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.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

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.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

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.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

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 The volume and cortical thickness of gray matter in patients with multiple sclerosis (MS) and neuromyelitis optica (NMO) were compared and analyzed by voxel⁃based morphometry (VBM) and surface⁃based morphometry (SBM), and the differences in the structural changes of gray matter in the two diseases were discussed. Methods A total of 21 MS patients, 16 NMO patients and 19 healthy controls were scanned by routine MRI sequence. The data were processed and analyzed by VBM and SBM method based on the statistical parameter tool SPM12 of Matlab2014a platform and the small tool CAT12 under SPM12. Results Compared with the normal control group (NC), after Gaussian random field (GRF) correction, the gray matter volume in MS group was significantly reduced in left superior occipital, left cuneus, left calcarine, left precuneus, left postcentral, left central paracentral lobule, right cuneus, left middle frontal, left superior frontal and left superior medial frontal (P<0. 05). After family wise error (FWE) correction, the thickness of left paracentral, left superiorfrontal and left precuneus cortex in MS group was significantly reduced (P<0. 05). Compared with the NC group, after GRF correction, the gray matter volume in the left postcentral, left precentral, left inferior parietal, right precentral and right middle frontal in NMO group was significantly increased (P<0. 05). In NMO group, the volume of gray matter in left middle occipital, left superior occipital, left inferior temporal, right middle occipital, left superior frontal orbital, right middle cingulum, left anterior cingulum, right angular and left precuneus were significantly decreased (P<0. 05). Brain regions showed no significant differences in cortical thickness between NMO groups after FWE correction. Compared with the NMO group, after GRF correction, the gray matter volume in the right fusiform and right middle frontal in MS group was increased significantly(P<0. 05). In MS group, the gray matter volume of left thalamus, left pallidum, left precentral, left middle frontal, left middle temporal, right pallidum, left inferior parietal and right superior parietal were significantly decreased (P<0. 05). After FWE correction, the thickness of left inferiorparietal, left superiorparietal, left supramarginal, left paracentral, left superiorfrontal and left precuneus cortex in MS group decreased significantly (P<0. 05). Conclusion The atrophy of brain gray matter structure in MS patients mainly involves the left parietal region, while NMO patients are not sensitive to the change of brain gray matter structure. The significant difference in brain gray matter volume between MS patients and NMO patients is mainly located in the deep cerebral nucleus mass.

Most patients with differentiated thyroid cancer have a good prognosis after radioiodine-131 therapy,but a small number of patients are insensitive to radioiodine-131 therapy and even continue to develop disease.At present,some targeted drugs can improve progression-free survival in patients with radioactive iodine-refractory differentiated thyroid cancer(RAIR-DTC),such as sorafenib and levatinib,have been approved for the treatment of RAIR-DTC.However,due to the presence of primary and acquired drug resistance,drug efficacy in these patients is unsatisfactory.This review introduces the acquired drug resistance mechanism of sorafenib in the regulation of mitogen-activated protein kinase(MAPK)and phosphatidylinositol-3-kinase(PI3K)pathways and proposes related treatment strategies,in order to provide a reference for similar drug resistance mechanism of sorafenib and effective treatment of RAIR-DTC.