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.

Objective: Multiple cohort studies have investigated the potential link between anesthesia and dementia. However, mixed findings necessitate closer examination. This study aimed to investigate the association between anesthesia exposure and the incidence of dementia, considering different anesthesia types and anesthetic agents. Methods: This nationwide cohort study utilized data from the South Korean Health Insurance Review and Assessment Service database, covering 62,541 participants, to investigate the correlation between anesthesia exposure and dementia incidence. Results: Results revealed an increased risk of dementia in individuals who underwent general (hazard ratio [HR], 1.318;95% confidence interval [CI], 1.061−1.637) or regional/local anesthesia (HR, 2.097; 95% CI, 1.887−2.329) compared to those who did not. However, combined general and regional/local anesthesia did not significantly increase dementia risk (HR, 1.097; 95% CI, 0.937−1.284). Notably, individual anesthetic agents exhibited varying risks; desflurane and midazolam showed increased risks, whereas propofol showed no significant difference. Conclusion This study provides unique insights into the nuanced relationship between anesthesia, individual anesthetic agents, and the incidence of dementia. While confirming a general association between anesthesia exposure and dementia risk, this study also emphasizes the importance of considering specific agents. These findings under-score the need for careful evaluation and long-term cognitive monitoring after anesthesia.

Objective: Multiple cohort studies have investigated the potential link between anesthesia and dementia. However, mixed findings necessitate closer examination. This study aimed to investigate the association between anesthesia exposure and the incidence of dementia, considering different anesthesia types and anesthetic agents. Methods: This nationwide cohort study utilized data from the South Korean Health Insurance Review and Assessment Service database, covering 62,541 participants, to investigate the correlation between anesthesia exposure and dementia incidence. Results: Results revealed an increased risk of dementia in individuals who underwent general (hazard ratio [HR], 1.318;95% confidence interval [CI], 1.061−1.637) or regional/local anesthesia (HR, 2.097; 95% CI, 1.887−2.329) compared to those who did not. However, combined general and regional/local anesthesia did not significantly increase dementia risk (HR, 1.097; 95% CI, 0.937−1.284). Notably, individual anesthetic agents exhibited varying risks; desflurane and midazolam showed increased risks, whereas propofol showed no significant difference. Conclusion This study provides unique insights into the nuanced relationship between anesthesia, individual anesthetic agents, and the incidence of dementia. While confirming a general association between anesthesia exposure and dementia risk, this study also emphasizes the importance of considering specific agents. These findings under-score the need for careful evaluation and long-term cognitive monitoring after anesthesia.

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.

Objective: Multiple cohort studies have investigated the potential link between anesthesia and dementia. However, mixed findings necessitate closer examination. This study aimed to investigate the association between anesthesia exposure and the incidence of dementia, considering different anesthesia types and anesthetic agents. Methods: This nationwide cohort study utilized data from the South Korean Health Insurance Review and Assessment Service database, covering 62,541 participants, to investigate the correlation between anesthesia exposure and dementia incidence. Results: Results revealed an increased risk of dementia in individuals who underwent general (hazard ratio [HR], 1.318;95% confidence interval [CI], 1.061−1.637) or regional/local anesthesia (HR, 2.097; 95% CI, 1.887−2.329) compared to those who did not. However, combined general and regional/local anesthesia did not significantly increase dementia risk (HR, 1.097; 95% CI, 0.937−1.284). Notably, individual anesthetic agents exhibited varying risks; desflurane and midazolam showed increased risks, whereas propofol showed no significant difference. Conclusion This study provides unique insights into the nuanced relationship between anesthesia, individual anesthetic agents, and the incidence of dementia. While confirming a general association between anesthesia exposure and dementia risk, this study also emphasizes the importance of considering specific agents. These findings under-score the need for careful evaluation and long-term cognitive monitoring after anesthesia.

Objective: Multiple cohort studies have investigated the potential link between anesthesia and dementia. However, mixed findings necessitate closer examination. This study aimed to investigate the association between anesthesia exposure and the incidence of dementia, considering different anesthesia types and anesthetic agents. Methods: This nationwide cohort study utilized data from the South Korean Health Insurance Review and Assessment Service database, covering 62,541 participants, to investigate the correlation between anesthesia exposure and dementia incidence. Results: Results revealed an increased risk of dementia in individuals who underwent general (hazard ratio [HR], 1.318;95% confidence interval [CI], 1.061−1.637) or regional/local anesthesia (HR, 2.097; 95% CI, 1.887−2.329) compared to those who did not. However, combined general and regional/local anesthesia did not significantly increase dementia risk (HR, 1.097; 95% CI, 0.937−1.284). Notably, individual anesthetic agents exhibited varying risks; desflurane and midazolam showed increased risks, whereas propofol showed no significant difference. Conclusion This study provides unique insights into the nuanced relationship between anesthesia, individual anesthetic agents, and the incidence of dementia. While confirming a general association between anesthesia exposure and dementia risk, this study also emphasizes the importance of considering specific agents. These findings under-score the need for careful evaluation and long-term cognitive monitoring after anesthesia.

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: The number of patients presenting with vaccination-related cardiovascular symptoms after receiving mRNA vaccines (mRNA-VRCS) is increasing. We investigated the incidence of vaccine-related adverse events (VAEs), including myocarditis and pericarditis, in patients with mRNA-VRCS after receiving BNT162b2-Pfizer-BioNTech and mRNA-1273-Moderna vaccines. Materials and Methods: We retrospectively collected data on patients presenting with mRNA-VRCS who visited the outpatient clinic of two tertiary medical centers. Clinical characteristics, laboratory findings, echocardiographic findings, and electrocardiographic findings were evaluated. VAE was defined as myocarditis or pericarditis in patients after mRNA vaccination. Clinical outcomes during short-term follow-up, including emergency room (ER) visit, hospitalization, or death, were also assessed among the patients. Results: A total of 952 patients presenting with mRNA-VRCS were included in this study, with 89.7% receiving Pfizer-BioNTech and 10.3% receiving Moderna vaccines. The mean duration from vaccination to symptom was 5.6±7.5 days. VAEs, including acute myocarditis and acute pericarditis, were confirmed in 11 (1.2%) and 10 (1.1%) patients, respectively. The VAE group showed higher rates of dyspnea, echocardiography changes, and ST-T segment changes. During the short-term follow-up period of 3 months, the VAE group showed a higher hospitalization rate compared to the control group; there was no significant difference in ER visit (p=0.320) or mortality rates (p>0.999). Conclusion Amongst the patients who experienced mRNA-VRCS, the total incidence of VAEs, including acute myocarditis and pericarditis, was 2.2%. Patients with VAEs showed higher rates of dyspnea, echocardiographic changes, and ST-T segment changes compared to those without VAEs. With or without the cardiovascular events, the prognosis in patients with mRNA-VRCS was favorable.