Gastrin-releasing peptide precursor (ProGRP) is a bioactive precursor of GRP and might play an important role as an emerging tumor marker in early cancer diagnosis. It might also be abnormal in the nonmalignant disease and renal function abnormalities. The present study was undertaken to investigate the changes of ProGRP levels in patients with kidney injuries, especially with chronic kidney disease (CKD), determine the upper reference intervals and clinical diagnostic value of ProGRP in CKD, and thus help oncologists in interpreting ProGRP levels and making clinical judgments of malignances. 676 individuals were enrolled in this cross-sectional study and divided into five groups: healthy control (n=194), CKD (n=272), nephrotic syndrome (NS) (n=137), antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) (n=41), and urinary tract infection (UTI) (n=32). A total of 27 features including age, gender, and 25 laboratory markers were analyzed. Machine learning algorithms were built for the diagnostic models of CKD. Statistical analysis was performed by R software. It was shown that serum ProGRP level in CKD was significantly higher than that in healthy controls, UTI and NS (P < 0.01). The upper reference limit of ProGRP was 188.42 pg/ml for CKD, 245.40 pg/ml for CKD IV-V, and 97.25 pg/ml for NS. Compared with the healthy control, the level of serum ProGRP in CKD stages II, III, IV-V was significantly increased and elevated progressively with CKD grade (P < 0.01). Random Forest (RF) model works best among 4 building machine learning algorithms. 5 vital indicators, ProGRP, estimated glomerular filtration rate (eGFR), urea, albumin (ALB), and direct bilirubin (DBIL), were selected to establish RF model for diagnosing CKD with an area under the curve (AUC) of 0.96 (95% confidence interval [CI]: 0.94-0.97) and high sensitivity (0.89) and specificity (0.92). This study demonstrates that the level of ProGRP in patients with CKD, nephrotic syndrome or AAV, was significantly higher than that in the healthy population. The machine learning model of ProGRP with DBIL, eGFR, ALB, and urea, could provide good clinical value for CKD evaluation.

Objective: Respiratory tract infections (RTIs) are imposing a great economic burden, morbidity, and mortality on the healthcare system. Eleven common respiratory pathogens, based on age of onset, clinical setting, season and calendar year during and after the COVID-19 pandemic, were analyzed to optimize clinical diagnosis and prevention. Methods: In this retrospective cross-sectional study, respiratory pathogen testing results of 9882 samples from RTI patients from January 2020 to December 2025 were included to statistically analyze the positivity, distribution and trend of change. Multiplex PCR combined with capillary electrophoresis was used to simultaneously detect 11 respiratory targets. Results: The overall positive detection rate was 24.71%, with the top three pathogens being human rhinovirus (HRV) (6.20%), influenza A virus (InfA) (4.28%), and respiratory syncytial virus (RSV) (4.13%). The patients aged under 18 had the highest detection rates (≤6 years: 39.80%; 7 to 17 years: 37.95%). HRV, RSV and Mycoplasma pneumoniae (MP) were the primary pathogens among pediatric patients, while InfA was predominant in adults (including those >65 years). The 11 pathogens were most common in emergency department patients (29.14%), followed by outpatient patients (28.57%). InfA was the most predominant virus, followed by HRV and RSV, in the intensive care unit, emergency department, and outpatient settings. The detection rate was highest in autumn (33.22%), followed by winter (26.61%). MP had the highest rate in autumn, and InfA was the most frequent in winter. During the COVID-19 pandemic (2020–2022), the respiratory pathogen spectrum was dominated by HRV, RSV, and human parainfluenza virus (HPIV). And the prevalent pathogens shifted to InfA, MP and HRV during 2023–2025. Co-detections were observed in 8.03% of the positive cases, and 82.65% occurred in patients aged < 18 years. The dominant co-detections were HRV + MP, HRV + RSV and HRV + HPIV. Conclusions: The distribution of respiratory pathogens exhibited specific variations based on age, clinical setting, season, and calendar year, suggesting that differentiated prevention should be developed for different patients.