Purpose: The optimal sequencing of targeted therapies and the role of primary tumor resection (PTR) in KRAS wild-type metastatic colorectal cancer (mCRC) remain unclear. This study compared survival outcomes in patients treated with first-line cetuximab plus FOLFIRI (folinic acid, 5-fluorouracil, and irinotecan) versus bevacizumab plus FOLFIRI, followed by second-line oxaliplatin-based chemotherapy and later-line trifluridine/tipiracil or regorafenib. Methods: This retrospective cohort study used Taiwan’s National Health Insurance Research Database and the Taiwan Cancer Registry. Patients diagnosed with mCRC between 2013 and 2019 were included if they received first-line cetuximab or bevacizumab plus FOLFIRI, followed by later-line trifluridine/tipiracil or regorafenib. Patients were stratified by PTR status. Primary endpoints were overall survival and survival during trifluridine/tipiracil or regorafenib treatment. Secondary endpoints included time to treatment discontinuation (TTD) and TTD during trifluridine/tipiracil or regorafenib therapy. Stabilized inverse probability of treatment weighting was used for adjustment. Results: Among 559 patients, 278 were assigned to the non-PTR group and 281 to the PTR group. In the non-PTR group, the cetuximab cohort demonstrated significantly longer survival during trifluridine/tipiracil or regorafenib therapy (6.2 months vs. 4.9 months; hazard ratio [HR], 0.72) and longer TTD1 (the interval between initiation of first-line therapy and the start of second-line chemotherapy; 11.8 months vs. 9.5 months; HR, 0.67) than the bevacizumab cohort. Survival differences between regimens were less pronounced among patients who underwent PTR. Conclusion First-line cetuximab plus FOLFIRI may confer a survival advantage over bevacizumab in patients with KRAS wild-type mCRC without PTR, including during later-line therapy with trifluridine/tipiracil or regorafenib, whereas bevacizumab appears to provide more consistent benefits in those with PTR.

Background/Aims: Pancreatic stenting reduces post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP) and aids in cannulation in difficult cases. However, conservative endoscopists may stop at this step, resulting in suboptimal outcomes. This study assessed the efficacy of transpancreatic precut sphincterotomy (TPS) as a rescue procedure following pancreatic stenting. Methods: Between March 2013 and November 2018, 82 patients underwent pancreatic stenting at our institution prior to successful biliary cannulation. TPS was introduced in April 2016, and patients were divided into Before TPS and After TPS groups. The outcomes included cannulation success, PEP incidence, and predictors of TPS conversion. Results: There were 43 and 39 patients in the Before TPS and After TPS groups, respectively. Twenty-two patients (56.4%) underwent conversion to TPS in the After TPS group. The After TPS group had a higher bile duct cannulation rate (89.7% vs. 72.1%) than the Before TPS group, but this difference was not statistically significant (p=0.054). Multivariate analysis showed that age >50 years (odds ratio [OR], 0.181; p=0.021) and being in the After TPS group (OR, 0.712; p=0.039) were independently associated with reduced PEP risk. Haraldsson Type 2 and Type 4 papillae carried a relatively high TPS conversion rate. Conclusions A stepwise cannulation strategy that incorporates TPS after pancreatic stenting minimizes the need for advanced techniques and improves PEP outcomes.

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.

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: Emerging evidence suggests that air pollution exposure may increase the risk of Parkinson’s disease (PD). We aimed to investigate the association between exposure to fine particulate matter (PM2.5) and the risk of incident PD nationwide. Methods: We utilized data from the Taiwan National Health Insurance Research Database, which is spatiotemporally linked with air quality data from the Taiwan Environmental Protection Administration website. The study population consisted of participants who were followed from the index date (January 1, 2005) until the occurrence of PD or the end of the study period (December 31, 2017). Participants who were diagnosed with PD before the index date were excluded. To evaluate the association between exposure to PM2.5 and incident PD risk, we employed Cox regression to estimate the hazard ratio and 95% confidence interval (CI). Results: A total of 454,583 participants were included, with a mean (standard deviation) age of 63.1 (9.9) years and a male proportion of 50%. Over a mean follow-up period of 11.1 (3.6) years, 4% of the participants (n = 18,862) developed PD. We observed a significant positive association between PM2.5 exposure and the risk of PD, with a hazard ratio of 1.22 (95% CI, 1.20–1.23) per interquartile range increase in exposure (10.17 μg/m3) when adjusting for both SO2 and NO2. Conclusion We provide further evidence of an association between PM2.5 exposure and the risk of PD. These findings underscore the urgent need for public health policies aimed at reducing ambient air pollution and its potential impact on PD.

Background: and Purpose This study aimed to investigate early changes in interstitial fluid (ISF) flow in patients with severe carotid stenosis after carotid angioplasty and stenting (CAS). Methods: We prospectively recruited participants with carotid stenosis ≥80% undergoing CAS at our institute between October 2019 and March 2023. Magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI), and the Mini-Mental State Examination (MMSE) were performed 3 days before CAS. MRI with DTI and MMSE were conducted within 24 hours and 2 months after CAS, respectively. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was calculated from the DTI data to determine the ISF status. Increments were defined as the ratio of the difference between post- and preprocedural values to preprocedural values. Results: In total, 102 participants (age: 67.1±8.9 years; stenosis: 89.5%±5.7%) with longitudinal data were evaluated. The DTI-ALPS index increased after CAS (0.85±0.15; 0.85 [0.22] vs. 0.86±0.14; 0.86 [0.21]; P=0.022), as did the MMSE score (25.9±3.7; 24.0 [4.0] vs. 26.9±3.4; 26.0 [3.0]; P<0.001). Positive correlations between increments in the DTI-ALPS index and MMSE score were found in all patients (rs=0.468; P<0.001). Conclusion An increased 24-hour post-CAS DTI-ALPS index suggests early improvement in ISF flow efficiency. The positive correlation between the 24-hour DTI-ALPS index and 2-month MMSE score increments suggests that early ISF flow improvement may contribute to long-term cognitive improvement after CAS.