Purpose: To assess the effectiveness of the prostate health index (PHI) in patients with no index lesions on multiparametric magnetic resonance imaging (mpMRI) or with negative findings on past prostate biopsy if there was an index lesion on mpMRI. Materials and Methods: Patients without an index lesion on MRI or with a negative result on combined biopsy for index lesions were assessed. Patients who underwent transperineal mapping biopsy among those suspected of having prostate cancer (PCa) due to persistently elevated prostate-specific antigen (PSA) levels were analyzed. Results: Of the 291 patients, 82 (28.2%) were diagnosed with PCa. Sixty-five of 291 patients had negative finding in previous combined biopsy. In total, 226 patients did not have any index lesions. The mean age of the PCa group was 64.33±8.88 years and that of the non-cancer group was 59.88±10.26 years (p<0.001). The PHI was 46.75±28.22 in the PCa group and 37.74±17.37 in the noncancer group (p=0.001), and the prostate volume was 41.52±15.77 mL in the PCa group and 50.78±23.97 mL in the non-cancer group (p=0.001). In multivariate analysis, age (odds ratio [OR] 1.096, p<0.001), PHI (OR 1.021, p=0.005), and prostate volume (OR 0.954, p<0.001) were identified as significant factors for PCa detection. The optimal cutoff value of the PHI for PCa detection was 44.6 and the PHI density (PHID) was 0.88. Conclusions In patients with elevated PSA levels but no index lesions on mpMRI or negative biopsy findings, PHI and PHID demonstrated significant potential for improving PCa detection.

Background: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing. Methods: We developed novel urine test strips and an AI algorithm for image capture.Sample images from the Chungnam National University Sejong Hospital were collected to train a k-nearest neighbor classification algorithm to read the strips. A mobile application was developed for image capturing and processing. We assessed the accuracy, sensitivity, specificity, and ROC area under the curve for 10 parameters. Results: In total, 2,612 urine test strip images were collected. The AI algorithm demonstrated 98.7% accuracy in detecting urinary nitrite and 97.3% accuracy in detecting urinary glucose. The sensitivity and specificity were high for most parameters. However, this system could not reliably determine the specific gravity. The optimal time for capturing the test strip results was 75 secs after dipping. Conclusions The AI-based program accurately interpreted urine test strips using smartphone cameras, offering an accessible and efficient method for urinalysis. This system can be used for immediate analysis and remote testing. Further research is warranted to refine test parameters such as specific gravity to enhance accuracy and reliability.

Background: It is well known that a large number of patients with diabetes also have dyslipidemia, which significantly increases the risk of cardiovascular disease (CVD). This study aimed to evaluate the efficacy and safety of combination drugs consisting of metformin and atorvastatin, widely used as therapeutic agents for diabetes and dyslipidemia. Methods: This randomized, double-blind, placebo-controlled, parallel-group and phase III multicenter study included adults with glycosylated hemoglobin (HbA1c) levels >7.0% and <10.0%, low-density lipoprotein cholesterol (LDL-C) >100 and <250 mg/dL. One hundred eighty-five eligible subjects were randomized to the combination group (metformin+atorvastatin), metformin group (metformin+atorvastatin placebo), and atorvastatin group (atorvastatin+metformin placebo). The primary efficacy endpoints were the percent changes in HbA1c and LDL-C levels from baseline at the end of the treatment. Results: After 16 weeks of treatment compared to baseline, HbA1c showed a significant difference of 0.94% compared to the atorvastatin group in the combination group (0.35% vs. −0.58%, respectively; P<0.0001), whereas the proportion of patients with increased HbA1c was also 62% and 15%, respectively, showing a significant difference (P<0.001). The combination group also showed a significant decrease in LDL-C levels compared to the metformin group (−55.20% vs. −7.69%, P<0.001) without previously unknown adverse drug events. Conclusion The addition of atorvastatin to metformin improved HbA1c and LDL-C levels to a significant extent compared to metformin or atorvastatin alone in diabetes and dyslipidemia patients. This study also suggested metformin’s preventive effect on the glucose-elevating potential of atorvastatin in patients with type 2 diabetes mellitus and dyslipidemia, insufficiently controlled with exercise and diet. Metformin and atorvastatin combination might be an effective treatment in reducing the CVD risk in patients with both diabetes and dyslipidemia because of its lowering effect on LDL-C and glucose.

This study aimed to assess the prognostic role of body mass index (BMI) in patients with metastatic renal cell carcinoma (mRCC) treated with first-line immune checkpoint inhibitor (ICI)-based therapy. We searched for relevant studies in the MEDLINE, Embase, and Cochrane Library databases. The initial search yielded 599 records, of which seven articles (2,517 patients) were selected for analysis. Patients with a high BMI had a favorable overall survival (OS) based on hazard ratio (HR) (crude HR 0.69, 95% confidence interval [CI] 0.57–0.83, p<0.0001; adjusted (a)HR 0.75, 95% CI 0.59–0.95, p=0.02), but not relative risk (RR 0.88, 95% CI 0.67–1.16, p=0.37). In the subgroup analysis, patients with a high BMI had better OS in the ICI with tyrosine kinase inhibitor (TKI) subgroup (aHR 0.71, 95% CI 0.55–0.92, p=0.01), while no significant difference was found in the ICI-only subgroup (aHR 1.02, 95% CI 0.56–1.87, p=0.95). Adjusted statistics for progression-free survival (PFS) were assessable in predominantly ICI-only studies and demonstrated a favorable outcome for patients with a low BMI (aHR 1.67, 95% CI 1.14–2.45, p=0.01). In conclusion, the impact of high BMI varies depending on the treatment type, exhibiting a favorable correlation with OS within ICI with TKI subgroup, but indicating an adverse association with PFS in the ICI-only subgroup. Further research is needed to clarify the influence of BMI by stratifying patients into ICI-only and ICI with TKI treatment to provide more insights.