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: 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.

Male infertility (MI) and male sexual dysfunction (MSD) can often coexist together due to various interplay factors such as psychosexual, sociocultural and relationship dynamics. The presence of each form of MSD can adversely impact male reproduction and treatment strategies will need to be individualized based on patients’ factors, local expertise, and geographical socioeconomic status. The Asia Pacific Society of Sexual Medicine (APSSM) and the Asian Society of Men’s Health and Aging (ASMHA) aim to provide a consensus statement and practical set of clinical recommendations based on current evidence to guide clinicians in the management of MI and MSD within the Asia-Pacific (AP) region. A comprehensive, narrative review of the literature was performed to identify the various forms of MSD and their association with MI. MEDLINE and EMBASE databases were searched for the following English language articles under the following terms: “low libido”, “erectile dysfunction”, “ejaculatory dysfunction”, “premature ejaculation”, “retrograde ejaculation”, “delayed ejaculation”, “anejaculation”, and “orgasmic dysfunction” between January 2001 to June 2022 with emphasis on published guidelines endorsed by various organizations. This APSSM consensus committee panel evaluated and provided evidence-based recommendations on MI and clinically relevant MSD areas using a modified Delphi method by the panel and specific emphasis on locoregional socioeconomic-cultural issues relevant to the AP region. While variations exist in treatment strategies for managing MI and MSD due to geographical expertise, locoregional resources, and sociocultural factors, the panel agreed that comprehensive fertility evaluation with a multidisciplinary management approach to each MSD domain is recommended. It is important to address individual MI issues with an emphasis on improving spermatogenesis and facilitating reproductive avenues while at the same time, managing various MSD conditions with evidence-based treatments. All therapeutic options should be discussed and implemented based on the patient’s individual needs, beliefs and preferences while incorporating locoregional expertise and available resources.

In Singapore, 9.03 million doses of the mRNA COVID-19 vaccines by Pfizer-BioNTech and Moderna have been administered, and 4.46 million people are fully vaccinated. An additional 87,000 people have been vaccinated with vaccines in World Health Organization's Emergency Use Listing. The aim of this review is to explore the reported cardiac adverse events associated with different types of COVID-19 vaccines. A total of 42 studies that reported cardiac side effects after COVID-19 vaccination were included in this study. Reported COVID-19 vaccine-associated cardiac adverse events were mainly myocarditis and pericarditis, most commonly seen in adolescent and young adult male individuals after mRNA vaccination. Reports of other events such as acute myocardial infarction, arrhythmia and stress cardiomyopathy were rare. Outcomes of post-vaccine myocarditis and pericarditis were good. Given the good vaccine efficacy and the high number of cases of infection, hospitalisation and death that could potentially be prevented, COVID-19 vaccine remains of overall benefit, based on the current available data.
Adolescent ; Humans ; Male ; Young Adult ; COVID-19/prevention & control* ; COVID-19 Vaccines/adverse effects* ; Myocarditis/etiology* ; Pericarditis ; RNA, Messenger ; Vaccination/adverse effects*

Humans ; COVID-19/epidemiology* ; Singapore/epidemiology* ; Oximetry ; Hypoxia

BACKGROUND: Cervical cancer remains a major public health issue for the Uyghur women and other women living mainly in rural areas of Xinjiang. This study aims to investigate the distribution of human papillomavirus (HPV) infection and cervical cancer in rural areas of Xinjiang, China. METHODS: Cervical cancer screening was performed on rural women aged 35 to 64 years from Xinjiang, China in 2017 through gynecological examination, vaginal discharge smear microscopy, cytology, and HPV testing. If necessary, colposcopy and biopsy were performed on women with suspicious or abnormal screening results. RESULTS: Of the 216,754 women screened, 15,518 received HPV testing. The HPV-positive rate was 6.75% (1047/15,518). Compared with the age 35-44 years group, the odds ratios (ORs) of HPV positivity in the age 45-54 years and 55-64 years groups were 1.18 (95% confidence interval [CI]: 1.02-1.37) and 1.84 (95% CI: 1.53-2.21), respectively. Compared with women with primary or lower education level, the ORs for HPV infection rates of women with high school and college education or above were 1.37 (95% CI: 1.09-1.72) and 1.62 (95% CI: 1.23-2.12), respectively. Uyghur women were less likely to have HPV infection than Han women, with an OR (95% CI) of 0.78 (0.61-0.99). The most prevalent HPV types among Xinjiang women were HPV 16 (24.00%), HPV 33 (12.70%), and HPV 52 (11.80%). The detection rate of cervical intraepithelial neoplasia (CIN)2+ was 0.14% and the early diagnosis rate of cervical cancer was 85.91%. The detection rates of vaginitis and cervicitis were 19.28% and 21.32%, respectively. CONCLUSIONS The HPV infection rate in Xinjiang is low, but the detection rate of cervical cancer and precancerous lesions is higher than the national average level. Cervical cancer is a prominent public health problem in Xinjiang, especially in southern Xinjiang.
Adult ; Alphapapillomavirus ; China/epidemiology* ; Early Detection of Cancer ; Female ; Humans ; Middle Aged ; Papillomaviridae/genetics* ; Papillomavirus Infections/epidemiology* ; Rural Population ; Uterine Cervical Neoplasms/epidemiology*