BACKGROUND:Osteonecrosis of the femoral head is one of the refractory diseases in orthopedic diseases.The natural collapse course of osteonecrosis of the femoral head under different stages and types affects the progression and prognosis of the disease. OBJECTIVE:To explore the progression of natural collapse within 5 years in patients under the different classifications of China-Japan Friendship Hospital(CJFH)with stage Ⅱ osteonecrosis of the International Association for Research Circulation Osseous(ARCO),and to analyze the collapse rate and collapse risk of the femoral head under the different classifications of CJFH. METHODS:A retrospective study was performed to select patients diagnosed with ARCO Ⅱ stage osteonecrosis of the femoral head without collapse in the Honghui Hospital Affiliated to Xi'an Jiaotong University from October 2016 to October 2017.According to whether it collapsed,the number of hips was divided into the collapse group(n=82)and the non-collapsed group(n=70).The collapse risk of patients with osteonecrosis of the femoral head under different CJFH classifications,as well as the collapse time,number of collapses,and collapse rate within 5 years were counted,and then the Kaplan-Meier survival curve of the femoral head under different classification of CJFH was plotted. RESULTS AND CONCLUSION:(1)A total of 97 patients with 152 hips were enrolled,and 82 hips collapsed during the follow-up period,with a total collapse rate of 53.9%,of which the collapse rates of M type,C type,L1 type,L2 type,and L3 type were 0.0%,36.7%,51.4%,72.2%,and 77.8%,respectively,and the comparison between the groups was statistically significant(P<0.05).(2)In terms of collapse risk,the collapse risk of L1 type was 1.704 times that of C-type(P>0.05),while the collapse risks of L2 type and L3 type were 3.866 times and 6.423 times that of C type(P<0.05),respectively.(3)In terms of the Kaplan-Meier survival curve,the median survival time of the femoral head of ARCO Ⅱ stage patients was 3 years,with a 95%confidence interval of 2.885-3.471 years,and the survival rates of the femoral head at the first,third and fifth years were 65.1%(99/152),50.7%(77/152),and 46.1%(70/152),respectively.(4)These findings conclude that different CJFH classifications affect the collapse rate of ARCO Ⅱ stage osteonecrosis of the femoral head patients,among which L3 type patients have the highest collapse rate,followed by L2 type and L1 type patients;C type patients have a lower collapse rate,and M type patients do not collapse,which indicates that the preservation of the lateral column of the femoral head is of great significance for the natural collapse course of osteonecrosis of the femoral head.

Objective To discuss the clinical application value of super selective renal artery embolization-assisted(SRAE-assisted)laparoscopic partial nephrectomy(LPN).Methods A retrospective analysis of the clinical data of patients with stage T1 renal carcinoma,who received LPN,was conducted.The patients were divided into SRAE group(performing LPN without adopting renal hilum vascular clamping)and VC group(performing LPN with adopting renal hilum vascular clamping).The time spent for operation,amount of intraoperative blood loss,and preoperative and postoperative renal functions were compared between the two groups.According to the warm ischemia time(WIT),the patients of the VC group were subdivided into WIT＜25 min subgroup and WIT≥25 min subgroup,and the preoperative and postoperative renal functions were compared between the two subgroups.Results A total of 59 patients with renal carcinoma were enrolled in this study,including 12 patients in SRAE group and 47 patients in VC group.In VC group,WIT＜25 min subgroup had 33 patients and WIT≥25 min subgroup had 14 patients.In both SRAE group and VC group,no patient was referred to open surgery or total nephrectomy.No patient in SRAE group was referred to traditional LPN.The time spent for operation in SRAE group and VC group was 100.50(73.75,132.50)min and 120.00(90.00,145.00)min respectively,the difference between the two groups was not statistically significant(P＞0.05).The postoperative estimated glomerular filtration rate(eGFR)in SRAE group was 100.56(82.85,106.81),which was remarkably higher than 84.66(70.84,94.85)in VC group(P＜0.05).The postoperative serum creatinine level in VC group was 90.50(77.10,104.90)μmol/L,which was strikingly higher than 72.24(65.97,80.27)μmol/L in SRAE group(P＜0.05).The amount of intraoperative blood loss in SRAE group was 50(50,50)mL,which was lower than 50(50,100)mL in VC group(P＜0.05).In VC group,the postoperative eGFR in WIT≥25 min subgroup was 66.13(47.08,82.50),which was lower than 90.80(77.18,98.78)in WIT＜25 min subgroup(P＜0.05).During the postoperative one-year follow-up,no recurrence was observed in both groups.Conclusion Compared with traditional LPN,SRAE-assisted LPN doesn't need to obstruct the renal hilus during surgery,which can avoid the ischemic impairment of the residual renal function and reduce the amount of intraoperative blood loss,moreover,it doesn't increase the operation time,doesn't increase the incidence of complications such as postoperative bleeding,etc.and doesn't affect the curative efficacy and patient's prognosis.

Aim To explore the feasibility of using machine learning algorithms combined with coronary computed tomography(CT)derived perivascular fat attenuation index(FAI)and plaque information to evaluate myocardial ischemia in stable coronary heart disease patients.Methods A retrospective analysis was conducted on the clinical and imaging data of patients who underwent preoperative coronary CT angiography(CCTA),invasive coronary angiography(ICA),and flow reserve fraction(FFR)measurements at Zhongshan Hospital Affiliated to Fudan University from April 2019 to October 2021.206 patients with stable coronary heart disease were selected.The semi-automatic plaque analysis software was used for quantification of plaque and lumen parameters and perivascular FAI measurement,with man-ual delineation of a 40 mm segment of the coronary artery starting 10 mm from the ostium for perivascular FAI measure-ment.Differences in plaque characteristics,perivascular FAI,and coronary perivascular FAI between stable coronary heart disease patients with FFR≤0.8 and FFR＞0.8 were compared.The diagnostic performance of combining perivascu-lar FAI,coronary perivascular FAI,and plaque features using machine learning algorithms for myocardial ischemia in stable coronary heart disease patients was evaluated through ROC curves.Results 206 stable coronary heart disease patients were divided into FFR≤0.8 group(50 cases)and FFR＞0.8 group(156 cases).The mean periplaque FAI of patients with FFR≤0.8 was-69.28±5.65 HU,significantly higher than that of patients with FFR＞0.8 at-80.10±7.75 HU(P＜0.001).Further analysis was conducted using machine learning models,including XGBoost,random forest,and Logistic regression models,all of which had an accuracy rate of over 0.8 in diagnosing myocardial ischemia.Among them,the XGBoost model performed the best with an accuracy of 0.903,an F1 value of 0.774,and an AUC of 0.931,in-dicating its high effectiveness in diagnosing myocardial ischemia.Conclusion The combination of FAI and machine learning algorithm XGBoost model is a new method for diagnosing myocardial ischemia,which has better diagnostic value in evaluating myocardial ischemia in stable coronary heart disease patients.

China prospective multicenter birth cohort (Prospective Omics Health Atlas birth cohort, POHA birth cohort) study was officially launched in 2022. This study, in collaboration with 12 participating units, aims to establish a high-quality, multidimensional cohort comprising 20 000 naturally conceived families and assisted reproductive families. The study involves long-term follow-up of parents and offspring, with corresponding biological samples collected at key time points. Through multi-omics testing and analysis, the study aims to conduct multi-omics big data research across the entire maternal and infant life cycle. The goal is to identify new biomarkers for maternal and infant diseases and provide scientific evidence for risk prediction related to maternal diseases and neonatal health.

Ambystoma mexicanum/immunology* ; Amputation ; Animals ; Biomarkers/metabolism* ; Blastomeres/immunology* ; Cell Lineage/immunology* ; Connective Tissue Cells/immunology* ; Epithelial Cells/immunology* ; Forelimb ; Gene Expression ; High-Throughput Nucleotide Sequencing ; Humans ; Immunity ; Peroxiredoxins/immunology* ; Regeneration/immunology* ; Regenerative Medicine/methods* ; Single-Cell Analysis/methods*

Objective To profile the differentially expressed genes in the muscle of polymyositis (PM) patients.Methods A mRNA microarray analysis was performed to profile mRNAs from 5 treatment-naive PM patients and 5 healthy controls.Gene Ontology and KEGG pathway analyses were applied to delineate the functional roles of the differentially expressed mRNAs.Quantitative real-time PCR analysis was conducted to validate the microarray data.The Student's t-test was used to analyze the statistical significance of the microarray results,and Benjamini-Hochberg FDR was used for multiple-test correction.Results Microarray analysis revealed that a total of 1 905 mRNAs (787 up-regulated and 1 118 down-regulated) were significantly differentially expressed in PM patients compared with the healthy controls (fold change＞2,P＜0.05).Six mRNAs were selected to analyze by quantitative RT-PCR to validate their expression levels and the results were consistent with that of the microarray analysis,and thus provide reliable validation for the microarray results.Gene ontology and KEGG pathway analysis for the differentially expressed mRNAs revealed that these genes were mainly involved in the biological process of infection and cytotoxic effect.In addition,there were some common signaling pathways that shared by PM and other autoimmune diseases.Conclusion There are differences in gene expressions between PM patients and healthy controls.The muscle damage in PM patients may be due to multi gene involvement and multi gene regulation.

Purpose To explore the independent predictors of malignant solitary pulmonary nodule (SPN) manifesting as ground-glass nodule (GGN),and to establish a prediction model.Materials and Methods The clinical data and CT images of 362 patients (group A) with pathological-confirmed SPN appearing as GGN in Shanghai Chest Hospital Shanghai Jiaotong University from January 2014 to December 2015 were retrospectively analyzed.The independent predictors of malignant SPN were identified,and the clinical prediction model was established.Another 119 SPN patients in Affiliated Zhoushan Hospital of Wenzhou Medical University were selected as group B to verify the diagnostic efficiency of the prediction model.Results Using multivariate Logistic regression analysis,clear border (OR=6.274,P＜0.01),smooth edge (OR=0.391,P＜0.01),lobulation (OR=3.387,P＜0.01),pleural retraction sign (OR=2.430,P＜0.01),and vocule sign (OR=3.076,P＜0.01)were identified as independent predictors of malignant SPN.The area of the model under the ROC curve was 0.859 with 95％ CI (0.804-0.903).The diagnostic accuracy rate,sensitivity,specificity,positive predictive value and negative predictive value were 85.92％,91.03％,81.97％,92.03％ and 73.53％,respectively.Conclusion In this study,the independent predictors of malignant SPN appearing as GGN were identified,and the prediction model was established.The model can accurately identify SPN and provide effective help for early diagnosis of SPN.

Non-governmental organizations ( NGOs) are playing an increasingly significant role in global health governance. This study selected ten key NGOs that play an important role in global health affairs and summarized the tools employed by NGOs participating in global health governance, including nine dimensions:“Generating informa-tion and evidence/intelligence”, “Cooperation ( Partnerships )”, “Participation”, “Consultation”, “Transparen-cy”,“Organizational adequacy/system design”, “Formulating policy / strategic direction”, “Responsibility” and“Regulation”. Four types of NGOs including Operational, Supportive, Advocacy and Integrated ones presented com-monness and their priorities in the selection of tools to participate in global health governance. Meanwhile, China should strive to nurture local NGOs, which should pay attention to“Transparency”,“Participation” and“Cooperation ( Partnerships)”.

Background and objective hTe solitary pulmonary nodule (SPN) is a common and challenging clini-cal problem, especially solid SPN. hTe object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs.Methods We had a retrospective review of 317 sol-id SPNs (group A) having a ifnal diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, letf or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air broncho-gram sign, vocule sign, cavity and calciifcation. By using univariate and multivariate analysis, we found the independent predic-tors of malignancy of solid SPNs and subsequently established a clinical prediction model. hTen, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model.Results MultivariateLogistic regression analysis was used to identify eight clini-cal characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calciifcation) as independent predictors of malignancy of in solid SPNs. hTe area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and speciifcity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%.Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.

Purpose To evaluate the CT features and pathological manifestations of the solid components of mixture ground-glass opacity (GGO) in adenocarcinoma in situ (AIS), minimally invasive adenocarcinom (MIA) and invasive adenocarcinoma (IAC), to analyze the qualitative diagnosis value of solid components of mixture GGO in the diagnosis of AIS, MIA and IAC, to provide reference for the selection of clinical treatment.Materials and Methods Eighteen patients with AIS, 53 patients with MIA and 28 patients with IAC (the maximum diameter smaller than 2 cm) proved by surgery and pathology with CT features appearing as mixture GGO were retrospectively analyzed, CT features of the solid components in three groups were analyzed and compared with pathology.Results The solid components in AIS mainly appeared as punctiform or polygon, with extensive distribution, solid nodules were usually single (17 cases, 94.44%), located in the middle of the lesion (14 cases, 77.78%), with clear binderies (16 cases, 88.89%) and the same density with vessels in the same axis (13 cases, 72.22%); the majority of solid components in MIA appeared as circular or elliptical (33 cases, 62.26%), less than or equal to 5 mm (48 cases, 90.57%), with eccentric or multi-point distribution (45 cases, 84.90%), the boundaries were less sharp (40 cases, 75.47%), with slightly lower density than that of the vasculars in the same level (34 cases, 64.15%); the solid components in IAC mainly appeared as irregular lesions (21 cases, 75.00%), lager than 5 mm (24 cases, 85.71%), with eccentric growth (20 cases, 71.43%) and less sharp boundary (15 cases, 53.57%), the integration of multiple nodules could also be observed. There were statistically significant differences in the CT features of solid components within the lesions among the three groups (P<0.01).Conclusion It is possible to predict the pathological typing and the prognosis of pulmonary mixture GGO in a certain extent according to the different CT features of the solid components in it, and to guide clinical treatment principles.