There is significant inter-individual variation of pharmacokinetics and pharmacody-namics in patients receiving tacrolimus(TAC)for an-ti-rejection therapy,which cause the rejection or toxic action.Based on results of therapeutic drug monitoring and pathophysiological index of trans-plant patients,the individualized dosing regimen can be designed and adjusted by using model in-formed precision dosing(MIPD).The patients'clini-cal outcome can be improved.In the consensus,the different methods of MIPD used for patients re-ceived TAC for anti-rejection therapy were intro-duced,which can be used for the designing and ad-justing doing regimen,predicting adverse drug reac-tion,improving medication adherence and econom-ics during therapy.

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 develop an artificial intelligence（AI）-based sequential ultrasound-magnetic resonance imaging（US-MRI）diagnostic strategy to optimize the imaging workflow for ovarian masses.Methods:A total of 1 120 patients with pathologically confirmed ovarian masses who underwent both preoperative pelvic ultrasound and MRI between January 2021 and December 2023 at Women's Hospital，Zhejiang University School of Medicine were retrospectively included. Patients were randomly divided into the training（ n=672）and internal test set（ n=448）at a ratio of 6∶4. An external test set（ n=128）was established at the Forth Affiliated Hospital of School of Medicine. Deep learning was used for automated segmentation of MRI lesions，followed by radiomic feature extraction and machine learning classification to construct both a US-MRI multimodal model and sequential US-MRI strategy. Diagnostic performance and potential healthcare cost-saving effects were evaluated across strategies. Results:In the internal test set（ n=448），the AI-based sequential US-MRI strategy achieved a F1 score of 0.863 and a diagnostic accuracy of 82.14%，with no significant difference compared to the US-MRI multi-modal model（ P>0.05）. The sequential strategy identified 82 cases（18.30%，82/448）of patients as low-risk true negatives during initial ultrasound screening，suggesting a potential to reduce the need for MRI examinations in future clinical practice. In the external test set（ n=128），the strategy achieved an F1 score of 0.800 and a confirmed diagnosis rate of 85.94%，with a theoretical reduction of 26.56%（34 cases）in MRI utilization while maintaining a diagnostic accuracy rate higher than that of the multi-modal model（82.18%）. Conclusions:The AI-based US-MRI sequential diagnostic strategy demonstrates favorable diagnostic accuracy while offering the potential to optimize MRI utilization. This approach may enhance the efficiency of imaging resource allocation and reduce healthcare burden in the management of ovarian masses.

Skeletal muscle dysfunction is a common extra-pulmonary complication in patients with chronic ob-structive pulmonary disease(COPD),significantly impacting exercise capacity and quality of life,leading to a poorer prognosis and increased mortality.Oxidative stress closely associates with the development and progression of skeletal muscle dysfunction in COPD.Exercise,a core component of pulmonary rehabilitation,stands as the primary non-pharma-cological treatment for skeletal muscle dysfunction in COPD patients and exerts a positive modulating effect on oxidative stress.This paper reviews the effects of oxidative stress on skeletal muscle dysfunction in COPD and discusses the mecha-nisms by which exercise improves skeletal muscle dysfunction in COPD from an anti-oxidative stress perspective.It has been found that oxidative stress affects the structure and function of muscles in COPD patients by upregulating the protein hydrolysis system,disrupting mitochondrial function,and impairing calcium homeostasis.Mechanisms by which exercise modulates oxidative stress to improve skeletal muscle dysfunction include the activation of antioxidant genes such as silent mating type information regulation 2 homolog 1 and nuclear factor erythroid 2 related factor to enhance the body's antioxi-dant capacity,inhibiting muscle atrophy.Exercise also regulates mitochondrial reactive oxygen species metabolism,im-proving mitochondrial function,and reduces oxidase activity to protect sarcoplasmic reticulum calcium regulation.In con-clusion,the regulation of skeletal muscle oxidative stress by exercise is a crucial target for improving skeletal muscle dys-function in COPD.

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.

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 article reports a diagnostically and therapeutically challenging case of small intestinal marginal zone lymphoma. The patient presented with recurrent abdominal pain as the chief complaint, and imaging revealed multifocal small bowel wall thickening with high uptake, multisegmental luminal stenosis, and proximal dilation. Initial diagnostic workup, including gastroscopy, colonoscopy, and enteroscopy with biopsy, failed to establish a definitive diagnosis. Empirical anti-tuberculosis therapy was ineffective. A repeat enteroscopic biopsy performed over eight months after symptom onset eventually confirmed the diagnosis of mucosa-associated lymphoid tissue (MALT) extranodal marginal zone lymphoma. Despite three different chemotherapy regimens, the patient's intestinal obstruction symptoms persisted, with imaging still showing multifocal bowel wall thickening and hypermetabolic activity. A critical diagnostic dilemma arose regarding whether the PET/CT-positive lesions represented residual lymphoma or fibrotic scarring, whether further chemotherapy adjustments were warranted, and whether surgical resection was necessary. Multidisciplinary discussion concluded that imaging had limited discriminatory value in this scenario and that surgical intervention should be pursued if feasible. The patient successfully underwent partial small bowel resection, with postoperative pathology confirming no residual lymphoma but significant fibrotic changes. The patient has since resumed a normal diet, with body weight nearly restored to pre-illness levels. This case highlights that fibrotic transformation is a common sequela of treated marginal zone lymphoma and that PET/CT may misleadingly suggest residual disease, potentially leading to unnecessary chemotherapy. Timely surgical intervention is crucial in such scenarios.

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.

To investigate the clinical value of cardiac magnetic resonance feature tracking (CMR-FT) technology in the assessment of the right ventricle function in patients with immune checkpoint inhibitor (ICIs)-related myocarditis.