Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design

Objective To investigate the epidemiological characteristics and spatial distribution characteristics of Clonorchis sinensis human infections in Guangdong Province from 2016 to 2022, so as to provide insights into formulation of the clonorchiasis control measures in the province. Methods Xinhui District of Jiangmen City, Longmen County of Huizhou City and Wengyuan County of Shaoguan City in Guangdong Province were selected as fixed surveillance sites for human clonorchiasis from 2016 to 2022, and additional 10% to 15% counties (districts) endemic for clonorchiasis were sampled from Guangdong Province as mobile surveillance sites each year from 2016 to 2022. A village (community) was randomly selected from each surveillance site according to the geographical orientations of east, west, south, north and middle, and subjects were randomly sampled from each village (community). C. sinensis eggs were detected in subjects’ stool samples using the Kato-Katz technique, and the prevalence and intensity of C. sinensis infections were calculated. In addition, subjects’ gender, age, ethnicity, educational level and occupation were collected. The Guangdong Provincial 1:1 million electronic map in vector format was downloaded from the National Geomatics Center of China, and kernel density analysis and spatial autocorrelation analysis of C. sinensis human infections in Guangdong Province from 2016 to 2022 were performed using the software ArcGIS 10.7. Results A total of 153 188 residents were tested for C. sinensis infections in Guangdong Province from 2016 to 2022, including 75 596 men (49.35%) and 77 592 women (50.65%), and there were 5 369 residents infected with C. sinensis, with 3.50% overall prevalence of infections. The prevalence rates of severe, moderate and mild C. sinensis infections were 0.76%, 7.26% and 91.97% among C. sinensis-infected residents in Guangdong Province from 2016 to 2022, and there were age-, gender-, ethnicity-, occupation- and educational level-specific prevalence of C. sinensis human infections (χ² = 2 578.31, 637.33, 52.22, 2 893.28 and 1 139.33, all P values < 0.05). Global spatial autocorrelation analysis showed a cluster in the prevalence of C. sinensis human infections in Guangdong Province (Moran’s I = 0.63, Z = 27.31, P < 0.05). Kernel density analysis showed that the prevalence of C. sinensis human infections with a high kernel density in Guangdong Province was mainly distributed along the Zhujiang River basin in Pearl River Delta areas, followed by in eastern and northern Guangdong Province. In addition, local spatial autocorrelation analysis identified 73 high-high clusters of the prevalence of C. sinensis human infections in Guangdong Province. Conclusions The prevalence of C. sinensis human infections was high in Guangdong Province from 2016 to 2022, and mild infection was predominant among all clonorchiasis cases, with spatial clusters identified in the prevalence of C. sinensis human infections. Targeted clonorchiasis control measures are required among high-risk populations and areas.

Objective To study the application value of different transbronchial biopsies in the diagnosis of central lung cancer in elderly patients.Methods The clinical data of 97 elderly patients with central lung cancer diagnosed by pathology from June 2020 to June 2023 in the 923rd Hospital of Chinese People's Liberation Army Joint Logistic Support Force were retrospectively analyzed.According to the different initial transbronchial biopsy methods,the patients were divided into the endobronchial biopsy(EBB)group(n=51)and the conventional transbronchial needle aspiration(cTBNA)group(n=46).The histopathological results were statistically analyzed,and the first biopsy positive rates of EBB and cTBNA in the diagnosis of senile central lung cancer were calculated and compared.At the same time,the differences of biopsy tolerance and complications between the two groups were evaluated.Results The squamous cell carcinoma proportions in both groups were over 50%.There was no significant difference in the first biopsy positive rate between the two groups(P>0.05).The incidence of temporary retreat of the scope due to subjective tolerance in the EBB group was higher than that in the cTBNA group,and the difference was statistically significant(P<0.05).There was a statistically significant difference in the incidence of intraoperative complications of different grades between the two groups(P<0.001).Among them,the incidence of grade 2 and above complications during surgery in the EBB group was significantly higher than that in the cTBNA group(P<0.001).Conclusion For elderly patients with central lung cancer,the success rate of the first biopsy of EBB and cTBNA is roughly equivalent,but the incidence of postoperative complications of the latter is significantly lower than that of the former.cTBNA can be used as the first biopsy method for this population.

Objective To investigate the feasibility of constructing an animal model for training of otoscopic anatomy and surgical operation using living guinea pigs.Methods Eight healthy adult guinea pigs were used as ex-perimental animals to construct a model of endoscopic operation by opening the upper tympanic cavity and abrading the upper wall of the external acoustic meatus to establish a space for endoscopic observation and operation.The an-atomical opening of the temporal bone and basic surgical steps were performed by the same resident on eight guinea pigs.The resident assessed the difficulty and completion of the endoscopic operation and measured various dimen-sions,including the anteroposterior and superior/inferior diameters of the mastoid process,the posterolateral wall of the upper tympanic cavity,and the upper wall of the external acoustic meatus,as well as the maximal depth of entry of the endoscope.Results The fine structures of guinea pig tympanic chamber were clearly displayed under otoen-doscopy.Except for the two steps of free preservation of the chorda tympani nerve and exposure of the stapes after removal of the ossicles,the other steps,such as separation of the tympanic membrane from the malleus,exposure of the malleus-anvil complex,removal of the cochlea shell to observe the cochlea axis,and exposure of the tympanic segment of the facial nerve under the endoscope,were all easily accomplished.The anterior and posterior diameters of the mastoid after opening were 3.56±0.21 and 3.89±0.16 mm,respectively,and the anterior and posterior di-ameters of the upper tympanic cavity and the upper wall of the external acoustic meatus after opening were 5.60±0.09 and 6.02±0.10 mm,respectively.The maximum depth of entry of the otoscopic endoscope was 15.14±0.24 mm.Conclusion Using guinea pig as an animal model for otoscopic surgery training can provide a more realis-tic surgical experience,which is helpful for beginners to be trained in the basic surgical skills of otoscopic surgery and otoscopic anatomy.