Previous studies have identified critical roles of IL-27 in the pathological mechanisms of sepsis, and blockade of IL-27 may be a promising alternative therapy for sepsis. The purpose of this study was to evaluate the clinical relevance of IL-27 genetic polymorphisms in sepsis and to further characterize their effect on IL-27 expression and inflammatory processes following sepsis. A total of 885 septic patients and 1101 healthy controls were enrolled and genotyped for IL-27 genetic variants (rs153109/−964A > G and rs17855750/2905 T > G). Quantitative real-time PCR and enzyme-linked immunosorbent assays were performed to detect IL-27 expression and cytokine production. The effect of the rs153109 polymorphism on IL-27 promoter activity was evaluated using a luciferase reporter assay, and THP-1 cell apoptosis was calculated using an annexin V apoptosis assay. No significant differences in the genotype/allele frequencies were observed between patients with sepsis and healthy controls, suggesting that these two IL-27 polymorphisms may not influence susceptibility to sepsis. The -964AA genotype was overrepresented in patients with severe sepsis/septic shock relative to patients with the sepsis subtype, and the A allele was significantly associated with 28-mortality in sepsis. Patients carrying the -964AA genotype exhibited significantly higher expression levels of IL-27 than the GA/GG genotype carriers. The results of an in vitro (lipopolysaccharide (LPS))-stimulated experiment showed that this sepsis-associated high-risk AA genotype significantly increased IL-27 levels and enhanced TNF-α and IL-1β production in the peripheral blood mononuclear cells (PBMCs) upon exposure to LPS in vitro. Furthermore, luciferase reporter assays indicated that the high-risk -964A allele resulted in increased promoter activities compared to the non-risk allele in THP-1 and 293 T cells. Additionally, IL-27 treatment significantly enhanced TNF-α and IL-6 secretion and apoptosis of THP-1 cells upon LPS stimulation. These results provided evidence that the IL-27 -964A > G polymorphism functionally enhanced IL-27 expression and promoted sepsis-induced inflammatory responses, which ultimately resulted in promoting the progression of sepsis and poor prognosis.

Objective: Genome-wide non-invasive prenatal testing (GW-NIPT) for prenatal screening has been widely implemented. However, the related clinical data is still insufficient. Here, we evaluated the clinical performance of GW-NIPT as a first-tier screening test for detecting fetal aneuploidy and copy number variation (CNV). Methods: The study included 59,877 pregnant women who underwent GW-NIPT at Shenzhen Baoan Women’s and Children’s Hospital, China, from November 2017 to May 2021. NIPT was performed on the BGISEQ-500 platform. Fetal karyotype analysis, chromosomal microarray analysis (CMA) and fluorescence in situ hybridization were used for invasive diagnostic procedures, and postnatal outcomes were collected. Results: Among 59,877 pregnant women who underwent GW-NIPT, 59,771 were successfully tested. Of these, 499 (0.83%) were identified with 504 high-risk fetal chromosomal abnormalities, including 5 cases each carrying two distinct abnormalities. Follow-up analysis demonstrated that GW-NIPT sensitivity exceeded 97% for fetal aneuploidies and was 63.6% for CNV (≥5 Mb). The positive predictive values for T21, T18, T13, sex chromosome aneuploidy, rare autosomal aneuploidy, and CNV (≥5 Mb) were calculated as 83.1%, 25.8%, 10.3%, 51.9%, 2.0%, and 33.9%, respectively. For confirmed fetal mosaicism, the detection rate of NIPT was 70.6%, which was consistent with that of CMA (70.6%). Conclusions: GW-NIPT has high sensitivity in screening fetal aneuploidy and moderate clinical utility in detecting CNV and fetal mosaicism, demonstrating that GW-NIPT holds significant application value in current and future prenatal screening procedures.

Background: Neonatal early-onset sepsis (EOS), occurring within 72 h of birth, is a major cause of neonatal morbidity and mortality worldwide. The urgent need for rapid, accurate diagnosis is underscored by the condition's severity. Current diagnostic methods are hampered by non-specific clinical signs, leading to underdiagnosis or overtreatment. This highlights a crucial gap in neonatal care. Methods: This retrospective study analyzed data from 1613 full-term pregnant women at a single center in Shenzhen, China (2022), including 69 EOS cases. Ten machine learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost) were developed using maternal prenatal predictors. Data preprocessing involved imputation, standardization, and Lasso feature selection. Models were evaluated using 5-fold cross-validation, and the SMOTE technique was applied to address class imbalance. Results: Among ten machine learning models, XGBoost and Random Forest demonstrated the highest discriminative ability (AUC=0.87). While a default threshold yielded low sensitivity, a threshold optimized for a clinical screening objective (0.04) achieved a sensitivity of 92.8% and a specificity of 73.1%. Key predictors identified included maternal temperature and inflammatory markers. Conclusion: This study demonstrates that machine learning models based on maternal factors have the potential to serve as high-sensitivity screening tools for EOS. Tuning the decision threshold is a critical step to maximize clinical utility, which involves a necessary trade-off between sensitivity and specificity. This approach provides a framework for developing and evaluating clinically-oriented prediction models to improve neonatal care.