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.

Objective To investigate the effect of varying blood pressure stratification on renal function in the diabetic population.Methods A prospective cohort study was conducted,enrolling 9 489 diabetic patients from a total of 101 510 Kailuan Group employees who underwent health examinations between July 2006 and October 2007.The follow-up period was(8.6±4.0)years.Participants were categorized into four groups based on their baseline blood pressure levels:normal blood pressure(systolic blood pressure<120 mmHg and diastolic blood pressure<80 mmHg),elevated blood pressure(systolic blood pressure 120-130 mmHg and diastolic blood pressure<80 mmHg),stage 1 hypertension(systolic blood pressure 130-140 mmHg and/or diastolic blood pressure 80-90 mmHg),and stage 2 hypertension(systolic blood pressure≥140 mmHg and/or diastolic blood pressure≥90 mmHg).The incidence density of chronic kidney disease(CKD)was compared among these groups.A multivariate Cox proportional hazards regression model was employed to assess the effects of different blood pressure levels on renal function in diabetic patients,with the stability of the results confirmed using a multivariate time-dependent Cox proportional hazards model.Sensitivity analysis was conducted after excluding cases of cardiovascular disease(CVD)during follow-up,and cases using antihypertensive and antidiabetic medications at baseline.Results(1)At baseline,stage 1 hypertension patients demonstrated statistically significant higher differences with age and body mass index(BMI)compared to normal blood pressure group(P<0.05).(2)By the end of the follow-up,2 294 cases of CKD were identified,including 1 117 cases of estimated glomerular filtration rate(eGFR)decline and 1 575 cases of urinary protein.The incidences density of CKD,eGFR decline and urinary protein for stage 1 hypertension group were 39.4,16.3 and 25.5 per thousand person-years,respectively,all of which were statistically significant different from normal blood pressure group(log-rank test,P<0.01).(3)Multivariate Cox regression analysis revealed that,compared to the normal blood pressure group,stage 1 hypertension was associated with a 29%increased risk of CKD(HR=1.29,95%CI 1.09-1.52)and a 40%increased risk of eGFR decline(HR=1.40,95%CI 1.08-1.80)in diabetic individuals.Conclusion Stage 1 hypertension significantly increases the risk of CKD and eGFR decline in diabetic individuals,with a particularly notable effect on the risk of eGFR decline.

Objective:To explore the stimulation conditions,optimal culture time and infection time of C57BL/6J mice CD3+T cells in vitro,so as to improve the infection efficiency of CD 19 chimeric antigen receptor T cells(mCD19 CAR-T).Methods:Purified C57BL/6J mice CD3+T cells were cultured in anti-CD3/CD28 coated,anti-CD3 coated+soluble anti-CD28 and anti-CD3 coated,respectively.The cells were stimulated in above three conditions for 12 h and 24 h,following with 24 h,48 h and 72 h incubation and then the number of cell clones was recorded.C57BL/6J mice CD3+T cells were stimulated for 12 h,24 h,and 36 h under the above three conditions,then interleukin(IL)-2(100 U/ml)was added.The number of cell clones was recorded under microscope at 24 h,48 h,and 72 h of culture.After 24 h of stimulation,CD3+T cells derived from C57BL/6J mice were infected with retrovirus for 48 h to establish mCD19 CAR-T cells,and the percentage of GFP+CAR-T cells was detected by flow cytometry.Results:The infection efficiency of mCD19 CAR-T cells derived from C57BL/6J mice was only 5.23％under the optimized conditions of mCD19 CAR-T cells derived from BALB/c mice.The number of clones of C57BL/6J mice CD3+T cells was the highest in anti-CD3 coated+soluble anti-CD28 group after stimulated for 24 h and followed cultured for 48 h.After 24 hours of stimulation under the above conditions and 48 hours of culture with IL-2,the number of T cell proliferating clones in the anti-CD3 coated+soluble anti-CD28 group was significantly increased compared with the same group without IL-2,and the infection efficiency of CAR-T cells in this group reached 17.63％±4.17％.Conclusion:The optimal conditions for constructing CAR-T cells from C57BL/6J mice CD3+T cells are different from those of BABL/c mice.T cells stimulated by anti-CD3 coated+soluble anti-CD28+IL-2 can obtain mCD19 CAR-T cells with the highest efficiency after retrovirus infection.

Objective:To investigate the effects of down-regulation of p21 activated kinase 1(PAK1)on the proliferation,differentiation,and apoptosis of myeloproliferative neoplasm(MPN)cells(6133/MPL)with thrombopoietin receptor MPL mutation at codon 515(MPLW515L)and survival of 6133/MPL mice.Methods:Interference with the protein level of PAK1 in 6133/MPL cells was assessed using lentivirus-mediated shRNA transfection technology.CCK-8 assay was used to detect the effect of down-regulation of PAK1 on the proliferation viability of 6133/MPL cells,and colony-forming ability was measured by cell counting.Flow cytometry was used to detect the PAK1 kinase activity on the ability of polyploid DNA formation and cell apoptosis in 6133/MPL cells.The expression of cyclin D1,cyclin D3 and apoptosis-related protein Bax was detected by Western blot.The infiltration of tumor cells in spleen and bone marrow of 6133/MPL mice were detected by HE staining.Results:Down-regulation of PAK1 inhibited the proliferation and reduced the ability of cell colony formation of 6133/MPL cells.After knocking down PAK1,the content of polyploid DNA in 6133/MPL cells increased from 31.8 to 57.5％and 48.0％,and the proportion of apoptosis increased approximately to 10.8％.Down-regulation of PAK1 led to a reduction of infiltration of tumor cells in liver and bone marrow of 6133/MPL mice,thereby prolonging survival time.Conclusion:Down-regulation of PAK1 can significantly inhibit the growth of 6133/MPL cells,promote the formation of polyploid DNA,induce 6133/MPL cell apoptosis,and prolong the survival time of 6133/MPL mice.

Background: This retrospective study investigates the complications, particularly subacromial osteolysis (SAO), associated with hook plate (HP) fixation, in the treatment of unstable distal clavicle fractures characterized by complete coracoclavicular (CC) ligament rupture. The decision-making process for employing HP in fractures of this nature, such as Neer types IIB and V and Cho classification IIC, involves considerations of distal fragment size and displacement. While HP offers advantages in clinical practice, it is not without complications, with SAO being a notable concern. Factors such as non-anatomic hook tip placement and fracture classification may influence the risk of SAO. Methods: The study comprises a retrospective analysis of unstable distal clavicle fractures treated with HP at our institution from 2019 to 2022. Exclusions include non-displaced fractures, those treated with other locking plates, and pathologic fractures. A total of 91 patients with displaced distal clavicle fractures underwent open reduction and internal fixation with HP. Cho classification was employed to differentiate cases with CC ligament rupture. Patient demographics, classifications, postoperative radiographs, distal fragment size, plate position, timing of implant removal, and complications, including SAO, were recorded. Results: Among the 91 patients, 32 were classified as Cho IIB, 43 as Cho IIC, and 16 as Cho IID. Ninety-one percent exhibited solid union before implant removal. The prevalence of SAO was 43.8%, 76.7%, and 62.5% in Cho IIB, IIC, and IID, respectively. Univariate analysis revealed a significant difference only in Cho classification (p = 0.014). Binary logistic regression identified Cho classification type IIC as the sole risk factor for SAO (p = 0.021; odds ratio, 4.48; 95% confidence interval, 1.56–12.87). Conclusions Cho type IIC fractures, characterized by CC ligament deficiency causing horizontal instability, demonstrated the highest SAO rate. In contrast, Neer type IIB fractures retained the trapezoid ligament, and Neer type V fractures had intact CC ligaments, resulting in lower SAO rates. Biomechanically, combining HPs with CC ligament reconstruction provided better structural stability than using HPs alone in treating Cho type IIC fractures.

Background/Aims: Ineffective esophageal motility (IEM) is common in patients with gastroesophageal reflux disease (GERD) and can be associated with poor esophageal contraction reserve on multiple rapid swallows. Alterations in the esophageal microbiome have been reported in GERD, but the relationship to presence or absence of contraction reserve in IEM patients has not been evaluated. We aim to investigate whether contraction reserve influences esophageal microbiome alterations in patients with GERD and IEM. Methods: We prospectively enrolled GERD patients with normal endoscopy and evaluated esophageal motility and contraction reserve with multiple rapid swallows during high-resolution manometry. The esophageal mucosa was biopsied for DNA extraction and 16S ribosomal RNA gene V3-V4 (Illumina)/full-length (Pacbio) amplicon sequencing analysis. Results: Among the 56 recruited patients, 20 had normal motility (NM), 19 had IEM with contraction reserve (IEM-R), and 17 had IEM without contraction reserve (IEM-NR). Esophageal microbiome analysis showed a significant decrease in microbial richness in patients with IEM-NR when compared to NM. The beta diversity revealed different microbiome profiles between patients with NM or IEM-R and IEM-NR (P = 0.037). Several esophageal bacterial taxa were characteristic in patients with IEM-NR, including reduced Prevotella spp.and Veillonella dispar, and enriched Fusobacterium nucleatum. In a microbiome-based random forest model for predicting IEM-NR, an area under the receiver operating characteristic curve of 0.81 was yielded. Conclusions In symptomatic GERD patients with normal endoscopic findings, the esophageal microbiome differs based on contraction reserve among IEM. Absent contraction reserve appears to alter the physiology and microbiota of the esophagus.