Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

BackgroundIn recent years， the incidence of depression among adolescents has been increasing steadily， posing a serious threat to their physical and mental health and even leading to severe consequences such as self-harm and suicide. At the same time， the detection rate of subclinical depression symptoms among adolescents is even higher. Although these symptoms do not meet the clinical diagnostic criteria， they have significantly affected their quality of life， and their persistence over time may further develop into depression. Therefore， in-depth exploration of adolescent depression symptoms and the predictive factors holds significant practical significance and research value. However， up to now， no large-scale investigation and research on depression symptoms among children and adolescents has been conducted in Inner Mongolia Autonomous Region. ObjectiveTo understand the prevalence of depressive symptoms among children and adolescents in Inner Mongolia Autonomous Region， in order to provide references for formulating scientific and effective prevention strategies and intervention measures. MethodsBy using the cluster stratified random sampling method， 6 281 students from the third grade of primary school to the second grade of high school in 12 leagues and cities of Inner Mongolia Autonomous Region were selected in March 2024. A self-designed questionnaire and the Self-rating Depression Scale （SDS） were used for on-site investigation. ResultsA total of 6 058 （96.45%） children and adolescents completed the valid questionnaire survey， and 2 728 cases （45.03%） were found to have depressive symptoms. There were statistically significant differences in the detection rates of depressive symptoms among children and adolescents of different genders， ages， whether they were only children， different family types， family monthly income， parents' educational levels， and whether the mother was employed （χ²=33.769， 40.618， 48.593， 29.972， 142.648， 195.999， 168.190， 5.445， P<0.05 or 0.01）.The results of the Logistic regression analysis showed that for children and adolescents， being female， aged between 12 and 16， over 16 years old， not being an only child， living in a reconstituted family， having a monthly family income of less than 5 000 yuan， and having parents with an education level of primary school or below were predictors of depressive symptoms （OR=1.241， 1.427， 1.273， 1.177， 1.549， 1.278， 1.462， 1.417， 1.514， 1.929， 1.660， 1.528， P<0.05 or 0.01）. ConclusionThe detection rate of depressive symptoms among children and adolescents in Inner Mongolia Autonomous Region is relatively high. Factors that may predict depressive symptoms in children and adolescents include female gender， ages between 12 and 16， ages over 16 years old， non-only children， families with a restructured structure， monthly family income of less than 5 000 yuan， and parents with an education level of primary school or below. ［Funded by Science and Technology Planning Project of the Inner Mongolia Autonomous Region （number， 2022YFSH0119）］

Ferroptosis is a key driver of the onset and progression of chronic obstructive pulmonary disease （COPD）. By exploring the role of ferroptosis in COPD from the perspective of "qi deficiency with retention and stagnation"， it is considered that mitochondrial dysfunction and imbalanced antioxidant defenses are the microscopic manifestations of "qi deficiency"， whereas iron accumulation and lipid peroxide deposition constitute the pathological basis of "retention and stagnation". In traditional Chinese medicine （TCM）， the treatment principle is tonifying deficiency and benefiting qi， scattering retention and unblocking stagnation. Its mechanism involves improving the antioxidant system and mitochondrial function to enhance cellular resistance to ferroptosis， as well as relieving pulmonary iron overload， excessive lipid peroxidation， and inflammatory factor release to reduce the accumulation of pathological products， thereby exerting therapeutic effects on COPD.

Background Ozone (O₃) is a major air pollutant. The existing monitoring system has uneven distribution of sites, insufficient coverage in underdeveloped areas, and low temporal resolution, making it difficult to obtain hourly data. This limits the dynamic identification of pollution and the formulation of prevention and control strategies. Objective To construct an hourly O₃ concentration estimation model based on ensemble machine learning, aiming to improve the accuracy of pollution exposure assessment and explore O₃ health impacts. Methods This study integrated land use regression modeling with modern machine learning techniques, employing random forest and XGBoost algorithms to construct base models, and stacking integration using non-negative least squares. The ensemble model was trained and validated across China using high-resolution, multi-source geographic data (e.g., meteorologicaldata, population density, land cover types, and aerosol optical thickness). It was tested in Taiyuan City, combined with a distributed lag non-linear model to analyze the association between O₃ and emergency admissions. Results The constructed ensemble model performed well in predicting O₃ concentration, with a higher coefficient of determination (R²) and a lower root-mean-square deviation (RMSE) compared to the single models. The R² improved from 0.90 to 0.92, and the RMSE decreased from 11.41 to 10.62, enhancing both prediction accuracy and generalization ability. In the application to Taiyuan City, the model successfully imputed the hourly-level data for the entire year. The distributed lag non-linear model analysis revealed that the relative risk (RR) values for the 6th to 8th days following O₃ exposure were 1.14 (95%CI: 1.01, 1.29), 1.16 (95%CI: 1.02, 1.31), and 1.14 (95%CI: 1.01, 1.29), respectively, which were significantly higher than 1, indicating a significant lagged association (lagged 6-8 d) between O₃ and the number of emergency room visits. Conclusion A high-precision, hourly-level O₃ concentration estimation model is successfully constructed by combining the land use regression model with an ensemble machine learning approach to provide a scientific basis for environmental policy formulation and public health intervention. The application of the model verifies its generalization ability and practical application value, which can provide a new technical framework for subsequent environmental health research.

Objective To identify core targets of hepatocellular carcinoma (HCC) by using bioinformatics and specific algorithms, explore their relationships with immune cells, and investigate the causal relationships between immune cells and HCC through Mendelian randomization. Methods Relevant genes associated with the development of HCC were screened using the GEO and TCGA databases. Immune infiltration analysis was conducted using GSVA and CIBERSORT algorithms. A bidirectional Mendelian randomization analysis was then performed to explore the causal relationships between immune cells and HCC. Results A total of 284 HCC-related genes were identified, with 120 genes recognized within the protein interaction network. Immune infiltration analysis revealed significant correlations between key genes and immune cells. Mendelian randomization results indicated that HLA DR on CD33⁺ HLA DR⁺ CD14dim (OR=1.097, 95%CI: 1.002–1.201, P=0.045, P_Bonferroni=0.091) and CD8 on CD28⁺ CD45RA⁺ CD8⁺ T cell (OR=1.123, 95%CI: 1.027–1.228, P=0.011, P_Bonferroni=0.022) were the risk factors for HCC. Conversely, HLA DR⁺⁺ monocyte absolute count was identified as a protective factor for HCC (OR=0.812, 95%CI: 0.702–0.938, P=0.005, P_Bonferroni=0.139). Conclusion The occurrence and development of liver cancer may be related to CDK1, CCNB1, and CDC20, showing a high degree of correlation with Th2 cells, T helper cells, Th17 cells, and DCs. Mendelian randomization shows that HLA DR on CD33⁺HLA DR⁺ CD14dim and CD8 on CD28⁺CD45RA⁺CD8⁺T cells are associated with an increased risk of HCC. The risk of hepatocellular carcinoma is associated with a decrease in the level of HLA DR⁺⁺monocyte absolute count.

In maintaining normal function and activation processes, glycolysis, lipid metabolism, and amino acid metabolism play key roles in the energy demand of platelets. In the resting state, platelets primarily rely on glycolysis and aerobic oxidation to generate energy. Upon activation, platelets preferentially utilize glycolysis, as it can more rapidly provide the required ATP. In addition to glycolysis, platelets can also utilize glycogen and fatty acids as additional energy sources. The ATP provided by fatty acid oxidation is crucial for platelet activation. Additionally, during platelet storage, distinctive changes in energy metabolism occur. In the early stages of storage, platelets primarily rely on glycolysis and the pentose phosphate pathway (PPP) to generate energy. In the mid-storage phase, there is an increase in tricarboxylic acid cycle (TCA) metabolism. In the later stages of storage, cellular metabolism gradually declines. The regulation and flexibility of these metabolic pathways play a critical role in the survival and function of platelets in different states.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopamine (DA) neurons in the substantia nigra pars compacta (SNpc), primarily manifesting as motor dysfunctions such as resting tremor, muscle rigidity, and bradykinesia. According to the classical model of basal ganglia motor control, approximately half of the medium spiny neurons (MSNs) in the striatum are D1-MSNs, which constitute the direct pathway. These neurons express D₁-dopamine receptor (D₁R) and substance P, and they mainly participate in the selection, initiation, and execution of movements. The other half are D2-MSNs, which constitute the indirect pathway. These neurons express D₂-dopamine receptor (D₂R) and adenosine 2A receptors and are involved in inhibiting unnecessary movements or terminating ongoing movements, thereby adjusting movement sequences to perform more precise motor behaviors. The direct pathway in the striatum modulates the activity of motor cortex neurons by exciting D1-MSNs through neurotransmitters such as glutamate (Glu), allowing the motor cortex to send signals more freely to the motor system, thus facilitating the generation and execution of specific motor behaviors. Studies using D1-Cre and D2-Cre mice with neurons labeled for D₁R and D₂R have shown that both types of neurons are involved in the execution of movements, with D1-MSNs participating in movement initiation and D2-MSNs in inhibiting actions unrelated to the target movement. These findings suggest that the structural and functional plasticity of D1-MSNs and D2-MSNs in the basal ganglia circuitry enables motor learning and behavioral regulation. Additionally, when SNpc DA neurons begin to degenerate, D1-MSNs are initially affected but do not immediately cause motor impairments. In contrast, when D2-MSNs undergo pathological changes, they are first activated by upstream projecting neurons, leading to the inhibition of most motor behaviors and resulting in motor dysfunction. Therefore, it is hypothesized that motor impairments such as bradykinesia and initiation difficulties are more closely related to the functional activity of D2-MSNs. The extracellular signal-regulated kinase (Erk)/mitogen-activated protein kinase (MAPK) signaling pathway has been identified as a critical modulator in the pathophysiology of PD. Recent findings indicate that Erk/MAPK signaling pathway can mediate DA and Glu signaling in the central nervous system, maintaining normal functional activity of striatal MSNs and influencing the transmission of motor control signals. Within this complex regulatory network, the Erk/MAPK signaling pathway plays a key role in transmitting motor information to downstream neurons, regulating normal movements, avoiding unnecessary movements, and finely tuning motor behaviors. Our laboratory’s previous research found that 4 weeks of aerobic exercise intervention improved motor dysfunction in PD mice by inhibiting the Erk1/2 signaling upstream of striatal MSNs, primarily involving the Erk1/2 signaling in D2-MSNs rather than D1-MSNs. This review summarizes the neurobiological mechanisms of Erk/MAPK signaling pathway in D2-MSNs for the prevention and treatment of motor dysfunction in PD. By exploring the role of this signaling pathway in regulating motor abnormalities and preventing motor dysfunction in the central nervous system of PD, this review provides new theoretical perspectives for related mechanistic research and therapeutic strategies.