Exploration of Programmed Cell Death-Associated Characteristics and Immune infiltration in Neonatal Sepsis: New insights From Bioinformatics analysis and Machine Learning

BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition. METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. to further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes. RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay. CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000)

Construction of Key Performance Indicators in the Balance Score Card Approach - (A Case Study) on Adoption of the CMMI Methodology

台灣組織企業所面臨外在環境急劇的變化，使得經營方面面臨空前的挑戰。企業為求生存及發展，紛紛尋求各種發展策略以提昇競爭優勢。紛紛建立或導入平衡計分卡（Balanced Score Card; BSC），並運用其財務，顧客，內部流程與學習成長四構面來平衡發展，以協助釐清並訂定組織策略目標，進而透過以BSC所發展一系列的執行計畫，達成組織企業永續經營之目的，而BSC其中最重要也最不易發展的一個環節就是各項關鍵績效指標(Key Performance Indicator; KPI)的建立與衡量。能力成熟度整合模式（Capability Maturity Model Integration；CMMI）是軟體組織所運用於協助專案發展時如何降低重工，提升品質績效上有重大貢獻的管理方法，其中又以在能力成熟度第二級度量與分析流程領域與第四級上的量化績效管理方法對BSC在發展關鍵績效指標上之應用有顯著的相關。本研究目的為建構企業組織關鍵績效衡量指標，並試著以CMMI方法為基礎，尋求發展另一種模式作為建構之參考，期能提升企業組織整體績效，輔助達成組織策略目標。The Taiwan enterprises are on the enormously external environment change which causes the management needs to face the unprecedented challenge. The enterprises are striving for the survival and seek kinds of development strategy to improve the competitive ability. The enterprises begin to implement the Balanced Scorecard, BSC, to achieve their competitive capacity strategy. With adopting finance aspect, the customer aspect, the internal work flow aspect and the learning aspect, the enterprises can construct and identify a balance development strategy of organization as well as clear the goal of the organization strategy. By execution of the series of plans developed by BSC, the enterprises can achieve their business goal. However, the most important and difficult of the developing of BSC is to construct the rule and measurement of the Key Performance Indicator, KPI. The methodology of Capability Maturity Model Integration, CMMI, is the key approach for software organization to assist the project management for reducing task rework, and improving the quality management of software. Upon CMMI level second, the flow of Measurement and Analysis, and level fourth, Quantification Performance Management, CMMI has the remarkable correlation on helping BSC on development the key performance indicators. The purpose of this research is to construct the enterprise key performance Indicators by using CMMI methodology as a foundation to build up a model which can help on accomplishment the strategy of the enterprises. In and improve the performance of the organizations.口試委員審定書 iii謝 iv文摘要 v文摘要 vi 錄 vii目錄 ix目錄 x一章 緒 論 1一節、研究背景與動機 1二節、研究背景 2二章 文獻探討 3一節、平衡計分卡文獻探討 3二節、關鍵績效指標 13三節、CMMI能力成熟度模型 14三章 BSC與CMMI整合KPI建構模式研究 27一節、KPI建構模式發展 27二節、BSC與CMMI流程應用之KPI建構模式 27四章 建構軟體組織BSC與CMMI整合之KPI 31一節、個案組織簡介 31二節、建構發展個案軟體組織平衡計分卡KPIs 32三節、軟體組織平衡計分卡KPIs之修訂 38五章 結論與建議 43一節、研究結論 43二節、研究貢獻 43三節、研究限制與建議 44考文獻 46目錄2-1 平衡計分卡組織遠景與策略與四構面關係圖32-2 BSC 架構下執行策略如何增進股東價值鍊62-3 顧客滿意度的企業價值鍊72-4 內部流程改善價值鏈82-5 組織內部學習與成長之貢獻模式82-6 完整的平衡計分卡企業組織願景策略轉化績效衡量架構圖92-7 BSC四構面與企業組織策略與願景關聯圖102-8 願景與長短期策略執行目標關聯圖112-9 組織營運目標轉化為部門及個人目標架構圖122-10 階層式表述之CMMI模型架構152-11 能力成熟度的五個階段及其對組織之影響162-12 CMMI整體導入與評鑑的流程結構253-1 KPI建構循環模式274-1 個案組織整體策略地圖314-2 個案組織策略地圖334-3 修正後策略地圖384-4 KPIs績效衡量管理循環模式42目錄2-1 平衡計分卡特色與效益…………………52-2 成熟度等二級與相關流程領域…………172-3 成熟度等三級與相關流程領域 ………182-4 成熟度等四級與相關流程領域 ………202-5 成熟度等五級與相關流程領域 ………202-6 CMMI列舉參考度量項目 ………………232-7 CMMI展示成果……………………………254-1 個案組織總體KPI指標…………………324-2 轉化策略為營運衡量指標………………344-3 顧客構面所發展衡量指標與來源……354-4 財務構面所發展衡量指標與來源………354-5 內部流程構面所發展衡量指標與來源…364-6 學習成長流程構面所發展衡量指標與來源…364-7 受訪者對KPI指標之建議………………………374-8 顧客構面所發展衡量指標與來源（修正後）……394-9 財務構面所發展衡量指標與來源（修正後）……394-10 內部流程構面所發展衡量指標與來源（修正後） …394-11 學習成長流程構面所發展衡量指標與來源（修正後） 4

Predicting Risk of Insulin Resistance in a Chinese Population with Polycystic Ovary Syndrome: Designing and Testing a New Predictive Nomogram

Background. This research is aimed at establishing and internally validating the risk nomogram of insulin resistance (IR) in a Chinese population of patients with polycystic ovary syndrome (PCOS). Methods. We developed a predictive model based on a training dataset of 145 PCOS patients, and data were collected between March 2018 and May 2019. The least absolute shrinkage and selection operator regression model was used to optimize function selection for the insulin resistance risk model. Multivariable logistic regression analysis was used to construct a prediction model integrating the function selected in the regression model of the least absolute shrinkage and selection operator. The predicting model’s characteristics of prejudice, disease, and lifestyle were analyzed using the C-index, the calibration diagram, and the study of the decision curve. External validity was assessed using the validation of bootstrapping. Results. Predictors contained in the prediction nomogram included occupation, disease durations (years), BMI, current use of metformin, and activities. With a C-index of 0.739 (95 percent confidence interval: 0.644–0.830), the model showed good differentiation and proper calibration. In the interval validation, a high C-index value of 0.681 could still be achieved. Examination of the decision curve found that the IR nomogram was clinically useful when the intervention was determined at the 11 percent IR potential threshold. Conclusion. This novel IR nomogram incorporates occupation, disease durations (years), BMI, current use of metformin, and activities. This nomogram could be used to promote the estimation of individual IR risk in patients with PCOS

Additional file 1: Table S1. of 5Îą-reductase activity in women with polycystic ovary syndrome: a systematic review and meta-analysis

PRISMA 2009 Checklist. (DOC 70 kb

Effects of Exercise Combined with Finasteride on Hormone and Ovarian Function in Polycystic Ovary Syndrome Rats

Exercise can reduce androgen and insulin levels in polycystic ovary syndrome (PCOS) patients. Finasteride is also presumed to improve the developing follicle environment. Therefore, the aim of this study was to observe the effects of the combination of exercise and finasteride therapy on hormone levels and ovarian morphology in rats with polycystic ovary syndrome. Forty female rats were randomly divided into five groups (n=8 each group): the PCOS sedentary group (P-Sed), PCOS exercise group (P-Ex), PCOS finasteride and sedentary group (P-FSed), and PCOS finasteride and exercise group (P-FEx), and healthy, age-matched rats were used as controls (CO). The results indicated that the levels of FINS in the P-FEx group were significantly lower than those in the P-Sed and P-FSed groups, while the ratio of fasting blood glucose (FBG)/fasting serum levels of insulin (FINS) in the P-FEx group was significantly higher than that in the P-Sed and P-FSed groups. Compared to the P-FEx group, serum levels of TT (total testosterone) in the P-Sed and P-FSed groups were significantly increased. The thickness of the follicular membrane and the number of atresia follicles in the P-FEx and CO groups were significantly lower than those in the P-Sed group, but there was no significant difference between the P-Ex and P-Sed groups. These results show that the combined usage of exercise and finasteride does not alter the effects of exercise on increasing insulin sensitivity and reducing androgen levels. There is also a synergistic effect of exercise and finasteride on the morphology of the ovary, including a reduced number of atresia follicles and thickness of the follicular membrane

Additional file 2: of 5Îą-reductase activity in women with polycystic ovary syndrome: a systematic review and meta-analysis

The search strategy and excluded studies with reasons. (DOCX 28 kb

Additional file 3: Table S2. of 5Îą-reductase activity in women with polycystic ovary syndrome: a systematic review and meta-analysis

Data from the studies included in the meta-analysis. (DOCX 17 kb

Identification of Novel Gene Signature Associated with Cell Glycolysis to Predict Survival in Hepatocellular Carcinoma Patients

Purpose. As hepatocellular carcinoma (HCC) is a complex disease, it is hard to classify HCC with a specific biomarker. This study used data from TCGA to create a genetic signature for predicting the prognosis of HCC patients. Methods. In a group of HCC patients (n = 424) from TCGA, mRNA profiling was carried out. To recognize gene sets that differed significantly between HCC and normal tissues, an enrichment study of genes was carried out. Cox relative hazard regression models have been used to identify genes that are significantly associated with overall survival. To test the function of a prognostic risk parameter, the following multivariate Cox regression analysis was used. The log-rank test and Kaplan–Meier survival estimates were used to test the significance of risk parameters for predictive prognoses. Results. Eight genes have been identified as having a significant link to overall survival (PAM, NUP155, GOT2, KDELR3, PKM, NSDHL, ENO1, and SRD5A3). The 377 HCC patients were divided into eight-gene signature-based high/low-risk subgroups. The eight-gene signature’s prognostic ability was unaffected by a number of factors. Conclusion. To predict the survival of patients with HCC, an eight-gene signature associated with cellular glycolysis was then identified. The findings shed light on cellular glycolysis processes and the diagnosis of patients with low HCC prognoses

Figures and cell types of vaginal smears.

<p><b>A</b>: preoestrus, loaded mainly with epithelial cell; <b>B</b>: estrus, loaded mainly with keratinocytes; <b>C</b>:metoestrus,displaying epithelial cell, keratinocytes, and leukocytes; <b>D</b>:diestrus, displaying full of leukocytes (magnification ×100).</p