Objective Objective To analyze the potential spatial factors affecting the genetic differentiation of Oncomelania hupensis robertsoni in Yunnan Province. Methods A total of 13 administrative villages were selected from schistosomiasis-endemic areas of Yunnan Province as O. hupensis snail sampling sites. At least 200 snails were collected in each site, and the spatial variable data of each site were recorded, including longitude, latitude and altitude. Thirty active and Schistosoma japonicum uninfected O. hupensis snails were selected from each sampling site by means of the crawling method and the cercarial shedding method. Genomic DNA was extracted from O. hupensis snails. Following PCR amplification, purification of PCR amplification products and sequencing, the gene sequences of O. hupensis snail samples were spliced and edited using the DNAstar software and the NCBI database to yield the complete mitochondrial sequences of O. hupensis snails at each sampling site, and the mitochondrial genetic distance matrix of O. hupensis robertsoni was calculated at each sampling site. The geographical coordinates of each sampling site were marked using the software ArcGIS 10.2, and the straight-line geographical distance between each sampling site was calculated. The altitude difference, longitude difference and latitude difference between each sampling site were calculated using the Excel software, and the correlation between the mitochondrial genetic distance matrix of O. hupensis robertsoni and each spatial variable matrix was examined by using the Mantel test at 13 sampling sites in Yunnan Province. Results Among the 13 O. hupensis snail sampling sites in Yunnan Province, the largest mitochondrial genetic distance of O. hupensis robertsoni snail populations was seen between Anding Village, Nanjian Yi Autonomous County and Caizhuang Village, Midu County (26.244 2), and the largest geographical distance was seen between Dongyuan Village, Gucheng District and Cangling Village, Chuxiong County (272.64 km). The highest altitude difference was seen between Anding Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1 086.10 m), and the largest longitude difference was found between Qiandian Village, Eryuan County and Cangling Village, Chuxiong County (1.86°), while the largest latitude difference was measured between Leqiu Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1.81°). In addition, the mitochondrial genetic distance of O. hupensis robertsoni snail populations was positively correlated with altitude at 13 snail sampling sites in Yunnan Province (r = 0.542 8, P < 0.001), and showed no significant correlations with geographical distance (r = 0.093 4, P > 0.05), longitude (r = −0.199 5, P > 0.05) or latitude (r = 0.205 7, P > 0.05). Conclusion Altitude may be a potential spatial factor affecting the genetic differentiation of O. hupensis robertsoni in Yunnan Province.

ObjectiveThis Study was conducted to investigate the interaction mechemisms between gutmicrobiota dysregulation and ischemic stroke by establishing a rat model of ischemic stroke and employing fecal microbiota transplantation (FMT). MethodsA preliminary experiment was conducted to establish an antibiotic-induced pseudo-sterile (ABX) rat model through antibiotic treatment, and a cerebral ischemia model was prepared using the middle cerebral artery occlusion (MCAO) method. Fecal microbiota from stroke patients and healthy individuals were transplanted via FMT, followed by behavioral testing. 16S rRNA sequencing was used to analyze the microbial community, hematoxylin and eosin (HE) staining to observe histopathological status, transmission electron microscopy (TEM) to examine the tight junction structure of the small intestine, and enzyme-linked immunosorbent assay (ELISA) to detect levels of inflammatory factors and intestinal barrier-related markers. Results16S rRNA sequencing of fecal samples showed that compared with the normal control group and the metronidazole group, the abundance and diversity of fecal microorganisms in the quadruple antibiotic group were significantly reduced, indicating successful establishment of the ABX model. After transplanting fecal microbiota from stroke patients into ABX rats, significant changes in gut microbiota composition were observed. Behavioral tests revealed that the MCAO model group showed significant decreases in both horizontal movement and vertical exploration abilities. ELISA results indicated that IL-17 concentration in the ABX+mFMT (antibiotic-treated+model fecal microbiota transplantation) group was lower than in the ABX+cFMT (antibiotic-treated+control fecal microbiota transplantation) group, suggesting that IL-17 may serve as a key inflammatory indicator for evaluating the impact of stroke intervention on gut microbiota. Triphenyltetrazolium chloricle staining (TTC) staining suggested that gut microbiota intervention may increase the risk of stroke. HE staining showed that, except for the control group, all groups exhibited ischemic changes and inflammatory infiltration in brain tissues. TEM revealed that microvilli of small intestinal epithelial cells in the ABX+mFMT group were sparser than those in the ABX+cFMT group, indicating that microbial intervention affects intestinal barrier function. ConclusionThe ABX model established using broad-spectrum antibiotics showed no significant differences in physiological characteristics compared to normal rats, and the findings were consistent with those from germ-free rat models. Stroke prognosis appears to be influenced by intestinal dysbiosis, accompanied by significantly elevated levels of the pro-inflammatory cytokine IL-17, which may exacerbate neural injury via the gut-brain axis. Behavioral experiments indicated that transplantation of gut microbiota from stroke rats impaired cognitive function. Furthermore, IL-17 demonstrated sensitivity to alterations in the gut microbiota, suggesting its potential as a key therapeutic target for stroke intervention.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveThis Study was conducted to investigate the interaction mechemisms between gutmicrobiota dysregulation and ischemic stroke by establishing a rat model of ischemic stroke and employing fecal microbiota transplantation (FMT). MethodsA preliminary experiment was conducted to establish an antibiotic-induced pseudo-sterile (ABX) rat model through antibiotic treatment, and a cerebral ischemia model was prepared using the middle cerebral artery occlusion (MCAO) method. Fecal microbiota from stroke patients and healthy individuals were transplanted via FMT, followed by behavioral testing. 16S rRNA sequencing was used to analyze the microbial community, hematoxylin and eosin (HE) staining to observe histopathological status, transmission electron microscopy (TEM) to examine the tight junction structure of the small intestine, and enzyme-linked immunosorbent assay (ELISA) to detect levels of inflammatory factors and intestinal barrier-related markers. Results16S rRNA sequencing of fecal samples showed that compared with the normal control group and the metronidazole group, the abundance and diversity of fecal microorganisms in the quadruple antibiotic group were significantly reduced, indicating successful establishment of the ABX model. After transplanting fecal microbiota from stroke patients into ABX rats, significant changes in gut microbiota composition were observed. Behavioral tests revealed that the MCAO model group showed significant decreases in both horizontal movement and vertical exploration abilities. ELISA results indicated that IL-17 concentration in the ABX+mFMT (antibiotic-treated+model fecal microbiota transplantation) group was lower than in the ABX+cFMT (antibiotic-treated+control fecal microbiota transplantation) group, suggesting that IL-17 may serve as a key inflammatory indicator for evaluating the impact of stroke intervention on gut microbiota. Triphenyltetrazolium chloricle staining (TTC) staining suggested that gut microbiota intervention may increase the risk of stroke. HE staining showed that, except for the control group, all groups exhibited ischemic changes and inflammatory infiltration in brain tissues. TEM revealed that microvilli of small intestinal epithelial cells in the ABX+mFMT group were sparser than those in the ABX+cFMT group, indicating that microbial intervention affects intestinal barrier function. ConclusionThe ABX model established using broad-spectrum antibiotics showed no significant differences in physiological characteristics compared to normal rats, and the findings were consistent with those from germ-free rat models. Stroke prognosis appears to be influenced by intestinal dysbiosis, accompanied by significantly elevated levels of the pro-inflammatory cytokine IL-17, which may exacerbate neural injury via the gut-brain axis. Behavioral experiments indicated that transplantation of gut microbiota from stroke rats impaired cognitive function. Furthermore, IL-17 demonstrated sensitivity to alterations in the gut microbiota, suggesting its potential as a key therapeutic target for stroke intervention.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

The crystalline lens of the eye is recognized as one of the most radiosensitive tissues in the human body. While the International Commission on Radiological Protection (ICRP) has classified ionizing radiation (IR)-induced cataracts as a tissue reaction (deterministic effect) and subsequently reduced the occupational equivalent dose limit for the lens, significant uncertainties remain regarding the precise dose threshold and the complex biological pathways driving lens opacification. This review provides a comprehensive synthesis of current knowledge concerning radiation-induced lens damage, integrating epidemiological exposure characteristics with dose-response modeling and mechanistic molecular insights. First, we analyze exposure characteristics through four epidemiological dimensions: dose, time, space, and population. Clinical evidence suggests that radiation cataracts—particularly posterior subcapsular opacities—exhibit a distinct latency period that is inversely correlated with dose. We highlight that risk is not confined to acute high-dose scenarios (such as in atomic bomb survivors) but is increasingly relevant in chronic low-dose occupational settings (e.g., interventional radiology) and medical diagnostics (e.g., CT scans). Crucially, individual susceptibility is modified by genetic background, age, and environmental co-factors, complicating risk assessment. Second, we critically examine the dose-effect relationship. Although the ICRP suggests a threshold of 0.5 Gy, emerging data challenge the traditional threshold model, with some studies advocating for a linear non-threshold (LNT) relationship. We further discuss the critical roles of radiation quality and dose rate. High linear energy transfer (LET) radiation demonstrates a significantly higher relative biological effectiveness (RBE) for cataractogenesis compared to low-LET radiation. Paradoxically, and unlike many other tissues, the lens may exhibit an “inverse dose-rate effect,” where fractionated or protracted exposures potentially enhance biological damage—a finding that challenges classical radiobiological paradigms. Third, drawing upon the “cataractogenic load” hypothesis and the unique physiological constraints of the lens, this review elucidates the multidimensional molecular mechanisms driving radiation-induced opacification. Key mechanisms include four aspects. (1) DNA damage and repair: IR induces DNA double-strand breaks (DSBs) that, due to the lens’ limited repair capacity (modulated by genes such as ATM, Ptch1, and Ercc2), lead to the accumulation of damage. (2) Antioxidant defense system: dysfunction of the Nrf2/HO-1 antioxidant axis results in redox imbalances, triggering NF-κB-mediated inflammation and protein aggregation. (3) Cell proliferation and senescence: IR disrupts cell cycle regulation, causing a dichotomy of effects—driving premature senescence in some cell populations (evidenced by ATM nuclear foci) while inducing aberrant proliferation via growth factor upregulation (FGF2, TGFβ) in others. (4) Cell migration and adhesion: activation of the Wnt/β‑catenin pathway and alterations in the E-cadherin complex promote the abnormal migration of epithelial cells to the posterior capsule, a hallmark of radiation-induced cataracts. In conclusion, radiation-induced cataractogenesis is a multifactorial process in which genetic susceptibility and environmental stressors converge to overwhelm the lens’ homeostatic thresholds. Future research must prioritize longitudinal cohort studies to refine dose thresholds and employ multi-omics approaches to map the crosstalk between DNA damage responses and matrix remodeling. Establishing a robust mechanistic model is essential for developing targeted radioprotective strategies and optimizing radiation protection standards for occupational and medical safety.

ObjectiveThis paper aims to investigate the efficacy and related actions of Guizhi Fulingwan in intervening in the mice with colitis-associated colon cancer （CAC） based on the immunosuppressive microenvironment associated with myeloid-derived suppressor cells （MDSCs）. MethodsSixty male C57BL/6 mice were randomly assigned to a blank group， a model group， an aspirin group （0.04 g·kg^-1）， and low-， medium-， and high-dose Guizhi Fulingwan groups （4.87， 9.75， and 19.50 g·kg^-1）， with ten mice per group. The CAC mouse model was established via combined induction of azoxymethane （AOM）/dextran sulphate sodium （DSS）. Drug intervention commenced in week five， with continuous intragastric administration for nine weeks. The food intake， body weight， fecal characteristics， and haematochezia were observed and recorded， and disease activity index （DAI） scores were calculated according to scoring criteria. Hematoxylin and eosin （HE） staining was used to observe the histopathological changes in the colon tissues of the mice. Immunohistochemistry was used to determine proliferating cell nuclear antigen-67 （Ki67） expression in the colon tissues， and enzyme-linked immunosorbent assay （ELISA） was used to detect the contents of interleukin-6 （IL-6）， IL-1β， and tumor necrosis factor-α （TNF-α） in the serum of the mice. Flow cytometry was employed to determine the proportion levels of MDSCs， CD4⁺ T cells， and CD8⁺ T cells in the spleen tissues of the mice. The mRNA expressions of MDSC-associated effector molecules， including arginase 1 （Arg1） and inducible nitric oxide synthase （iNOS）， were detected by real-time quantitative polymerase chain reaction （Real-time PCR）. After that， an in vitro co-culture model of MDSCs and CD8⁺ T cells was established， and drug-containing serum of Guizhi Fulingwan was used for intervention. The Flow cytometry was employed to assess the effects of drug-containing serum of Guizhi Fulingwan with different concentrations on the levels of reactive oxygen species （ROS） and iNOS in MDSCs and the proliferation of CD8⁺ T cells. The levels of granzyme B （GZMB） and interferon-γ （IFN-γ） in cell supernatant were detected by ELISA. ResultsCompared with those in the control group， the mice in the model group exhibited significantly reduced body weight， elevated DAI scores， shortened colon length （P<0.01）， increased number of tumors and Ki67 expression （P<0.01）， and significantly elevated contents of IL-6， IL-1β， and TNF-α in the serum （P<0.01）. Significant increases in the number of MDSCs were observed in mouse spleens， alongside marked reductions in the levels of CD4⁺ T and CD8⁺ T cells （P<0.01）. Furthermore， the mRNA expressions of MDSC function-associated effector molecules Arg1 and iNOS were significantly upregulated （P<0.01）. Compared with those in the model group， the mice in the middle-dose Guizhi Fulingwan group exhibited increased body weight and significantly decreased DAI scores （P<0.05， P<0.01）. The mice in the middle- and high-dose Guizhi Fulingwan groups exhibited significantly improved colon shortening， significantly decreased number of tumors and Ki67 expression （P<0.05， P<0.01）， and significantly decreased contents of IL-6， IL-1β， and TNF-α in the serum （P<0.05， P<0.01）. Furthermore， administration of Guizhi Fulingwan markedly reduced MDSC infiltration in the spleen of the mice， with different degrees of increase in the levels of both CD4⁺ T and CD8⁺ T cells （P<0.05， P<0.01）， alongside significant decreases in the mRNA expressions of Arg1 and iNOS （P<0.05， P<0.01）. In vitro cell co-culture shows that administration of drug-containing serum of Guizhi Fulingwan significantly decreases the activity levels of ROS and iNOS in MDSCs and promotes the proliferation of CD8⁺ T cells and the secretion of GZMB and IFN-γ （P<0.05， P<0.01）. ConclusionGuizhi Fulingwan can reduce pro-inflammatory cytokine secretion and inhibit tumor proliferation in the colon tissues of CAC mice. Its potential mechanism may involve reducing MDSC infiltration， enhancing effector T cells， particularly CD8⁺ T cell response， and improving the tumor immunosuppressive microenvironment.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Retinal vein occlusion(RVO)is the second most common blinding retinal vascular disease, and its secondary macular edema(ME)is an important cause of visual function impairment in patients. Intravitreal injection of anti-vascular endothelial growth factor(VEGF)drugs serves as the first-line treatment, yet it is confronted with such issues as the need for repeated injections and non-response in some patients. Imaging and laboratory biomarkers play a crucial role in the early accurate diagnosis, prediction of disease progression, and evaluation of visual prognosis of RVO-ME. This study systematically reviews the research progress of imaging and laboratory biomarkers related to the prognosis of RVO-ME after anti-VEGF treatment in recent years, covering imaging biomarkers like central retinal thickness and ellipsoid zone integrity, as well as laboratory biomarkers such as serum APLN and aqueous humor IL-6. It summarizes the associations between different biomarkers and the prognosis of anti-VEGF therapy, aiming to provide a basis for the early accurate assessment and optimization of individualized treatment for RVO-ME patients, which holds significant clinical reference value.