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 explore medium and long term efficacy of oblique lateral interbody fusion(OLIF)in treating lumbar specific infection.Methods From October 2017 to January 2021,24 patients with lumbar specific infection were treated by OLIF combined with vertebral screw internal fixation,including 15 males and 9 females,aged from 27 to 61 years old with an average of(43.0±15.0)years old;the courses of disease ranged from 6 to 24 months with an average of(14.0±7.0)months;7 patients with L2-L3,12 patients with L3-L4 and 5 patients with L4-L5;19 patients with tuberculosis infection and 5 patients with brucella infection.The amount of intraoperative blood loss,operative time and complications were recorded,and erythro-cyte sedimentation rate(ESR),C-reactive protein(CRP),visual analogue scale(VAS),Japanese Orthopaedic Association(JOA)score and American Spinal Injury Association(ASIA)rating were compared before and one month after opertaion.Re-sults All patients were followed up from 9 to 24 months with an average of(13.0±6.0)months.Operative time was(132.5±21.4)min,and intraoperative blood loss was(227.3±43.1)ml.ESR and CRP were decreased from(82.34±18.62)mmol·h-1 and(53.08±21.84)mg·L-1 before operation to(33.52±17.31)mmol·h-1 and(15.48±8.36)mg·L-1 at one month after opera-tion,respectively(P＜0.05).VAS was decreased from(7.52±1.36)before opertaion to(1.74±0.87)at one month after opera-tion(P＜0.05).JOA was increased from(17.86±3.95)before operation to(24.72±3.19)at one month after operation(P＜0.05).Four patients had neurological symptoms before operation,and were classified to grade D according to ASIA classifica-tion,who were recovered to grade E at 1 month after operation.One patient was suffered from psoas major muscle injury after operation,and returned to normal at 3 weeks.One patient was suffered from abdominal distension and difficulty in defecation,and relieved after gastrointestinal decompression and enema.No complications such as abdominal organ injury and poor wound healing occurred in all patients.Conclusion OLIF combined with vertebral screw internal fixation is a new minimally invasive surgical method for the treatment of lumbar specific infection,especially the lesion located on the middle lumbar vertebra.It has advantages of less trauma,short operation time,less blood loss,convenient operation,complete removal of the lesion,safety and effectiveness,and has good medium-and long-term efficacy for lumbar specific infection.

Objective To investigate the application effect of F4.8 visualized puncture assisted microchannel percutaneous nephroli-thotomy(mPCNL)in the treatment of patients with calyceal diverticular calculus.Methods A total of 86 patients with calyceal diverticular calculus from January 2020 to January 2023 in Hefei First People's Hospital were selected and divided into the observation group and the conventional group according to random number table method,with 43 cases in each group.Patients in the conventional group were given mPCNL,while patients in the observation group were given F4.8 visualized puncture assisted mPCNL.The stone clearance rate immediately after surgery and 4 weeks after surgery,surgery and postoperative recovery,complications,as well as pre-and post-operative trauma stress indicators[serum cortisol(Cor),norepinephrine(NE),C-reactive protein(CRP),interleukin-6(IL-6)],interlobar renal artery hemodynamic indicators[peak systolic velocity(Vmax),end diastolic velocity(EDV),resistance index(RI)],and renal function indicators[urine kidney injury molecule-1(KIM-1),24 h urinary protein quantitation(24 h Upro),blood urea nitrogen(BUN),serum creatinine(Scr)]were compared between the two groups.Results The stone clearance rate immediately after surgery in the observation group was higher than that in the conventional group,the intraoperative blood loss was less than that in the conventional group,and the operation time,postoperative ambulation time,indwelling nephrostomy tube time,and hospitalization time were shorter than those in the conventional group,and the differences were statistically significant(P<0.05).The serum levels of Cor,NE,CRP and IL-6 1 day and 3 days after surgery in the two groups were higher than those before operation(P<0.05),while the serum levels of Cor,NE,CRP and IL-6 1 day and 3 days after surgery in the observation group were lower than those in the conventional group(P<0.05).The Vmax and EDV of renal interlobar artery 1 day after surgery in the observation group decreased compared with those before operation(P<0.05),while RI increased(P<0.05).The Vmax and EDV of renal interlobar artery 1 day and 3 days after surgery in the conventional group decreased compared with those before operation(P<0.05),while RI increased(P<0.05).The Vmax and EDV of renal interlobar artery 1 day and 3 days after surgery in the observation group were higher than those in the conventional group,while RI was lower than that in the conventional group,and the differences were statistically significant(P<0.05).The levels of KIM-1,24 h Upro,BUN,and Scr of urine 7 days after surgery in both groups were lower than those before surgery,and those in the observation group were lower than those in the conventional group,with statistically significant differences(P<0.05).There was no statistically significant difference in the incidence of complication between the two groups(P>0.05).Conclusion F4.8 visualized puncture assisted mPCNL for the treatment of calyceal diverticular calculus can improve surgical effect,reduce surgical trauma stress,and promote renal function recovery.