ObjectiveTo observe the mechanism of Guihuang formula in regulating the activation of NOD-like receptor protein 3 （NLRP3） inflammasome and inhibiting pyroptosis in the treatment of type Ⅲ prostatitis. Methods（1） In an animal experiment， 50 Sprague Dawley （SD） rats were randomly divided into a blank group， a model group， and low-dose， medium-dose， and high-dose groups of Guihuang formula， with 10 rats in each group. Except for the blank group， the type Ⅲ prostatitis rat model was prepared for the other four groups.After the modeling was successful， the blank group and the model group were given normal saline intragastrically， and the low-dose， medium-dose， and high-dose groups of Guihuang formula were given intragastrically with Guihuang formula （4.9， 9.8， 19.6 g·kg^-1）. After 30 days of intragastrical administration， samples were taken for detection. Inflammatory cell infiltration in prostate tissue was observed by hematoxylin-eosin （HE） staining， and serum IL-1β and IL-18 levels were measured by enzyme-linked immunosorbent assay （ELISA）. Serum malondialdehyde （MDA）， superoxide dismutase （SOD）， and glutathione peroxidase （GSH-Px） levels were determined by biochemistry. NLRP3 expression in prostate tissue was assessed by immunohistochemistry， and the expression of NLRP3， cysteine-aspartic acid protease-1 （Caspase-1）， and gasdermin D （GSDMD） in prostate tissue was measured by Western blot. （2） In a cell experiment， human normal prostate epithelial cells （RWPE-1 cells） were divided into a blank group， a model group， a Guihuang formula group， and an NLRP3 inhibitor group （MCC950 group）. Except for the blank group， the other three groups were stimulated by 100 μg·L^-1 lipopolysaccharide （LPS） for 4 h and 5 mol·L^-1 adenosine triphosphate （ATP） for 30 min to prepare the pyroptosis model. After successful modeling， blank serum was given to the blank group and the model group. 6.25 μg·mL^-1 Guihuang formula drug-containing serum was added to the Guihuang formula group， and MCC950 was added to the MCC950 group on the basis of the model group. Propidium iodide （PI） uptake and Caspase-1 expression were detected by flow cytometry， and lactate dehydrogenase （LDH） level in the cell supernatant was measured by biochemistry. Interleukin （IL）-1β and IL-18 levels of the cell supernatant were determined by ELISA， and the expression of NLRP3， Caspase-1， and GSDMD was detected in Western blot. Results（1） For the animal experiment， compared with the blank group， the model group showed significant infiltration of inflammatory cells in prostate tissue， while the low-dose， medium-dose， and high-dose groups of Guihuang formula showed reduced infiltration of acinar inflammatory cells， reduced degree of glandular epithelial degeneration and interstitial edema， and significantly reduced degree of damage. Compared with those in the blank group， the levels of IL-1β and IL-18 in the serum of the model group were significantly increased （P<0.01）. Compared with the model group， the low-dose， medium-dose， and high-dose groups of Guihuang formula showed a significant decrease in serum IL-1β and IL-18 levels （P<0.01）. Compared with that in the blank group， the serum MDA level in the model group significantly increased （P<0.01）. Compared with that in the model group， the MDA level in the low-dose， medium-dose， and high-dose groups of Guihuang formula was significantly reduced （P<0.01）. Compared with those in the blank group， the levels of SOD and GSH-Px in the serum of the model group significantly decreased （P<0.05）. Compared with the model group， the low-dose， medium-dose， and high-dose groups of Guihuang formula showed a significantly increase in SOD （P<0.01）. Compared with the model group， the low-dose， medium-dose， and high-dose groups of Guihuang formula showed a significantly increase in GSH-Px （P<0.05）. Immunohistochemistry showed that compared with the blank group， the model group had high expression of NLRP3 molecule in prostate tissue. The expression of NLRP3 in the low-dose， medium-dose， and high-dose groups of Guihuang formula was significantly lower than that in the model group. Compared with those in the blank group， the expression levels of NLRP3， Caspase-1， and GSDMD proteins in the prostate tissue of the model group were significantly increased （P<0.01）. Compared with those in the model group， the expression levels of NLRP3， Caspase-1， and GSDMD proteins in the low-dose， medium-dose， and high-dose groups of Guihuang formula were significantly inhibited （P<0.01）. （2） For the cell experiment， compared with that in the blank group， the PI uptake rate of RWPE-1 cells in the model group significantly increased （P<0.01）. Compared with that in the model group， the PI uptake rate of the Guihuang formula group and the inhibitor group significantly decreased （P<0.01）. Compared with that in the blank group， the expression of Caspase-1 in the model group was significantly higher （P<0.01）. Compared with that in the model group， the Caspase-1 in the Guihuang formula group and the inhibitor group significantly decreased （P<0.01）. Compared with the blank group， the model group showed an increase in LDH release （P<0.01）. Compared with the model group， the Guihuang formula group and the inhibitor group showed a significantly decrease in LDH release （P<0.01）. Compared with those in the blank group， the levels of IL-1β and IL-18 in the supernatant of the model group were significantly increased （P<0.01）. Compared with the model group， the Guihuang formula group and the inhibitor group showed a significantly decrease in the levels of IL-1β and IL-18 （P<0.01）. Compared with those in the blank group， the expression levels of NLRP3， Caspase-1， and GSDMD proteins significantly increased in the model group （P<0.01）. Compared with those in the model group， the protein expression levels of NLRP3， Caspase-1， and GSDMD were significantly reduced in the Guihuang formula group and inhibitor group （P<0.01）. ConclusionGuihuang formula can inhibit the activation of Caspase-1， prevent GSDMD cleavation and lysis， and inhibit cell pyrodeath in the treatment of type Ⅲ prostatitis by inhibiting the activation of NLRP3 inflammasome.

ObjectiveTo identify the pathogen species responsible for the wilt disease of Tetradium ruticarpum in Chongqing， investigate there biological characteristics， and screen effective fungicides， so as to provide a theoretical basis for disease control in production. MethodsThe pathogen was isolated via the tissue culture method. Pathogenicity was verified according to Koch's postulates. The pathogen was identified based on morphological characteristics and multi-gene phylogenetic analysis. The mycelial growth rate method was used for biological characterization of the pathogen and fungicide screening. ResultsThe pathogen colonies were nearly circular with irregular edges， white， short， velvety aerial hyphae， and pale purple undersides. Macroconidia were colorless， sickle-shaped， with 3-5 septa， while microconidia were transparent， elliptical， aseptate or with 1-2 septa. Multi-gene phylogenetic analysis showed that the pathogen clustered in the same clade as Fusarium fujikuroi with 100% support， which， combined with morphological characteristics， identified the pathogen causing wilt of T. ruticarpum in Chongqing as F. fujikuroi. The optimal conditions for the mycelial growth of F. fujikuroi were mung bean agar （MBA） with glucose as the carbon source， beef extract and yeast powder as nitrogen sources， 28 ℃， pH 7.0， and alternating light/dark conditions. The optimal conditions for sporulation were potato dextrose agar （PDA） with glucose as the carbon source， beef extract as the nitrogen source， 28 ℃， pH 7.0， and complete darkness. Among chemical fungicides， phenazine-1-carboxylic acid exhibited the strongest inhibitory effect on F. fujikuroi. Shenqinmycin and tetramycin were the most effective bio-fungicides. ConclusionThis study is the first to report F. fujikuroi as the causal agent of wilt disease in T. rutaecarpa. The chemical fungicide phenazine-1-carboxylic acid and the bio-fungicides shenqinmycin and tetramycin showed strong inhibitory effects against F. fujikuroi.

ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

This paper systematically elaborates on the key points of diagnosis and differential diagnosis of salivary gland tumors characterized by a substantial amount of extracellular mucus as a main or prominent feature, and clarifies the core differential features. The term "mucus-rich" specifically denotes that mucus is a major component of the tumor, rather than a focal or minor one. This phenomenon is associated with distinct histogenetic mechanisms: it may result from specific genetic mutations (e.g., AKT1 E17K in mucinous adenocarcinoma) that drive ductal epithelial differentiation into mucus-secreting cells, or from myoepithelial cells secreting glycosaminoglycans that form a myxoid stroma. Salivary gland tumors with abundant extracellular mucus include mucinous cystadenoma, sialadenoma papilliferum-like intraductal papillary tumors, mucinous myoepithelioma, pleomorphic adenoma with mucin-rich stroma, mucinous adenocarcinoma, low-grade mucoepidermoid carcinoma, mucin-rich salivary duct carcinoma and intestinal-type adenocarcinoma. The diagnosis of these tumors is complicated by the dual nature of extracellular mucus: while it is a defining feature of some entities, it can also obscure key diagnostic architectural features in others, leading to histological overlap and inconspicuous diagnostic areas. Given the frequent histological morphological overlap among these tumors, immunohistochemical findings and molecular characteristics have emerged as crucial differential diagnostic criteria. Core differential diagnostic points include the following: histologically, there must be meticulous identification of typical structures obscured by mucin (such as squamoid cells in mucoepidermoid carcinoma and apocrine features in salivary duct carcinoma); in immunohistochemical staining, CK20 is useful for distinguishing intestinal-type adenocarcinoma (positive) from mucinous adenocarcinoma (negative), while androgen receptor aids in differentiating salivary duct carcinoma (positive) from mucoepidermoid carcinoma (negative); and molecular testing plays a critical role in definitive diagnosis (e.g., the AKT1 E17K mutation for mucinous adenocarcinoma, MAML2 rearrangement for mucoepidermoid carcinoma, and MEF2C::SS18 fusion for microsecretory adenocarcinoma). This paper systematically summarizes the core pathological features and differential diagnostic points of mucin-rich salivary gland tumors, aiming to provide a practical reference for clinical pathological diagnosis.

In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

Objective: To analyze the characteristics of road traffic injuries (RTI) among urban and rural residents in Zhejiang Province, so as to provide a basis for developing targeted RTI prevention and control strategies. Methods: In April 2023, permanent residents from 13 counties (cities, districts) in Zhejiang Province were selected using a multistage stratified random sampling method. Basic information and RTI occurrences within the past 12 months were collected through questionnaire surveys. RTI incidence and characteristics of RTI among urban and rural residents were analyzed. Results: A total of 36 980 individuals were surveyed, including 18 327 males (49.56%) and 18 653 females (50.44%). The median age was 56.00 (interquartile range, 28.00) years. There were 442 person-times of RTI, with an incidence of 1.20%. The rural incidence was significantly higher than the urban (1.33% vs. 1.05%, P<0.05). The incidence of RTI increased with age and decreased with higher educational attainment (both P<0.05). The majority of RTI occurred on streets/urban areas, accounting for 59.28%. In urban, streets/urban areas were the primary locations, accounting for 76.84%. In rural, streets/urban areas and intercity highways were the main sites, accounting for 46.03% and 40.48%, respectively. There was a statistically significant difference in the composition of RTI locations between urban and rural (P<0.05). The primary treatment approach of RTI was outpatient/emergency care, accounting for 61.99%. There was a statistically significant difference in the proportion of treatment approaches of RTI between urban and rural (P<0.05). Electric bicycles were involved in 67.87% of RTI, and 54.29% of motor vehicle occupants used seat belts. No statistically significant differences were observed in the composition of transportation modes or seat belt usage rates between urban and rural (both P>0.05). The lower limb was the most commonly injured sites, accounting for 42.31%. Mild injury was predominant, accounting for 50.90%, and complete recovery was predominant outcome, accounting fo 69.68%. The median rest period was 13.50 (interquartile range, 27.25) days. The median medical expenses was 1 200.00 (interquartile range, 5 700.00) yuan. No statistically significant differences were observed between urban and rural in terms of injury sites, injury severity, outcome, rest period, or medical expenses (all P>0.05). Conclusions RTI incidence is higher among rural residents, the elderly, and lower education levels residents in Zhejiang Province. It is recommend optimizing road safety infrastructure on streets/urban areas and intercity highways, prioritizing prevention of electric bicycles RTI, strengthening safety education for high-risk population, and increasing the usage rate of safety devices.