Objective This study aimed to explore the prognostic value of 18F-FDG PET/CT Metabolic and Heterogeneity Parameters Combined with Clinical Features Before Definitive Chemoradiotherapy(D-CRT)in predicting the prognosis of esophageal squamous cell carcinoma(ESCC)Patients.Methods A retrospective analysis was conducted on clinical data from 106 patients with ESCC who received D-CRT at the first affiliated Hospital of University of Science and Technology of China between January 2017 and December 2021.All patients underwent 18F-FDG PET/CT examination before the treatment.The primary tumor′s metabolic and heterogeneity parameters were obtained through data processing.All patients were followed up for overall survival.The Kaplan-Meier method and Cox proportional hazards models were used to analyze the association between clinical features,tumor metabo-lism and heterogeneity parameters and patient prognosis.Results The 1-and 1.5-year overall survival rates of all patients were 77.4%and 51.9%.The median survival time was 20 months.Univariate analysis showed that N stage,M stage,metabolic tumor volume,total lesion glycolysis,heterogeneity index-2(HI-2),and coefficient of variation with a threshold of 40%maximum standard uptake value(CV40%)were correlated with the prognosis of ESCC(all P<0.05).Multivariate analysis showed that N stage and CV40%were independent predictors of prognosis in patients with ESCC(P = 0.039 and P<0.001,respectively).Conclusion N stage and tumor metabolic heterogeneity parameter CV40%,which offering a degree of predictive value,are closely related to the prognosis of patients with ESCC treated with D-CRT.

OBJECTIVE To review the current research progress on pharmacoeconomics evaluation related to spinal muscular atrophy （SMA）， in order to provide valuable insights for clinical treatment， screening and medical insurance payment decision- making. METHODS A computerized search was conducted across multiple databases including PubMed， Web of Science， Embase， Scopus， Cochrane Library， EBSCOhost， CNKI， VIP， CBM and Wanfang database as well as other important health technology assessment （HTA） websites， such as National Institute for Health and Care Research，International Society of Technology Assessment in Health Care， Agency for Healthcare Research and Quality， etc. The pharmacoeconomics evaluation studies related to SMA were collected from the inception to December 31st， 2023. The literature/reports were rigorously screened based on predefined inclusion and exclusion criteria by two researchers， and the essential information from the included literature/ reports was extracted using Excel 2019. The quality of the included literature/reports was evaluated by Consolidated Health Economic Evaluation Reporting Standards 2022. RESULTS Finally， 9 articles and 15 HTA reports were included， with overall good quality of literature， but poor quality of HTA reports. There were a total of 24 studies on the pharmacoeconomics evaluation of SMA， including treatment options such as nusinersen sodium， sovaprevir， risperidone， and best supportive therapy.The review results showed that nusinersen sodium was not cost-effective in the treatment of SMA； there was no consensus on the economic viability of treatment options such as risperidone and sovaprevir； newborn/prenatal screening combined withmedication therapy was cost-effective. CONCLUSIONS newborn/prenatal screening combined with SMA medication therapy demonstrates economic advantages. It is suggested to further investigate the cost-effectiveness of new SMA drugs and SMA screening in China， taking localization parameters and medical insurance prices into account， and gradually incorporate SMA screening into the scope of neonatal genetic disease detection， in order to alleviate the financial burden of patients’ families and healthcare systems.

Alzheimer's disease(AD)is the most common neurodegenerative disease,typically affecting individuals aged 65 and older.Mild cognitive impairment is generally regarded as a precursor stage of AD.The underlying molecular and cellular processes of AD can be visualized in vivo using PET/CT technology.Multi-modal imaging based on Aβ PET,tau PET,and FDG PET within the ATN framework can effectively characterize the core neuropathological features of AD.Many novel PET tracers have recently been developed and applied to deeply probe the pathophysiological dysfunction and internal environmental alterations in AD.Therefore,this review will summarize the recent advances of amyloid,tau protein,FDG,and neuroinflammatory PET probes in AD research within the ATN framework.

Objective To explore the polymorphism and distribution of apolipoprotein E(ApoE)gene in patients with hyperlipidemia.Methods A total of 370 hyperlipidemia patients diagnosed at Hangzhou Ninth People's Hospital from October 2023 to June 2024 were selected.Gene polymorphism was detected in the blood samples of the patients using fluorescence polymerase chain reaction,and the distribution of gene polymorphism among different genders and age groups within this population was compared.Results ①The polymorphism distribution of the two genetic loci(ApoE 388T＞C and ApoE 526C＞T)in the studied sample population conforms to Hardy-Weinberg equilibrium,indicating that it is representative of the population;②Among the 370 patients,the distribution of the ApoE gene was as follows:E3/E3(66.49％),E3/E4(20.27％),E2/E3(8.65％),E2/E4(1.89％),E2/E2(1.35％),and E4/E4(1.35％).The proportion of the patient population carrying the E4 allele was about 1.88 times higher than that of those carrying the E2 allele;③The distribution differences of ApoE gene polymorphism were not related to the patients'gender and age(P＞0.05).Conclusion The distribution of ApoE gene polymorphism among hyperlipidemia patients at a hospital in Hangzhou is uneven and is not related to gender or age groups.Therefore,it is recommended to prioritize the detection of ApoE gene in patients before using statin medications to assess treatment effectiveness.

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.
Male ; Humans ; Prostate/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Magnetic Resonance Imaging/methods* ; Imaging, Three-Dimensional/methods* ; Prostatic Neoplasms/diagnostic imaging*

Bone defects caused by different causes such as trauma, severe bone infection and other factors are common in clinic and difficult to treat. Usually, bone substitutes are required for repair. Current bone grafting materials used clinically include autologous bones, allogeneic bones, xenografts, and synthetic materials, etc. Other than autologous bones, the major hurdles of rest bone grafts have various degrees of poor biological activity and lack of active ingredients to provide osteogenic impetus. Bone marrow contains various components such as stem cells and bioactive factors, which are contributive to osteogenesis. In response, the technique of bone marrow enrichment, based on the efficient utilization of components within bone marrow, has been risen, aiming to extract osteogenic cells and factors from bone marrow of patients and incorporate them into 3D scaffolds for fabricating bone grafts with high osteoinductivity. However, the scientific guidance and application specification are lacked with regard to the clinical scope, approach, safety and effectiveness. In this context, under the organization of Chinese Orthopedic Association, the Expert consensus for the clinical application of autologous bone marrow enrichment technique for bone repair ( version 2023) is formulated based on the evidence-based medicine. The consensus covers the topics of the characteristics, range of application, safety and application notes of the technique of autologous bone marrow enrichment and proposes corresponding recommendations, hoping to provide better guidance for clinical practice of the technique.

Objective:To explore the risk factors of poor renal prognosis in patients with coronary artery bypass surgery (CABG)-associated acute kidney injury (AKI), and establish a preliminary clinical risk prediction model for chronic kidney disease (CKD) progression in CABG-associated AKI patients, and evaluate its predictive efficacy.Methods:It was a retrospective, observational cohort study. The study subjects were patients who underwent CABG at Central China Fuwai Hospital from January 1, 2018 to December 31, 2020, with a baseline estimated glomerular filtration rate (eGFR)>60 ml·min -1·(1.73 m 2) -1 and postoperative complication of AKI. The patients were followed up for 90 days after discharge from hospital. The endpoint event was defined as progression to CKD after 90 days of the occurrence of CABG-associated AKI. The patients were divided into CKD group and non-CKD group based on whether they experienced endpoint events. The baseline clinical data were compared between the two groups. The logistic regression model was used to analyze the risk factors of endpoint event. The receiver-operating characteristic curve was drawn to evaluate the performance of the clinical risk prediction model for predicting poor renal prognosis in CABG-associated AKI patients. Results:A total of 124 CABG-associated AKI patients were enrolled in the study, including 95 males and 29 females, aged (62.57±9.61) years old. Thirty-eight patients (30.8%) progressed to new-onset CKD 90 days after CABG-associated AKI. Compared with non-CKD group, CKD group had lower preoperative hemoglobin ( t=2.778, P=0.006) and baseline eGFR ( t=3.603, P<0.001), higher proportion of women ( χ2=10.714, P=0.001), and higher preoperative blood NT-proBNP ( Z=-2.150, P=0.030) and discharged serum creatinine ( Z=-5.290, P<0.001). The multivariate logistic regression analysis results revealed that female ( OR=5.179, 95% CI 1.535-17.477, P=0.008), high preoperative blood NT-proBNP ( OR=1.049, 95% CI 1.004-1.095, P=0.032), low baseline eGFR ( OR=0.928, 95% CI 0.889-0.968, P=0.001), and high serum creatinine at discharge ( OR=1.019, 95% CI 1.009-1.029, P<0.001) were independent influencing factors of CABG-associated AKI to CKD. The clinical risk prediction model including female, preoperative blood NT-proBNP, preoperative baseline eGFR, and serum creatinine at discharge produced a moderate performance for predicting CABG-associated AKI to CKD ( AUC=0.872, 95% CI 0.806-0.939, P<0.001). Conclusion:Patients with CABG-associated AKI are at high risks for new-onset CKD. Female, preoperative high NT-proBNP, preoperative low baseline eGFR and high serum creatinine at discharge can help identify patients with a high risk of CABG-associated AKI to CKD.

Objective:To construct an artificial intelligence-assisted diagnosis system to recognize the characteristics of Helicobacter pylori ( HP) infection under endoscopy, and evaluate its performance in real clinical cases. Methods:A total of 1 033 cases who underwent 13C-urea breath test and gastroscopy in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2020 to March 2021 were collected retrospectively. Patients with positive results of 13C-urea breath test (which were defined as HP infertion) were assigned to the case group ( n=485), and those with negative results to the control group ( n=548). Gastroscopic images of various mucosal features indicating HP positive and negative, as well as the gastroscopic images of HP positive and negative cases were randomly assigned to the training set, validation set and test set with at 8∶1∶1. An artificial intelligence-assisted diagnosis system for identifying HP infection was developed based on convolutional neural network (CNN) and long short-term memory network (LSTM). In the system, CNN can identify and extract mucosal features of endoscopic images of each patient, generate feature vectors, and then LSTM receives feature vectors to comprehensively judge HP infection status. The diagnostic performance of the system was evaluated by sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC). Results:The diagnostic accuracy of this system for nodularity, atrophy, intestinal metaplasia, xanthoma, diffuse redness + spotty redness, mucosal swelling + enlarged fold + sticky mucus and HP negative features was 87.5% (14/16), 74.1% (83/112), 90.0% (45/50), 88.0% (22/25), 63.3% (38/60), 80.1% (238/297) and 85.7% (36 /42), respectively. The sensitivity, specificity, accuracy and AUC of the system for predicting HP infection was 89.6% (43/48), 61.8% (34/55), 74.8% (77/103), and 0.757, respectively. The diagnostic accuracy of the system was equivalent to that of endoscopist in diagnosing HP infection under white light (74.8% VS 72.1%, χ2=0.246, P=0.620). Conclusion:The system developed in this study shows noteworthy ability in evaluating HP status, and can be used to assist endoscopists to diagnose HP infection.

Objective:To assess the diagnostic efficacy of upper gastrointestinal endoscopic image assisted diagnosis system (ENDOANGEL-LD) based on artificial intelligence (AI) for detecting gastric lesions and neoplastic lesions under white light endoscopy.Methods:The diagnostic efficacy of ENDOANGEL-LD was tested using image testing dataset and video testing dataset, respectively. The image testing dataset included 300 images of gastric neoplastic lesions, 505 images of non-neoplastic lesions and 990 images of normal stomach of 191 patients in Renmin Hospital of Wuhan University from June 2019 to September 2019. Video testing dataset was from 83 videos (38 gastric neoplastic lesions and 45 non-neoplastic lesions) of 78 patients in Renmin Hospital of Wuhan University from November 2020 to April 2021. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD for image testing dataset were calculated. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD in video testing dataset for gastric neoplastic lesions were compared with those of four senior endoscopists.Results:In the image testing dataset, the accuracy, the sensitivity, the specificity of ENDOANGEL-LD for gastric lesions were 93.9% (1 685/1 795), 98.0% (789/805) and 90.5% (896/990) respectively; while the accuracy, the sensitivity and the specificity of ENDOANGEL-LD for gastric neoplastic lesions were 88.7% (714/805), 91.0% (273/300) and 87.3% (441/505) respectively. In the video testing dataset, the sensitivity [100.0% (38/38) VS 85.5% (130/152), χ2=6.220, P=0.013] of ENDOANGEL-LD was higher than that of four senior endoscopists. The accuracy [81.9% (68/83) VS 72.0% (239/332), χ2=3.408, P=0.065] and the specificity [ 66.7% (30/45) VS 60.6% (109/180), χ2=0.569, P=0.451] of ENDOANGEL-LD were comparable with those of four senior endoscopists. Conclusion:The ENDOANGEL-LD can accurately detect gastric lesions and further diagnose neoplastic lesions to help endoscopists in clinical work.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.