BACKGROUND:The Twin-block orthodontic appliance is commonly used for the correction of Class Ⅱ malocclusion.Its mechanism of action in stimulating mandibular growth has been confirmed in many studies,but its impact on maxillary growth is not very clear. OBJECTIVE:By establishing a finite element model to analyze the stress distribution of the maxillary complex,surrounding bone sutures,and maxillary dentition in patients with Class Ⅱ malocclusion wearing Twin-block orthodontic appliances. METHODS:One patient with Class Ⅱ malocclusion who underwent orthodontic treatment at Qingdao Hospital/Qingdao Municipal Hospital of Shandong Rehabilitation University was selected.The bite force data of the patient when wearing the Twin-block orthodontic appliance was measured,and CBCT data were collected.A finite element model was established,including the maxillary complex,peripheral sutures,Twin-block orthodontic appliance,and maxillary dentition.ABAQUS software was used to simulate the stress distribution in the maxilla and maxillary dentition when the patient was wearing the Twin-block appliance. RESULTS AND CONCLUSION:The equivalent stress on the maxillary anterior teeth was significantly smaller than that on the posterior teeth,and the maximum equivalent stress on both sides of the teeth were 4.797 5 Mpa and 8.716 1 Mpa,respectively,which were located at the first premolar.The maximum displacements were presented at the maxillary incisors on both sides of the teeth,which were 0.080 5 mm and 0.081 0 mm,respectively.The maximum equivalent stress on the bone suture was 1.284 Mpa,which was mainly concentrated in the pterygopalatine suture and the frontal-maxillary suture on both sides,and there was almost no difference in the force of the rest of bone sutures;the maximum displacement of the bone suture was 0.07 mm,with the pterygopalatine suture having the largest displacement,followed by the frontal-maxillary suture.The maximal equivalent stress on the maxillary complex was 27.18 Mpa,which was mainly concentrated on both sides of the anterior pyriform foramen of the maxilla,around the nasofrontal suture and around the pterygopalatine suture at the posterior part of the jaws.The maximal displacement of the maxilla was 0.07 mm,which was mainly concentrated on the maxillary alveolar bone.All these findings show that the occlusal force acts on the maxillary complex through the Twin-block appliance,resulting in clockwise rotation of the maxilla and steepening of the dentition plane.Measures should be taken to compensate for this tendency,for example,by considering maxillary molar elongation and intrusion in the process of occlusion,which are not only able to flatten the occlusal plane,but facilitate the mandibular protraction,thereby further improving Class Ⅱ malocclusion orthodontic treatment.

Traditional Chinese medicine （TCM） offers a rich theoretical foundation and clinical experience for the prevention and treatment of cardiovascular-kidney-metabolic syndrome（CKM）， demonstrating unique advantage. Building on previous work in managing diabetes， its complications， and chronic kidney disease， our team has proposed a five-phase evolution theory of "constraint， heat， deficiency， stasis， and toxin" as the core pathogenesis. These phases correspond to the pathological progression of constraint of phlegm-dampness， constraint transforming into heat， heat damaging qi and yin， stasis accumulated in the collateral vessels， and toxin induced by deficiency and stasis. In the prevention and treatment of CKM by TCM， it is emphasized to integrate the concept of "treating disease before it arises" with constitution theory， and incorporate the "2-5-8" prevention and treatment strategy， which combines prevention with treatment， tailors interventions to different phases， and employs comprehensive treatment modalities. Our goal is to leverage TCM's holistic advantages in preventing and treating CKM.

ObjectiveTo evaluate the efficacy of Shexiang Baoxinwan in treating stable angina pectoris with Qi stagnation and blood stasis syndrome in patients with coronary artery disease （CAD） complicated with anxiety and depression and explore its underlying mechanisms. MethodsThis study employed a randomized， double-blind， and placebo-controlled clinical trial design. Patients admitted to the hospital were randomly assigned to the observation group and the control group， with 52 patients in each group. Patients in the observation and control groups received Shexiang Baoxinwan and placebo， respectively， both in combination with conventional Western medication. The dose was 45.0 mg， three times daily， for a total duration of eight weeks. The primary outcome was the Seattle Angina Questionnaire （SAQ） scores before and after treatment. Secondary outcomes included changes in traditional Chinese medicine （TCM） syndrome score， the patient health questionnaire-9 （PHQ-9）， generalized anxiety disorder-7 （GAD-7）， inflammatory markers ［interleukin-18 （IL-18）， interleukin-10 （IL-10）， tumor necrosis factor-alpha （TNF-α）， CD40， etc.］， monoamine neurotransmitters ［e.g.， dopamine （DA）］， vascular endothelial function markers ［e.g.， endothelin-1（ET-1）］， adipokines， and ischemia-modified albumin （IMA）. Adverse reactions were also recorded. ResultsA total of 92 patients completed the study， with 44 in the observation group and 48 in the control group. Compared with baseline， both groups showed significant decreases in PHQ-9， GAD-7， and TCM syndrome scores following treatment （P<0.05）， along with a significant increase in SAQ scores （P<0.05）. In the observation group， DA levels were significantly increased （P<0.05）， while levels of IL-18， TNF-α， CD40， ET-1， and IMA were decreased （P<0.05）. In contrast， the control group exhibited significantly increased CD40 levels （P<0.05）. Compared with the control group after treatment， the observation group showed significant improvements in the SAQ dimensions of physical limitation， angina stability， treatment satisfaction， and disease perception， as well as in TCM syndrome score， PHQ-9 score， IL-18， CD40， ET-1， and IMA （P<0.05）. No adverse reactions were observed in either group during treatment. ConclusionShexiang Baoxinwan can improve anxiety and depression， alleviate angina symptoms， and reduce TCM symptoms of Qi stagnation and blood stasis in CAD patients. The mechanism may involve anti-inflammation， improvement of vascular endothelial function， reduction of IMA， and increase of monoamine neurotransmitter levels.

ObjectiveTo evaluate the efficacy of Shexiang Baoxinwan in treating stable angina pectoris with Qi stagnation and blood stasis syndrome in patients with coronary artery disease （CAD） complicated with anxiety and depression and explore its underlying mechanisms. MethodsThis study employed a randomized， double-blind， and placebo-controlled clinical trial design. Patients admitted to the hospital were randomly assigned to the observation group and the control group， with 52 patients in each group. Patients in the observation and control groups received Shexiang Baoxinwan and placebo， respectively， both in combination with conventional Western medication. The dose was 45.0 mg， three times daily， for a total duration of eight weeks. The primary outcome was the Seattle Angina Questionnaire （SAQ） scores before and after treatment. Secondary outcomes included changes in traditional Chinese medicine （TCM） syndrome score， the patient health questionnaire-9 （PHQ-9）， generalized anxiety disorder-7 （GAD-7）， inflammatory markers ［interleukin-18 （IL-18）， interleukin-10 （IL-10）， tumor necrosis factor-alpha （TNF-α）， CD40， etc.］， monoamine neurotransmitters ［e.g.， dopamine （DA）］， vascular endothelial function markers ［e.g.， endothelin-1（ET-1）］， adipokines， and ischemia-modified albumin （IMA）. Adverse reactions were also recorded. ResultsA total of 92 patients completed the study， with 44 in the observation group and 48 in the control group. Compared with baseline， both groups showed significant decreases in PHQ-9， GAD-7， and TCM syndrome scores following treatment （P<0.05）， along with a significant increase in SAQ scores （P<0.05）. In the observation group， DA levels were significantly increased （P<0.05）， while levels of IL-18， TNF-α， CD40， ET-1， and IMA were decreased （P<0.05）. In contrast， the control group exhibited significantly increased CD40 levels （P<0.05）. Compared with the control group after treatment， the observation group showed significant improvements in the SAQ dimensions of physical limitation， angina stability， treatment satisfaction， and disease perception， as well as in TCM syndrome score， PHQ-9 score， IL-18， CD40， ET-1， and IMA （P<0.05）. No adverse reactions were observed in either group during treatment. ConclusionShexiang Baoxinwan can improve anxiety and depression， alleviate angina symptoms， and reduce TCM symptoms of Qi stagnation and blood stasis in CAD patients. The mechanism may involve anti-inflammation， improvement of vascular endothelial function， reduction of IMA， and increase of monoamine neurotransmitter levels.

Objective To explore the feasibility of long non-coding RNAs (lncRNAs) as biomarkers for radiation-induced intestinal injury. Methods Mice were exposed to 15 Gy of ⁶⁰Co γ-rays to the abdominal area. The pathological changes in intestinal tissues were analyzed at 72 h post-irradiation to confirm the successful establishment of the radiation-induced intestinal injury model. Real-time quantitative PCR was conducted to detect the expression of candidate radiation-responsive lncRNAs in the jejunum, jejunal crypts, colon tissues, and plasma of irradiated mice. Human intestinal epithelial cell line HIEC-6 and human colon epithelial cell line NCM460 were exposed to 0, 5, 10, and 15 Gy of ⁶⁰Co γ-rays. The expression levels of candidate lncRNAs were measured at 4, 24, 48, and 72 h post-irradiation to observe their changes with the irradiation dose. Results Pathological analysis showed that abdominal irradiation with 15 Gy successfully established an acute radiation-induced intestinal injury mouse model. Real-time quantitative PCR showed that Dino, Lncpint, Meg3, Dnm3os, Trp53cor1, Pvt1, and Neat1 were significantly upregulated following the occurrence of radiation-induced intestinal injury (P < 0.05). Among them, Meg3 and Dnm3os in mouse plasma were significantly upregulated (P < 0.05), while Gas5 was significantly downregulated (P < 0.05). In HIEC-6 and NCM460 cells, the expression levels of DINO, MEG3, DNM3OS, and GAS5 showed dose-dependent patterns at certain time points (P < 0.05). Conclusion The lncRNAs encoded by MEG3, DNM3OS, and GAS5 in intestinal epithelial cells are responsive to ionizing radiation. Consistent differential expression changes were detected in mouse plasma and intestinal tissues, indicating their potential as biomarkers for radiation-induced intestinal injury.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.