Objective To analyze the prevalence characteristics and influencing factors of metabolic syndrome (MS) in people aged 35-75 years in Hubei Province. Methods The follow-up data from 2016 to 2022 in the early screening and comprehensive intervention project for high-risk cardiovascular population in Hubei Province were collected. SAS 9.4 software was used to conduct 2-test and multivariate logistic regression to analyze the prevalence of MS and its influencing factors. Results Among the 89 199 subjects, 24 757 were affected by MS, with a prevalence rate of 27.75% and a standardized rate of 23.55%. Among the various components of MS, the prevalence of abnormal blood pressure was the highest, at 70.88%, and the standardized rate was 59.32%. Secondly, abnormal blood glucose was 36.26%, and the standardized rate was 30.04%. Central obesity was 33.12%, and the standardized rate was 30.28%. Hypertriglyceridemia was 32.90%, and the standardized prevalence rate was 32.70%. The rate of low HDL-C syndrome was 10.25%, and the standardized rate was 11.67%. The results of multivariate logistic regression analysis showed that the risk of MS increased with age, and the risk of MS in urban residents was lower than that in rural residents (OR=0.835, 95%CI: 0.77-0.886). Administrative and professional workers had a higher risk of MS than farmers (OR=1.313, 95%CI:1.194-1.445). Overweight, obesity, central obesity, history of self-reported hypertension, history of self-reported diabetes, and history of self-reported dyslipidemia were associated with a higher risk of MS, and the differences were statistically significant (P < 0.001). Conclusion The prevalence of MS is high in people aged 35-75 years in Hubei Province. On the basis of comprehensive intervention, focus monitoring should be strengthened to control the risk factors of MS and reduce the risk of cardiovascular and cerebrovascular diseases.

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.

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.

Objectives:This study aimed to explore the association between perceived mental stress (MS), lymphocyte subset variations, and coronary lesion severity in patients with coronary artery disease (CAD).Methods:Patients with CAD were enrolled in this study from September 2023 to May 2024. Perceived Stress Scale-14 (PSS-14) was used to evaluate MS during the last 1 month. Lymphocyte subsets were analyzed, including the percentage and absolute counts of CD3 +, CD3 +CD4 +, CD3 +CD8 +, CD3 -CD19 +, CD3 -CD56 +16 +, and the Th/Ts ratio. Statistical analysis was conducted using SPSS 24.0. Results:This study recruited patients with 323 CAD, with an average age of 61 (56, 68) years, including 203 males and 120 females. According to the PSS-14, a score of 14-42 and 43-70 were categorized as normal and increased MS, respectively. Patients with CAD with increased MS had significantly higher Gensini scores than those with normal MS [37(19,64) vs. 28(12,50), Z=-2.19, P=0.029]. Male CAD patients with increased MS exhibited significantly higher Gensini scores [39(20, 58) vs. 26(12, 45), Z=-2.37, P=0.018], levels of CD3 +CD8 +%[28.3%(23.6%,36.6%) vs. 25.9%(21.0%,32.4%), Z=-2.05, P=0.041], and CD3 +CD8 +absolute value [485 (346, 675) vs. 396 (309, 510) cells/μl, Z=-2.55, P=0.011] than those with normal MS. In male patients with CAD, a positive correlation was observed between Gensini scores (correlation coefficient: 0.181, P=0.011), PSS-14 scores, and CD3 +CD8 +absolute value (correlation coefficient: 0.162, P=0.038). Conclusion:This study reveals a positive correlation between MS and coronary stenosis severity, with notable sex differences. In male patients with CAD, higher levels of MS are associated with more severe coronary stenosis. The potential underlying mechanism may involve the regulation of lymphocyte subsets .

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.

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.

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

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.

ObjectiveTo investigate the change in the activity of hepatitis B virus （HBV）-specific CD8⁺ T cells after pegylated interferon-α-2b （PEG-IFN-α-2b） therapy in HBeAg-negative patients with chronic HBV infection. MethodsA total of 53 HBeAg-negative patients with chronic HBV infection who attended The First Affiliated Hospital of Xinxiang Medical University and Tangdu Hospital of Air Force Mdical University from April 2020 to June 2022 were enrolled and treated with PEG-IFN-α-2b （180 μg/week， subcutaneous injection） antiviral therapy. The study endpoint was HBsAg clearance （course of treatment<48 weeks） or 48 weeks （course of treatment≥48 weeks）. Peripheral blood mononuclear cells were isolated at baseline and study endpoint， and peripheral blood T cell counts were measured. Enzyme-linked immunospot assay was used to measure the frequency of HBV-specific CD8⁺ T cells secreting perforin， granzyme B， and interferon-γ. A total of 17 HLA-A^*02-restricted patients were selected， and CD8⁺ T cells were purified to establish direct- and indirect-contact co-culture systems for HBV-specific CD8⁺ T cells and HepG2.2.15 cells. The level of lactate dehydrogenase in supernatant was measured to calculate the mortality rate of HepG2.2.15 cells， and the levels of HBV DNA， cytotoxic molecules， and cytokines in supernatant were also measured. Flow cytometry was used to measure the expression of apoptosis ligands， and the cytotoxicity of HBV-specific CD8⁺ T cells was evaluated. The independent samples t-test or the paired t-test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test or the Wilcoxon rank-sum test was used for comparison of non-normally distributed continuous data between two groups. ResultsThe HBsAg clearance rate at study endpoint was 30.19% （16/53）. There were no significant differences in peripheral blood T cell counts （CD3⁺， CD4⁺， and CD8⁺ T cells） between baseline and study endpoint （P>0.05）. At study endpoint， there was a significant increase in the frequency of HBV-specific CD8⁺ T cells secreting perforin， granzyme B， and interferon-γ （U=177.50， t=11.90， U=186.50， all P<0.001）， and the patients with HBsAg clearance had a significantly higher frequency of such HBV-specific CD8⁺ T cells than those without HBsAg clearance （U=120.50， t=2.73， U=121.50， all P<0.01）. In the direct- and indirect-contact co-culture systems at study endpoint， HBV-specific CD8⁺ T cells induced a significant reduction in HBV DNA in the supernatant of HepG2.2.15 cells （all P<0.001） and significant increases in the secretion of interferon-γ and tumor necrosis factor-α （all P<0.05）； in the direct-contact co-culture system， HBV-specific CD8⁺ T cells induced significant increases in the mortality rate of HepG2.2.15 cells （13.62%±3.27% vs 11.39%±2.40%， t=2.27， P=0.030） and the secretion of perforin and granzyme B （t=72.50， U=52.50， both P<0.05）. In the direct- and indirect-contact co-culture systems， compared with HBV-specific CD8⁺ T cells from the patients without HBsAg clearance， the HBV-specific CD8⁺ T cells from patients with HBsAg clearance had a significantly greater reduction in HBV DNA （P<0.05） and significant increases in the secretion of interferon-γ and tumor necrosis factor-α （P<0.05）. ConclusionPEG-IFN-‍α‍-2b therapy can help to achieve a relatively high HBsAg clearance rate in HBeAg-negative patients with chronic HBV infection， and the activity of HBV-specific CD8⁺ T cells is significantly enhanced， which is closely associated with HBsAg clearance.

BACKGROUND:Fasudil has a regulatory effect on mitochondrial dynamics in the brain of Alzheimer's disease mice and can inhibit neuroinflammation,but whether it can reduce the toxicity of β-amyloid protein by regulating mitophagy-NLRP3 inflammasome pathway remains unclear.OBJECTIVE:To investigate the regulatory effect of fasudil on β-amyloid 1-42-induced apoptosis and mitophagy and NLRP3 inflammasome in human derived neuroblastoma cell line SH-SY5Y cells.METHODS:SH-SY5Y cells were inoculated into the pore plate.After adhesion,cells were divided into three groups for intervention:No drug was added to the control group;20 μmol/L β-amyloid 1-42 was added to the model group,and 20 μmol/L β-amyloid 1-42 and 15 mg/L fasudil were added to the fasudil group at the same time.After 24 hours of intervention,the cell activity was detected by MTT assay and apoptosis was detected by TUNEL staining.The expression of apoptosis-related proteins was detected by qRT-PCR and western blot assay.The expression of mitochondrial autophagy related proteins was detected by immunofluorescence staining and western blot assay.The expression of NLRP3 inflammasome related proteins was detected by immunofluorescence staining and western blot assay.RESULTS AND CONCLUSION:(1)Compared with control group,the cell activity of the model group was decreased and the apoptosis rate was increased(P<0.05).Compared with model group,cell activity in the fasudil group was increased and apoptosis rate was decreased(P<0.05).(2)The results of qRT-PCR and western blot assay showed that compared with the control group,the expression of Bax mRNA and protein was increased in the model group(P<0.05),while the expression of Bcl-2 mRNA and protein was decreased(P<0.05).Compared with the model group,the expression of Bax mRNA and protein was decreased(P<0.05),and the expression of Bcl-2 mRNA and protein was increased(P<0.05)in the fasudil group.(3)The results of immunofluorescence staining and western blot assay showed that compared with the control group,the expressions of PINK1,Parkinson's disease protein and LC3 protein were decreased(P<0.05),while the expression of p62 protein was increased(P<0.05)in the model group.Compared with model group,the expression levels of PINK1,Parkinson's disease protein,and LC3 protein were increased(P<0.05),while the expression of p62 protein was decreased(P<0.05)in the fasudil group.(4)The results of immunofluorescence staining and western blot assay showed that compared with the control group,the expression levels of NLRP3,ASC,Caspase-1,and interleukin1β protein were increased in the model group(P<0.05).Compared with the model group,the expression levels of NLRP3,ASC,Caspase-1,and interleukin1β were decreased in the fasudil group(P<0.05).(5)The results show that fasudil can reduce the apoptosis of SH-SY5Y cells induced by β-amyloid 1-42,and its mechanism may be related to the activation of mitophagy and the inhibition of NLRP3 inflammasome activation.