Objective:To compare the efficacy and safety of LY01011,a recombinant anti-RANKL fully human monoclonal antibody injection,versus denosumab in the treatment of bone metastases from solid tumors.Methods:A randomized,double-blind,positive drug parallel-controlled,multicenter clinical trial was conducted.A total of 850 subjects were randomly assigned(1:1)to either the experimental group(424 subjects)or the control group(426 subjects).The experimental group received 13 doses of LY01011,while the control group received 3 doses of denosumab followed by 10 doses of LY01011.Results:The primary efficacy endpoint was the natural logarithmic change from baseline in urinary N-terminal telopeptide of type I collagen corrected by urinary creatinine(uNTX/uCr)at week 13.The change was-1.740(0.042 0)in the experimental group and-1.745(0.042 1)in the control group.The least-squares mean difference between groups was 0.005(90%CI:-0.088 to 0.097),indicating no statistically significant difference(P>0.05).Safety profiles,including treatment-emergent adverse events,laboratory tests,vital signs,physical examinations,and electrocardiograms,were comparable between groups(P>0.05).Conclusions:LY01011 demonstrated biosimilarity to denosumab,with favorable safety profile,tolerability,and potential for clinical application.

Objective To observe the value of 18F-prostate specific membrane antigen(PSMA)-1007 PET/MRI for diagnosing seminal vesicle invasion(SVI)of prostatic cancer(PCa).Methods Totally 92 male patients with PCa who underwent radical prostatectomy were retrospectively enrolled and divided into positive group(n=26)and negative group(n=66)based on postoperative pathology showed SVI or not.PET/MRI parameters,including maximum standard uptake value(SUVmax),minimum apparent diffusion coefficient(ADCmin),mean apparent diffusion coefficient(ADCmean),SUVmax/ADCmin,SUVmax/ADCmean,PSMA tumor volume(PSMA-TV)and total lesion PSMA(TL-PSMA)were compared between groups.The receiver operating characteristic curve was drawn,and the efficacy of each parameter for diagnosing SVI was analyzed.Results Among 92 cases of PCa,18F-PSMA-1007 PET/MRI showed 30 cases with SVI and 62 cases without SVI,with accuracy of 73.91％,sensitivity of 61.54％,specificity of 78.79％,positive predictive value of 53.33％and negative predictive value of 83.87％.Significant differences of ADCmin,PSMA-TV and TL-PSMA were found between groups(all P＜0.05).The area under the curve(AUC)of SUVmax,ADCmin,ADCmean,SUVmax/ADCmin,SUVmax/ADCmean,PSMA-TV and TL-PSMA for diagnosing SVI of PCa was 0.554,0.341,0.396,0.603,0.581,0.755 and 0.705,respectively.The AUC of PSMA-TV was higher than other parameters except for TL-PSMA,with sensitivity of 84.60％and specificity of 56.10％.Conclusion 18 F-PSMA-1007 PET/MRI was helpful for diagnosing SVI of PCa.

Objective To observe the value of 18F-prostate specific membrane antigen(PSMA)-1007 PET/MRI for diagnosing seminal vesicle invasion(SVI)of prostatic cancer(PCa).Methods Totally 92 male patients with PCa who underwent radical prostatectomy were retrospectively enrolled and divided into positive group(n=26)and negative group(n=66)based on postoperative pathology showed SVI or not.PET/MRI parameters,including maximum standard uptake value(SUVmax),minimum apparent diffusion coefficient(ADCmin),mean apparent diffusion coefficient(ADCmean),SUVmax/ADCmin,SUVmax/ADCmean,PSMA tumor volume(PSMA-TV)and total lesion PSMA(TL-PSMA)were compared between groups.The receiver operating characteristic curve was drawn,and the efficacy of each parameter for diagnosing SVI was analyzed.Results Among 92 cases of PCa,18F-PSMA-1007 PET/MRI showed 30 cases with SVI and 62 cases without SVI,with accuracy of 73.91％,sensitivity of 61.54％,specificity of 78.79％,positive predictive value of 53.33％and negative predictive value of 83.87％.Significant differences of ADCmin,PSMA-TV and TL-PSMA were found between groups(all P＜0.05).The area under the curve(AUC)of SUVmax,ADCmin,ADCmean,SUVmax/ADCmin,SUVmax/ADCmean,PSMA-TV and TL-PSMA for diagnosing SVI of PCa was 0.554,0.341,0.396,0.603,0.581,0.755 and 0.705,respectively.The AUC of PSMA-TV was higher than other parameters except for TL-PSMA,with sensitivity of 84.60％and specificity of 56.10％.Conclusion 18 F-PSMA-1007 PET/MRI was helpful for diagnosing SVI of PCa.

Objective:To compare the efficacy and safety of LY01011,a recombinant anti-RANKL fully human monoclonal antibody injection,versus denosumab in the treatment of bone metastases from solid tumors.Methods:A randomized,double-blind,positive drug parallel-controlled,multicenter clinical trial was conducted.A total of 850 subjects were randomly assigned(1:1)to either the experimental group(424 subjects)or the control group(426 subjects).The experimental group received 13 doses of LY01011,while the control group received 3 doses of denosumab followed by 10 doses of LY01011.Results:The primary efficacy endpoint was the natural logarithmic change from baseline in urinary N-terminal telopeptide of type I collagen corrected by urinary creatinine(uNTX/uCr)at week 13.The change was-1.740(0.042 0)in the experimental group and-1.745(0.042 1)in the control group.The least-squares mean difference between groups was 0.005(90%CI:-0.088 to 0.097),indicating no statistically significant difference(P>0.05).Safety profiles,including treatment-emergent adverse events,laboratory tests,vital signs,physical examinations,and electrocardiograms,were comparable between groups(P>0.05).Conclusions:LY01011 demonstrated biosimilarity to denosumab,with favorable safety profile,tolerability,and potential for clinical application.

AIM: To investigate the preoperative ocular symptoms and the characteristics of asymptomatic ocular surface abnormalities in hospitalized patients with primary pterygium.METHODS: Cross-sectional study. Hospitalized patients diagnosed with primary pterygium and scheduled to receive pterygium excision surgery at the Xiamen Eye Center of Xiamen University from August 2022 to October 2022 were enrolled. Ocular surface disease index questionnaire(OSDI), six examinations including non-invasive tear film break-up time, Schirmer I test, tear meniscus height, lid margin abnormality, meibomian gland dropout and tear film lipid layer thickness, and anterior segment optical coherence tomography(AS-OCT)were performed and statistically analyzed.RESULTS: A total of 178 cases(178 eyes), with a mean age of 54.39±10.75 years old, were recruited, including 75 males(42.1%)and 103 females(57.9%). The average values of ocular surface parameters in these patients included OSDI: 11.47±9.69, tear film break-up time: 7.10±3.86 s; tear meniscus height: 0.16±0.07 mm, Schirmer I test values: 14.39±7.29 mm/5 min, and pterygium thickness: 504.74±175.87 μm. Totally 161 eyes(90.4%)presented with abnormal lid margin, 44 eyes(24.7%)presented with meibomian gland dropout score ≥4, 52 eyes(29.2%)presented with low lipid layer thickness. In the 6 objective examinations, abnormalities in at least 4 of these tests were found in 85.4% of eyes. Pterygium morphology was classified into four grades: 10 eyes(5.6%)of grade Ⅰ, 93 eyes(52.2%)of grade Ⅱ, 60 eyes(33.7%)of grade Ⅲ, and 15 eyes(8.4%)of grade Ⅳ. In patients with a higher grade of pterygium, the tear film break-up time was lower, and the proportion of abnormal lid margin was also significantly higher(P<0.05). The patients were further divided into two subgroups, including 121 eyes(68.0%)with normal OSDI <13 in the normal group and 57 eyes(32.0%)with OSDI ≥13 in the abnormal group. No significant difference was found in the proportion of meibomian gland dysfunction between the two groups of patients(71.9% vs. 71.9%, P=0.872). In addition, there were differences in the number of abnormal objective examinations(4.11±0.85 vs. 4.91±0.99, P<0.001).CONCLUSIONS: Asymptomatic ocular surface abnormalities were present preoperatively in patients hospitalized for primary pterygium. A comparable high incidence of structural or functional meibomian gland dysfunction existed in pterygium patients with or without apparent ocular discomfort. More attention should be paid to the ocular surface abnormalities in those asymptomatic patients before primary pterygium surgery.

Hepatocellular carcinoma(HCC),commonly known as primary liver cancer,is a major cause of malignant tumors and cancer-related deaths in China,accounting for approximately 85％of all cancer cases in the country.Several guidelines have been used to diagnose and treat liver cancer.However,these guidelines provide a broad definition for classifying advanced liver cancer,with an emphasis on a singular approach,without considering treatment options for individual patients.Therefore,it is necessary to establish a comprehensive and practical expert consensus,specifically for China,to enhance the diagnosis and treatment of HCC using the Delphi method.The classification criteria were refined for Chinese patients with HCC,and the corresponding optimal treatment regimen recommendations were developed.These recommendations took into account various factors,including tumor characteristics,vascular tumor thrombus grade,distant metastasis,liver function status,portal hypertension,and the hepatitis B virus replication status of patients with primary HCC,along with treatment prognosis.The findings and rec-ommendations provide detailed,scientific,and reasonable individualized diagnosis and treatment strategies for clinicians.

Objective To evaluate the scientificity,transparency and applicability of Chinese guidelines and expert consensuses on nursing published in 2022,in order to improve the quality of guidelines and consensuses.Methods Databases including Medline,Embase,Web of Science,CBM,CNKI,WanFang database,Chinese Medical Journal,and related websites were electronically searched,as well as China Hong Kong,Macao and Taiwan medical journals,to collect Chinese guidelines and expert consensuses on nursing from January to December 2022.STAR tool was used to evaluate the quality of each guidelines and consensuses by 3 assessors independently.Total score,scoring rate of each domain and item were adopted to analyze the outcomes.Results A total of 3 guidelines and 33 expert consensuses were included.The total guidelines and expert consensuses STAR score(33.5±14.3).The quality of guidelines and consensuses was low.The quality of guidelines was moderate with average score of 55.1,and the quality of consensuses was low with average score of 31.5.The included guidelines and consensuses had a highest score rate(52.4％)in the domain of recommendation.Among 39 items of STAR tool,the top 4 items including listing participants and institutions,explaining additional instructions for implementation,describing consensus method,and listing references for recommendations had a high score rate of 100％,83.3％,77.8％,75.0％respectively.However,the items of registration,providing registration information,protocols being searched on public platforms and explaining the role of funding had a low score rate,urgent need for attention and upgrading.Conclusion The overall quality of the Chinese guidelines and expert consensuses on nursing published in 2022 was low.As a medical and nursing practice guidance document,the quality of guidelines and expert consensuses should be improved by encouraging registration,strengthening management of interest conflict,enhancing the rigor of guideline developing process,and expanding the dissemination.

Humans ; MicroRNAs/genetics* ; Cell Line, Tumor ; Osteosarcoma/pathology* ; Neoplasm Invasiveness ; Cell Proliferation ; Bone Neoplasms/pathology* ; Cell Movement ; Neoplasm Metastasis

Objective:To compare 3D-printing-assisted surgery and conventional surgery in the treatment of Schazker type Ⅵ tibial plateau fractures.Methods:A retrospective study was conducted to analyze the clinical data of 50 patients with type Ⅵ tibial plateau fracture who had been treated from January 2019 to December 2021 at the 5 Departments of Orthopedics in The First Affiliated Hospital of Nanchang University, The First People's Hospital of Jiujiang, Pingkuang General Hospital, Ganzhou People's Hospital, and Nanchang Hongdu Hospital of Traditional Chinese Medicine. The patients were divided into 2 groups according to their different treatment methods. In the 3D printing group of 25 cases treated by 3D-printing-assisted surgery, there were 14 males and 11 females, with an age of (42.5±9.1) years; in the conventional group of 25 cases treated by conventional surgery, there were 13 males and 12 females with an age of (42.2±9.3) years. The 2 groups were compared in terms of operation time, intraoperative blood loss, intraoperative fluoroscopy frequency, fracture healing time, postoperative complications, the Rasmussen radiological scores and the American Hospital for Special Surgery (HSS) knee function scores at 6 and 12 months after operation.Results:There was no significant difference in the preoperative general data between the 2 groups, indicating comparability ( P>0.05). The operation time [(125.4±10.6) min], intraoperative blood loss [(206.2±16.3) mL], intraoperative fluoroscopy frequency [(9.2±2.7) times] and fracture healing time [(3.0±0.7) months] in the 3D printing group were all significantly less than those in the conventional group [(168.2±14.1) min, (303.2±20.4) mL, (15.5±3.5) times and (4.1±0.8) months] while the Rasmussen radiological scores (17.6±1.2 and 17.9±0.6) and HSS knee scores (90.8±6.4 and 91.5±5.6) at 6 and 12 months after operation in the 3D printing group were all significantly higher than those in the conventional group (16.2±2.6 and 16.7±2.2; 84.5±9.2 and 87.6±8.0) (all P<0.05). In the 3D printing group, there were 1 case of wound infection and 1 case of wound dehiscence after operation. In the conventional group, there were 2 cases of wound skin necrosis, 3 cases of wound dehiscence, 1 case of traumatic arthritis, 2 cases of wound infection, and 1 case of screw loosening. The incidence of complications in the 3D printing group (8.0%, 2/28) was significantly lower than that in the conventional group (36.0%, 9/25) ( P<0.05). Conclusion:In the treatment of Schatzker type VI tibial plateau fractures, compared with conventional surgery, 3D-printing-assisted surgery can lead to better curative outcomes, because it is conducive to lowering surgical difficulty, reducing postoperative complications, and promoting fracture union and functional recovery of the knee.

Objective:To investigate the application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma (HCC) after resection.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 375 patients with HCC who underwent radical resection in the Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College from January 2012 to January 2017 were collected. There were 304 males and 71 females, aged from 21 to 79 years, with a median age of 57 years. According to the random numbers showed in the computer, 375 patients were divided into training dataset consisting of 300 patients and validation dataset consisting of 75 patients, with a ratio of 8∶2. Machine learning algorithms including logistic regression (LR), supporting vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used to construct survival prediction models for HCC after resection, so as to identify the optimal machine learning algorithm prediction model. A COX nomogram prediction model for predicting postoperative survival in patients with HCC was also constructed. Comparison of performance for predicting postoperative survival of HCC patients was conducted between the optimal machine learning algorithm prediction model and the COX nomogram prediction model. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up and survival of patients in the training dataset and validation dataset; (3) construction and evaluation of machine learning algorithm prediction models; (4) construction and evaluation of COX nomogram prediction model; (5) evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model. Follow-up was performed using outpatient examination or telephone interview to detect survival of patients up to December 2019 or death. Measurement data with normal distribution were expressed as Mean± SD, and comparison between groups was analyzed by the paired t test. Measurement data with skewed distribution were expressed as M ( P25, P75) or M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were represented as absolute numbers. Comparison between groups was performed using the chi-square test when Tmin ≥5 and N ≥40, using the calibration chi-square test when 1≤ Tmin ≤5 and N ≥40, and using Fisher exact probability when Tmin <1 or N <40. The Kaplan-Meier method was used to calculate survival rate and draw survival curve. The COX proportional hazard model was used for univariate analysis, and variables with P<0.2 were included for the Lasso regression analysis. According to the lambda value, variables affecting prognosis were screened for COX proportional hazard model to perform multivariate analysis. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: cases without microvascular invasion or with microvascular invasion, cases without liver cirrhosis or with liver cirrhosis of the training dataset were 292, 8, 105, 195, respectively, versus 69, 6, 37, 38 of the validation dataset, showing significant differences between the two groups ( χ2=4.749, 5.239, P<0.05). (2) Follow-up and survival of patients in the training dataset and validation dataset: all the 375 patients received follow-up. The 300 patients in the training dataset were followed up for 1.1-85.5 months, with a median follow-up time of 50.3 months. Seventy-five patients in the validation dataset were followed up for 1.0-85.7 months, with a median follow-up time of 46.7 months. The postoperative 1-, 3-year overall survival rates of the 375 patients were 91.7%, 79.5%. The postoperative 1-, 3-year overall survival rates of the training dataset were 92.0%, 79.7%, versus 90.7%, 81.9% of the validation dataset, showing no significant difference in postoperative survival between the two groups ( χ2=0.113, P>0.05). (3) Construction and evaluation of machine learning algorithm prediction models. ① Selection of the optimal machine learning algorithm prediction model: according to information divergence of variables for prediction of 3 years postoperative survival of HCC, five machine learning algorithms were used to comprehensively rank the variables of clinicopathological factors of HCC, including LR, SVM, DT, RF, and ANN. The main predictive factors were screened out, as hepatitis B e antigen (HBeAg), surgical procedure, maximum tumor diameter, perioperative blood transfusion, liver capsule invasion, and liver segment Ⅳ invasion. The rank sequence 3, 6, 9, 12, 15, 18, 21, 24, 27, 29 variables of predictive factors were introduced into 5 machine learning algorithms in turn. The results showed that the area under curve (AUC) of the receiver operating charateristic curve of LR, SVM, DT, and RF machine learning algorithm prediction models tended to be stable when 9 variables are introduced. When more than 12 variables were introduced, the AUC of ANN machine learning algorithm prediction model fluctuated significantly, the stability of AUC of LR and SVM machine learning algorithm prediction models continued to improve, and the AUC of RF machine learning algorithm prediction model was nearly 0.990, suggesting RF machine learning algorithm prediction model as the optimal machine learning algorithm prediction model. ② Optimization and evaluation of RF machine learning algorithm prediction model: 29 variables of predictive factors were sequentially introduced into the RF machine learning algorithm to construct the optimal RF machine learning algorithm prediction model in the training dataset. The results showed that when 10 variables were introduced, results of grid search method showed 4 as the optimal number of nodes in DT, and 1 000 as the optimal number of DT. When the number of introduced variables were not less than 10, the AUC of RF machine learning algorithm prediction model was about 0.990. When 10 variables were introduced, the RF machine learning algorithm prediction model had an AUC of 0.992 for postoperative overall survival of 3 years, a sensitivity of 0.629, a specificity of 0.996 in the training dataset, an AUC of 0.723 for postoperative overall survival of 3 years, a sensitivity of 0.177, a specificity of 0.948 in the validation dataset. (4) Construction and evaluation of COX nomogram prediction model. ① Analysis of postoperative survival factors of HCC patients in the training dataset. Results of univariate analysis showed that HBeAg, alpha fetoprotein (AFP), preoperative blood transfusion, maximum tumor diameter, liver capsule invasion, and degree of tumor differentiation were related factors for postoperative survival of HCC patients [ hazard ratio ( HR)=1.958, 1.878, 2.170, 1.188, 2.052, 0.222, 95% confidence interval ( CI): 1.185-3.235, 1.147-3.076, 1.389-3.393, 1.092-1.291, 1.240-3.395, 0.070-0.703, P<0.05]. Clinico-pathological data with P<0.2 were included for Lasso regression analysis, and the results showed that age, HBeAg, AFP, surgical procedure, perioperative blood transfusion, maximum tumor diameter, tumor located at liver segment Ⅴ or Ⅷ, liver capsule invasion, and degree of tumor differentiation as high differentiation, moderate-high differentiation, moderate differentiation, moderate-low differentiation were related factors for postoperative survival of HCC patients. The above factors were included for further multivariate COX analysis, and the results showed that HBeAg, surgical procedure, maximum tumor diameter were independent factors affecting postoperative survival of HCC patients ( HR=1.770, 8.799, 1.142, 95% CI: 1.049- 2.987, 1.203-64.342, 1.051-1.242, P<0.05). ② Construction and evaluation of COX nomogram prediction model: the clinicopathological factors of P≤0.1 in the COX multivariate analysis were induced to Rstudio software and rms software package to construct COX nomogram prediction model in the training dataset. The COX nomogram prediction model for predicting postoperative overall survival had an consistency index of 0.723 (se=0.028), an AUC of 0.760 for postoperative overall survival of 3 years in the training dataset, an AUC of 0.795 for postoperative overall survival of 3 years in the validation dataset. The verification of the calibration plot in the training dataset showed that the COX nomogram prediction model had a good prediction performance for postoperative survival. COX nomogram score=0.627 06×HBeAg (normal=0, abnormal=1)+ 0.134 34×maximum tumor diameter (cm)+ 2.107 58×surgical procedure (laparoscopy=0, laparotomy=1)+ 0.545 58×perioperative blood transfusion (without blood transfusion=0, with blood transfusion=1)-1.421 33×high differentiation (non-high differentiation=0, high differentiation=1). The COX nomogram risk scores of all patients were calculated. Xtile software was used to find the optimal threshold of COX nomogram risk scores. Patients with risk scores ≥2.9 were assigned into high risk group, and patients with risk scores <2.9 were assigned into low risk group. Results of Kaplan-Meier overall survival curve showed a significant difference in the postoperative overall survival between low risk group and high risk group of the training dataset ( χ2=33.065, P<0.05). There was a significant difference in the postoperative overall survival between low risk group and high risk group of the validation dataset ( χ2=6.585, P<0.05). Results of further analysis by the decision-making curve showed that COX nomogram prediction model based on the combination of HBeAg, surgical procedure, perioperative blood transfusion, maximum tumor diameter, and degree of tumor differentiation was superior to any of the above individual factors in prediction performance. (5) Evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model: prediction difference between two models was investigated by analyzing maximun tumor diameter (the important variable shared in both models), and by comparing the predictive error curve of both models. The results showed that the postoperative 3-year survival rates predicted by RF machine learning algorithm prediction model and COX nomogram prediction model were 77.17% and 74.77% respectively for tumor with maximum diameter of 2.2 cm ( χ2=0.182, P>0.05), 57.51% and 61.65% for tumor with maximum diameter of 6.3 cm ( χ2=0.394, P>0.05), 51.03% and 27.52% for tumor with maximum diameter of 14.2 cm ( χ2=12.762, P<0.05). With the increase of the maximum tumor diameter, the difference in survival rates predicted between the two models turned larger. In the validation dataset, the AUC for postoperative overall survival of 3 years of RF machine learning algorithm prediction model and COX nomogram prediction model was 0.723 and 0.795, showing a significant difference between the two models ( t=3.353, P<0.05). Resluts of Bootstrap cross-validation for prediction error showed that the integrated Brier scores of RF machine learning algorithm prediction model and COX nomogram prediction model for predicting 3-year survival were 0.139 and 0.134, respectively. The prediction error of COX nomogram prediction model was lower than that of RF machine learning algorithm prediction model. Conclusion:Compared with machine learning algorithm prediction models, the COX nomogram prediction model performs better in predicting 3 years postoperative survival of HCC, with fewer variables, which is easy for clinical use.