Aberrant NK cell profile in gestational diabetes mellitus with fetal growth restriction.

Gestational diabetes mellitus (GDM) is a gestational disorder characterized by hyperglycemia, that can lead to dysfunction of diverse cells in the body, especially the immune cells. It has been reported that immune cells, specifically natural killer (NK) cells, play a crucial role in normal pregnancy. However, it remains unknown how hyperglycemia affects NK cell dysfunction thus participates in the development of GDM. In this experiment, GDM mice were induced by an intraperitoneal injection of streptozotocin (STZ) after pregnancy and it has been found that the intrauterine growth restriction occurred in mice with STZ-induced GDM, accompanied by the changed proportion and function of NK cells. The percentage of cytotoxic CD27-CD11b+ NK cells was significantly increased, while the proportion of nourished CD27-CD11b- NK cells was significantly reduced in the decidua of GDM mice. Likewise, the same trend appeared in the peripheral blood NK cell subsets of GDM patients. What's more, after intrauterine reinfusion of NK cells to GDM mice, the fetal growth restriction was alleviated and the proportion of NK cells was restored. Our findings provide a theoretical and experimental basis for further exploring the pathogenesis of GDM.

Identifying a panel of nine genes as novel specific model in endometriosis noninvasive diagnosis.

OBJECTIVE: To study biomarkers to develop a novel diagnosis model for endometriosis and validate it using clinical samples. DESIGN: We used publicly available data sets and weighted gene coexpression network analysis to identify differentially expressed genes. Ten machine learning algorithms were used to develop an integrative model for predicting endometriosis. The accuracy and robustness of the model were validated using data sets and clinical samples. SETTING: Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China. PATIENT(S): The study included clinical patients between the ages of 20 and 40 years who required laparoscopic surgery and who had not undergone hormone therapy within the previous 3 months. All the healthy individuals had given birth to a child at least once in their lives. Patients with inflammatory conditions, malignant diseases, immune diseases, myoma, or adenomyosis were excluded. Paraffin blocks of the samples were collected (case, n = 5; control, n = 5). Blood samples of 58 individuals were collected (case, n = 28; control, n = 30). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): The areas under the receiver operator characteristic curve of our diagnostic model were measured for data sets and clinical samples. Multiplex immunohistochemical staining and real-time quantitative polymerase chain reaction assays were used for the validation of the model from tissue slides and peripheral blood samples. RESULT(S): A nine-gene panel endometriosis messenger RNA score (EMScore), was constructed to distinguish the patients with endometriosis from healthy individuals using algorithms. The EMScore accurately predicted endometriosis, and the areas under the receiver operator characteristic curve of our diagnostic model were 0.920, and 0.942 for tissue and blood samples, respectively. Moreover, the EMScore outperformed other acknowledged signatures for predicting endometriosis across seven clinical cohorts. Overall, the EMScore constitutes a sensitive and specific noninvasive diagnostic method for endometriosis. CONCLUSION(S): We developed the EMScore, a novel model that can aid in the diagnosis of endometriosis using peripheral blood samples. This study will contribute to the development of improved clinical noninvasive and sensitive diagnostic tools for endometriosis. These nine genes might be potential target molecules for treating endometriosis.

Comparative effectiveness and safety of 36 therapies or interventions for pregnancy outcomes with recurrent implantation failure: a systematic review and network meta-analysis.

PURPOSE: To investigate the effectiveness and safety of 36 different therapies for recurrent implantation failure (RIF) patients. METHODS: We searched PubMed, Embase, the Cochrane Library (CENTRAL), Web of Science, and China National Knowledge Internet (CNKI) from inception to August 24, 2022, with language in both English and Chinese. Randomized controlled trials (RCTs) and observational studies that provided data with one of pregnancy outcomes on RIF patients were included in the network meta-analysis (NMA). The odds ratios (OR) and 95% credible interval (CrI) on pregnancy outcomes were summarized by NMA with a random-effects model. We also analyzed data from only RCTs and compared whether the optimal treatment is the same for different failed embryo transfer attempts. RESULTS: The total of 29,906 RIF patients from 154 clinical studies (74 RCTs and 80 non-RCTs) were included in the NMA. In terms of implantation rate (IR), growth hormone (GH) (OR: 3.32, 95% CrI: 1.95-5.67) is the best treatment in all included studies; IVIG+PBMC (5.84, 2.44-14.1) is the best for clinical pregnancy rate (CPR); hyaluronic acid (HA) (12.9, 2.37-112.0) for live birth rate (LBR); and aspirin combined with glucocorticoids (0.208, 0.0494-0.777) for miscarriage rate (MR). The two-dimensional graphs showed that GH could maximize IR and CPR simultaneously; HA and GH could simultaneously increase IR and LBR to a large extent; HA could maximize IR and minimize MR. CONCLUSION: IVIG+PBMC, GH, and embryo medium enriched with HA could significantly improve pregnancy outcomes in patients with RIF. It appears that combination therapy is a potential administration strategy. TRIAL REGISTRATION: This study has been registered on PROSPERO (CRD42022353423).

Using time series vector features for annual cultivated land mapping: A trial in northern Henan, China.

Annual monitoring of the spatial distribution of cultivated land is important for maintaining the ecological environment, achieving a status quo of land resource management, and guaranteeing agricultural production. With the gradual development of remote sensing technology, it has become a common practice to obtain cultivated land boundary information on a large scale with the help of satellite Earth observation images. Traditional land use classification methods are affected by multiple types of land cover, which leads to a decrease in the accuracy of cultivated land mapping. In contrast, although the current advanced methods (such as deep learning) can obtain more accurate cultivated land mapping results than traditional methods, such methods often require the use of a massive amount of training samples, large computing power, and highly complex model tuning processes, increasing the cost of mapping and requiring the involvement of more professionals. This has hindered the promotion of related methods in mapping institutions. This paper proposes a method based on time series vector features (MTVF), which uses vector thinking to establish the features. The advantage of this method is that the introduction of vector features enlarges the differences between the different land cover types, which overcomes the loss of mapping accuracy caused by the influences of the spectra of different ground objects and ensures the calculation efficiency. Moreover, the MTVF uses a traditional method (random forest) as the classification core, which makes the MTVF less demanding than advanced methods in terms of the number of training samples. Sentinel-2 satellite images were used to carry out cultivated land mapping for 2020 in northern Henan Province, China. The results show that the MTVF has the potential to accurately identify cultivated land. Furthermore, the overall accuracy, producer accuracy, and user accuracy of the overall study area and four sub-study areas were all greater than 90%. In addition, the cultivated land mapping accuracy of the MTVF is significantly better than that of the maximum likelihood, support vector machine, and artificial neural network methods.

Quantitative analysis of differentially expressed milk fat globule membrane proteins between donkey and bovine colostrum based on high-performance liquid chromatography with tandem mass spectrometry proteomics.

This study was designed to provide novel insights into milk fat globule membrane (MFGM) proteins in donkey colostrum (DC) and bovine colostrum (BC) using quantitative proteomics. In total, 179 (DC) and 195 (BC) MFGM proteins were characterized, including 71 shared, 108 DC-specific, and 124 BC-specific proteins. Fifty-one shared proteins were selected as differentially expressed MFGM proteins, including 21 upregulated and 30 downregulated proteins in DC. Gene ontology analysis showed that these proteins were mainly enriched in cellular components, including the extracellular exosome, extracellular space, and plasma membrane. Additionally, they were further involved in metabolic pathways, including cholesterol metabolism, the peroxisome proliferator-activated receptor signaling pathway, and purine metabolism. Furthermore, several key protein factors with high connectivity were identified via protein-protein interaction analysis. These results provide more comprehensive knowledge of differences in the biological properties of MFGM proteins in DC and BC as well as pave the way for future studies of the nutritional and functional requirements of these important ingredients toward the development of dairy products based on multiple milk sources.

Novel insights into whey protein differences between donkey and bovine milk.

In this study, we characterized and compared the whey proteins from donkey and bovine milk using HPLC-MS/MS-based proteomics. A total of 989 and 1534 whey proteins were characterized in donkey and bovine milk, respectively. Furthermore, 623 whey proteins were found in both groups, and 229 differentially expressed whey proteins (DEWPs) were identified. Among the common proteins, 66 DEWPs were upregulated and 163 were downregulated in donkey milk compared to those in bovine milk. Gene Ontology analysis revealed the cellular components, biological processes, and molecular functions of these DEWPs. Metabolic pathway analysis suggested that most DEWPs were associated with endocytosis, platelet activation, and phagocytosis. These results improve our understanding of the differences between donkey and bovine whey proteins and provide important information regarding these proteins as nutritional and functional factors in dairy product formulations from multiple milk sources.