Introduction: Diabetic nephropathy is a major complication of type 1 diabetes mellitus (T1DM) and a leading cause of endstage renal disease. This study aims to correlate long-term glycemic control and disease duration with renal decline, measured via estimated glomerular filtration rate (eGFR) and urinalysis proteinuria. Methodology: A retrospective cohort study was conducted among 15 patients with T1DM at Hospital Bentong. Independent variables were 3-year mean glycated hemoglobin A1c (HbA1c) and duration of diabetes. Dependent variables included the change in eGFR derived from baseline and latest serum creatinine (calculated using the CKD-EPI 2021 equation), and the shift in urine protein severity, mapped to an ordinal scale, over a 3-year follow-up. Multivariate linear regression and Spearman’s rank correlation were utilized for analysis. Results: The cohort consisted of 5 male patients and 10 female patients with a mean initial presentation HbA1c of 11.4%, a mean 3-year HbA1c of 9.15%, and a median disease duration of 11 years. Baseline eGFR was preserved (mean 117.15 mL/ min/1.73 m²). Regression analysis revealed that for every 1% increase in mean HbA1c, eGFR declined significantly by 9.6 mL/min/1.73 m² (p = 0.002). Furthermore, each additional year of disease duration independently reduced eGFR by 1.1 units (p = 0.008). Spearman validation confirmed the strong negative correlation between HbA1c and eGFR (Rho = -0.664, p = 0.006). Conversely, the correlation between 3-year mean HbA1c and worsening proteinuria was not statistically significant (Rho = -0.067, p = 0.827), and patient gender did not significantly impact progression (p = 0.129). Conclusion Poor long-term glycemic control and longer duration of diabetes mellitus are strong independent predictors of decline in functional glomerular filtration in T1DM. While worsening proteinuria may not be detected during a short 3-year window using a standard urinalysis (UFEME), a more sensitive test such as Urine Albumin-to-Creatinine Ratio may better detect early structural kidney damage.
Diabetes Mellitus, Type 1 ; Proteinuria

Introduction: The 2021 IDF-DAR risk calculator had been previously evaluated in multiple studies and subsequently widely accepted and applied in clinical practice as a practical standardized tool for patient risk stratification. Recently updated, the 2026 IDF-DAR Risk calculator enables a more individualized, evidence-related evaluation of patientrelated and disease-related risk factors, incorporating modern diabetes technologies, including continuous glucose monitoring (CGM), automated insulin delivery (AID) systems, and advanced insulin formulations to enhance risk stratification. This tool allows medical professionals to tailor Ramadan practices based on overall factors toward promoting safe fasting. Methodology: This prospective multicentre observational study recruited adults with Type 1 and Type 2 diabetes attending public hospitals nationwide. People with diabetes (PwD) intending to perform Ramadan fasting were invited to participate and assessed using the 2026 IDF-DAR Risk Calculator in the 6-week pre-Ramadan period between 30th January and 19th March 2026. Results: A total of 458 PwD were evaluated and stratified into low (15.7%), moderate (41%), and high risk (43.3%) categories. Most participants had Type 2 diabetes (83.6%), with 60.3% having a disease duration exceeding 10 years and 43% exhibiting poor glycemic control (hemoglobin A1c >9%). Insulin therapy was used by 76.4% of participants, including two individuals with Type 1 diabetes using AID systems. Most participants reported no recent hypoglycemia (76.4%), 81.0% performed glucose monitoring, and 3.3% used CGM. Severe comorbidities were uncommon, with 1.1% having unstable macrovascular disease and 4.4% advanced chronic kidney disease (estimated glomerular filtration rate <30). Notably, 72.2% received structured Ramadan education. Conclusion Majority of PwD attending tertiary diabetes clinics were in the moderate- to high-risk category and intended to fast despite medical advice against fasting in some cases. Although most participants were on insulin therapy, hypoglycemia was low in the pre-Ramadan period. Integration of modern technologies, advanced insulin therapies, and structured education may support safer fasting practices.
Risk Assessment ; Diabetes Mellitus ; Hospitals ; Fasting

Objective To systematically evaluate the efficacy and safety of single-port thoracoscopic surgery (SPTS) in the treatment of spontaneous pneumothorax. Methods Computer searches were conducted in PubMed, The Cochrane Library, Web of Science, Embase, CNKI, WanFang, and the Chinese Medical Association databases to collect randomized controlled trials (RCTs) and cohort studies on the comparison of efficacy and safety of SPTS and three-port thoracoscopic surgery (TPTS) for the treatment of spontaneous pneumothorax from their inception to March 2024. The Cochrane RCT bias risk assessment tool and the Newcastle-Ottawa Scale (NOS) were used to assess the quality of RCTs and cohort studies, respectively. Meta-analysis was performed using RevMan 5.4.1. Results A total of 68 studies were included, comprising 23 RCTs and 45 cohort studies with a total of 5403 patients. The NOS scores of the cohort studies were 7-8 points. Meta-analysis results showed that compared with TPTS, SPTS had less intraoperative blood loss [SMD=−1.58, 95%CI (−1.93, −1.22), P<0.001], shorter postoperative hospital stay [SMD=−1.05, 95%CI (−1.29, −0.82), P<0.001], shorter postoperative drainage tube placement time [SMD=−0.75, 95%CI (−1.00, −0.50), P<0.001], fewer postoperative complications [OR=0.34, 95%CI (0.26, 0.45), P<0.001], fewer postoperative recurrences [OR=0.48, 95%CI (0.32, 0.72), P<0.001], and less pain at 24, 48, and 72 h postoperatively [SMD=−1.71, 95%CI (−2.13, −1.30), P<0.001; SMD=−1.70, 95%CI (−2.35, −1.06), P<0.001; SMD=−1.72, 95%CI (−2.16, −1.29), P<0.001]. Conclusion SPTS is safe and effective in the treatment of spontaneous pneumothorax with high clinical value and can be further promoted in clinical practice. Considering the limitations in the number and quality of included studies, researches with larger sample sizes and higher quality are needed to validate the above conclusions.

Cardiac surgery is associated with high risks, significant trauma, and long recovery periods. With advances in cardiac surgery techniques, the mortality rate and incidence of complications have been steadily decreasing. Patient-reported outcomes have gradually become an important area of research in postoperative recovery of cardiac surgery. The use of patient-reported outcome measures (PROMs) in this field helps to reflect patients' physiological, psychological, and social functioning during recovery, and provides scientific evidence for clinical interventions, which may further improve prognosis and enhance patient recovery experience. This paper reviews the dimensions of PROMs in the field of cardiac surgery recovery, the current status of existing PROMs scales, and the progress of their application, while also identifies the limitations of the existing tools. Finally, it explores future research directions for PROMs in cardiac surgery patients.

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.