Background: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

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.

Background: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

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.

Background: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

Objectives: The effectiveness of combining romosozumab (ROMO) with denosumab (Dmab) remains uncertain.We compare the six-month effects of Dmab plus monthly ROMO versus Dmab plus daily teriparatide (TPTD) on bone mineral density (BMD) in treatment-naïve postmenopausal women with osteoporosis. Methods: This retrospective cohort study analyzed 26 treatment-naïve postmenopausal women with primary osteoporosis. Participants received either a monthly regimen of ROMO and Dmab (N = 14) or a daily regimen of TPTD plus Dmab (N = 12). BMD at the lumbar spine, total hip, and femoral neck was measured at baseline, 3 months, and 6 months by dual-energy X-ray absorptiometry. Serum levels of C-terminal telopeptide (CTX) and procollagen type I N-terminal propeptide (P1NP) were assessed at the same intervals. Results: Both regimens significantly improved lumbar spine BMD at 6 months (ROMO + Dmab: +9.75%; TPTD +Dmab: +7.42%). Improvements in total hip and femoral neck BMD were modest and similar between groups (~2%). Serum CTX and P1NP were significantly suppressed in both groups at 3 months, but P1NP suppression waned in the TPTD + Dmab group by 6 months. No statistically significant differences in BMD or marker changes were detected between the two regimens. Conclusions Both combination therapies effectively improve lumbar spine BMD over 6 months. The ROMO +Dmab regimen yielded numerically greater increases with fewer injections.

Objective: Lumbar foraminal stenosis (FS) is a prevalent cause of radiculopathy, affecting 8%–11% of the population, particularly in individuals with degenerative lumbar scoliosis (DLS). Although minimally invasive techniques such as uniportal endoscopic decompression have emerged, standardized guidelines for FS treatment in DLS patients remain lacking. This study evaluated the clinical outcomes of uniportal endoscopic decompression in treating FS in DLS patients, focusing on lesion location and surgical outcomes. Methods: This retrospective study included 22 patients with FS and DLS treated between September 2019 and November 2023. Patients were categorized based on whether the lesion was located at the fractional curve or major scoliotic curve. All underwent uniportal endoscopic decompression. Clinical outcomes were assessed using the MacNab criteria over a minimum 12-month follow-up. Predictors of poor outcomes and recurrence, such as the disc wedge angle (DWA) to Cobb angle (CA) ratio, were analyzed using the chi-square test, Mann-Whitney U-test, and the Pearson correlation test. Results: This study included 22 patients with FS and DLS. The fractional curve group showed better clinical outcomes (81.3% “good” or “excellent”) compared to the major scoliotic curve group (33.4%), although the difference was not statistically significant (p=0.102). However, revision rates were significantly higher in the major scoliotic curve group (66.7% vs. 12.5%, p=0.011). The DWA/CA ratio and fusion timing showed no significant associations within the major scoliotic curve group. Conclusion Uniportal endoscopic decompression was effective for FS in the fractional curve, providing sustained symptom relief. However, for FS in the major scoliotic curve, particularly with a high DWA/CA ratio, fusion surgery may be necessary to ensure long-term stability. A comprehensive preoperative evaluation, including the DWA/CA ratio and whole-spine balance assessment, is crucial for optimizing surgical outcomes.