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.

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: Particulate matter (PM) < 2.5 μm (PM METHODS: We obtained DNA methylation and exercise data of 496 participants (aged between 30 and 70 years) from the Taiwan Biobank (TWB) database. We also extracted PM RESULTS: DLEC1 methylation and PM CONCLUSIONS We found significant positive associations between PM
Adult ; Aged ; Air Pollutants/adverse effects* ; DNA Methylation/drug effects* ; Environmental Exposure/adverse effects* ; Exercise ; Female ; Humans ; Male ; Middle Aged ; Particulate Matter/adverse effects* ; Taiwan ; Tumor Suppressor Proteins/metabolism*

OBJECTIVE: To investigate the efficacy of frankincense and myrrha in the treatment of acute interstitial cystitis/painful bladder syndrome (IC/PBS). METHODS: The effects of frankincense and myrrha on the proliferation and migration of primary human urothelial cells (HUCs) were assessed in vitro. In the animal study, 48 virgin female rats were randomized into 4 groups (12 in each group): (1) control group (saline-injected control); (2) cyclophosphamide (CYP) group (intraperitoneal injected 150 mg/kg CYP); (3) CYP + pentosan polysulfate sodium group (orally received 50 mg/kg pentosan polysulfate sodium); and (4) CYP + frankincense and myrrha group [orally received frankincense (200 mg/kg) and myrrha (200 mg/kg)]. Rats orally received pentosan polysulfate sodium or frankincense and myrrha on day 1, 2, and 3. The experiments were performed on day 4. Pain and cystometry assessment behavior test were performed. Voiding interval values were assessed in rats under anesthesia. Finally, immunohistochemistry and Western blot were used to confirm the location and level, respectively, of cell junction-associated protein zonula occludens-2 (ZO-2) expression. RESULTS: Low dose frankincense and myrrha increased cell proliferation and migration in HUCs compared with control (P<0.05). Rats with acute IC/PBS rats exhibited lower voiding interval values, pain tolerance, and ZO-2 expression (P<0.05). Voiding interval values and pain tolerance were higher in the frankincense and myrrha group than CYP group (P<0.05). ZO-2 expression in the bladder was increased in the CYP + pentosan polysulfate and frankincense + myrrha groups compared with the CYP-induced acute IC/PBS group (P<0.05). CONCLUSION frankincense and myrrha modulate urothelial wound healing, which ameliorates typical features of acute IC/PBS in rats.