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.

Objective To explore the role of X chromosome encoded epigenetic regulator UTX in NK cell-mediated anti-tumor activity.Methods Male Ncr1-iCre mice were crossed with female UTXfl/fl mice to generate F1 Ncr1-iCre+UTXfl/-male mice,which were further crossed with female UTXfl/fl mice to obtain male Ncr1-iCre-UTX fl/-control mice(M-Con)and NK-specific deletion of UTX male mice Ncr1-iCre+UTXfl/-(M-KO),as well as female Ncr1-iCre-UTXfl/fl control mice(F-Con)and UTX-deficient female mice Ncr1-iCre+UTXfl/fl(F-KO).UTX-deficient mice were injected with melanoma cell line B16F10 via tail vein to observe pulmonary metastatic tumor nodules.Moreover,flow cytometry was applied to detect the proportion and quantity of pulmonary NK cells(CD3-CD19-NK1.1+),maturation makers KLRG1 and CD11b,activation receptors NKG2D and CD69,and effector molecules,including perforin,granzyme B,CD107a,and IFN-γ.Then pulmonary NK cells were sorted and co-cultured with B16F10 cells,and the apoptosis of the melanoma cells was measured with flow cytometry.Results Compared with the M-Con mice,the M-KO mice presented less number of pulmonary tumor nodules(P<0.05),increased proportion and quantity of NK cells in the tumor microenvironment(P<0.01),though no obvious changes in the ratio of NK maturation makers KLRG1 to CD11b,enhanced expression level of cytotoxic molecule perforin(P<0.01),but no changes in the expression of effector molecule granzyme B,degranulation marker CD107a and cytokine IFN-γ in NK cells.Co-culture of NK cells and B16F10 cells promoted the apoptosis of tumor cells(P<0.05).Compared with the F-Con mice,the F-KO mice had no statistical difference in the number of pulmonary tumor nodules,but larger proportion and number of NK cells(P<0.05),decreased ratio of KLRG1 to CD11b(P<0.01),elevated level of perforin but decreased levels of granzyme B,CD107a and IFN-γ in NK cells(P<0.01).The co-culture of NK cells and B16F10 cells reduced the apoptosis of tumor cells in F-KO female mice(P<0.05).Conclusion NK-specific deletion of UTX regulates pulmonary metastasis of melanoma in a sex-dependent manner.

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 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.

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.