The cardiovascular risk profile of middle-aged women with polycystic ovary syndrome

Objectives: Contradictory results have been reported regarding the association between polycystic ovary syndrome (PCOS) and cardiovascular disease (CVD). We assessed the cardiometabolic phenotype and prevalence of CVD in middle-aged women with PCOS, compared with age-matched controls from the general population, and estimated 10-year CVD risk and cardiovascular health score. Design: A cross-sectional study. Participants: 200 women aged >45 with PCOS, and 200 age-matched controls. Measurements: Anthropometrics, insulin, lipid levels, prevalence of metabolic syndrome and type II diabetes. Ten-year Framingham risk score and the cardiovascular health score were calculated, and carotid intima-media thickness (cIMT) was measured. Results: Mean age was 50.5 years (SD = 5.5) in women with PCOS and 51.0 years (SD = 5.2) in controls. Increased waist circumference, body mass index and hypertension were more often observed in women with PCOS (P <.001). In women with PCOS, the prevalence of type II diabetes and metabolic syndrome was not significantly increased and lipid levels were not different from controls. cIMT was lower in women with PCOS (P <.001). Calculated cardiovascular health and 10-year CVD risk were similar in women with PCOS and controls. Conclusions: Middle-aged women with PCOS exhibit only a moderately unfavourable cardiometabolic profile compared to age-matched controls, even though they present with an increased BMI and waist circumference. Furthermore, we found no evidence for increased (10-year) CVD risk or more severe atherosclerosis compared with controls from the general population. Long-term follow-up of women with PCOS is necessary to provide a definitive answer concerning lon

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings

Sialic acid glycoengineering using N-acetylmannosamine and sialic acid analogs

Contains fulltext : 207220.pdf (publisher's version ) (Closed access) Contains fulltext : 207220po.pdf (postprint version ) (Open Access)Sialic acids cap the glycans of cell surface glycoproteins and glycolipids. They are involved in a multitude of biological processes and aberrant sialic acid expression is associated with several pathologies. Sialic acids modulate the characteristics and functions of glycoproteins and regulate cell-cell as well as cell-extracellular matrix interactions. Pathogens such as influenza virus use sialic acids to infect host cells and cancer cells exploit sialic acids to escape from the host's immune system. The introduction of unnatural sialic acids with different functionalities into surface glycans enables the study of the broad biological functions of these sugars and presents a therapeutic option to intervene with pathological processes involving sialic acids. Multiple chemically modified sialic acid analogs can be directly utilized by cells for sialoglycan synthesis. Alternatively, analogs of the natural sialic acid precursor sugar N-Acetylmannosamine (ManNAc) can be introduced into the sialic acid biosynthesis pathway resulting in the intracellular conversion into the corresponding sialic acid analog. Both, ManNAc and sialic acid analogs, have been employed successfully for a large variety of glycoengineering applications such as glycan imaging, targeting toxins to tumor cells, inhibiting pathogen binding, or altering immune cell activity. However, there are significant differences between ManNAc and sialic acid analogs with respect to their chemical modification potential and cellular metabolism that should be considered in sialic acid glycoengineering experiments

C-2 auxiliaries for stereoselective glycosylation based on common additive functional groups

Contains fulltext : 216239.pdf (publisher's version ) (Open Access

Structure-Activity Relationship of Metabolic Sialic Acid Inhibitors and Labeling Reagents

[Image: see text] Sialic acids cap the glycans of cell surface glycoproteins and glycolipids. They are involved in a multitude of biological processes, and aberrant sialic acid expression is associated with several pathologies, such as cancer. Strategies to interfere with the sialic acid biosynthesis can potentially be used for anticancer therapy. One well-known class of sialylation inhibitors is peracetylated 3-fluorosialic acids. We synthesized 3-fluorosialic acid derivatives modified at the C-4, C-5, C-8, and C-9 position and tested their inhibitory potency in vitro. Modifications at C-5 lead to increased inhibition, compared to the natural acetamide at this position. These structure–activity relationships could also be applied to improve the efficiency of sialic acid metabolic labeling reagents by modification of the C-5 position. Hence, these results improve our understanding of the structure–activity relationships of sialic acid glycomimetics and their metabolic processing

Structure-Activity Relationship of Fluorinated Sialic Acid Inhibitors for Bacterial Sialylation

Contains fulltext : 235687.pdf (Publisher’s version ) (Open Access

Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study

OBJECTIVE: To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort. DATA SOURCES: Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes. DESIGN : Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort. SETTING: Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL). PARTICIPANTS: 38,379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort. OUTCOME MEASURE: Incident type 2 diabetes. RESULTS: The review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non- invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably. CONCLUSIONS: Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes

Data underlying the research of Chemoenzymatic Synthesis of Sialic Acid Derivatives Using Immobilized N-Acetylneuraminate Lyase in a Continuous Flow Reactor

NMR research data related to a continuous flow process involving N‐acetylneuraminate lyase (NAL) immobilized on Immobead 150P to prepare Neu5Ac derivatives. Batch experiments with Immobead‐NAL showed equal activity as the native enzyme. By using a fivefold excess of either N‐acetyl‐D‐mannosamine (ManNAc) or pyruvate the conversion and isolated yield of Neu5Ac were significantly improved. To further increase the efficiency of the process, a flow setup was designed providing a chemoenzymatic entry into a series of N‐functionalized Neu5Ac derivatives in conversions of 48‐82%, and showing excellent stability over 1 week of continuous use