OBJECTIVE: To compare the registration accuracy of cone beam computed tomography (CBCT) images while registering to virtual craniodentofacial patients based on soft tissue and the dentition registration method. METHODS: Virtual dentofacial patients out of 13 selected participants who needed CBCT scanning were established by impression with a registered-block impression (RBI) based on digital dental images, three-dimensional (3D) facial images and maxillofacial CBCT images. CBCT images were processed in the Mimics software program, establishing the craniofacial virtual patients based on CBCT images (CCTs). Registration between virtual patients from RBI and CCT, using the soft tissue in lower half face (STE) and dentition (DTN) as the reference area, respectively, forming two kinds of virtual craniofacial patients based on digital dental images, 3D facial images and skeletal images of CBCT (hiding the soft tissue and dental casts from CBCT). Three-dimensional deviation analysis was performed in the upper half face and lower half face of facial images from CBCT between two kinds of virtual craniodentofacial patients and compared with 3D facial images from RBI and recorded as root mean square error (RMSE). Paired-t test was used to compare the deviations of RMSEs between the upper and lower half of the face and the upper half of the face of facial images from CCT, respectively, between the two kinds of virtual craniodentofacial patients based on STE and DTN methods. RESULTS: Paired-t tests showed that there was no statistically significant difference between the upper and lower half faces of facial images from CCT between STE and DTN (P>0.05), but the deviation of RMSEs of the upper half face of facial images from CCT in STE was smaller than those in DTN [(1.696±0.420) mm vs. (1.752±0.424) mm, P < 0.01]. CONCLUSION The registration accuracy of CBCT registered in virtual craniodentofacial patients using soft tissue as the reference area was higher.
Humans ; Cone-Beam Computed Tomography/methods* ; Imaging, Three-Dimensional/methods* ; Male ; Face/anatomy & histology* ; Female ; Adult ; Image Processing, Computer-Assisted/methods* ; Young Adult ; User-Computer Interface

OBJECTIVES: To explore the mechanism of cinnamic acid (CA) for improving doxorubicin-induced myocardial injury (DIC) in mice. METHODS: Network pharmacology analysis was used to obtain the key targets of CA and DIC. Male C57BL/6J mice were randomized into Sham, DOX, CA (25, 50 and 100 mg/kg)+DOX, and CA+Ferrostatin-1+DOX groups, and their myocardial function and pathology were examined by echocardiography and HE staining. Serum levels of CK-MB, LDH, MDA, IL-6, TNF‑α and myocardial ROS level were detected, and the expression levels of TLR4 and ferroptosis pathway proteins in myocardial tissue were detected by Western blotting. Cultured murine cardiomyocytes (HL-1 cells) with or without transfection with a small interfering RNA targeting TLR4 (si-TLR4) were treated with DOX or Erastin, and the cellular ROS content was measured by DCFH-DA staining; the expression level of GPX4 was detected using immunofluorescence staining. RESULTS: Network pharmacology analysis suggested that CA may improve DIC through TLR4 signaling. DOX treatment caused obvious myocardial injury in mice, which showed significantly increased serum levels of CK-MB, LDH, MDA, IL-6, TNF-α and myocardial ROS level with decreased myocardial levels of SLC7A11 and GPX4 proteins and increased levels of TLR4 and PTGS2 proteins. All these changes in the mouse models were significantly alleviated by treatment with CA, and the mice receiving CA or ferrostatin-1 treatment exhibited increased myocardial expressions of SLC7A11 and GPX4 proteins and lowered expressions of TLR4 and PTGS2 proteins. In cultured HL-1 cells, treatment with DOX and Erastin both obviously increased intracellular ROS level and decreased cellular GPX4 expression level, and these changes were strongly attenuated by TLR4 interference. CONCLUSIONS CA, as a potent herbal monomer, can effectively alleviate DIC in mice by inhibiting TLR4-mediated ferroptosis.
Animals ; Ferroptosis/drug effects* ; Toll-Like Receptor 4/metabolism* ; Myocytes, Cardiac/metabolism* ; Mice, Inbred C57BL ; Mice ; Male ; Doxorubicin/adverse effects* ; Cinnamates/pharmacology* ; Signal Transduction ; Reactive Oxygen Species/metabolism*

The construction of a new cultural system for high-quality development in public hospitals serves as a crucial pillar for achieving their high-quality advancement.During this developmental processe stablishing a cultural framework that aligns with the new development model holds particular significance.Through content analysis methodology,it identifies 18 core elements of the new cultural system for high-quality development in public hospitals.Furthermore it synthesizes seven implementation pathways across three dimensions-organizational patientand employee perspectives:digital leadership organizational reform capability talent innovation capability resource integration capability normative constraint force value co-creation capability and employee support capability.These findings provide both theoretical and practical references for cultivating new cultural constructs that facilitate high-quality development in public hospitals.

Objective To compare the medication rules of traditional Chinese medicine in the treatment of granulomatous lobular mastitis(GLM)and plasma cell mastitis(PCM).Methods The relevant literatures on GLM and PCM therapy published by CNKI,VIP,Wanfang Data Knowledge Service Platform and SinoMed from the establishment of the database to June 2024 were searched.The prescription was screened,and the frequency,nature and flavour,channel tropism were analyzed,and association rule analysis and cluster analysis were carried out.Results The top four most frequently used traditional Chinese medicines in the treatment of GLM and PCM were Chaihu,Pugongying,Zaojiaoci and Danggui.In the two-item association,the highest confidence levels for the treatment of the two were Huzhang→Muli and Ezhu→Sanleng.In the three-item association,the highest confidence levels for the treatment of the two were Huzhang→Jinyinhua-Pugongying and Chaihu→Yujin-Danggui.In the cluster analysis,the drugs for both could be clustered into four categories.Conclusion This study compares the medication rules of traditional Chinese medicine in the treatment of GLM and PCM,reflecting similarities and differences,and provides data reference for clinical compound research and development.

Objective: To investigate the correlations among clinicopathological features, histopathological growth patterns and prognosis of extrapulmonary multiple metastatic(stage ⅣB) pulmonary adenocarcinoma with epidermal growth factor receptor(EGFR) mutations. Methods : A total of 488 eligible patients with adenocarcinoma of stage ⅣB. Clinicopathological data,EGFRgene mutation subtypes, metastatic sites, histopathological growth patterns and survival information were collected. The chi-square test(χ2test) and Fisher's exact probability method were used to detect the correlation between the metastasis status and various clinical characteristics; the Kaplan-Meier method was used to conduct survival analysis on the median Progression-Free Survival(PFS) under different clinical characteristics. Cox univariate and multivariate regression analyses were conducted to evaluate the impact of various clinical characteristics on prognosis. Results : The metastatic patterns of stage ⅣB pulmonary adenocarcinoma withEGFRmutations was correlated with histopathological growth patterns(P<0.05). In the group with multiple metastases in a single organ, the proportion of micropapillary type in the group with multiple metastases in a single organ was higher than that in the group with multiple-organ metastases(51.1%vs41.1%), while the proportion of solid type in the group with multiple-organ metastases was higher than that in the group with multiple metastases in a single organ(23.8%vs14.2%). Multiple brain or multiple bone metastases were correlated with histopathological growth patterns and tumor differentiation degree. Compared with the multiple bone metastases group, the proportion of acinar type decreases in the multiple brain metastasis group, while the proportion of micropapillary type increased. Moreover, the proportion of poorly differentiated tumors increased significantly(P<0.05). Compared with multiple bone metastases, the proportion of poorly differentiated tumors significantly increases in the group with multiple brain metastases. The median progression-free survival(PFS) of patients with a predominant solid growth pattern was shorter than that of patients with other growth patterns(12.7 monthsvs17.8 months,P<0.05). The PFS of patients in the poorly differentiated group was worse than that in the moderately differentiated group(15.6 monthsvs17.8 months,P<0.05). There were significant differences in PFS among patients with common sensitive mutations and rare mutationsEGFR(17.3 monthsvs10.2 months,P<0.01). Cox proportional hazards regression model suggested that solid growth pattern, poor differentiation and rare single gene mutation were adverse prognostic factors. Conclusion In stage ⅣB pulmonary adenocarcinoma patients withEGFRmutations, both the metastatic patterns and metastatic sites are significantly correlated with the histopathological growth patterns of tumors. Moreover, theEGFRmutation subtypes as well as the histopathological growth patterns and differentiation degree of tumors significantly affect the prognosis of patients.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

Objective·To develop an early diagnostic model for small cell lung cancer(SCLC)based on differences in serum metabolite expression profiles between patients with SCLC and those with benign pulmonary diseases,using machine learning algorithms.Methods·Serum samples were collected from 29 SCLC patients and 67 patients with benign lung diseases at Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,as the training cohort.An independent external validation cohort included 20 SCLC patients and 40 patients with benign lung diseases from Gansu Provincial Cancer Hospital.A total of 69 serum metabolites were quantitatively analyzed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).The XGBoost Classifier was employed to rank metabolite importance,and a forward feature selection strategy based on XGBoost was used to identify a subset of key metabolites.Diagnostic models were constructed using AdaBoost,random forest(RF),and light gradient boosting machine(LGBM)algorithms.Model performance was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC),and validated on the external test cohort.Results·Principal component analysis(PCA)and orthogonal projections to latent structures-discriminant analysis(OPLS-DA)of the training cohort revealed distinct metabolic profiles between SCLC and benign lung disease patients.Based on feature importance rankings,six key metabolites were selected to construct the MTB-6 diagnostic model.Among the models,AdaBoost achieved the best performance,with an AUC of 0.943,sensitivity of 75.0%,and specificity of 90.9%in the training cohort.In the external test cohort,the model demonstrated robust performance with an AUC of 0.921,sensitivity of 80.0%,and specificity of 87.5%.Conclusion·The MTB-6 model,based on six serum metabolites and the AdaBoost algorithm,exhibits excellent diagnostic performance and holds potential for the differential diagnosis of SCLC and benign pulmonary diseases.

BACKGROUND:Polyamines play a crucial role in tissue calcification.Spermidine/spermine N1-acetyltransferase 1(SAT1),as a key rate-limiting enzyme regulating intracellular polyamine metabolism,has been associated with various pathological processes.However,its role in vascular calcification remains unclear.OBJECTIVE:To investigate the role of SAT1 in rat vascular smooth muscle cell calcification.METHODS:(1)Bioinformatics analysis:Differential expression of SAT1 in human carotid atherosclerotic plaques and their surrounding healthy carotid artery tissues were using GEO datasets.PanglaoDB database was used to analyze SAT1 expression abundance and localization across different cell types through single-cell sequencing.(2)Rat vascular smooth muscle cells were divided into three groups:a control group cultured in DMEM medium,a calcification group induced by DMEM medium containing 10 mmol/L β-glycerophosphate sodium and 3 mmol/L calcium chloride,and the 50,100 μmol/L diacetylaminotriazamidine groups treated with the SAT1 inhibitor,diacetylaminotriazamidine,in addition to the calcification medium.After 7-10 days of culture,alizarin red S staining was performed,and cellular calcium content and alkaline phosphatase activity were assessed.Western blot was used to detect the protein expression of Runt-related transcription factor 2,bone morphogenetic protein 2,alpha-smooth muscle actin,and SAT1.Immunofluorescence staining was conducted to examine the expression of Runt-related transcription factor 2 and SAT1.RESULTS AND CONCLUSION:(1)Bioinformatics analysis revealed significantly upregulated expression of SAT1 and Runt-related transcription factor 2(P<0.05)in carotid atherosclerotic plaques compared with healthy carotid tissues(P<0.05).Single-cell sequencing database analysis confirmed SAT1 expression in vascular smooth muscle cells.(2)Compared with the control group,the calcification group showed significantly increased Runt-related transcription factor 2,bone morphogenetic protein 2,SAT1,calcium content,and alkaline phosphatase activity,while alpha-smooth muscle actin expression was significantly decreased(all P<0.05).Compared with the calcification group,the 50 and 100 μmol/L diacetylaminotriazamidine groups showed significantly decreased Runt-related transcription factor 2,bone morphogenetic protein 2,calcium content,and alkaline phosphatase activity,while alpha-smooth muscle actin expression was significantly increased(all P<0.05).(3)Immunofluorescence experiments demonstrated that compared with the calcification group,the expression intensity of Runt-related transcription factor 2 was significantly reduced in the 50 and 100 μmol/L diacetylaminotriazamidine groups.Overall,SAT1 may promote vascular smooth muscle cell calcification by upregulating Runt-related transcription factor 2 expression.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.