Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

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: The FAVOR (Comparison of Quantitative Flow Ratio Guided and Angiography Guided Percutaneous Intervention in Patients with Coronary Artery Disease) III China trial demonstrated that percutaneous coronary intervention (PCI) lesion selection using quantitative flow ratio (QFR) measurement, a novel angiography-based approach for estimating fractional flow reserve, improved two-year clinical outcomes compared with standard angiography guidance. This study aimed to assess the cost-effectiveness of QFR-guided PCI from the perspective of the current Chinese healthcare system. METHODS: This study is a pre-specified analysis of the FAVOR III China trial, which included 3825 patients randomized between December 25, 2018, and January 19, 2020, from 26 centers in China. Patients with stable or unstable angina pectoris or those ≥72 hours post-myocardial infarction who had at least one lesion with a diameter stenosis between 50% and 90% in a coronary artery with a ≥2.5 mm reference vessel diameter by visual assessment were randomized to a QFR-guided strategy or an angiography-guided strategy with 1:1 ratio. During the two-year follow-up, data were collected on clinical outcomes, quality-adjusted life-years (QALYs), estimated costs of index procedure hospitalization, outpatient cardiovascular medication use, and rehospitalization due to major adverse cardiac and cerebrovascular events (MACCE). The primary analysis calculated the incremental cost-effectiveness ratio (ICER) as the cost per MACCE avoided. An ICER of ¥10,000/MACCE event avoided was considered economically attractive in China. RESULTS: At two years, the QFR-guided group demonstrated a reduced rate of MACCE compared to the angiography-guided group (10.8% vs . 14.7%, P <0.01). Total two-year costs were similar between the groups (¥50,803 ± 21,121 vs . ¥50,685 ± 23,495, P = 0.87). The ICER for the QFR-guided strategy was ¥3055 per MACCE avoided, and the probability of QFR being economically attractive was 64% at a willingness-to-pay threshold of ¥10,000/MACCE avoided. Sensitivity analysis showed that QFR-guided PCI would become cost-saving if the cost of QFR were below ¥3682 (current cost: ¥3800). Cost-utility analysis yielded an ICER of ¥56,163 per QALY gained, with a 53% probability of being cost-effective at a willingness-to-pay threshold of ¥85,000 per QALY gained. CONCLUSION: In patients undergoing PCI, a QFR-guided strategy appears economically attractive compared to angiographic guidance from the perspective of the Chinese healthcare system. TRIAL REGISTRATION ClinicalTrials.gov , NCT03656848.
Humans ; Cost-Benefit Analysis ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Angiography/methods* ; Middle Aged ; Aged ; Coronary Artery Disease/surgery* ; Quality-Adjusted Life Years ; Fractional Flow Reserve, Myocardial/physiology*

Objective: Chest pain, a common emergency department presentation, requires rapid evaluation. The current gold standard of care is serial troponin measurements from blood draws along with an electrocardiogram and clinical presentation. However, an optical technology-based noninvasive wearable device, the Infrasensor, which can rapidly and transcutaneously assesses cardiac troponin I (cTnI), was recently developed. We aimed to perform a pilot study to evaluate the performance of the Infrasensor in cTnI defined cohorts. Methods: This was a 10-hospital prospective observational study in healthy US subjects with a normal cTnI level and in patients with an elevated local cTnI level. Healthy subjects as determined by a negative questionnaire and bloodwork underwent 3-minute Infrasensor measurement and levels of high-sensitivity cTnI (hs-cTnI), n-terminal pro-B-type natriuretic peptide (NTproBNP), creatinine, and glycosylated hemoglobin (HbA1c) were evaluated from blood. Patients with elevated cTnI had the same Infrasensor and blood sample measurements. Using a fivefold cross-validation technique, cTnI-based binary classification models that did and did not include age were trained on 80% and validated on 20% of the dataset (n=168; elevated hs-cTnI equally partitioned into five nearly equally-sized subsets ). Results: Of 840 patients, 727 (87.5%) had normal (nonelevated) cTnI levels while the remaining 113 had elevated cTnI. Median age was 61 years (interquartile range, 52–71 years) for the elevated cohort and 48 years (interquartile range, 32–57 years) for the nonelevated. Overall, 416 (50.5%) were female, with 33 of 113 (29.2%) in the elevated cohort and 383 of 727 (52.7%) in the nonelevated cohort. Overall, the sensitivity, specificity, negative and positive predictive values of the Infrasensor for identifying elevated cTnI were 0.90, 0.70, 0.98 and 0.48, respectively, with a C-statistic of 0.90 (95% confidence interval, 0.89–0.99). Conclusion The Infrasensor accurately identifies elevated cTnI within 3 minutes of application.

Background: and Purpose Tenecteplase is a thrombolytic agent with pharmacological advantages over alteplase and has been shown to be noninferior to alteplase for acute ischemic stroke in randomized trials. However, evidence pertaining to the safety and efficacy of tenecteplase in patients from different ethnic groups is lacking. The aim of this systematic review and metaanalysis was to investigate ethnicity-specific differences in the safety and efficacy of tenecteplase versus alteplase in patients with acute ischemic stroke. Methods: Following an International Prospective Register of Systematic Reviews (PROSPERO)- registered protocol (CRD42023475038), three authors conducted a systematic review of the PubMed/MEDLINE, Embase, Cochrane Library, and CINAHL databases for articles comparing the use of tenecteplase with any thrombolytic agent in patients with acute ischemic stroke up to November 20, 2023. The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Two independent authors extracted data onto a standardized data collection sheet. A pairwise meta-analysis was conducted in risk ratios (RR). Results: From 34 studies (59,601 participants), the rate of complete recanalization was significantly higher (P<0.01) in Asian (RR: 1.91, 95% confidence interval [CI]: 1.30 to 2.80) versus Caucasian patients (RR: 0.99, 95% CI: 0.87 to 1.14). However, Asian patients (RR: 1.18, 95% CI: 0.87 to 1.62) had significantly higher (P=0.01) rates of mortality compared with Caucasian patients (RR: 1.10, 95% CI: 1.00 to 1.22). Caucasian patients were also more likely to attain a modified Rankin Scale (mRS) score of 0 to 2 at follow-up (RR: 1.14, 95% CI, 1.10 to 1.19) compared with Asian (RR: 1.00, 95% CI, 0.95 to 1.05) patients. There was no significant difference in the rate of symptomatic intracranial hemorrhage (P=0.20) and any intracranial hemorrhage (P=0.83) between Asian and Caucasian patients. Conclusion Tenecteplase was associated with significantly higher rates of complete recanalization in Asian patients compared with Caucasian patients. However, tenecteplase was associated with higher rates of mortality and lower rates of mRS 0 to 2 in Asian patients compared with Caucasian patients. It may be beneficial to study the variations in response to tenecteplase among patients of different ethnic groups in large prospective cohort studies.