Landscape and progress of global peptide drugs in obstetrics and gynaecology.

Peptides have gained popularity in the global market during recent years and have been placed between small molecule drugs and biologics. However, little is known about the comprehensive landscape of peptide drugs in obstetrics and gynaecology. Herein, we analysed new peptide drug-related clinical trials in obstetrics and gynaecology registered on ClinicalTrials.gov. The number and percentage were used for statistical analysis, and a time trend analysis was conducted by calculating the annual growth rate. We aimed to provide the first overview of the changing landscape and status of global peptide drugs in this prospective field, including exploring drug targets, the cutting-edge oncotherapy of peptide vaccines and peptide-drug conjugates, and unsolved challenges with oral administration.

[A late-type star spectra outlier data mining system].

In M star population, some special objects, which may be of magnetic activity, may be giant stars, or may be of other rare properties, are very important for the follow-up observation and the scientific research on galactic structure and evolution. For local bias of M-type star spectral characteristic lines contained in subspace, a late-type star spectra outlier data mining system is given in the present paper. Firstly, for the sample of M stellar spectral characteristic lines indices, its distribution characteristics in attribute spaces are measured by using the sparse factor and sparsity coefficient, and then this sample is discretized and dimension-reduced to the spectral subspace. Secondly, local outlier subspaces are extracted by PSO (particle swarm optimization) algorithm and identified. Additionally, the effects of sparse coefficient and sparse factor on the number of outliers are discussed by experiments on the sample of SDSS M stellar spectral line index set, and the outliers are compared with spectral type provided by SDSS. In this way, the feasibility and value of this system were validated.

LC-MS/MS untargeted lipidomics uncovers placenta lipid signatures from intrahepatic cholestasis of pregnancy.

Aims: Intrahepatic cholestasis of pregnancy (ICP) stands as the predominant liver disorder affecting pregnant women, with a prevalence ranging from 0.2% to 15.6%. While ICP is known to heighten the chances of perinatal mortality and morbidity, its pathogenesis remains elusive, and therapeutic options are limited. The objective of this study was to explore the characteristic lipid signature in placentas collected from normal pregnancies and those with mild and severe intrahepatic cholestasis of pregnancy. This research aims to clarify the pathogenesis and identify lipid biomarker for ICP through LC-MS/MS based lipidomic analysis. Methods and materials: Placenta samples were collected from 30 normal pregnancy women and 30 mild and severe ICP women respectively. Women with normal pregnancy and ICP were recruit from April 2021 to July 2022 in Chengdu, China. And LC-MS/MS based lipidomic analysis was used to explore the characteristic placental lipids in mild and severe ICP. Results: Fourty-four lipids were differentially expressed both in mild and severe ICP placenta. The pathway analysis revealed these lipids are mainly enriched in glycerophospholipid metabolism and autophagy pathway. Weighted correlation network analysis (WGCNA) identified the correlation network module of lipids highly related to ICP. Using multiple logistic regression analysis, we identified three and four combined metabolites that had an area under receiver operating characteristic curves (AUC) ≥ 0.90. Conclusion: Our results systematically revealed the lipid signature in mild and severe ICP placenta. The results may provide new insight into the treatment and early prediction of ICP.

Intrahepatic cholestasis of pregnancy: insights into pathogenesis and advances in omics studies.

Intrahepatic cholestasis of pregnancy (ICP) is the most common pregnancy-specific liver disease. It is characterized by pruritus, abnormal liver function and elevated total bile acid (TBA) levels, increasing the risk of maternal and fetal adverse outcomes. Its etiology remains poorly elucidated. Over the years, various omics techniques, including metabolomics, microbiome, genomics, etc., have emerged with the advancement of bioinformatics, providing a new direction for exploring the pathogenesis, diagnosis and treatment of ICP. In this review, we first summarize the role of bile acids and related components in the pathogenesis of ICP and then further illustrate the results of omics studies.

The role of serum lipid in predicting coronary artery lesions and intravenous immunoglobulin resistance in Kawasaki disease: a cohort study.

OBJECTIVE: To assess the predictive value of the serum lipid profile for initial intravenous immunoglobulin (IVIG) resistance and coronary artery lesions (CALs) in patients with Kawasaki disease (KD). METHODS: This retrospective cohort study enrolled patients with KD and divided them into IVIG-responsive and IVIG-resistant groups. They were also stratified based on the presence of CALs (CALs and non-CALs groups). Clinical, echocardiographic and biochemical values were evaluated. A subgroup analysis was performed on complete and incomplete KD. Predictors of initial IVIG resistance and CALs were determined by multivariate logistic regression analysis. RESULTS: A total of 649 KD patients were enrolled: 151 had CALs and 76 had initial IVIG resistance. Low-density lipoprotein cholesterol (LDL-C) was significantly lower in the IVIG-resistant group than in the IVIG-responsive group. LDL-C and apolipoprotein (Apo) B were significantly lower in the CALs group compared with the non-CALs group. Multivariate logistic regression failed to identify the serum lipid profile (LDL-C, Apo A or Apo B) as an independent risk factor for initial IVIG resistance or CALs in KD patients. CONCLUSION: KD patients might have dyslipidaemia in the acute phase, but the serum lipid profile might not be suitable as a single predictor for initial IVIG resistance or CALs.

[A post-processing method of classification rule on stellar spectra].

Automatic classification and analysis of observational data is of great significance along with the gradual implementation of LAMOST Survey, which will obtain a large number of spectra data. In classification rules extracted, there is often a great deal of redundancy which will reduce the classification efficiency and quality seriously. In the present paper, a post-processing method of star spectra classification rule based on predicate logic is presented by using predication to describe the classification rules and logical reasoning to eliminate redundant rules. In the end, some experimental results on LAMOST's stellar spectra data show that, with no classification accuracy reduction, the efficiency of auto classification is significantly improved.

[Automatic classification method of star spectrum data based on classification pattern tree].

Frequent pattern, frequently appearing in the data set, plays an important role in data mining. For the stellar spectrum classification tasks, a classification rule mining method based on classification pattern tree is presented on the basis of frequent pattern. The procedures can be shown as follows. Firstly, a new tree structure, i. e., classification pattern tree, is introduced based on the different frequencies of stellar spectral attributes in data base and its different importance used for classification. The related concepts and the construction method of classification pattern tree are also described in this paper. Then, the characteristics of the stellar spectrum are mapped to the classification pattern tree. Two modes of top-to-down and bottom-to-up are used to traverse the classification pattern tree and extract the classification rules. Meanwhile, the concept of pattern capability is introduced to adjust the number of classification rules and improve the construction efficiency of the classification pattern tree. Finally, the SDSS (the Sloan Digital Sky Survey) stellar spectral data provided by the National Astronomical Observatory are used to verify the accuracy of the method. The results show that a higher classification accuracy has been got.

A survey on federated learning: challenges and applications.

Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model. Its unique distributed training mode and the advantages of security aggregation mechanism are very suitable for various practical applications with strict privacy requirements. However, with the deployment of FL mode into practical application, some bottlenecks appear in the FL training process, which affects the performance and efficiency of the FL model in practical applications. Therefore, more researchers have paid attention to the challenges of FL and sought for various effective research methods to solve these current bottlenecks. And various research achievements of FL have been made to promote the intelligent development of all application areas with privacy restriction. This paper systematically introduces the current researches in FL from five aspects: the basics knowledge of FL, privacy and security protection mechanisms in FL, communication overhead challenges and heterogeneity problems of FL. Furthermore, we make a comprehensive summary of the research in practical applications and prospect the future research directions of FL.

Association of intraoperative hypotension and severe postoperative complications during non-cardiac surgery in adult patients: A systematic review and meta-analysis.

Background: Intraoperative hypotension (IOH) is a common side effect of non-cardiac surgery that might induce poor postoperative outcomes. The relationship between the IOH and severe postoperative complications is still unclear. Thus, we summarized the existing literature to evaluate whether IOH contributes to developing severe postoperative complications during non-cardiac surgery. Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane Library, Web of Science, and the CBM from inception to 15 September 2022. The primary outcomes were 30-day mortality, acute kidney injury (AKI), major adverse cardiac events (myocardial injury or myocardial infarction), postoperative cognitive dysfunction (POCD), and postoperative delirium (POD). Secondary outcomes included surgical-site infection (SSI), stroke, and 1-year mortality. Results: 72 studies (3 randomized; 69 non-randomized) were included in this study. Low-quality evidence showed IOH resulted in an increased risk of 30-day mortality (OR, 1.85; 95% CI, 1.30-2.64; P < .001), AKI (OR, 2.69; 95% CI, 2.15-3.37; P < .001), and stroke (OR, 1.33; 95% CI, 1.21-1.46; P < .001) after non-cardiac surgery than non-IOH. Very low-quality evidence showed IOH was associated with a higher risk of myocardial injury (OR, 2.00; 95% CI, 1.17-3.43; P = .01), myocardial infarction (OR, 2.11; 95% CI, 1.41-3.16; P < .001), and POD (OR, 2.27; 95% CI, 1.53-3.38; P < .001). Very low-quality evidence showed IOH have a similar incidence of POCD (OR, 2.82; 95% CI, 0.83-9.50; P = .10) and 1-year-mortality (OR, 1.66; 95% CI, 0.65-4.20; P = .29) compared with non-IOH in non-cardiac surgery. Conclusion: Our results suggest IOH was associated with an increased risk of severe postoperative complications after non-cardiac surgery than non-IOH. IOH is a potentially avoidable hazard that should be closely monitored during non-cardiac surgery.

Predictive value of albumin for intravenous immunoglobulin resistance in a large cohort of Kawasaki disease patients.

BACKGROUND: Intravenous immunoglobulin (IVIG) has been the mainstay of treatment for Kawasaki disease (KD) over the past decades. However, 10-20% of KD patients are resistant to IVIG treatment which puts those patients at high risk of coronary artery lesions (CALs). Therefore, it is important to predict whether patients will be resistant to IVIG before the treatment. This study aimed to investigate the risk factors for IVIG non-responsive patients with KD. METHODS: This study enrolled patients diagnosed with KD and divided them into two groups, IVIG responders and IVIG non-responders. We compared the differences in demographics and clinical data between the two groups. Differences among the groups were analyzed by ANOVA and Chi-square analysis. Predictors of IVIG resistance were determined by multiple logistic regression analysis and receiver operating characteristic (ROC) curve analysis. RESULTS: In total, 907 KD patients were reviewed, with 841 IVIG responders and 66 IVIG non-responders. Patients in IVIG responders were younger than IVIG non-responders. The length of hospitalization of the IVIG non-responders was significantly longer than IVIG responders. The neutrophils%, C-reaction protein (CRP), and CRP/albumin ratio in IVIG responders were significantly lower than in IVIG non-responders (P < 0.05). The lymphocyte% and Albumin in IVIG responders were significantly higher than in IVIG non-responders. Multivariable logistic regression analysis demonstrated that albumin (OR = 0.881, 95% CI, 0.781 to 0.994, p-value = 0.039) was an independent risk factor for predicting IVIG resistance. The area under the ROC curve was 0.644, with a cut-off of ≤ 33.4 g/L determined by Youden's index. The sensitivity and specificity in predicting IVIG resistance were 40.91% and 83.47%, respectively. CONCLUSION: Albumin can serve as a potential predicting marker for IVIG resistance in KD. A lower albumin level may be useful for identifying KD patients with a high risk of IVIG resistance to guide further therapy strategies.