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During the Coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is universally susceptible to all types of populations. In addition to the elderly and children becoming the groups of great concern, pregnant women carrying new lives need to be even more alert to SARS-CoV-2 infection. Studies have shown that pregnant women infected with SARS-CoV-2 can lead to brain damage and post-birth psychiatric disorders in offspring. It has been widely recognized that SARS-CoV-2 can affect the development of the fetal nervous system directly or indirectly. Pregnant women are recommended to mitigate the effects of COVID-19 on the fetus through vaccination, nutritional supplements, and psychological support. This review summarizes the possible mechanisms of the nervous system effects of SARS-CoV-2 infection on their offspring during the pregnancy and analyzes the available prophylactic and treatment strategies to improve the prognosis of fetal-related neuropsychiatric diseases after birth.

AimsThis study aims to analyse the factors associated with prognosis in hospitalized patients with heart failure, particularly the role of depressive symptoms, and to develop a prediction model for depressive symptoms based on clinical characteristics in hospitalized patients with heart failure. Methods and results Baseline information was collected at admission, and patients were followed up after discharge. The endpoint events were being hospitalized for heart failure or all-cause death. Depressive symptoms were evaluated and defined via the Patient Health Questionnaire (PHQ)-2 and PHQ-9. The bidirectional elimination was used to screen independent predictors of heart failure with depression symptoms. The least absolute shrinkage and selection operator (LASSO) optimized the predictor variables, and the prediction model was constructed. The model was internally validated by the bootstrap sampling method (Bootstrap), and its performance was assessed by discrimination and calibration. The mean age of patients with heart failure was 69.43 ± 12.15 years, and the proportion of males was 66.67%. The prevalence of depressive symptoms in hospitalized patients with heart failure was 46.83%, and the prevalence of moderate/severe depressive symptoms was 11.62%. Eighty cases (30.30%) were readmitted for heart failure, and 13 cases (4.92%) were all-cause deaths. Depressive symptoms (HR = 2.43, 95% CI: 1.55-3.80) and the PHQ-9 score (HR = 1.11, 95% CI: 1.06-1.16) were both independent risk factors for endpoint events (P < 0.001). For heart failure patients combined with depressive symptoms, obesity (OR = 0.27, 95% CI: 0.09-0.77, P = 0.015), N-terminal brain natriuretic peptide precursor (NT-proBNP) level (lnNT-proBNP: OR = 1.55, 95% CI: 1.20-2.01, P < 0.001) and red blood cell distribution width (RDW) (OR = 1.26, 95% CI: 1.08-1.47, P = 0.004) were the independent factors. Six variables, including cardiovascular disease hospitalization history, obesity, renal insufficiency, NT-proBNP level, neutrophil ratio and RDW, were included to construct the prediction model. The area under the curve (AUC) was 0.730 in the original data, and the calibration curve was approximately distributed along the reference line in Bootstrap (500 resamplings), indicating the high level of discrimination and calibration of this model. Conclusions Depressive symptoms and the PHQ-9 score are both independent risk factors for the prognosis of hospitalized patients with heart failure. In hospitalized patients with heart failure, the risk prediction model developed in this study has good predictive power for depressive symptoms.

Aims The aim of this study is to analyze the sarcopenia index (SI), based on serum creatinine to cystatin C ratio, in heart failure (HF) patients, especially HF with preserved ejection fraction (HFpEF) patients, and to develop a prediction model for the diagnosis of HFpEF. Methods There were 229 HF patients and 73 healthy controls (HCs) enrolled in this study. Binary logistic regression model was used to analyze the influence factors of HFpEF. A prediction model was constructed and optimized based on the least absolute shrinkage and selection operator (LASSO), displayed by nomogram and verified internally by the bootstrap sampling method (Bootstrap). Results SI was significantly different between the HF and HC groups (67.9 ± 13.0 vs. 98.6 ± 31.5). Atrial fibrillation (AF) (OR 6.336, 95% CI 2.511-15.988, P < 0.001) and SI (OR0.948, 95% CI 0.914-0.983, P = 0.004) were independently associated with HFpEF. Nine indicators, including SI, were included in the prediction model. The area under the curve (AUC) was 0.902. In Bootstrap (500 resamples), the calibration curve was distributed approximately along the reference line. The prediction models with the additional features of AF and SI showed a significantly higher value of AUC (0.902 vs. 0.855, P < 0.01). Conclusions Low SI is an independent risk factor for hospitalized HF patients, especially HFpEF patients. HFpEF was better identified using this diagnostic prediction model, and the diagnostic efficacy of the model was significantly improved by two features, including SI and AF.

BackgroundCardiomyocyte death is an important pathophysiological basis for ischemic cardiomyopathy (ICM). Many studies have suggested that ferroptosis is a key link in the development of ICM. We performed bioinformatics analysis and experiment validation to explore the potential ferroptosis-related genes and immune infiltration of ICM.MethodsWe downloaded the datasets of ICM from the Gene Expression Omnibus database and analyzed the ferroptosis-related differentially expressed genes (DEGs). Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and protein–protein interaction network were performed to analyze ferroptosis-related DEGs. Gene Set Enrichment Analysis was used to evaluate the gene enrichment signaling pathway of ferroptosis-related genes in ICM. Then, we explored the immune landscape of patients with ICM. Finally, the RNA expression of the top five ferroptosis-related DEGs was validated in blood samples from patients with ICM and healthy controls using qRT-PCR.ResultsOverall, 42 ferroptosis-related DEGs (17 upregulated and 25 downregulated genes) were identified. Functional enrichment analysis indicated several enriched terms related to ferroptosis and the immune pathway. Immunological analysis suggested that the immune microenvironment in patients with ICM is altered. The immune checkpoint-related genes (PDCD1LG2, LAG3, and TIGIT) were overexpressed in ICM. The qRT-PCR results showed that the expression levels of IL6, JUN, STAT3, and ATM in patients with ICM and healthy controls were consistent with the bioinformatics analysis results from the mRNA microarray.ConclusionOur study showed significant differences in ferroptosis-related genes and functional pathway between ICM patients and healthy controls. We also provided insight into the landscape of immune cells and the expression of immune checkpoints in patients with ICM. This study provides a new road for future investigation of the pathogenesis and treatment of ICM.