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.

Aim To investigate the role and mecha-nism of CXC chemokine receptor 3(CXCR3)in hepa-toblastoma(HB).Methods The expression of CX-CR3 was detected by immunohistochemical and West-ern blot in 16 cases of HB tissue and adjacent normal liver tissue.The HB cells(Huh-6 and HepT1)were transfected with Con-shRNA,CXCR3-shRNA1,and CXCR3-shRNA2,respectively,and then divided into the Con-shRNA group,CXCR3-shRNA1 group,and CXCR3-shRNA2 group.Cell proliferation was detected by CCK-8 assay and EdU staining.Cell migration and invasion were detected by scratch and Transwell as-says.The expressions of β-catenin,c-Myc,cyclin D1,MMP-7 and MMP-9 were detected by Western blot.The tumor formation and tumor volume in each group were assessed using nude mouse xenograft tumor model,while the expressions of MMP-9 and Ki67 in tumor tissue were examined by immunohistochemistry.Results The expression of CXCR3 was up-regulated in HB tissue(P＜0.01).Compared to the Con-shR-NA group,the viability,proliferation,migration and invasion of Huh-6 and HepT1 cells in the CXCR3-shR-NA1 and CXCR3-shRNA2 groups were reduced(P＜0.01),the expressions of the Wnt/β-catenin signaling pathway related proteins were attenuated(P＜0.01),the tumor grew slowly and the volume was significantly reduced(P＜0.01),and the expressions of MMP-9 and Ki67 in tumor tissue decreased(P＜0.01).Con-clusions Downregulation of CXCR3 hinders the pro-liferation and migration of HB cells,potentially as-cribed to the attenuation of Wnt/β-catenin signaling regulation.

A triplex RT-qPCR assay with human genes as internal references was established for detection of the Dengue and Zika viruses(DENV and ZIKV,respectively).The conserved regions of the four serotypes of DENV,along with the NS1 gene of ZIKV and the human β-actin gene,which is stably ex-pressed in various human tissues,were targeted by three sets of specific primers and probes.Standard plasmids for four se-rotypes of DENV,ZIKV,and β-actin were constructed as pos-itive controls.Optimal reaction conditions were determined through an L9(34)orthogonal experiment.The specificity,sensitivity,and coverage of the assay were verified and evalua-ted clinically,and the consistency was evaluated against a com-mercial kit for detection of DENV.The triplex RT-qPCR assay established exhibited no non-specific cross reactions with 12 similar arboviruses.The detection sensitivity for DENV and ZIKV were 2.99 and 2.18 copies/μL,respectively,and the intra-group and inter-group repeatability coefficients of variation were within 1.5％.As compared to the commercial kit,the proposed assay obtained positive results for 13 epidemic strains of DENV.Bland-Altman consistency analysis confirmed that the consistency of the detection results of clinical positive samples between the commercial kit and the proposed assay was 92.59％.The highly specific and sensitive triplex RT-qPCR assay with internal references is an effective tool for early and rapid differential identification of DENV and ZIKV.

Objective:To explore the feasibility of establishing combat readiness blood bank with low titer group O whole blood and group A plasma.Methods:The Galileo automatic blood analyzer was used to detect the titers of IgM anti-A and anti-B antibodies in the samples of group O blood donors and IgM anti-B titer in the samples of group A blood donors.Group O blood donors with antibody titers below 128 were selected and included in the mobile blood bank for combat readiness,group A plasma with anti-B titer lower than 128 and group O whole blood with antibody titers below 128 were included in the combat readiness entity blood bank.Results:A total of 1 452 group O blood donors were selected,and the anti-A/B antibody titers were detected.Both antibody titers were distributed below 512,and both peak values of sample distribution were at titer 4.The proportion of samples with titers＞128 for both antibodies was relatively low.There was a significant positive correlation between the titers of the two antibodies(r=0.383),and the proportion of samples with IgM anti-A titer higher than IgM anti-B titer was relatively high.1 335(91.94％)group O blood donors with IgM anti-A and anti-B antibody titers＜128 could be included in the mobile blood bank.The anti-B titer of group A blood was detected in 512 cases and the results showed that as the antibody titer increased,the proportion of blood donors gradually decreased.99.8％of group A blood donors had anti-B antibody titer less than 128,and only one case did not meet the inclusion criteria.Conclusion:The proportion of group O blood donors whose whole blood meet the low antibody titer standard is high,and almost all plasma of group A blood donors meet the low titer standard,which improves the blood supply rate in emergencies.

Objective To explore the influence of perfectionism on work immersion of clinical nurses, and to analyze the roles of perceived stress and social connection in the relationship between perfectionism and work immersion. Methods A total of 646 clinical nurses from three tertiary-A hospitals in Guangzhou City were selected as the research subjects using the convenience sampling method. The perfectionism, perceived stress, social connectedness, and work immersion of clinical nurses were assessed using the Chinese version of the Frost Multidimensional Perfectionism Scale, Perceived Stress Scale, Social Connectedness Scale and Work Immersion Scale. Results The average scores for perfectionism, perceived stress, social connectedness, and work immersion among the clinical nurses were (80.3±12.6), (42.5±8.3), (88.1±16.8), and (42.5±8.3) points, respectively. Perceived stress partially mediated the relationship between perfectionism and work immersion, with an effect value of 0.06 and 95% confidence interval (CI) of (0.027-0.096), accounting for 21.6% of the total effect. Social connectedness moderated the initial path and direct path which perfectionism affected work immersion [standardized partial regression coefficients were -0.15 and 0.21, and 95%CI were (-0.210--0.082 ) and (0.140-0.281), respectively, both P<0.01). Conclusion Perfectionism may directly or indirectly affect the work immersion of clinical nurses, and perceived stress plays a partial mediating role, while social connectedness acts as a moderator in this relationship

This video presents a case of L4–5 unstable spondylolisthesis treated with full-endoscopic transforaminal lumbar interbody fusion (Endo-TLIF), emphasizing the GUARD (Glider Used as a Rotary Device) technique for nerve root protection. This innovative approach involves controlled rotation of the cage glider before cage insertion to minimize the risk of nerve root injury, a significant complication in Endo-TLIF procedures. The GUARD technique, validated in previous cadaveric studies, provides enhanced safety during cage insertion by protecting the nerve root. A 48-year-old woman with a 3-year history of progressive low back pain and bilateral lower extremity radiculopathy (right-sided predominance) was diagnosed with L4–5 unstable spondylolisthesis and spinal stenosis. After failure of conservative management, she underwent uniportal full-endoscopic facet-resecting transforaminal lumbar interbody fusion using the GUARD technique. Postoperatively, the patient experienced significant symptomatic improvement and resolution of radiculopathy, without any intraoperative nerve root injury or postoperative neurological deficits. This case demonstrates the effectiveness of the GUARD technique in reducing neurological complications and improving patient outcomes.