Objective: Postmenopausal osteoporosis (PMOP), a chronic systemic metabolic disease prevalent in middle-aged and elderly women, heavily relies on bone mineral density (BMD) measurement as the diagnostic indicator. In this study, we investigated serum microRNAs (miRNAs) as a possible screening tool for PMOP. Methods: This investigation recruited 83 eligible participants from 795 community-dwelling postmenopausal women between June 2020 and August 2021. The miRNA expression profiles in the serum of PMOP patients were evaluated via miRNA microarray (six PMOP patients and four postmenopausal women without osteoporosis (n-PMOP) as controls). Subsequently, results were verified in independent sample sets (47 PMOP patients and 26 n-PMOP controls) using quantitative real-time PCR. In addition, the target genes and main functions of the differentially expressed miRNAs were explored by bioinformatics analysis. Results: Four highly expressed miRNAs in the serum of patients (hsa-miR-144-5p, hsa-miR-506-3p, hsa-miR-8068, and hsa-miR-6851-3p) showed acceptable disease-independent discrimination performance (area under the curve range: 0.747–0.902) in the training set and verification set, outperforming traditional bone turnover markers. Among four key miRNAs, hsa-miR-144-5p is the only one that can simultaneously predict changes in BMD in lumbar spine 1–4, total hip, and femoral neck (β = −0.265, p = 0.022; β = −0.301, p = 0.005; and β = −0.324, p = 0.003, respectively). Bioinformatics analysis suggested that the differentially expressed miRNAs were targeted mainly to YY1, VIM, and YWHAE genes, which are extensively involved in bone metabolism processes. Conclusions: Bone-metabolism-related serum miRNAs, such as hsa-miR-144-5p, hsa-miR-506-3p, hsa-miR-8068, and hsa-miR-6851-3p, can be used as novel biomarkers for PMOP diagnosis independent of radiological findings and traditional bone turnover markers. Further study of these miRNAs and their target genes may provide new insights into the epigenetic regulatory mechanisms of the onset and progression of the disease.

Original Article A new haematological model for the diagnosis and prognosis of severe community-acquired pneumonia: a single-center retrospective study Xiaohe Zheng, Zena Huang, Dong Wang, Shiyao Pan, Yating Zhao, Jin Li, Jianqing Zhang, Manman Ye, Shihong Zhang

Background: Urine testing as a routine screening programme, abnormal test results can be suggestive to clinicians but can sometimes be overlooked, and the establishment of a diagnostic model can better assist clinicians in identifying potential problems. BLD (blood), LEU (leukocyte), PRO (protein) and GLU (glucose) are the four most important parameters in urine testing, and the accuracy of their results is a key concern for clinicians, so it is essential to verify the accuracy of their results. In this study, we evaluated the analytical and clinical performance of Mindray’s automatic urine dry chemistry analyzer, the UA-5600 (Hereinafter referred to as the (UA-5600), and the test strips configured with the instrument, and developed a machine-learning (ML) model for kidney disease screening from the results of 11 parameters output from the UA-5600 with the aim of detecting abnormal urine test results. Methods: Urine samples from outpatients and inpatients at The First Affiliated Hospital of Sun Yat-sen University were collected from August to September 2022 to evaluate the performance of the Mindray UA-5600 dry chemistry analyzer and test strips. The evaluation of the UA-5600 and its test strips focused on the agreement of the urine BLD and LEU readings with the RBC (red blood cell) and WBC (white blood cell) counts obtained by the Mindray EH-2090 urine formed element analyzer. We also compared the PRO and GLU readings with the results of the Mindray BS-2800M biochemistry analyzer. Urine samples from outpatients and inpatients were retrospectively analysed and grouped according to LIS diagnosis. Additionally, eight ML models for kidney disease screening were developed using 11 parameters measured by the UA-5600. And the model was validated by the validation set. Results: The UA-5600 had an 89.55% concordance rate for BLD and a 91.04% concordance rate for LEU compared to the EH-2090 analyzer. When benchmarked against the BS-2800M, the concordance rates for PRO and GLU were 94.14% and 95.20%, respectively. A total of 1,691 samples were used for the construction of the ML models, of which 346 patients (135 males and 211 females, age range: 18 to 98 years) diagnosed with renal disease, and 1,345 patients (397 males and 948 females, age range: 18 to 92 years) with non-renal disease diagnosed with other conditions. Notably, the Naïve Bayes (NB) model, which was built from the UA-5600 parameters, demonstrated superior predictive capabilities for renal disease, with an area under the receiver operating characteristic curve of 0.9470, a sensitivity of 0.7767, and a specificity of 0.9457. Conclusions: The Mindray UA-5600 demonstrates robust detection abilities for both BLD and LEU, and its results for PRO and GLU align closely with those obtained from the chemistry analyzer. The NB model has a good screening ability and shows promise as an effective screening tool.