The rapid development of artificial intelligence （AI） technology provides new opportunities for the modernisation of traditional Chinese medicine （TCM） diagnosis. By analysing the foundation， research progress and difficulties of the combination of AI and TCM diagnosis， it is concluded that AI has made remarkable development in intelligence-driven modernization of TCM tongue diagnosis， pulse diagnosis， listening and smelling diagnosis and text processing， and there are useful explorations in the field of constructing data-driven TCM diagnostic model and multidisciplinary integration of TCM diagnostic models. However， the current integration of AI technology in TCM diagnosis still faces many challenges， such as the scarcity and uneven quality of clinical data， the limited ability of AI algorithms to express TCM thinking model of syndrome differentiation and empirical knowledge， and the possible existence of ethical and privacy issues. By systematically sorting out the current research status and development direction of AI-empowered TCM diagnostics， it is proposed to promote the application of AI technology in TCM diagnostics in four aspects， namely， strengthening the construction of TCM big data and talent cultivation， encouraging cross-disciplinary cooperation， improving the legal and ethical framework， and promoting the popularity of the technology in primary care， so as to enhance the modernisation of TCM diagnostics.

Stroke is a critical health issue in China, and carotid artery stenosis and plaque play key roles in its prevalence. Despite the acknowledged significance of this condition, detailed information regarding the prevalence of carotid artery stenosis and plaque across the Chinese population has been scarce. This study analyzed data from the China Stroke High-risk Population Screening and Intervention Program for 2020-2021, focusing on 194 878 Chinese adults aged 40 years and above. It assessed the prevalence of carotid artery stenosis and plaque and identified their associated risk factors. Results revealed a standardized prevalence of 0.40% for carotid artery stenosis and 36.27% for carotid plaque. Notably, the highest rates of stenosis were observed in north and south China at 0.61%, while southwestern China exhibited the highest plaque prevalence at 43.17%. Key risk factors included older age, male gender, hypertension, diabetes, stroke, smoking, and atrial fibrillation. This study highlights significant geographical and demographic disparities in the prevalence of these conditions, underlining the urgent need for targeted interventions and policy reforms. These measures are essential for reducing the incidence of stroke and improving patient outcomes, addressing this significant health challenge in China.
Humans ; China/epidemiology* ; Male ; Female ; Prevalence ; Middle Aged ; Carotid Stenosis/epidemiology* ; Risk Factors ; Aged ; Adult ; Plaque, Atherosclerotic/epidemiology* ; Stroke/epidemiology* ; Aged, 80 and over

AIM:To investigate alterations in the expression levels of inflammatory cytokines and subsets of CD8+ T cells in the peripheral blood of patients with diabetic retinopathy(DR).METHODS:Retrospective study. A total of 40 patients with type 2 diabetes admitted to Xi'an People's Hospital(Xi'an Fourth Hospital)from April to July 2022 were recruited for this study and categorized into two groups: 20 cases in the simple type 2 diabetes mellitus(DM)group, and 20 cases in the DR group. Additionally, 20 healthy individuals undergoing routine physical examinations served as the control group. The expression levels of cytokines, including interleukin(IL)-6, IL-8, and IL-10 in peripheral blood were quantified using ELISA. Flow cytometry was employed to analyze the expression of programmed cell death-1(PD-1), T cell immunoglobulin domain and mucin domain protein-3(TIM-3), CD28, and CD57 on CD8+ T cells.RESULTS:The peripheral blood expression of IL-6, IL-8, and IL-10 inflammatory cytokines were significantly elevated in DR patients as detected by ELISA(all P<0.001); flow cytometry analysis showed that the expression of PD-1, TIM-3, and CD57 were elevated in peripheral blood CD8+ T cells of DR patients(all P<0.001), and the expression of CD28 was decreased(all P<0.001).CONCLUSION:In DR patients, CD8+ T cells may undergo depletion and senescence as a result of elevated pro-inflammatory cytokines, including IL-6, IL-8, and IL-10.

Climate change poses a significant threat to public health. Existing studies on the health impacts of climate change focus primarily on chronic and infectious diseases, with limited research on injury. To address this gap, this issue launched a special column titled "Climate change and injuries". Through interdisciplinary integration of atmospheric science and public health, we explored the impact of climate change on injury risk and burden from diverse perspectives. This initiative aims to deepen a comprehensive understanding of the impact of climate change on human health, enhance China's capacity to address climate-related health risks, reduce associated disease burden, and provide scientific evidence for targeted adaptation measures.

Background Previous studies found that high temperature and heatwave increase the risk of traffic injuries. The complex road conditions in Yunnan Province result in frequent traffic accidents. However, there is limited evidence on the correlation between heatwave and traffic injuries in Yunnan Province. Objective To assess the association between heatwave events and traffic injuries, to estimate its disease burden, and to identify relevant sensitive groups. Methods We collected data on traffic injury cases and concurrent meteorological information from four surveillance sites in Yunnan Province, China: Dali, Lufeng, Zhaoyang, and Qilin from May to September each year from 2015 to 2023. Traffic injury cases refer to patients who visited the outpatient or emergency departments of local surveillance hospitals for the first time due to traffic injuries. Meteorological data were derived from the fifth generation atmosphericreanalysis dataset of the global climate provided by the European Centre for Medium-Range Weather Forecasts. A time-stratified case-crossover design combined with distributed lag non-linear model was used to analyze the association between short-term exposure to heatwave and traffic injuries. We also conducted subgroup analyses by sex, age, occupation, injury cause, activity at the time of injury occurrence, and severity of injury. Results A total of 34764 traffic injury surveillance cases were included in the analysis. The risk of traffic injuries occurred during heatwave was 1.13 times (95%CI: 1.07, 1.20) greater than those occurred during non-heatwave, and 2.64% (95%CI: 1.49%, 3.73%) of traffic injuries were attributed to heatwave exposure throughout the study period. The results of stratified analysis showed greater impacts of heatwave on the risk of traffic injuries in female [odds ratio (OR)=1.17, attributable fraction (AF)=3.23%], people aged 15-64 years (OR=1.13, AF=2.68%), farmers/workers (OR=1.22, AF=4.07%), people with non-motor vehicle traffic injuries (OR=1.17, AF=3.24%), people who were driving or riding when injuries occurred (OR=1.12, AF=2.43%), and people with minor injuries (OR=1.18, AF=3.49%) ( P<0.05). Longer duration [excess risk (ER)=0.99%] and greater intensity (ER=4.12%) of heatwave had greater impacts on the risk of traffic injuries (P<0.05). Conclusion Heatwave events are associated with an increased risk of traffic injuries. Female, people aged 15-64, farmers/workers, people with non-motor vehicle traffic injuries, those injured while driving/riding a vehicle, and people with minor injuries are particularly vulnerable. The findings suggest that targeted mitigation and adaptation measures should be taken to address the risk of traffic injuries associated with extreme climate events.

Background Injury poses a serious threat to human health. As global warming continues to intensify, there is an urgent need to explore the impact of temperature changes on injury deaths. However limited research has focused on this issue. Objective To investigate the relationship between daily mean temperature change (Tm) and injury death, as well as to estimate the associated future death burden in Hunan Province. Methods We employed an individual-level, time-stratified case-crossing design to establish a conditional logistic regression model to analyze the exposure-response relationship between daily mean temperature change and injury death in Hunan Province from 2013 to 2018. Consequently, we conducted subgroup analysis of gender, age group, and injury type. Finally, we estimated the excess burden of injury death attributable to temperature changes under a sustainable development path [low emission scenario (SSP1-2.6)], regional competition path [high emission scenario (SSP3-7.0)], or fossil fuel development path [very high emission scenario (SSP5-8.5)]. Results The study collected 155577 injury deaths in Hunan Province. The results indicated that for each 1 ℃ increment in daily mean temperature, the cumulative excess risk (CER) of injury mortality among Hunan residents increased by 0.71% (95%CI: 0.53%, 0.90%). Notably, the CER for intentional injuries (1.06%, 95%CI: 0.51%, 1.61%) was higher than that for unintentional injuries (0.66%, 95%CI: 0.46%, 0.87%). Warmer temperatures were positively associated with CER of mortality due to drowning, falls, transport injuries, assaults, and suicides, while they were negatively associated with mortality due to poisoning. Compared with 2010–2019, for 2090–2099, the excess mortality rate (EMR) due to ambient temperature increase in Hunan Province under the SSP5-8.5 scenario (14.62/100000, 95%CI: 10.81/100000, 18.40/100000) would be higher than that of the SSP3-7.0 scenario (10.28/100000, 95%CI: 7.58/100000, 12.97/100000) and the SSP1-2.6 scenario (3.35/100000, 95%CI: 2.46/100000, 4.23/100000); the EMR would be around 2.5 times among men (20.64/100000, 95%CI: 14.44/100000, 26.8/100000) than that among women (8.33/100000, 95%CI: 3.98/100000, 12.6/100000). Among unintentional injuries, the leading contributors to EMR would be drowning (4.46/100000), falls (3.96/100000), and transport injuries (2.96/100000). And among intentional injuries, the EMR for suicide (2.08/100000) would be higher than that for assault (0.47/100000). Conclusion Climate warming will increase the total burden of injury-related deaths among residents in Hunan Province, especially in the absence of effective mitigation strategies and measures. It is necessary to strengthen the prevention and control of severe injuries closely related to temperature changes, such as drowning, falls, traffic, and suicides injuries, in order to reduce injury deaths associated with climate change.

Background Under the background of global warming, research on association between ambient temperature and risk of injury is needed. Objective To examine the effect of temperature on injury in Bao'an district, Shenzhen and identify the sensitive population, thereby providing a scientific basis for formulating prevention and control strategies and measures of injury. Methods The injury reports from the Injury Surveillance System and the meteorological data of Bao'an District between 2018 to 2022 were collected. The meteorological data were sourced from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) land reanalysis data. Based on time-stratified case-crossover design, conditional logistic regression combined with distributed lag nonlinear model was used to evaluate the exposure-response association between ambient temperature and injury. The stratified analyses were further conducted by gender, age, and causes of injury. Results A total of 156205 injury cases were collected from the injury surveillance sentinel hospitals in Bao'an District of Shenzhen from 2018 to 2022, and the median of daily average temperature during the same period was 20.0 ℃. The exposure-response curve showed that the risk of injury was positively correlated with temperature. The linearized analysis revealed that each 1°C increase in temperature was associated with a 1.05% (95%CI: 0.68%, 1.42%) rise in injury risk (ER). Notably, the effect was greater in females (ER=1.31%, 95%CI: 0.67%, 1.94%) than males (ER=0.92%, 95%CI: 0.47%, 1.37%). Among age groups, adults ＞60 years faced the highest risk (ER=1.91%, 95%CI: −0.36%, 4.24%). The top three temperature-associated injury risks were sharp instrument injuries (ER=2.19%, 95%CI: 1.16%, 3.22%), animal injuries (ER=1.71%, 95%CI: 0.86%, 2.56%), and blunt injuries (ER=071%, 95%CI: −0.08%, 1.51%). The impact of temperature on unintentional injuries (ER=1.11%, 95%CI: 0.72%, 1.49%) was higher than that on intentional injuries (ER=0.43%, 95%CI: −0.85%, 1.72%). Severe injuries (ER=2.20%, 95%CI: −3.09%, 7.77%) were more affected by temperature than mild injuries (ER=1.00%, 95%CI: 0.58%, 1.43%) and moderate injuries (ER=1.15%, 95%CI: 0.42%, 1.89%). Conclusion The increase in ambient temperature associates with injury occurrence, and the impact of temperature exhibits obvious heterogeneity among different populations, indicating that targeted intervention measures should be taken for different populations and types of injuries.

Climate change is a significant environmental concern in the 21^st century. It can directly and indirectly affect public health, making it a major public health problem. This review focused on the relationship between meteorological factors and health-related behaviors such as physical activity, sleep, dietary behavior, and social contact behavior. The results indicated that meteorological factors such as temperature, relative humidity, wind speed, atmospheric pressure, and rainfall are associated with the four health behaviors. Physical activity levels are negatively associated with temperature, relative humidity, and rainfall. Sleep quality tends to decrease under extreme temperatures and extreme weather events. Dietary behavior is influenced by high and low temperatures, low pressure, and daylight duration, which in turn affects appetite and food intake. Social contact frequency decreases in response to extreme temperatures, increased rainfall, and extreme weather events. However, there are differences in the strength and direction of the associations between meteorological factors and health-related behaviors in various studies. Research on the combined effects of multiple meteorological factors exposure on health-related behaviors is rare, and the mechanisms underlying the associations are unclear. Therefore, there is a need for more multi-center, large-scale studies to explore the biological and behavioral mechanisms behind these associations, which will help clarify the complex effects of meteorological factors on human health behaviors and provide scientific evidence for policy-making, thereby mitigating the negative impact of climate change on public health.

Falls are one of the most common injuries, with a high prevalence and risk of disability, which poses the greatest threat to the health of the elderly. Falls are influenced by a variety of factors, including environmental factors such as slippery pavements, poor meteorological conditions, and inadequate lighting. The Sixth Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC) stated that climate change has become more frequent and stronger in recent years, which may threat people's health and lives. This review summarized the progress of epidemiological studies on the association between meteorological factors (e.g. temperature, precipitation, frost, and snowfall) and falls, and found that most of the studies focused on the association between temperature and falls, and the results of different studies varied greatly. While the studies on the association between other meteorological factors and falls were relatively few. Moreover, the mechanisms behind the associations were unclear. Therefore, we point out that it is necessary to further conduct large-sample and multi-centre studies to evaluate the effects of exposure to multiple meteorological factors, and further to explore the mechanisms of the associations, which may provide scientific evidence for better response to climate change.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.