The pathophysiological process of ischemic stroke (IS) is highly complex. Current clinical treatments are limited by a narrow therapeutic time window and are inadequate for improving residual neurological dysfunction. Therefore, animal models have become essential tools for exploring the pathological mechanisms of this disease from both traditional Chinese medicine (TCM) and Western medicine perspectives,as well as for evaluating innovative therapeutic strategies. This review systematically outlines existing IS modeling methods and cutting-edge advances. It comparatively integrates the multi-layered pathogeneses in Western medicine, including excitotoxicity, oxidative stress, inflammatory responses, blood-brain barrier injury, ferroptosis, and PANoptosis, with the core TCM pathogenesis characterized by disorders of Qi and blood and functional imbalance caused by "wind, fire/heat, phlegm, blood stasis, and deficiency." Furthermore, this paper categorizes and compares the specific modeling techniques, evaluation indicators, and respective advantages and limitations of focal cerebral ischemia models, global cerebral ischemia models, and TCM disease-syndrome combined models, such as the Qi deficiency and blood stasis syndrome. Literature review suggests that, by exploring emerging cell death mechanisms like PANoptosis and introducing environmental stress or dietary interventions on the basis of ischemic modeling, specific TCM syndromes associated with IS can be preliminarily screened out and replicated. It provides a feasible approach for objectively elucidating the biological basis of TCM interventions involving multiple targets and pathways. Given that traditional single-disease models fail to fully capture the complexity and individual heterogeneity of the cerebral network microenvironment in clinical patients, future research on IS animal models should focus on the deep integration of TCM and Western medicine in pathogenesis. By leveraging multidisciplinary technologies, such as gene editing, high-resolution in vivo imaging, artificial intelligence-driven multi-omics analysis, and brain organoids, researchers can construct high-biomimetic, multi-dimensional models that closely mirror clinical scenarios, thereby effectively improving the success rate of translating innovative IS therapies from basic research to clinical application.

[Objective] : To explore the clinical characteristics and methods for syndrome differentiation prediction, as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ischemic stroke (AIS). [Methods] : This study employed a retrospective case-control design to analyze patients with AIS who received inpatient treatment at the Neurology Department of The First Hospital of Hunan University of Chinese Medicine from January 1, 2013 to December 31, 2022. AIS patients meeting the diagnostic criteria for Qi deficiency and blood stasis syndrome were stratified into case group, while those without Qi deficiency and blood stasis syndrome were stratified into control group. The demographic characteristics (age and gender), clinical parameters [time from onset to admission, National Institutes of Health Stroke Scale (NIHSS) score, and blood pressure], past medical history, traditional Chinese medicine (TCM) diagnostic characteristics (tongue and pulse), neurological symptoms and signs, imaging findings [magnetic resonance imaging-diffusion weighted imaging (MRI-DWI)], and biochemical indicators of the two groups were collected and compared. The indicators with statistical difference (P < 0.05) in univariate analysis were included in multivariate logistic regression analysis to evaluate their predictive value for the diagnosis of Qi deficiency and blood stasis syndrome, and the predictive model was constructed by receiver operating characteristic (ROC) curve analysis. [Results] : The study included 1 035 AIS patients, with 404 cases in case group and 631 cases in control group. Compared with control group, patients in case group were significantly older, had extended onset-to-admission time, lower diastolic blood pressure, and lower NIHSS scores (P < 0.05). Case group showed lower incidence of hypertension history (P < 0.05). Regarding tongue and pulse characteristics, pale and dark tongue colors, white tongue coating, fine pulse, astringent pulse, and sinking pulse were more common in case group. Imaging examinations demonstrated higher proportions of centrum semiovale infarction, cerebral atrophy, and vertebral artery stenosis in case group (P < 0.05). Among biochemical indicators, case group showed higher proportions of elevated fasting blood glucose and glycated hemoglobin (HbA1c), while lower proportions of elevated white blood cell count, reduced hemoglobin, and reduced high-density lipoprotein cholesterol (HDL-C) (P < 0.05). Multivariate logistic regression analysis identified significant predictors for Qi deficiency and blood stasis syndrome including: fine pulse [odds ratio (OR) = 4.38], astringent pulse (OR = 3.67), superficial sensory abnormalities (OR = 1.86), centrum semiovale infarction (OR = 1.57), cerebral atrophy (OR = 1.55), vertebral artery stenosis (OR = 1.62), and elevated HbA1c (OR = 3.52). The ROC curve analysis of the comprehensive prediction model yielded an area under the curve (AUC) of 0.878 [95% confidence interval (CI) = 0.855 – 0.900]. [Conclusion] This study finds out that Qi deficiency and blood stasis syndrome represents one of the primary types of AIS. Fine pulse, astringent pulse, superficial sensory abnormalities, centrum semiovale infarction, cerebral atrophy, vertebral artery stenosis, elevated blood glucose, elevated HbA1c, pale and dark tongue colors, and white tongue coating are key objective diagnostic indicators for the syndrome differentiation of AIS with Qi deficiency and blood stasis syndrome. Based on these indicators, a syndrome differentiation prediction model has been developed, offering a more objective basis for clinical diagnosis, and help to rapidly identify this syndrome in clinical practice and reduce misdiagnosis and missed diagnosis.

Objective To investigate the preoperative prediction of the expression level of programmed cell death ligand 1(PD-L1)in non-small cell lung cancer(NSCLC)by a nomogram model constructed with clinical data,conventional CT signs and spectral CT parameters.Methods A retrospective analysis was conducted on 52 patients with pathologically confirmed NSCLC and undergoing preoperative spectral CT examination.The patients were categorized into positive and negative groups according to PD-L1 expression level,and their clinical data,conventional CT signs and spectral CT parameters were collected.Specifically,clinical data included gender,age,Ki-67 and tumor markers;conventional CT signs included tumor density,margins,calcification,spiculation,lobulation,pleural indentation and cavitation;and spectral CT parameters measured in the arterial and venous phases included effective atomic number(Eff-Z),iodine concentration(IC),water concentration(WC)and normalized iodine concentration(NIC).Intergroup differences were analyzed,and multivariate Logistic regression was used to identify independent predictors and establish the prediction model which was evaluated for prediction performance and accuracy using receiver operating characteristic(ROC)curves,calibration curve and decision curve analyses.Results For clinical data,only the difference in gender between two groups had statistical significance(P<0.05).The spectral CT parameters(IC,NIC and Eff-Z)in the arterial and venous phases of PD-L1 positive group were all greater than those of PD-L1 negative group,with statistically significant differences(P<0.05).Multivariate Logistic regression analysis identified gender(P=0.024),venous-phase Eff-Z(P=0.002),and venous-phase IC(P=0.003)as independent predictive factors for PD-L1 expression.The nomogram prediction model constructed with these independent predictors had an area under curve of 0.80,a sensitivity of 88.00%,and a specificity of 59.00%.The calibration curve showed that the predicted values had a high consistency with the actual values.The decision curve revealed that when the high-risk threshold was between 0.10 and 0.83,the model could achieve the maximum net benefit.Conclusion The nomogram model constructed with spectral CT parameters and clinical data has certain value in predicting the expression level of PD-L1 in NSCLC.

Objective:In order to effectively educate students on tooth defect diseases and restoration techniques, the teaching and research team at the School of Stomatology of Qingdao University developed an integrated course operative dentistry. This course is specifically designed to align with the undergraduate curriculum reform requirements of "Three Early, Two Multiple and One Integration".Methods:The teaching practice of operative dentistry was conducted with the stomatology undergraduates of grade 2020 and 2021. The course group redesigned the curriculum by reorganizing teaching content, rebuilding teaching resources, reconstructing teaching mode, and reshaping the evaluation system. Evaluation of the curriculum design included test scores, satisfaction questionnaires, and teacher self-evaluation. Statistical analysis was performed using SPSS 27.0 software, including independent sample t-tests for numerical variables and Fisher's exact test or Wilcoxon rank sum test for categorical variables. Results:Through the integrated course learning, 92.68% (38/41) of the students of grade 2020 and 97.44% (38/39) of the students of grade 2021 can basically or completely master various dental defect repair schemes and understand the importance of preserving natural tooth. 82.93% (34/41) of the students of grade 2020 and 100.00% (39/39) of the students of grade 2021 could basically or completely master the key points of various dental defect restorations.Conclusions:Through the practice of teaching, it has been confirmed that the integrated course of operative dentistry is beneficial to students' mastering the knowledge and skills of dental defect diseases and their restoration in a more comprehensive way, and is conducive to the cultivation of clinical thinking, hands-on ability and the patient-centered, comprehensive problem-solving thinking, which is in line with the guidance on the innovation and development of medical education.

Objective:Comprehensive characterization of B-cell phenotypes and spatial distribution in oral lichen planus (OLP) and related oral lichenoid lesions (OLL)(OLP/OLL), with an emphasis on transcriptomic profiling and functional analysis, to uncover the epigenetic mechanisms underlying B cell-mediated immune regulation within the oral mucosal microenvironment.Methods:Single-cell RNA sequencing raw data were sourced from the GSE211630 database, encompassing samples from 2 cases of erosive OLP (EOLP), 3 cases of non-erosive OLP (NEOLP) and 1 healthy control (NORMAL). Following stringent quality control, the data underwent normalization, selection of highly variable genes and batch effect correction. Subsequent analyses included dimensionality reduction and unsupervised clustering to identify distinct cell populations. This study collected pathological specimens from 3 OLP/OLL patients and 3 healthy controls who were treated at the Department of Oral Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine from January 2021 to December 2023. Using 10X Genomics Visium HD spatial transcriptomics technology, tissue sections were processed through dewaxing, staining and histological imaging, enabling the reconstruction of nucleic acid structures and the capture of gene expression profiles. Data analysis included quality assessment, gene quantification, normalization, dimensionality reduction and clustering. Furthermore, cell type deconvolution was performed using the robust cell type decomposition algorithm, integrating single-cell transcriptomic data to accurately predict and spatially resolve cell type distributions within the tissue microenvironment.Results:After integrating single-cell data from EOLP, NEOLP and NORMAL, cells were classified into seven major categories: B/plasma cells, endothelial cells, epithelial cells, fibroblasts, myeloid cells, smooth muscle cells and T/natural killer cells. The proportion of B/plasma cells varied significantly among the three groups, accounting for 10.7% (1 693/15 815), 3.8% (833/21 653) and 0.4% (47/11 556) of the total cells respectively. Further clustering analysis of B/plasma cells identified four distinct subpopulations: naive B cells, activated B cells, memory B cells and plasma cells. In the EOLP group, these subpopulations constituted 25.9% (348/1 344), 45.9% (617/1 344), 3.3% (45/1 344) and 24.9% (334/1 344) of the B/plasma cells respectively. In the NEOLP group, they represented 31.6% (195/617), 59.6% (368/617), 0.2% (1/617) and 8.6% (53/617). Howerer, only plasma cells were detected in the NORMAL group. Spatial analysis revealed that B cells were actively involved in the formation of tertiary lymphoid structures (TLS) at various stages in OLP/OLL samples, with a prominent structural organization observed in secondary follicle-like TLS. Within these structures, the expressions of T cells marker gene CD3E and B cells marker gene MS4A1 were significantly elevated. Additionally, in secondary follicle-like TLS, the gene encoding follicular dendritic cell secreted protein, germinal center marker gene B cell lymphoma 6 and the gene for activation induced cytidine deaminase also showed strong expression. In OLP/OLL samples, plasma cell marker gene CD38, immunoglobulin (IGH) G3, IGHG1, IGHM, IGHD, IGHE, imunoglobulin Kappa constant, immunoglobulin alpha 1, immunoglobulin Lambda constant 1 and complement gene C3 all exhibited high levels of expression.Conclusions:Compared to normal mucosa, extensive B-cell infiltration is observed in both OLP and OLL, accompanied by significant differences in B-cell phenotypes and proportions. B cells appear to play a central role in local immune responses, primarily through the formation of TLS. However, the precise functional mechanisms underlying their involvement require further investigation.

Objective:Constructing a risk prediction model to assess the impact of various factors on the need for auxiliary bone grafting with implant placement following alveolar ridge preservation (ARP) in posterior teeth.Methods:According to the sample size calculation formula, the sample size was calculated using the pmsampsize package of R 4.1.3 software, based on inclusion and exclusion criteria, a total of 110 posterior teeth in 98 patients who underwent ARP at the Department of Periodontology, School and Hospital of Stomatology, China Medical University, from January 2018 to May 2024 were conducted. Teeth were randomly divided into modeling group and validation group with 7∶3 ratio according to the random number table. The modeling group was divided into direct implantation group and auxiliary bone grafting group on the basis of whether auxiliary bone grafting was performed 6 months after ARP. Univariate and multivariate analyses were conducted to identify factors influencing auxiliary bone grafting with implant placement following ARP. Nomogram was constructed using R software. Receiver operator characteristic (ROC) curve and calibration curve were drawn to evaluate model differentiation and consistency. The decision curve analysis (DCA) was used to assess the clinical application value of the model.Results:Age ( OR=1.06, P=0.001), maximum attachment loss (AL) ( OR=1.75, P<0.001), reason of tooth extraction ( OR=12.73, P<0.001), smoking [<10 cigarettes/d ( OR=7.59, P<0.001);≥10 cigarettes/d ( OR=28.12, P<0.001)] and stage of periodontitis [stage Ⅱ ( OR=2.57, P=0.430); stage Ⅲ ( OR=21.00, P=0.007); stage Ⅳ ( OR=76.50, P<0.001)] influenced the necessity for auxiliary bone grafting with implant placement after ARP. After multivariate analysis of the above influencing factors, it was found that smoking [<10 cigarettes/d ( OR=7.02, P=0.009);≥10 cigarettes/d ( OR=10.27, P=0.026)] was an independent risk factor for the need of auxiliary bone grafting with implant placement after ARP. The area under the ROC curve for internal verification was 0.90 (95 %CI: 0.84-0.97), and the H-L goodness of fit test results were χ 2=4.79, P=0.780, indicating a good agreement. The area under the externally verified ROC curve was 0.97 (95 %CI: 0.92-1.00), suggesting that the fitting effect was slightly lower than that of the modeling group, and the predicted value of the model was slightly lower than the true value, which might underestimate the risk of additional surgery in patients. Results:of H-L goodness of fit test were χ 2=5.03, P=0.754. The DCA curve showed that when the probability of high-risk threshold was between 0.06 and 0.93, the clinical application value of the prediction model was higher. Conclusions:Age, smoking, reason of tooth extraction, stage of periodontitis, and maximum AL of the affected teeth were related to the necessity for auxiliary bone grafting with implant placement 6 months after ARP. Smoking was an independent risk factor for auxiliary bone grafting surgery. The constructed nomogram model had good discrimination and consistency.

Objective:To compare the value of different imaging modalities in evaluating early efficacy of 90Y-selective internal radiation therapy (SIRT) for liver malignant tumors. Methods:From September 2021 to December 2023, a retrospective analysis was conducted on 43 patients (32 males, 11 females; age (55.8±14.7) years) with liver malignant tumors who received 90Y-SIRT at Hainan Cancer Hospital and Boao Super Hospital. The injection dosage of 90Y was 1.5(1.2, 2.4)GBq. Clinical and imaging data of patients before and after treatment for multimodal evaluation were collected, including MRI plain and enhanced scans, as well as diffusion weighted imaging (DWI). Plain scan images were evaluated for the treatment efficacy by response evaluation criteria in solid tumors (RECIST) 1.1 version, while enhanced MRI was scored into 1-5 based on changes in the target area, including increased low-density range, necrotic features, and decreased enhancement. The minimum apparent diffusion coefficient (ADC min) of DWI increased by 20% compared to baseline was determined to be responsive. Wilcoxon signed rank test was used to analyze data, and ROC curve analysis was used to analyze the diagnostic efficacy of different imaging modalities (Delong test). Results:All patients had baseline MRI data, 39 underwent MRI at 1-month after treatment, and 22 underwent MRI at 3-month after treatment. Based on MRI plain scan images, the target lesions showed partial remission (PR) in 10.26%(4/39) of patients, progressive disease (PD) in 5.13%(2/39) of patients, and stable disease (SD) in 84.61%(33/39) of patients at 1-month after treatment; while there were 40.91%(9/22) PR, 9.09%(2/22) PD and 50.00%(11/22) SD at 3-month after treatment. Based on DWI images, 35.90%(14/39) and 68.18%(15/22) of patients were considered responsive at 1-month and 3-month after treatment, respectively. Compared with baseline, the differences of tumor sizes, enhancement degree of target lesions and ADC min at 1-month and 3-month after treatment were statistically significant ( Z values: from -3.88 to -2.39, all P<0.05). Compared with the tumor size and enhancement degree, the AUCs of ADC min were the highest at 1-month (0.701) and 3-month (0.953) after treatment ( Z values: 0.40-2.29, all P<0.05). Conclusions:MRI plain scan, MRI enhancement and DWI are effective in the evaluation the efficacy of 90Y-SIRT for liver malignant tumors at 1-month and 3-month after treatment. ADC min is superior to tumor size and enhancement degree in diagnostic efficacy.

Objective:To construct an evaluation indicator system for accessories of patient monitor,so as to provide a basis for clinical management,equipment procurement,and technical improvement for accessories of medical monitor.Methods:The initial selection indicators of corresponding accessories of three types of monitoring of medical monitors,including electrocardiogram(ECG),blood oxygen saturation(SpO2)and non-invasive blood pressure(NIBP),were determined through literature research,expert consultation,and actual investigation.The Delphi method was adopted to conduct two rounds of questionnaire consultation for experts from clinical medicine,biomedical engineering and other fields in medical institutions included Sichuan Provincial People's Hospital,The Affiliated Hospital of Southwest Medical University and Medical Institute of Chengdu Institute of Metrology Verification and Testing.The evaluation indicators were screened and optimized,and the Analytic Hierarchy Process(AHP)was used to calculate the weights of each indicator.The consistency test was conducted to verify the rationality of the evaluation indicator system.Results:The evaluation indicator system for accessories of medical monitor included three first-level indicators:clinical value,cost value,and management value.The number of second-level indicators about ECG,SpO2 and NIBP of evaluation indicator system were respectively 10,9 and 8,and the number of third-level indicators of that were respectively 20,19,and 14.In the first-level indicators,the clinical value had the highest weight,with 72.49%for ECG,70.88%for SpO2 and 70.32%for NIBP.In the second-level indicators,the accuracy(28.70%for ECG,38.13%for SpO2 and 43.03%for NIBP)and safety(27.47%for ECG,26.48%for SpO2 and 23.06%for NIBP)were the core indicators.The weights of cost value and management value were between 14.62%and 17.41%,and between 12.27%and 12.89%,respectively.Conclusion:The evaluation indicator system for accessories of medical monitor integrates multi-dimensional expert opinions and quantitative analysis,highlights the priority principle for clinical performance.It can provide theoretical support for optimizing selection about accessory for medical institutions,and improving quality of monitoring,and promoting standardized management in the industry.

Objective:Comprehensive characterization of B-cell phenotypes and spatial distribution in oral lichen planus (OLP) and related oral lichenoid lesions (OLL)(OLP/OLL), with an emphasis on transcriptomic profiling and functional analysis, to uncover the epigenetic mechanisms underlying B cell-mediated immune regulation within the oral mucosal microenvironment.Methods:Single-cell RNA sequencing raw data were sourced from the GSE211630 database, encompassing samples from 2 cases of erosive OLP (EOLP), 3 cases of non-erosive OLP (NEOLP) and 1 healthy control (NORMAL). Following stringent quality control, the data underwent normalization, selection of highly variable genes and batch effect correction. Subsequent analyses included dimensionality reduction and unsupervised clustering to identify distinct cell populations. This study collected pathological specimens from 3 OLP/OLL patients and 3 healthy controls who were treated at the Department of Oral Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine from January 2021 to December 2023. Using 10X Genomics Visium HD spatial transcriptomics technology, tissue sections were processed through dewaxing, staining and histological imaging, enabling the reconstruction of nucleic acid structures and the capture of gene expression profiles. Data analysis included quality assessment, gene quantification, normalization, dimensionality reduction and clustering. Furthermore, cell type deconvolution was performed using the robust cell type decomposition algorithm, integrating single-cell transcriptomic data to accurately predict and spatially resolve cell type distributions within the tissue microenvironment.Results:After integrating single-cell data from EOLP, NEOLP and NORMAL, cells were classified into seven major categories: B/plasma cells, endothelial cells, epithelial cells, fibroblasts, myeloid cells, smooth muscle cells and T/natural killer cells. The proportion of B/plasma cells varied significantly among the three groups, accounting for 10.7% (1 693/15 815), 3.8% (833/21 653) and 0.4% (47/11 556) of the total cells respectively. Further clustering analysis of B/plasma cells identified four distinct subpopulations: naive B cells, activated B cells, memory B cells and plasma cells. In the EOLP group, these subpopulations constituted 25.9% (348/1 344), 45.9% (617/1 344), 3.3% (45/1 344) and 24.9% (334/1 344) of the B/plasma cells respectively. In the NEOLP group, they represented 31.6% (195/617), 59.6% (368/617), 0.2% (1/617) and 8.6% (53/617). Howerer, only plasma cells were detected in the NORMAL group. Spatial analysis revealed that B cells were actively involved in the formation of tertiary lymphoid structures (TLS) at various stages in OLP/OLL samples, with a prominent structural organization observed in secondary follicle-like TLS. Within these structures, the expressions of T cells marker gene CD3E and B cells marker gene MS4A1 were significantly elevated. Additionally, in secondary follicle-like TLS, the gene encoding follicular dendritic cell secreted protein, germinal center marker gene B cell lymphoma 6 and the gene for activation induced cytidine deaminase also showed strong expression. In OLP/OLL samples, plasma cell marker gene CD38, immunoglobulin (IGH) G3, IGHG1, IGHM, IGHD, IGHE, imunoglobulin Kappa constant, immunoglobulin alpha 1, immunoglobulin Lambda constant 1 and complement gene C3 all exhibited high levels of expression.Conclusions:Compared to normal mucosa, extensive B-cell infiltration is observed in both OLP and OLL, accompanied by significant differences in B-cell phenotypes and proportions. B cells appear to play a central role in local immune responses, primarily through the formation of TLS. However, the precise functional mechanisms underlying their involvement require further investigation.

Objective:Constructing a risk prediction model to assess the impact of various factors on the need for auxiliary bone grafting with implant placement following alveolar ridge preservation (ARP) in posterior teeth.Methods:According to the sample size calculation formula, the sample size was calculated using the pmsampsize package of R 4.1.3 software, based on inclusion and exclusion criteria, a total of 110 posterior teeth in 98 patients who underwent ARP at the Department of Periodontology, School and Hospital of Stomatology, China Medical University, from January 2018 to May 2024 were conducted. Teeth were randomly divided into modeling group and validation group with 7∶3 ratio according to the random number table. The modeling group was divided into direct implantation group and auxiliary bone grafting group on the basis of whether auxiliary bone grafting was performed 6 months after ARP. Univariate and multivariate analyses were conducted to identify factors influencing auxiliary bone grafting with implant placement following ARP. Nomogram was constructed using R software. Receiver operator characteristic (ROC) curve and calibration curve were drawn to evaluate model differentiation and consistency. The decision curve analysis (DCA) was used to assess the clinical application value of the model.Results:Age ( OR=1.06, P=0.001), maximum attachment loss (AL) ( OR=1.75, P<0.001), reason of tooth extraction ( OR=12.73, P<0.001), smoking [<10 cigarettes/d ( OR=7.59, P<0.001);≥10 cigarettes/d ( OR=28.12, P<0.001)] and stage of periodontitis [stage Ⅱ ( OR=2.57, P=0.430); stage Ⅲ ( OR=21.00, P=0.007); stage Ⅳ ( OR=76.50, P<0.001)] influenced the necessity for auxiliary bone grafting with implant placement after ARP. After multivariate analysis of the above influencing factors, it was found that smoking [<10 cigarettes/d ( OR=7.02, P=0.009);≥10 cigarettes/d ( OR=10.27, P=0.026)] was an independent risk factor for the need of auxiliary bone grafting with implant placement after ARP. The area under the ROC curve for internal verification was 0.90 (95 %CI: 0.84-0.97), and the H-L goodness of fit test results were χ 2=4.79, P=0.780, indicating a good agreement. The area under the externally verified ROC curve was 0.97 (95 %CI: 0.92-1.00), suggesting that the fitting effect was slightly lower than that of the modeling group, and the predicted value of the model was slightly lower than the true value, which might underestimate the risk of additional surgery in patients. Results:of H-L goodness of fit test were χ 2=5.03, P=0.754. The DCA curve showed that when the probability of high-risk threshold was between 0.06 and 0.93, the clinical application value of the prediction model was higher. Conclusions:Age, smoking, reason of tooth extraction, stage of periodontitis, and maximum AL of the affected teeth were related to the necessity for auxiliary bone grafting with implant placement 6 months after ARP. Smoking was an independent risk factor for auxiliary bone grafting surgery. The constructed nomogram model had good discrimination and consistency.