Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To evaluate clinical outcomes of occlusion-guided vascularized folded fibula flap reconstruction with delayed implant restoration for mandibular defects after benign tumor resection.Methods:A total of 12 patients with benign mandibular tumors underwent free folded fibula flap reconstruction at the First Affiliated Hospital of Bengbu Medical University between January 2020 and December 2023, including 7 males and 5 females, aged 21-52 years. Six months after mandibular reconstruction, the internal fixation titanium plates were removed, and dental implants were placed using a preoperatively fabricated occlusal guide, followed by second-stage implant surgery and prosthetic restoration. Mandibular CT scans were obtained 6 months after reconstruction to compare the fitting accuracy between the preoperative virtual design and the actual reconstructed mandible. The implant stability quotient (ISQ) was measured 3 months after implant placement. Masticatory efficiency and Enneking lower limb function scores were evaluated at the following time points: before tumor surgery (T1), before implant placement (T2), 6 months (T3) and 9 months (T4) after implant crown restoration. One-way repeated measures ANOVA was used to analyze the masticatory efficiency and lower limb function scores.Results:The free folded fibula grafts were successfully performed via an intraoral approach in all 12 patients, with a 100% of survival rate. Mandibular defects included Brown class I in 6 cases, class II in 2 cases, and class III in 4 cases. A total of 42 implants were placed with successful osseointegration. The ISQ measured at 3 months post-placement was 64.10±4.18. At 6 months postoperatively, morphological analysis comparing the preoperative virtual surgical design with the actual postoperative reconstructed mandible revealed a reconstruction accuracy of 84.27%±4.23%. Significant differences were observed in Enneking scores and masticatory efficiency across all four time points (all P<0.001). Masticatory function showed significant improvement at T4 compared that at T2 [(88.06±3.66)% vs. (65.44±3.82)%, P<0.05]. Conclusion:Occlusal function-guided mandibular reconstruction with vascularized folded fibula flap after removal of benign mandibular tumors is a reliable method, which is associated with minimal donor-site morbidity and enables patients to restore precise occlusion and to achieve favorable masticatory efficiency.

The oropouche virus (OROV) poses a threat to pregnant women and fetuses, potentially causing fetal neurological defects and even stillbirth, which has caused global attention. OROV is an arthropod-borne virus belonging to the Orthobunyavirus genus in the Bunyavirales order, primarily transmitted by arthropods and causing oropouche fever. This article reviews the etiological characteristics, epidemiological distribution, clinical symptoms, detection methods, and prevention strategies of OROV. OROV is prevalent in Central and South America, with a sharp increase in cases reported in Brazil in 2024. The virus's symptoms resemble those of several other arthropod-borne viral diseases, which can lead to misdiagnosis. Currently, there are no specific drugs or vaccines available, and treatment is mainly supportive. Culicoides paraensis and Culex quinquefasciatus are among the significant vectors of OROV. Furthermore, the article analyzes the distribution of Culex quinquefasciatus in China, highlights the risk of imported cases, proposes targeted prevention and control strategies, and underscores the significance of international cooperation in disease prevention and control.

Enamel demineralization, the formation of white spot lesions, is a common issue in clinical orthodontic treatment. The appearance of white spot lesions not only affects the texture and health of dental hard tissues but also impacts the health and aesthetics of teeth after orthodontic treatment. The prevention, diagnosis, and treatment of white spot lesions that occur throughout the orthodontic treatment process involve multiple dental specialties. This expert consensus will focus on providing guiding opinions on the management and prevention of white spot lesions during orthodontic treatment, advocating for proactive prevention, early detection, timely treatment, scientific follow-up, and multidisciplinary management of white spot lesions throughout the orthodontic process, thereby maintaining the dental health of patients during orthodontic treatment.
Humans ; Consensus ; Dental Caries/etiology* ; Dental Enamel/pathology* ; Tooth Demineralization/etiology* ; Tooth Remineralization

Objective:To evaluate clinical outcomes of occlusion-guided vascularized folded fibula flap reconstruction with delayed implant restoration for mandibular defects after benign tumor resection.Methods:A total of 12 patients with benign mandibular tumors underwent free folded fibula flap reconstruction at the First Affiliated Hospital of Bengbu Medical University between January 2020 and December 2023, including 7 males and 5 females, aged 21-52 years. Six months after mandibular reconstruction, the internal fixation titanium plates were removed, and dental implants were placed using a preoperatively fabricated occlusal guide, followed by second-stage implant surgery and prosthetic restoration. Mandibular CT scans were obtained 6 months after reconstruction to compare the fitting accuracy between the preoperative virtual design and the actual reconstructed mandible. The implant stability quotient (ISQ) was measured 3 months after implant placement. Masticatory efficiency and Enneking lower limb function scores were evaluated at the following time points: before tumor surgery (T1), before implant placement (T2), 6 months (T3) and 9 months (T4) after implant crown restoration. One-way repeated measures ANOVA was used to analyze the masticatory efficiency and lower limb function scores.Results:The free folded fibula grafts were successfully performed via an intraoral approach in all 12 patients, with a 100% of survival rate. Mandibular defects included Brown class I in 6 cases, class II in 2 cases, and class III in 4 cases. A total of 42 implants were placed with successful osseointegration. The ISQ measured at 3 months post-placement was 64.10±4.18. At 6 months postoperatively, morphological analysis comparing the preoperative virtual surgical design with the actual postoperative reconstructed mandible revealed a reconstruction accuracy of 84.27%±4.23%. Significant differences were observed in Enneking scores and masticatory efficiency across all four time points (all P<0.001). Masticatory function showed significant improvement at T4 compared that at T2 [(88.06±3.66)% vs. (65.44±3.82)%, P<0.05]. Conclusion:Occlusal function-guided mandibular reconstruction with vascularized folded fibula flap after removal of benign mandibular tumors is a reliable method, which is associated with minimal donor-site morbidity and enables patients to restore precise occlusion and to achieve favorable masticatory efficiency.

This consensus was developed by the Orthodontic Society of the Chinese Stomatological Association to provide a systematic, scientific, and practical guideline for informed consent in orthodontic care. Orthodontic treatment is typically lengthy, highly individualized, and involves multiple factors such as growth and development, occlusal function, and facial esthetics. Rapid technological advances and diverse risk profiles make the traditional reliance on orthodontist experience or institutional templates insufficient to ensure patients′ full understanding and autonomous decision-making. To address this, the expert panel conducted extensive reviews of domestic and international guidelines, analyzed representative dispute cases, and performed multicenter patient-clinician surveys. Using a multi-round Delphi method, the group established a standardized informed consent framework covering the initial consultation, treatment, and retention phases. The consensus emphasizes that informed consent is not only a fundamental legal and ethical requirement but also a key step in building trust, improving patient compliance, and enhancing treatment satisfaction. Orthodontists should clearly and comprehensively explain treatment plans, potential risks, uncertainties, and associated costs, while respecting the autonomy of patients or guardians, and maintain continuous communication and dynamic evaluation throughout the treatment process. The release of this consensus provides unified and authoritative guidance for clinical orthodontics, helping to standardize informed consent, enhance its transparency, safeguard patient rights, reduce medical risks, and promote high-quality, sustainable development of orthodontic practice.

This consensus was developed by the Orthodontic Society of the Chinese Stomatological Association to provide a systematic, scientific, and practical guideline for informed consent in orthodontic care. Orthodontic treatment is typically lengthy, highly individualized, and involves multiple factors such as growth and development, occlusal function, and facial esthetics. Rapid technological advances and diverse risk profiles make the traditional reliance on orthodontist experience or institutional templates insufficient to ensure patients′ full understanding and autonomous decision-making. To address this, the expert panel conducted extensive reviews of domestic and international guidelines, analyzed representative dispute cases, and performed multicenter patient-clinician surveys. Using a multi-round Delphi method, the group established a standardized informed consent framework covering the initial consultation, treatment, and retention phases. The consensus emphasizes that informed consent is not only a fundamental legal and ethical requirement but also a key step in building trust, improving patient compliance, and enhancing treatment satisfaction. Orthodontists should clearly and comprehensively explain treatment plans, potential risks, uncertainties, and associated costs, while respecting the autonomy of patients or guardians, and maintain continuous communication and dynamic evaluation throughout the treatment process. The release of this consensus provides unified and authoritative guidance for clinical orthodontics, helping to standardize informed consent, enhance its transparency, safeguard patient rights, reduce medical risks, and promote high-quality, sustainable development of orthodontic practice.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

The oropouche virus (OROV) poses a threat to pregnant women and fetuses, potentially causing fetal neurological defects and even stillbirth, which has caused global attention. OROV is an arthropod-borne virus belonging to the Orthobunyavirus genus in the Bunyavirales order, primarily transmitted by arthropods and causing oropouche fever. This article reviews the etiological characteristics, epidemiological distribution, clinical symptoms, detection methods, and prevention strategies of OROV. OROV is prevalent in Central and South America, with a sharp increase in cases reported in Brazil in 2024. The virus's symptoms resemble those of several other arthropod-borne viral diseases, which can lead to misdiagnosis. Currently, there are no specific drugs or vaccines available, and treatment is mainly supportive. Culicoides paraensis and Culex quinquefasciatus are among the significant vectors of OROV. Furthermore, the article analyzes the distribution of Culex quinquefasciatus in China, highlights the risk of imported cases, proposes targeted prevention and control strategies, and underscores the significance of international cooperation in disease prevention and control.

Objective:To explore mechanism of Fasudil reducing Aβ1-42 induced BV2 cell injury based on NLRP3 inflamma-some.Methods:BV2 cells were divided into:normal control group,Aβ stimulation group,Aβ+Fasudil intervention group,Aβ+MCC950(NLRP3 inhibitor)intervention group.Cell morphology was observed under microscope.Cell activity was determined of by CCK8.NO release was measured by Griess.NLRP3,caspase 1 and IL-18 expressions were detected by immunofluorescence staining.NLRP3,ASC,caspase 1,IL-1β and IL-18 expressions were detected by Western blot.Results:Compared with normal control group,BV2 cells in Aβ stimulation group were activated and showed amoeba-like shape,cell activity was decreased,NO production was increased,NLRP3,ASC,caspase 1,IL-1β and IL-18 expressions were increased.Fasudil intervention and MCC950 intervention inhibited cell injury induced by Aβ1-42 in which BV2 cell morphology tended to be normal,cell activity was increased,while produc-tion of NO was reduced,and NLRP3,ASC,caspase 1,IL-1β and IL-18 expressions were down-regulated,there was no significant difference between Fasudil intervention group and MCC950 intervention group.Conclusion:Fasudil may alleviate Aβ1-42 induced BV2 cell injury and inflammatory reaction by inhibiting NLRP3 inflammasome activation.