Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan

AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Diagnostic Performance of Delirium Assessment Tools in Critically Ill Patients: A Systematic Review and Meta‐Analysis

Background Critical care nurses are in the best position to detect and monitor delirium in critically ill patients. Therefore, an optimum delirium assessment tool with strong evidence should be identified with critical care nurses to perform in the daily assessment. Aim To evaluate and compare the diagnostic performance of delirium assessment tools in diagnosing delirium in critically ill patients. Methods We searched five electronic databases including the Cochrane Library, PubMed, Embase, CINAHL, and a Chinese database for eligible diagnostic studies published in English or Mandarin up to December 2018. This diagnostic test accuracy meta‐analysis was limited to studies in intensive care unit (ICU) settings, using the Diagnostic and Statistical Manual of Mental Disorders (DSM) as a standard reference to test the accuracy of delirium assessment tools. Eligible studies were critically appraised by two investigators independently. The summary of evidence was conducted for pooling and comparing diagnostic accuracy by a bivariate random effects meta‐analysis model. The pooled sensitivities and specificities, summary receiver operating characteristic curve (sROC), the area under the curve (AUC), and diagnostic odds ratio (DOR) were calculated and plotted. The possibility of publication bias was assessed by Deeks’ funnel plot. Data Synthesis We identified and evaluated 23 and 8 articles focused on CAM‐ICU and ICDSC, respectively. The summary sensitivities of 0.85 and 0.87, and summary specificities of 0.95 and 0.91 were found for CAM‐ICU and ICDSC, respectively. The AUC of the CAM‐ICU was 0.96 (95% CI, 0.94–0.98), with DOR at 99 (95% CI, 55–177). The AUC of the ICDSC was 0.95 (95% CI, 0.92–0.96), and the DOR was 65 (95% CI, 27–153). Linking Evidence to Action CAM‐ICU demonstrated higher diagnostic test accuracy and is recommended as the optimal delirium assessment tool. However, the results should be interpreted with caution due to the between‐study heterogeneity of this diagnostic test accuracy meta‐analysis

Health big data analytics : current perspectives, challenges and potential solutions

Modern health information systems can generate several exabytes of patient data, the so called "Health Big Data", per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of Health Big Data as well as the challenges and solutions for health Big Data Analytics (BDA) – the process of extracting knowledge from sets of Health Big Data – and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA

Opportunities and Challenges of Cloud Computing to Improve Health Care Services

Cloud computing is a new way of delivering computing resources and services. Many managers and experts believe that it can improve health care services, benefit health care research, and change the face of health information technology. However, as with any innovation, cloud computing should be rigorously evaluated before its widespread adoption. This paper discusses the concept and its current place in health care, and uses 4 aspects (management, technology, security, and legal) to evaluate the opportunities and challenges of this computing model. Strategic planning that could be used by a health organization to determine its direction, strategy, and resource allocation when it has decided to migrate from traditional to cloud-based health services is also discussed

The Potential Role of Complement System in the Progression of Ovarian Clear Cell Carcinoma Inferred from the Gene Ontology-Based Immunofunctionome Analysis

Ovarian clear cell carcinoma (OCCC) is the second most common epithelial ovarian carcinoma (EOC). It is refractory to chemotherapy with a worse prognosis after the preliminary optimal debulking operation, such that the treatment of OCCC remains a challenge. OCCC is believed to evolve from endometriosis, a chronic immune/inflammation-related disease, so that immunotherapy may be a potential alternative treatment. Here, gene set-based analysis was used to investigate the immunofunctionomes of OCCC in early and advanced stages. Quantified biological functions defined by 5917 Gene Ontology (GO) terms downloaded from the Gene Expression Omnibus (GEO) database were used. DNA microarray gene expression profiles were used to convert 85 OCCCs and 136 normal controls into to the functionome. Relevant offspring were as extracted and the immunofunctionomes were rebuilt at different stages by machine learning. Several dysregulated pathogenic functions were found to coexist in the immunopathogenesis of early and advanced OCCC, wherein the complement-activation-alternative-pathway may be the headmost dysfunctional immunological pathway in duality for carcinogenesis at all OCCC stages. Several immunological genes involved in the complement system had dual influences on patients’ survival, and immunohistochemistrical analysis implied the higher expression of C3a receptor (C3aR) and C5a receptor (C5aR) levels in OCCC than in controls

Diagnostic Performance of Delirium Assessment Tools in Critically Ill Patients: A Systematic Review and Meta‐Analysis

Background Critical care nurses are in the best position to detect and monitor delirium in critically ill patients. Therefore, an optimum delirium assessment tool with strong evidence should be identified with critical care nurses to perform in the daily assessment. Aim To evaluate and compare the diagnostic performance of delirium assessment tools in diagnosing delirium in critically ill patients. Methods We searched five electronic databases including the Cochrane Library, PubMed, Embase, CINAHL, and a Chinese database for eligible diagnostic studies published in English or Mandarin up to December 2018. This diagnostic test accuracy meta‐analysis was limited to studies in intensive care unit (ICU) settings, using the Diagnostic and Statistical Manual of Mental Disorders (DSM) as a standard reference to test the accuracy of delirium assessment tools. Eligible studies were critically appraised by two investigators independently. The summary of evidence was conducted for pooling and comparing diagnostic accuracy by a bivariate random effects meta‐analysis model. The pooled sensitivities and specificities, summary receiver operating characteristic curve (sROC), the area under the curve (AUC), and diagnostic odds ratio (DOR) were calculated and plotted. The possibility of publication bias was assessed by Deeks’ funnel plot. Data Synthesis We identified and evaluated 23 and 8 articles focused on CAM‐ICU and ICDSC, respectively. The summary sensitivities of 0.85 and 0.87, and summary specificities of 0.95 and 0.91 were found for CAM‐ICU and ICDSC, respectively. The AUC of the CAM‐ICU was 0.96 (95% CI, 0.94–0.98), with DOR at 99 (95% CI, 55–177). The AUC of the ICDSC was 0.95 (95% CI, 0.92–0.96), and the DOR was 65 (95% CI, 27–153). Linking Evidence to Action CAM‐ICU demonstrated higher diagnostic test accuracy and is recommended as the optimal delirium assessment tool. However, the results should be interpreted with caution due to the between‐study heterogeneity of this diagnostic test accuracy meta‐analysis