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Abstract Background Preeclampsia is a severe obstetric disorder that significantly affects the maternal and neonatal peri-partum safety and long-term quality of life. However, there is limited research exploring the common mechanisms and potential clinical significance between early-onset preeclampsia and full-term preeclampsia from an immunological perspective. Methods In this study, data analysis was conducted. Initially, immune-related co-expressed genes involving both subtypes of preeclampsia were identified through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were further employed to investigate the shared pathways regulated by immune-related genes. Binary logistic regression identified co-expressed genes with diagnostic value for preeclampsia, and a diagnostic model was constructed. Gene Set Enrichment Analysis (GSEA) predicted the potential biological functions of the selected genes. Lasso and Cox regression analyses identified genes closely associated with gestational duration, and a risk score model was established. A 4-gene feature, immune-related gene model for predicting the risk of preterm birth in preeclamptic pregnant women, was developed and validated through qPCR experiments. Immune cell infiltration analysis determined differences in immune cell infiltration between the two subtypes of preeclampsia. Results This study identified 4 immune-related co-expressed genes (CXCR6, PIK3CB, IL1RAP, and OSMR). Additionally, diagnostic and preterm birth risk prediction models for preeclampsia were constructed based on these genes. GSEA analysis suggested the involvement of these genes in the regulation of galactose metabolism, notch signaling pathway, and RIG-I like receptor signaling pathway. Immune pathway analysis indicated that the activation of T cell co-inhibition could be a potential intervention target for immunotherapy in early-onset preeclampsia. Conclusion Our study provides promising insights into immunotherapy and mechanistic research for preeclampsia, discovering novel diagnostic and intervention biomarkers, and offering personalized diagnostic tools for preeclampsia.

Background: Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications, particularly in visual recognition tasks such as image and video analyses. There is a growing interest in applying this technology to diverse applications in medical image analysis. Automated three-dimensional Breast Ultrasound is a vital tool for detecting breast cancer, and computer-assisted diagnosis software, developed based on deep learning, can effectively assist radiologists in diagnosis. However, the network model is prone to overfitting during training, owing to challenges such as insufficient training data. This study attempts to solve the problem caused by small datasets and improve model detection performance. Methods: We propose a breast cancer detection framework based on deep learning (a transfer learning method based on cross-organ cancer detection) and a contrastive learning method based on breast imaging reporting and data systems (BI-RADS). Results: When using cross organ transfer learning and BIRADS based contrastive learning, the average sensitivity of the model increased by a maximum of 16.05%. Conclusion: Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced, and contrastive learning method based on BI-RADS can improve the detection performance of the model.

Abstract Background Pre-eclampsia (PE) is one of the leading causes of maternal and fetal morbidity/mortality during pregnancy, and alpha-2-macroglobulin (A2M) is associated with inflammatory signaling; however, the pathophysiological mechanism by which A2M is involved in PE development is not yet understood. Methods Human placenta samples, serum, and corresponding clinical data of the participants were collected to study the pathophysiologic mechanism underlying PE. Pregnant Sprague–Dawley rats were intravenously injected with an adenovirus vector carrying A2M via the tail vein on gestational day (GD) 8.5. Human umbilical artery smooth muscle cells (HUASMCs), human umbilical vein endothelial cells (HUVECs), and HTR-8/SVneo cells were transfected with A2M-expressing adenovirus vectors. Results In this study, we demonstrated that A2M levels were significantly increased in PE patient serum, uterine spiral arteries, and feto-placental vasculature. The A2M-overexpression rat model closely mimicked the characteristics of PE (i.e., hypertension in mid-to-late gestation, histological and ultrastructural signs of renal damage, proteinuria, and fetal growth restriction). Compared to the normal group, A2M overexpression significantly enhanced uterine artery vascular resistance and impaired uterine spiral artery remodeling in both pregnant women with early-onset PE and in pregnant rats. We found that A2M overexpression was positively associated with HUASMC proliferation and negatively correlated with cell apoptosis. In addition, the results demonstrated that transforming growth factor beta 1 (TGFβ1) signaling regulated the effects of A2M on vascular muscle cell proliferation described above. Meanwhile, A2M overexpression regressed rat placental vascularization and reduced the expression of angiogenesis-related genes. In addition, A2M overexpression reduced HUVEC migration, filopodia number/length, and tube formation. Furthermore, HIF-1α expression was positively related to A2M, and the secretion of sFLT-1 and PIGF of placental origin was closely related to PE during pregnancy or A2M overexpression in rats. Conclusions Our data showed that gestational A2M overexpression can be considered a contributing factor leading to PE, causing detective uterine spiral artery remodeling and aberrant placental vascularization.

The proteomic analysis from samples of patients with preeclampsia (PE) displayed a low level of ferritin light chains (FTL), but we do not know what the significance of reduced FTL in PE pathophysiology is. To address this question, we first demonstrated that FTL was expressed in first- and third-trimester cytotrophoblasts, including extravillous trophoblasts (EVTs), of the human placenta. Furthermore, a pregnant rat model of FTL knockdown was successfully established by intravenously injecting adenoviruses expressing shRNA targeting FTL. In pregnant rats with downregulated FTL, we observed PE-like phenotypes and impaired spiral arterial remodelling, implying a causal relationship between FTL downregulation and PE. Blocking ferroptosis with ferrostatin-1 (Fer-1) significantly rescued the above PE-like phenotypes in pregnant rats with FTL knockdown. Furthermore, using trophoblast cell line and chorionic villous explant culture assays, we showed that FTL downregulation induced cell death, especially ferroptosis, resulting in defective uterine spiral artery remodelling. Eventually, this conclusion from the animal model was verified in PE patients’ placental tissues. Taken together, this study revealed for the first time that FTL reduction during pregnancy triggered ferroptosis and then caused defective uterine spiral artery remodelling, thereby leading to PE.

Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.

Background: Preeclampsia (PE) is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Placental oxidative stress has been identified as a major pathway to the development of PE. Ferroptosis is a new form of regulated cell death that is associated with iron metabolism and oxidative stress, and likely mediates PE pathogenesis. The aim of the study was to identify the key molecules involved in ferroptosis to further explore the mechanism of ferroptosis in PE.Methods: Gene expression data and clinical information were downloaded from the GEO database. The limma R package was used to screen differentially expressed genes (DEGs) and intersected with ferroptosis genes. The GO and KEGG pathways were then analyzed. Next, hub genes were identified via weighted gene co-expression network analysis (WGCNA). Receiver operating curves (ROCs) were performed for diagnostic and Pearson’s correlation of hub genes and clinicopathological characteristics. Immunohistochemistry and Western blot analysis were used to verify the expression of hub genes.Results: A total of 3,142 DEGs were identified and 30 ferroptosis-related DEGs were obtained. In addition, ferroptosis-related pathways were enriched by GO and KEGG using DEGs. Two critical modules and six hub genes that were highly related to diagnosis of PE were identified through WGCNA. The analysis of the clinicopathological features showed that NQO1 and SRXN1 were closely correlated with PE characteristics and diagnosis. Finally, Western blot and immunohistochemistry analysis confirmed that the expression of the SRXN1 protein in the placental tissue of patients with PE was significantly elevated, while the expression of NQO1 was significantly decreased.Conclusions: SRXN1 and NQO1 may be key ferroptosis-related proteins in the pathogenesis of PE. The study may provide a theoretical and experimental basis for revealing the pathogenesis of PE and improving the diagnosis of PE.

BackgroundWiskott-Aldrich syndrome protein family member 2 (WASF2) has been shown to play an important role in many types of cancer. Therefore, it is worthwhile to further study expression profile of WASF2 in human cancer, which provides new molecular clues about the pathogenesis of ovarian cancer.MethodsWe used a series of bioinformatics methods to comprehensively analyze the relationship between WASF2 and prognosis, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and tried to find the potential biological processes of WASF2 in ovarian cancer. Biological behaviors of ovarian cancer cells were investigated through CCK8 assay, scratch test and transwell assay. We also compared WASF2 expression between epithelial ovarian cancer tissues and normal ovarian tissues by using immunohistochemical staining.ResultsIn the present study, we found that WASF2 was abnormally expressed across the diverse cancer and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the WASF2 expression level also significantly related to the TME. Our results also showed that the expression of WASF2 was closely related to immune infiltration and immune-related genes. In addition, WASF2 expression was associated with TMB, MSI, and antitumor drugs sensitivity across various cancer types. Functional bioinformatics analysis demonstrated that the WASF2 might be involved in several signaling pathways and biological processes of ovarian cancer. A risk factor model was found to be predictive for OS in ovarian cancer based on the expression of WASF2. Moreover, in vitro experiments, it was demonstrated that the proliferative, migratory and invasive capacity of ovarian cancer cells was significantly inhibited due to WASF2 knockdown. Finally, the immunohistochemistry data confirmed that WASF2 were highly expressed in ovarian cancer.ConclusionsOur study demonstrated that WASF2 expression was associated with a poor prognosis and may be involved in the development of ovarian cancer, which might be explored as a potential prognostic marker and new targeted treatments.

Background: Pre-eclampsia is a common pregnancy-specific disease. In this study, we used comprehensive bioinformatics to screen key genes related to the development of pre-eclampsia and explore their potential connections. Methods: Two data sets GSE44711 and GSE30186 from the GEO database were used. The experimental group data set GSE44711 includes 16 samples (8 normal group samples and 8 early-onset PE group samples); the validation group data set GSE30186 includes 12 samples (6 Samples from the normal group and 6 samples from the PE group). Use the limma R package to analyze the difference between the two data sets, perform enrichment analysis, GO and KEGG analysis on the experimental group data set GSE44711, then obtain the hub gene by cytoscape, and verify the Hub gene in the validation data set GES30186. Results: 826 DEGs were identified from GSE44711, including 458 up-regulated genes and 368 down-regulated genes; The 6 Hub genes (CXCR6, CD3D, CD8A, CD69, and IL5) identified are considered to have high credibility. Conclusion: Through our analysis, the functional difference between early onset preeclampsia and normal pregnancy patients was explained, and the Hub genes CXCR6, CD3D, CD8A, CD69 and IL5 were determined.

Considering the problem of discrete texture synthesis and the time for texturing, this paper proposes a novel framework for synthesizing texture images based on discrete example-based elements. We start with extracting texture feature distribution from exemplars and then produce discrete elements based on the cluster algorithm. After initializing a texture image, we propose a texture optimization algorithm based on heuristic searching to improve the quality of the texture image. Final, we use a texture transfer method based on Convolutional Neural Network (CNN) to stylize the optimized texture image. Our results show that the proposed texture synthesis method can significantly improve the quality of discrete texture synthesis and effectively shorten the time for texture generation.

Preeclampsia (PE) is the leading cause of maternal and fetal mortality and morbidity. Early and accurate diagnosis is critical to reduce mortality. Placental oxidative stress has been identified as a major pathway to the development of PE. Ferroptosis, a new form of regulated cell death, is associated with iron metabolism and oxidative stress, and has been suspected to play a role in the pathophysiology of PE, although the mechanism is yet to be elucidated. The identification of potential ferroptosis-related biomarkers is of great significance for the early diagnosis and treatment of PE. A gene expression dataset of peripheral blood samples was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially expressed genes (DEGs) were filtrated with the R package “limma”. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs were then conducted. Ferroptosis-related DEGs were screened by overlapping the ferroptosis-related genes with DEGs. The protein–protein interaction (PPI) network was used to identify the key ferroptosis-related DEGs. Enzyme-linked immunosorbent assay (ELISA) was used to validate changes in the selected key ferroptosis-related DEGs. The correlations between the key genes and clinical and pathological characteristics were analyzed. Finally, the diagnostic value of these key genes for PE was confirmed by a receiver operating characteristic (ROC) curve. A total of 5913 DEGs were identified and 45 ferroptosis-related DEGs were obtained. Besides, ferroptosis-related pathways were enriched by KEGG using DEGs. The PPI network showed that p53 and c-Jun were the critical hub genes. ELISA showed that p53 in the serum of PE patients was higher than that of the control group, while c-Jun was lower than that of the control group. Analysis of the clinicopathological features showed that p53 and c-Jun were correlated with the PE characteristics. Finally, based on the area under curve (AUC) values, c-Jun had the superior diagnostic power (AUC = 0.87, p < 0.001), followed by p53 (AUC = 0.75, p < 0.001). Our study identified that two key genes, p53 and c-Jun, might be potential diagnostic biomarkers of PE.

Nutrient inputs to forest ecosystems significantly influence aboveground plant community structure and ecosystem functioning. However, our knowledge of the influence of nitrogen (N) and/or phosphorus (P) inputs on belowground microbial communities in subtropical forests is still unclear. In this study, we used quantitative polymerase chain reaction and Illumina Miseq sequencing of the bacterial 16S rRNA gene to investigate bacterial abundance, diversity, and community composition in a Chinese fir plantation. The fertilization regimes were as follows: untreated control (CK), P amendment (P), N amendment (N), and N with P amendment (NP). Additions of N decreased soil pH and bacterial 16S rRNA gene abundance by 3.95 (from 4.69 to 3.95) and 3.95 × 109 copies g−1 dry soil (from 9.27 × 109 to 3.95 × 109 g−1 dry soil), respectively. Bacterial richness and diversity decreased with N addition (N and NP) rather than only P input. Proteobacteria, Acidobacteria, and Actinobacteria were the major phylum across all treatments. Nitrogen addition increased the relative abundance of Proteobacteria and Actinobacteria by 42.0 and 10.5%, respectively, while it reduced that of Acidobacteria by 26.5%. Bacterial community structure in the CK and P treatments was different from that in the N and NP treatments upon principle coordinates analysis. Phosphorus addition did not significantly affect soil bacterial communities, and no interactions between N and P inputs on microbial traits were observed. Soil pH and mineral N availability appeared to have a cooperative effect on bacterial abundance and community structure, with soil pH being the key influencing factor by canonical correspondence analysis. These results indicate that inorganic N rather than P fertilization affected both bacterial abundance and community composition in subtropical forests.

BACKGROUND: Preeclampsia is a significant pregnancy disorder with an unknown cause, mainly attributed to impaired spiral arterial remodeling. METHODS: Using RNA sequencing, we identified key genes in placental tissues from healthy individuals and preeclampsia patients. Placenta and plasma samples from pregnant women were collected to detect the expression of TPBG (trophoblast glycoprotein). Pregnant rats were injected with TPBG-carrying adenovirus to detect preeclamptic features. HTR-8/SVneo cells transfected with a TPBG overexpression lentiviral vector were used in cell function experiments. The downstream molecular mechanisms of TPBG were explored using RNA sequencing and single-cell RNA sequencing data. TPBG expression was knocked down in the lipopolysaccharide-induced preeclampsia-like rat model to rescue the preeclampsia features. We also assessed TPBG’s potential as an early preeclampsia predictor using clinical plasma samples. RESULTS: TPBG emerged as a crucial differentially expressed gene, expressed specifically in syncytiotrophoblasts and extravillous trophoblasts. Subsequently, we established a rat model with preeclampsia-like phenotypes by intravenously injecting TPBG-expressing adenoviruses, observing impaired spiral arterial remodeling, thus indicating a causal correlation between TPBG overexpression and preeclampsia. Studies with HTR-8/SVneo cells, chorionic villous explants, and transwell assays showed TPBG overexpression disrupts trophoblast/extravillous trophoblast migration/invasion and chemotaxis. Notably, TPBG knockdown alleviated the lipopolysaccharide-induced preeclampsia-like rat model. We enhanced preeclampsia risk prediction in early gestation by combining TPBG expression with established clinical predictors. CONCLUSIONS: These findings are the first to show that TPBG overexpression contributes to preeclampsia development by affecting uterine spiral artery remodeling. We propose TPBG levels in maternal blood as a predictor of preeclampsia risk. The proposed mechanism by which TPBG overexpression contributes to the occurrence of preeclampsia via its disruptive effect on trophoblast and extravillous trophoblast migration/invasion on uterine spiral artery remodeling, thereby increasing the risk of preeclampsia. [ABSTRACT FROM AUTHOR]