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Background. Preeclampsia (PE), which has a high incidence rate worldwide, is a potentially dangerous syndrome to pregnant women and newborns. However, the exact mechanism of its pathogenesis is still unclear. In this study, we used bioinformatics analysis to identify hub genes, establish a logistic model, and study immune cell infiltration to clarify the physiopathogenesis of PE. Methods. We downloaded the GSE75010 and GSE10588 datasets from the GEO database and performed weighted gene coexpression network analysis (WGCNA) as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The online search tool for the retrieval of interacting genes and Cytoscape software were used to identify hub genes, which were then used to establish a logistic model. We also analyzed immune cell infiltration. Finally, we verified the expression of the genes included in the predictive model via RT-PCR. Results. A total of 100 and 212 differently expressed genes were identified in the GSE75010 and GSE10588 datasets, respectively, and after overlapping with WGCNA results, 17 genes were identified. KEGG and GO analyses further indicated the involvement of these genes in bioprocesses, such as gonadotropin secretion, immune cell infiltration, and the SMAD and MAPK pathways. Additionally, protein-protein interaction network analysis identified 10 hub genes, six (FLT1, FLNB, FSTL3, INHA, TREM1, and SLCO4A1) of which were used to establish a logistic model for PE. RT-PCR analysis also confirmed that, except FSTL3, these genes were upregulated in PE. Our results also indicated that macrophages played the most important role in immune cell infiltration in PE. Conclusion. This study identified 10 hub genes in PE and used 6 of them to establish a logistic model and also analyzed immune cell infiltration. These findings may enhance the understanding of PE and enable the identification of potential therapeutic targets for PE.

Maternal sepsis results in poor outcomes such as fetal or maternal death. The incidence and mortality rates of maternal sepsis vary in different places because of differences in economic development, race and medical conditions. Identifying the clinical features and determining possible mechanisms for avoiding morbidity and preventing poor outcomes would benefit committed patients. Therefore, this was an epidemiological study at a maternity transfer center in Southeast China that aimed to identify local disease features of maternal sepsis. To investigate the incidence and risk factors associated with maternal sepsis and its progression to severe sepsis in a large population-based birth cohort. This local epidemiological study was conducted in at a tertiary care center in Guangzhou, China, from 2015 to 2019. A total of 74,969 pregnant women experiencing childbirth were included in this study; Of these, 74 patients with maternal sepsis were diagnosed according to the sepsis criterion, and 118 patients without sepsis in the same period were selected randomly as the control group to study possible reasons for postpartum sepsis. This retrospective analysis covered the entire period from the first trimester to puerperium. Clinical data were collected using the hospital's electronic medical record system. Multivariate logistic regression was used to analyze risk factors for maternal sepsis. The incidences of maternal sepsis, the maternal mortality, and the fetal mortality were 0.099%, 0.004%, and 0.007%, respectively. Septic shock was associated with a higher severity of illness. All poor outcomes (maternal or fetal death) occurred during pregnancy. Postpartum sepsis had the longest onset period, and was associated with premature rupture of fetal membranes and preeclampsia. Sepsis is an important cause of both maternal and fetal mortality. Herein, we describe an epidemiological study that evaluated the incidence, development, and prognosis of local maternal sepsis. Furthermore, the characteristics of maternal sepsis are likely due to unknown pathological mechanisms, and patients would benefit from identifying more effective treatments for maternal sepsis.Abbreviations: APACHE II = acute physiology and chronic health evaluation II, BMI = body mass index, ICU = intensive care unit, IVF = in vitro fertilization, MSCs = mesenchymal stem cells, omSOFA = obstetrically modified SOFA failure assessment, PROM = premature rupture of membrane.

Background Pregnant women with pulmonary hypertension (PH) have higher mortality rates and poor foetal/neonatal outcomes. Tools to assess these risk factors are not well established. Methods Predictive and prognostic nomograms were constructed using data from a “Development” cohort of 420 pregnant patients with PH, recorded between January 2009 and December 2018. Logistic regression analysis established models to predict the probability of adverse maternal and foetal/neonatal events and overall survival by Cox analysis. An independent “Validation” cohort comprised data of 273 consecutive patients assessed from January 2019 until May 2022. Nomogram performance was evaluated internally and implemented with online software to increase the ease of use. Results Type I respiratory failure, New York Heart Association functional class, N-terminal pro-brain natriuretic peptide $$\ge$$ ≥ 1400 ng/L, arrhythmia, and eclampsia with pre-existing hypertension were independent risk factors for maternal mortality or heart failure. Type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, New York Heart Association functional class, and N-terminal pro-brain natriuretic peptide $$\ge$$ ≥ 1400 ng/L were independent predictors of pulmonary hypertension survival during pregnancy. For foetal/neonatal adverse clinical events, type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, parity, platelet count, fibrinogen, and left ventricular systolic diameter were important predictors. Nomogram application for the Development and Validation cohorts showed good discrimination and calibration; decision curve analysis demonstrated their clinical utility. Conclusions The nomogram and its online software can be used to analyse individual mortality, heart failure risk, overall survival prediction, and adverse foetal/neonatal clinical events, which may be useful to facilitate early intervention and better survival rates.

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