Identification of potential diagnostic genes for atherosclerosis in women with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS), which is the most prevalent endocrine disorder among women in their reproductive years, is linked to a higher occurrence and severity of atherosclerosis (AS). Nevertheless, the precise manner in which PCOS impacts the cardiovascular well-being of women remains ambiguous. The Gene Expression Omnibus database provided four PCOS datasets and two AS datasets for this study. Through the examination of genes originating from differentially expressed (DEGs) and critical modules utilizing functional enrichment analyses, weighted gene co-expression network (WGCNA), and machine learning algorithm, the research attempted to discover potential diagnostic genes. Additionally, the study investigated immune infiltration and conducted gene set enrichment analysis (GSEA) to examine the potential mechanism of the simultaneous occurrence of PCOS and AS. Two verification datasets and cell experiments were performed to assess biomarkers' reliability. The PCOS group identified 53 genes and AS group identified 175 genes by intersecting DEGs and key modules of WGCNA. Then, 18 genes from two groups were analyzed by machine learning algorithm. Death Associated Protein Kinase 1 (DAPK1) was recognized as an essential gene. Immune infiltration and single-gene GSEA results suggest that DAPK1 is associated with T cell-mediated immune responses. The mRNA expression of DAPK1 was upregulated in ox-LDL stimulated RAW264.7 cells and in granulosa cells. Our research discovered the close association between AS and PCOS, and identified DAPK1 as a crucial diagnostic biomarker for AS in PCOS.

Astaxanthin Alleviates Inflammatory Response in Neonatal Necrotizing Enterocolitis Rats by Regulating NOD2/TLR4 Pathway.

Background: Necrotizing enterocolitis (NEC) is often associated with exaggerated activation of inflammatory response. Astaxanthin has been shown in studies to have a positive and advantageous effect on anti-inflammatory response. Hence, it is of great significance to study the protective effect of astaxanthin in NEC disease and its molecular mechanism. Objective: The present study was to investigate whether astaxanthin attenuates NEC rats and to explore its potential mechanism. Material and Methods. Hematoxylin-eosin staining was used to observe the pathological change of the intestinal tissue in NEC rats. Subsequently, we determined the anti-oxidative stress, anti-apoptosis, and anti-inflammation in astaxanthin with enzyme-linked immunosorbent assay kits, TUNEL staining, western blot, and immunohistochemistry assay. Furthermore, we added nucleotide-binding oligomerization domain 2 (NOD2) inhibitor to certify the molecular pathway of the astaxanthin in NEC rats. Results: Astaxanthin improved the pathological changes of the intestinal tissues. It restrained inflammation, oxidative stress, and protected cells from apoptosis in the intestinal tissue and serum of the NEC rats. Moreover, astaxanthin enhanced NOD2, whereas it suppressed toll-like receptor 4 (TLR4), nuclear factor-κB (NF-κB) pathway-related proteins. Apart from that, the NOD2 inhibitor offset the protective effect of the astaxanthin towards the NEC rats. Conclusion: The present study indicated that astaxanthin alleviated oxidative stress, inflammatory response, and apoptosis in NEC rats by enhancing NOD2 and inhibiting TLR4 pathway.

Design and analysis of management platform based on financial big data.

Traditional financial accounting will become limited by new technologies which are unable to meet the market development. In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. In addition, a financial data management platform based on distributed Hadoop architecture is designed, which combines MapReduce framework with the fuzzy clustering algorithm and the local outlier factor (LOF) algorithm, and uses MapReduce to operate in parallel with the two algorithms, thus improving the performance of the algorithm and the accuracy of the algorithm, and helping to improve the operational efficiency of enterprise financial data processing. The comparative experimental results show that the proposed platform can achieve the best the running efficiency and the accuracy of financial data classification compared with other methods, which illustrate the effectiveness and superiority of the proposed platform.

Bioinformatic Analysis and Machine Learning Methods in Neonatal Sepsis: Identification of Biomarkers and Immune Infiltration.

The disease neonatal sepsis (NS) poses a serious threat to life, and its pathogenesis remains unclear. Using the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified and functional enrichment analyses were conducted. Three machine learning algorithms containing the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) were applied to identify the optimal feature genes (OFGs). This study conducted CIBERSORT to present the abundance of immune infiltrates between septic and control neonates and assessed the relationship between OFGs and immune cells. In total, 44 DEGs were discovered between the septic and control newborns. Throughout the enrichment analysis, DEGs were primarily related to inflammatory signaling pathways and immune responses. The OFGs derived from machine learning algorithms were intersected to yield four biomarkers, namely Hexokinase 3 (HK3), Cystatin 7 (CST7), Resistin (RETN), and Glycogenin 1 (GYG1). The potential biomarkers were validated in other datasets and LPS-stimulated HEUVCs. Septic infants showed a higher proportion of neutrophils (p < 0.001), M0 macrophages (p < 0.001), and regulatory T cells (p = 0.004). HK3, CST7, RETN, and GYG1 showed significant correlations with immune cells. Overall, the biomarkers offered promising insights into the molecular mechanisms of immune regulation for the prediction and treatment of NS.

Identification of new biomarkers and immune infiltration characteristics of sepsis in very low birth weight infants.

Sepsis is a life-threatening condition, especially in very low birth weight (VLBW) infants, and its pathogenesis remains unclear. Effective biomarkers need to be found to diagnose and treat the disease at an early stage. The Gene Expression Omnibus (GEO) database was screened and analyzed for differentially expressed genes (DEGs) in VLBW infants with sepsis. DEGs were then analyzed for functional enrichment. A weighted gene co-expression network analysis (WCGNA) was performed to identify the key modules and genes. The optimal feature genes (OFGs) were created using three machine learning algorithms. The single-sample Gene Set Enrichment Analysis (ssGSEA) scored the degree of immune cell enrichment between septic and control patients, and the correlation between OFGs and immune cells was evaluated. A total of 101 DEGs were identified between the sepsis and control samples. DEGs were mainly associated with immune responses and inflammatory signaling pathways in the enrichment analysis. In the WGCNA analysis, the MEturquoise module was significantly correlated with sepsis in VLBW infants (cor = 0.57, P < 0.001). By intersecting OFGs derived from three machine learning algorithms, two biomarkers were identified: glycogenin 1 (GYG1) and resistin (RETN). The area under the curves of GYG1 and RETN was greater than 0.97 in the testing set. The ssGSEA indicated immune cells infiltration in septic VLBW infants, and GYG1 and RETN revealed close correlations with immune cells. New biomarkers offer promising insights into the diagnosis and treatment of sepsis in VLBW infants.

[Retrospective analysis of elective caesarean section and respiratory distress syndrome in the term neonates].

OBJECTIVE: Severe respiratory distress syndrome (RDS) caused by pulmonary surfactant (PS) deficiency is described not only in preterm infants but also in term babies delivered via caesarean section, especially before the onset of labour (elective caesarean section). Once RDS of term neonates happened, mechanical ventilation is needed, and the infants were at high risk of developing further complications such as persistent pulmonary hypertension of neonates (PPHN), pulmonary air leak and cardiovascular instability, even fatal outcome cannot be avoided. The present study aimed to analyze the association between the elective caesarean section and respiratory distress syndrome (RDS) in term neonates, and to determine the related factors and outcomes of RDS cases in neonatal intensive care unit (NICU) and neonatology ward. METHODS: A retrospective study was conducted at the NICU and the Neonatology Ward of A center (Children's Hospital of Zhejiang University) and the NICU of center B (Obstetrics and Gynecology Hospital of Zhejiang University) on 90 term infants who were diagnosed as RDS between June 2006 and June 2008. The general clinical data, mode of delivery, severity of the radiological sign, pulmonary surfactant (PS) application, the onset time and duration of mechanical ventilation, the ratio of PaO(2) to FIO(2) before mechanical ventilation, oxygenation index (OI), duration of oxygen supplementation, the length of hospital stay and complications including pulmonary air leaks (pneumothorax, pulmonary interstitial emphysema), PPHN, systemic hypotension and ventilator-associated pneumonia (VAP) were collected. The gestational age distribution was studied in RDS cases delivered by elective caesarean section, and the comparative analysis and non-conditional logistic regression analysis wer performed for clinical characteristics and risk factors between the RDS cases with or without complications. P < 0.05 was considered to be significant. RESULTS: There were 88 episodes of elective caesarean section in 90 RDS patients. The proportion of elective caesarean section was 97.8% (88/90). The number of RDS cases was reduced gradually with the increase of gestational age and the constituent ratio of RDS at 39 w and at 40 w was significantly lower than that of 37 w and 38 w. By logistic regression analysis, the onset time of mechanical ventilation > 12 h independently predicted complications in RDS of term neonates (odds ratio 12.667, 95% confidence interval, i.e., CI 1.455 to 110.300, P = 0.021). Moreover, there was a significant difference in the admission age (t = 11.833, P = 0.001), severity of the radiological findings (t = 4.85, P = 0.028), PS application (t = 11.911, P = 0.002), the onset time of mechanical ventilation (t =10.051, P = 0.018), the ratio of PaO(2) to FIO(2) before mechanical ventilation (chi(2) = 4.184, P = 0.005), OI > 25 (t = 4.737, P = 0.03), duration of oxygen supplementation (chi(2) = 10.475, P = 0.001), systemic hypotension (t = 11.020, P = 0.01) and the length of hospital stay (t = 9.827, P = 0.002) between the two centers. CONCLUSION: Severe RDS can occur in term babies after elective caesarean section, gestational age at the time of elective caesarean section may also be important for RDS in term neonates. The onset time of mechanical ventilation > 12 h independently predicted complications in RDS of term neonates. The main complications of RDS in term neonates were PPHN, pulmonary air leaks and systemic hypotension. Early diagnosis, early intervention can significantly reduce the complications, alleviate the severity and shorten their time for oxygen therapy and their length of stay in NICU in term RDS infants.

[A case-control study on green tea consumption and the risk of adult leukemia].

OBJECTIVE: To investigate whether green tea consumption can reduce the risk of adult leukemia. METHODS: A hospital-based matched case-control study was conducted in 2005 - 2006. We recruited 107 confirmed leukemia cases and 110 inpatient controls with orthopedic disease without leukemia or other malignancy matched on gender, age and hospitals that patients stayed. Related information were gathered on quantity, duration and frequency of tea consumption, demographic characteristics, exposure to radiation and occupational hazards, medications, using a validated questionnaire by face-to-face interview. Univariate and multivariate unconditional logistic regression analysis were used to estimate odds ratios (ORs) and associated 95% confidence intervals (CIs) with SPSS 11.5 software. RESULTS: Compared with non-tea-drinkers, the OR of those who consumed green tea was 0.58 (95% CI:0.34-1.00, P< 0.05) under univariate statistical analysis. The OR was 0.52 ( 95% CI: 0.28- 0.98, P = 0.04), using logistic regression to count for age, gender, residential area, smoking, level of education, exposure to radiation, benzene and organo-phosphorus. Compared with non-drinkers, the risk of adult leukemia declined with increasing quantity, duration, and frequency of green tea consumption. Tests for trend on dose-response was statistically significant (P < 0.01). CONCLUSION: A higher consumption of green tea seemed to be associated with a declined risk of adult leukemia. Tea consumption might be of help to human health planning projects.