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 the clinical efficacy and safety of one-stage posterior lesion removal and internal spinal fixation in patients with lumbar Brucellosis spondylitis.Methods The clinical data of 24 patients admitted from October 2017 to October 2022 were retrospectively analyzed,2 patients were lost to follow-up at 10 months after surgery,at the final 22 cases were included in the study,including 13 males and 9 females with an average age of(52.00±6.89)years old,were treated with one-stage posterior lesion removal and internal spinal fixation.The operation time,intraoperative bleeding,follow-up time,ery-throcyte sedimentation rate(ESR)and C-reactive protein(CRP)before and after operation were recorded.The pain visual ana-logue scale(VAS),Oswestry disability index(ODI),the Japanese Orthopaedic Association(JOA)score for neurofunction,American Spinal Injury Association(ASIA)spinal cord injury grade and modified MacNab criteria were ussed to evaluate the efficacy.Results All patients were followed up from 12 to 30 months with an average of(17.41±4.45)months.The operation time was 70 to 155 min with an average of(1 16.59±24.32)min;the intraoperative bleeding volume was 120 to 520 ml with an average of(275.00±97.53)ml.CRP and ESR levels decreased more significantly at 1 week and at the final follow-up than pre-operative levels(P＜0.05).VAS,JOA score and ODI at 1 week and at the latest follow-up were more significantly improved than preoperative results(P＜0.05).There was no significant difference between ASIA preoperative and 1 week after operation(P＞0.05),and a significant difference between preoperative and last follow-up(P＜0.05).In the final follow-up,21 patients had ex-cellent efficacy,1 patient had fair,and there was no recurrence during the follow-up.Conclusion One-stage transpedicular le-sion removal and internal spinal fixation,with few incisions and short operation time,helps the recovery of neurological func-tion,and the prognosis meets the clinical requirements,which can effectively control Brucella spondylitis.

Child ; Humans ; East Asian People ; Quality of Life ; Students ; Child Abuse, Sexual ; Young Adult ; Resilience, Psychological

Humans ; Transcatheter Aortic Valve Replacement ; Aortic Valve/surgery* ; Heart Valve Prosthesis Implantation ; Aortic Valve Stenosis/surgery* ; Treatment Outcome ; Heart Valve Prosthesis

Objective: To investigate the expression of CD24 gene in human malignant pleural mesothelioma (MPM) cells and tissues, and evaluate its relationship with clinicopathological characteristics and clinical prognosis of MPM patients. Methods: In February 2021, UALCAN database was used to analyze the correlation between CD24 gene expression and clinicopathological characteristics in 87 cases of MPM patients. The TIMER 2.0 platform was used to explore the relationship between the expression of CD24 in MPM and tumor immune infiltrating cells. cBioportal online tool was used to analyze the correlation between CD24 and MPM tumor marker gene expression. RT-qPCR was used to analyze the expressions of CD24 gene in human normal pleural mesothelial cell lines LP9 and MPM cell lines NCI-H28 (epithelial type), NCI-H2052 (sarcoma type), and NCI-H2452 (biphasic mixed type). RT-qPCR was performed to detect the expressions of CD24 gene in 18 cases of MPM tissues and matched normal pleural tissues. The expression difference of CD24 protein in normal mesothelial tissue and MPM tissue was analyzed by immunohistochemistry. A Kaplan-Meier model was constructed to explore the influence of CD24 gene expression on the prognosis of MPM patients, and Cox regression analysis of prognostic factors in MPM patients was performed. Results: The CD24 gene expression without TP53 mutation MPM patients was significantly higher than that of patients in TP53 mutation (P<0.05). The expression of CD24 gene in MPM was positively correlated with B cells (r(s)=0.37, P<0.001). The expression of CD24 gene had a positive correlation with the expressions of thrombospondin 2 (THBS2) (r(s)=0.26, P<0.05), and had a negative correlation with the expression of epidermal growth factor containing fibulin like extracellular matrix protein 1 (EFEMP1), mesothelin (MSLN) and calbindin 2 (CALB2) (r(s)=-0.31, -0.52, -0.43, P<0.05). RT-qPCR showed that the expression level of CD24 gene in MPM cells (NCI-H28, NCI-H2052 and NCI-H2452) was significantly higher than that in normal pleural mesothelial LP9 cells. The expression level of CD24 gene in MPM tissues was significantly higher than that in matched normal pleural tissues (P<0.05). Immunohistochemistry showed that the expressions of CD24 protein in epithelial and sarcoma MPM tissues were higher than those of matched normal pleural tissues. Compared with low expression of CD24 gene, MPM patients with high expression of CD24 gene had lower overall survival (HR=2.100, 95%CI: 1.336-3.424, P<0.05) and disease-free survival (HR=1.800, 95%CI: 1.026-2.625, P<0.05). Cox multivariate analysis showed that compared with the biphasic mixed type, the epithelial type was a protective factor for the prognosis of MPM patients (HR=0.321, 95%CI: 0.172-0.623, P<0.001). Compared with low expression of CD24 gene, high expression of CD24 gene was an independent risk factor for the prognosis of MPM patients (HR=2.412, 95%CI: 1.291-4.492, P=0.006) . Conclusion: CD24 gene and protein are highly expressed in MPM tissues, and the high expression of CD24 gene suggests poor prognosis in MPM patients.
Humans ; Mesothelioma, Malignant ; Mesothelioma/diagnosis* ; Lung Neoplasms/genetics* ; Pleural Neoplasms/diagnosis* ; Prognosis ; Biomarkers, Tumor/analysis* ; Extracellular Matrix Proteins ; CD24 Antigen/genetics*

OBJECTIVE: To analyze the clinical effect of decompression and bone grafting on osteonecrosis of the femoral head(ONFH) at different sites of necrotic lesions. METHODS: A total of 105 patients with ARCOⅡstage ONFH admitted from January 2017 to December 2018 were retrospectively analyzed. There were 71 males and 34 females, with an average age of (55.20±10.98) years old. The mean course of all patients was(15.91±9.85) months. According to Japanese Inveatigation Committee (JIC) classification, all patients were divided into 4 types:17 cases of type A, 26 cases of type B, 33 cases of type C1 and 29 cases of type C2. All four groups were treated with decompression of the pulp core and bone grafting. Visual analogue scale(VAS) and Harris hip joint score were used before and at 3, 6, 12, and 24 months after the operation, and the collapse of the femoral head was observed by X-ray examination within 2 years. RESULTS: All 105 patients were successful on operation without complications, and the mean follow-up duration was (24.45±2.75) months. Harris score showed that there was no statistical difference among four groups before surgery and 3, 6 months after surgery (P>0.05);at 12 and 24 months after surgery, there were significant differences among all groups (P<0.01). There were significant differences in intragroup Harris scores at preoperative and postoperative time points among four groups (P<0.01). VAS showed that there was no statistical difference among four groups before and 3, 6 months after surgery (P>0.05);at 12 and 24 months after surgery, there were significant differences among all groups (P<0.01). There were significant differences in VAS at preoperative and postoperative time points among four groups (P<0.01). None of the patients in four groups had femoral head collapse before and 3, 6 months after surgery. At 12 months after operation, there were 3 cases of femoral head collapse in group C and 4 cases in group C2(P>0.05);At 24 months after operation, 1 case of femoral head collapse occurred in group B, 6 cases in group C1 and 8 cases in group C2(P<0.05). CONCLUSION Core decompression and bone grafting can improve the effect of ONFH and hip preservation. The effect of hip preservation for ONFH is closely related to the location of the osteonecrosis lesion, so the influence of the location of lesion on the effect of hip preservation should be considered in clinical treatment, so as to make better preoperative hip preservation plan.
Male ; Female ; Humans ; Adult ; Middle Aged ; Aged ; Retrospective Studies ; Femur Head Necrosis/diagnosis* ; Femur Head/surgery* ; Treatment Outcome ; Decompression, Surgical ; Bone Transplantation