market exit among 2,143 adults aged 55 and older. Work conditions were aggregated by occupation based on data from the Occupational Safety and Health Surveys, which included nationally representative employees, and subsequently linked to HALST data. We examined the differential impact of psychosocial and physical work conditions on health-related labor market exit, compared to old age retirement, among individuals with and without pension coverage. Results Among 2,143 study participants, 7.3% left the labor market due to health reasons, and 39.9% reported not having a pension. Individuals with low job control (adjusted odds ratio [aOR] = 2.23, 95% confidence interval [CI] = 1.05 to 4.73) and high physical demands (aOR = 2.72, 95% CI = 1.26 to 5.85) were more likely to exit the labor market for health reasons compared to old age retirement. Among participants without a pension, adverse work conditions were significantly associated with labor market exit for health reasons. Conclusions Adverse work conditions were associated with labor market exit for health reasons particularly among older adults without pension coverage. Implementing policies to improve psychosocial work conditions and enhance the pension system is warranted.

Accumulating evidence has demonstrated that apoptotic vesicles (apoVs) derived from mesenchymal stem cells (MSCs; MSC-apoVs) are vital for bone regeneration, and possess superior capabilities compared to MSCs and other extracellular vesicles derived from MSCs (such as exosomes). The osteoinductive effect of MSC-apoVs is attributed to their diverse contents, especially enriched proteins or microRNAs (miRNAs). To optimize their osteoinduction activity, it is necessary to determine the unique cargo profiles of MSC-apoVs. We previously established the protein landscape and identified proteins specific to MSC-apoVs. However, the features and functions of miRNAs enriched in MSC-apoVs are unclear. In this study, we compared MSCs, MSC-apoVs, and MSC-exosomes from two types of MSC. We generated a map of miRNAs specific to MSC-apoVs and identified seven miRNAs specifically enriched in MSC-apoVs compared to MSCs and MSC-exosomes, which we classified as apoV-specific miRNAs. Among these seven specific miRNAs, hsa-miR-4485-3p was the most abundant and stable. Next, we explored its function in apoV-mediated osteoinduction. Unexpectedly, hsa-miR-4485-3p enriched in MSC-apoVs inhibited osteogenesis and promoted adipogenesis by targeting the AKT pathway. Tailored apoVs with downregulated hsa-miR-4485-3p exhibited a greater effect on bone regeneration than control apoVs. Like releasing the brake, we acquired more powerful osteoinductive apoVs. In summary, we identified the miRNA cargos, including miRNAs specific to MSC-apoVs, and generated tailored apoVs with high osteoinduction activity, which is promising in apoV-based therapies for bone regeneration.

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union ( IoU ) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

Phenotypic traits, such as plant height, internode length, and node count, are essential indicators of the growth status of tomato plants, carrying significant implications for research on genetic breeding and cultivation management. Deep learning algorithms such as object detection and segmentation have been widely utilized to extract plant phenotypic parameters. However, segmentation-based methods are labor-intensive due to their requirement for extensive annotation during training, while object detection approaches exhibit limitations in capturing intricate structural features. To achieve real-time, efficient, and precise extraction of phenotypic traits of seedling tomatoes, a novel plant phenotyping approach based on 2D pose estimation was proposed. We enhanced a novel heatmap-free method, YOLOv8s-pose, by integrating the Convolutional Block Attention Module (CBAM) and Content-Aware ReAssembly of FEatures (CARAFE), to develop an improved YOLOv8s-pose (IYOLOv8s-pose) model, which efficiently focuses on salient image features with minimal parameter overhead while achieving a superior recognition performance in complex backgrounds. IYOLOv8s-pose manifested a considerable enhancement in detecting bending points and stem nodes. Particularly for internode detection, IYOLOv8s-pose attained a Precision of 99.8%, exhibiting a significant improvement over RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose by 2.9%, 5.4%, 3.5%, and 5.4%, respectively. Regarding plant height estimation, IYOLOv8s-pose achieved an RMSE of 0.48 cm and an rRMSE of 2%, and manifested a 65.1%, 68.1%, 65.6%, and 51.1% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose, respectively. When confronted with the more intricate extraction of internode length, IYOLOv8s-pose also exhibited a 15.5%, 23.9%, 27.2%, and 12.5% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose. IYOLOv8s-pose achieves high precision while simultaneously enhancing efficiency and convenience, rendering it particularly well suited for extracting phenotypic parameters of tomato plants grown naturally within greenhouse environments. This innovative approach provides a new means for the rapid, intelligent, and real-time acquisition of plant phenotypic parameters in complex backgrounds.

Migraine is a medical condition with a severe recursive headache. The activation of the trigeminovascular system is an important mechanism. The neuropeptide calcitonin gene-related peptide (CGRP) plays a crucial role in the pathogenesis of migraine. Several other neuropeptides are also involved; however, their roles in migraine remain unclear. In this study, using a rat model of migraine induced by electrical stimulation of the trigeminal ganglia (TG) and an improved version induced with repeated stimulation, we observed the dynamic changes of these peptides in TG and blood. We demonstrated that the expression of CGRP, pituitary adenylate cyclase activating polypeptide (PACAP), neuropeptide Y (NPY), vasoactive intestinal peptide, and nociceptin in TG was significantly elevated and peaked at different time points after a single stimulation. Their levels in the blood plasma were significantly increased at 12 h after stimulation. The peptides were further elevated with repeated stimulation. The improved rat model of migraine with repeated stimulation of TG resulted in a more pronounced elevation of CGRP, PACAP, and NPY. Thus, the dynamic changes in neuropeptides after stimulation suggest that these neuropeptides may play an important role in the pathogenesis of migraine. Additionally, the migraine model with repetitive stimulation would be a novel model for future research.

Objective Health problems caused by long working hours and work stress have gained growing concerns in Japan, Korea, and Taiwan. In all the three countries, cardiovascular, cerebrovascular, and mental disorders attributed to heavy workloads or stressful work events are considered compensable occupational diseases by workers’ compensation systems. This study compared the trends of such cases and correlated the trends with changes in working hours during the period from 1980 to 2010. Methods Data on occupational diseases were obtained from official statistics of the workers’ compensation systems. Information on working hours was obtained from official statistics and national surveys of employees. Results While occupational cardiovascular, cerebrovascular, and mental disorders attributed to work stress were increasingly compensated in all the three countries, the averaged working hours and the percentage of employees with long working hours had been in decline discordantly. Conclusion Findings of this study suggested that reducing working hours alone is unlikely to reduce the problems of work stress. There is an urgent need to monitor and regulate a wider range of psychosocial work hazards. Especially, precarious employment and its associated health risks should be targeted for effective prevention of stress-related health problems in the workplace.

Bait costs constitute 40–50% of the total expenditure in river crab aquaculture, highlighting the critical need for accurately assessing crab growth and scientifically determining optimal feeding regimes across different farming stages. Current traditional methods rely on periodic manual sampling to monitor growth status and artificial feeding platforms to observe consumption and adjust bait input. These approaches are inefficient, disruptive to crab growth, and fail to provide comprehensive growth data. Therefore, this study proposes a machine vision-based monitoring system for river crab feeding platforms. Firstly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is applied to enhance underwater images of river crabs. Subsequently, an improved YOLOv11 (You Only Look Once) model is introduced and applied for multi-target detection and counting in crab ponds, enabling the extraction of information related to both river crabs and bait. Concurrently, underwater environmental parameters are monitored in real-time via an integrated environmental information sensing system. Finally, an information processing platform is established to facilitate data sharing under a “detection–processing–distribution” workflow. The real crab farm experimental results show that the river crab quality error rate was below 9.57%, while the detection rates for both corn and pellet baits consistently exceeded 90% across varying conditions. These results indicate that the proposed system significantly enhances farming efficiency, elevates the level of automation, and provides technological support for the river crab aquaculture industry.