A shape-context model for matching placental chorionic surface vascular networks

Shape matching algorithm to validate the tracing protocol of placental chorionic surface vessel networks

Diabetes and cancer: Findings from the <scp>REACTION</scp> study 糖尿病与恶性肿瘤：来自REACTION研究的发现

We agree with Dr. Kuller (1) that large-scale epidemiologic studies require substantial input from innovative, committed investigators across a range of disciplines—as in the UK Biobank—in deciding what questions to ask, what measurements to make, and what samples to collect, so that a wide range of hypotheses about many different conditions may be addressed (2). Given the scientific potential of the UK Biobank and the controversies surrounding its initiation (3), it is hard to imagine a stronger vested interest in its success for the investigators involved. We also agree that detailed phenotyping in large prospective cohort studies can allow more specific questions to be addressed (4). We disagree, however, that large-scale studies are easy to conduct or that data collection in the measures used is necessarily simple. For example, in the UK Biobank, touch-screen systems were developed to increase the precision and breadth of information that could be collected on such a large scale. Food frequency questions have been supplemented by Web-based diet diary information, and physical activity questions have been supplemented by wrist-worn activity monitors. Through careful design and in the context of specific hypotheses, retinal imaging has been obtained for 100,000 UK Biobank participants, and magnetic resonance imaging, dual-energy X-ray absorptiometry, and 3-dimensional carotid ultrasonography are being planned for 100,000 of them. All of these measures are designed by dedicated groups of actively involved experts, and data are collected using the same centralized approach to which Dr. Kuller objects. No one is paid on the basis of how many pieces of data they submit or per paper published. If there is a “top-down” (centralized?) approach, it is limited to the mechanics of establishing and running assessment centers and ensuring high-quality data collection. Few imaginative scientists have either an interest or expertise in the large-scale industrial processes needed to make such efforts successful; indeed, the most effective cohort investigators are often those who find capable managers to conduct such procedures for them. The successful merging of academic and management disciplines has been a major, and possibly unique, factor in the success of the UK Biobank. Common diseases may be caused by many exposures that interact with each other in complex ways. To study the effects of numerous different exposures, large studies involving many thousands of cases of a specific disease are likely to be required (5). One area of phenotyping that does not typically receive sufficient focus is ascertainment of a wide range of health outcomes that occur during follow-up. Misclassification of disease cases can substantially reduce statistical power to assess associations. Hence, in the UK Biobank, as in many other cohort studies, diagnostic accuracy is increased by using innovative methods (e.g., retrieval of imaging data and tissue samples) for the classification of outcomes. Establishing such epidemiologic resources requires more—not less—thought than would a study with a narrow focus on a few specific hypotheses. Prospective studies typically collect either large amounts of data on relatively few participants (“data depth”) or small amounts of data on many participants (“data breadth”). The centralized model we espoused (6), exemplified by Sweden's LifeGene, the UK Biobank, and other efforts, can achieve both data depth and data breadth in an efficient manner. In contrast with Dr. Kuller's assertion, “streamlined” does not mean simple, and “centralized” does not mean excluding scientists from the process. Rather, it means allowing scientists to focus on being innovative instead of distracting them with process. The most serious challenge to epidemiology today is the need for very large cohort studies which produce high-quality data that are widely available to the scientific community, for a broad range of uses, at an affordable cost. The centralized model is one proven way of achieving this, to help identify the “big winners” we all seek.

A low magnesium intake has been suggested to be associated with amyotrophic lateral sclerosis (ALS) in pathological and case-control studies, but prospective studies in humans are lacking.The relation between dietary intake of magnesium and ALS risk was explored in five large prospective cohort studies (the Nurses’ Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition Cohort, the Multiethnic Cohort Study, and the National Institutes of Health – AARP Diet and Health Study), comprising over 1,050,000 males and females contributing 1093 cases of ALS during a mean of 15 years of follow-up. Cox proportional hazards models were used within each cohort, and cohort-specific estimates were subsequently pooled using a random-effects model. Results demonstrated that dietary magnesium intake was not associated with ALS risk, relative risk 1.07, 95% confidence interval 0.88 − 1.31 comparing the highest quintile of intake with the lowest. This finding does not support a protective effect of magnesium intake on ALS risk. Further analyses should explore magnesium intake in combination with heavy metal exposure and genetic variants affecting magnesium absorption.

Large prospective cohort studies may offer an opportunity to study the etiology and natural history of rare cancers. Cancer diagnoses in observational cohort studies are often self-reported. Little information exists on the validity of self-reported cancer diagnosis, especially rare cancers, in Canada. This study evaluated the validity of self-reported cancer diagnosis in Alberta's Tomorrow Project (ATP), a provincial cohort in Canada. ATP data were linked to the Alberta Cancer Registry (ACR). The first instance of self-reported cancer in a follow-up survey was compared to the first cancer diagnosis in the ACR after enrollment. The sensitivity and positive predictive value (PPV) were estimated for the reporting of cancer status, reporting of common or rare cancer, and reporting of site-specific cancer. Logistic regression analysis explored factors associated with false positive, false negative, and incorrect cancer site reporting. In the 30,843 ATP participants who consented to registry linkage, there were 810 primary cancer diagnoses in the ACR and 959 self-reports of first cancer post-enrollment, for a cancer status sensitivity of 92.1% (95% CI: 90.0-93.9) and PPV of 77.8% (95% CI: 75.0-80.4). Compared to common cancers, rare cancers had a lower sensitivity (62.8% vs. 89.6%) and PPV (35.8% vs. 84.5%). Participants with a rare cancer were more likely to report an incorrect site than those with a common cancer. Rare cancers were less likely to be captured by active follow-up than common cancers. While rare cancer research may be feasible in large cohort studies, registry linkage is necessary to capture rare cancer diagnoses completely and accurately.

Post-stroke insomnia and the risk of post-stroke cognitive impairment and dementia: A large retrospective cohort study.

ObjectivesIn view of the debate of factors in Dupuytren’s disease, we aimed to describe its relationship with certain occupational factors, alcohol intake and smoking.SettingThe French GAZEL cohort (employees of Electricité...

This editorial comment focuses upon the idea of offering HAART to all HIV-infected pregnant women including those in developing countries and considers the barriers or problems associated with this concept as well as the positive outcomes. It states that more data is needed but that a strategy would be to ensure that women with CD4 cell count below 350 cells/ml in resource-limited settings receive HAART that would then be continued after breastfeeding cessation for maternal health.

The sit-to-stand muscle power test: An easy, inexpensive and portable procedure to assess muscle power in older people.

The body’s response to the stresses of hypoxia and/or hypercapnia is primarily to increase ventilation while maintaining adequate perfusion, especially to vital organs. This is a highly integrated response that involves both local and reflex mechanisms. Local effects of hypoxia and hypercapnia are vasodilatory and they vary among different vascular beds so as to increase perfusion of the more vital and hypoxia-sensitive organs, such as the brain and heart. Reflex responses, which are mediated by the peripheral and central chemoreflexes, increase ventilation and maintain or raise blood pressure by increasing cardiac output and by vasoconstriction in less vital and less oxygen-sensitive tissues, such as skeletal muscle. Hence, while attempting to correct the derangement in the blood gases by increasing ventilation and pulmonary perfusion, the body tries to maintain adequate perfusion of the vital organs such as the brain and heart by diverting flow from splanchnic and muscle vascular beds.

Large cohort study gained its popularity in biomedical research and demonstrated its application in exploring disease etiology and pathogenesis, improving the prognosis of disease, as well as reducing the burden of diseases. Data science is an interdisciplinary field that uses scientific methods from computer science and statistics to extract insights or knowledge from data in a specific domain. The results from the combination of the two would provide new evidence for developing the strategies and measures on disease prevention and control. This review included a brief introduction of data science, descriptions on characteristics of large cohort data according to the development of the study design, and application of data science at each stage of a large cohort study, as well as prospected the application of data science in the future large cohort studies.

Subjects matter: a survey of public opinions about a large genetic cohort study

Pancreatic neuroendocrine tumors (pNETs) are uncommon, comprising 3%-7% of pancreatic tumors. With increasing incidence due to advanced imaging techniques, there is a need for detailed characterization of these tumors. This study aims to describe the clinical features, diagnostic evaluations, and pathology characteristics of pNETs in a large cohort from a single tertiary center, and to compare these findings with other larger cohort studies. We conducted a retrospective analysis of 866 patients with pNETs who underwent surgical resection at Mayo Clinic, Rochester, from March 2000 to December 2019. Data on demographics, clinical presentation, laboratory tests, imaging, and pathology were extracted and analyzed. Descriptive statistics were used to summarize the data. The cohort had a median age of 57 years. Nonfunctional tumors were much more prevalent (77.5%), with functional tumors primarily being insulinomas (75.9%). Common presenting symptoms included gastrointestinal (45.3%) and nongastrointestinal symptoms (30.7%). Chromogranin A levels were elevated in 57.5% of patients. Imaging revealed enhancing lesions in most cases, with computed tomography scans performed in 90.9% of patients. Endoscopic ultrasound (EUS) identified tumors in 98.1% of cases, with EUS-FNA showing a sensitivity of 82%. Ki-67 index, used in 58.1% of cases, indicated grade 2 tumors as the most common (55.9%). Metastasis was observed in 39.4% of patients at the time of diagnosis, predominantly in the liver. This study provides a comprehensive description of pNET characteristics in a large surgical cohort. Findings highlight the predominance of nonfunctional tumors and the importance of imaging and EUS in diagnosis. The data can aid in inter-institutional comparisons and enhance understanding of pNETs, contributing to improved patient management and future research.

The women's health initiative observational and clinical trials cohort is one of the largest and most carefully characterized group of postmenopausal women who have undergone long-term follow-up. Although outcome assessment has focused on the outcomes of CVD, cancer, and fractures, a wealth of data have been collected, which could form the basis for researchers interested in autoimmune diseases to begin important studies. Baseline and follow-up blood samples and DNA samples are available upon application to the study's Design and Analysis Committee and publications can be planned based on the current dataset upon application to the Publications and Presentations Committee. In both of these cases, one of the principal investigators from the 40 clinical centers or the Central Coordinating Center would need to be involved as a collaborator. Upon obtaining appropriate IRB approval, it could be possible to recontact women to collect additional information to verify outcomes or obtain further follow-up data. In addition to the WHI, the National Institutes of Health have funded a number of other large cohort studies that could be of use for researchers of autoimmune diseases. These include several longitudinal studies of diabetes funded by the NIDDK and several other large cardiovascular cohorts funded by the National Heart, Blood and Lung institute (e.g. ARIC, CARDIA and CHS. In addition, the national Cancer Institute has funded studies of large cohorts of Whites, Blacks, Hispanics, and American Indians in the United States. In all these studies, individuals have undergone baseline assessment for environmental and lifestyle risk factors and are being followed for long-term health outcomes. Given the time and expense devoted to the Women's Health initiative and these other studies, it would be of great value if researchers interested in a wide variety of chronic diseases would make use of these rich sources of data.

Despite debilitating consequences, cancer-associated malnutrition often goes under-detected due to the lack of a standardised diagnostic tool (1). The Global Leadership Initiative on Malnutrition (GLIM) criteria was established in 2018 in order to standardise the diagnosis of malnutrition globally (2). The aim of this study was to determine the association between GLIM-diagnosed malnutrition and overall survival in a large cohort of patients with mixed-cancer types. This is the first study to stratify patients according to treatment intent and is one of the largest studies to identify reduced muscle mass using gold-standard CT analysis of body composition.Patients receiving anti-cancer treatment for solid tumours were enrolled in a cross-sectional study to examine nutritional status between 2011-2016. The GLIM criteria was retrospectively applied. CT images at the third lumbar vertebrae (L3) were used to quantify skeletal muscle index and categorised according to previously published cut-points. Survival analysis was carried out using Kaplan-Meier curves and Cox-Regression.Of 1405 patients enrolled, 52.5% were male. Mean age was 62 years (SD:12 years). The most common cancer diagnosis was gastrointestinal (44.5%) and 60.3% had metastatic disease. In total, 40.4% of participants were diagnosed with GLIM-malnutrition (14.8% had stage 1 moderate and 25.6% had stage 2 severe malnutrition). Median follow-up time was 102.4 months (95% CI 99.6–105.2 months). Median survival for those without malnutrition was 30.4 months (95% CI 23.5–37.2 months), versus 11.0 months (95% CI 6.6 – 15.4 months p&lt;0.001) for those with stage 1 moderate and 10.0 months (95% CI 8.1–11.9 months p&lt;0.001) for stage 2 severe malnutrition. Multivariate-analysis (controlling for gender, age, cancer site, GLIM-malnutrition and treatment intent) demonstrated a hazard ratio (HR) of death of 1.499 (95% CI 1.233–1.822, p&lt;0.001) for stage 1 moderate and HR 1.548 (1.322–1.800, p&lt;0.001) for stage 2 severe-malnutrition. The prevalence of stage 2 severe malnutrition was significantly higher in the palliative cohort (receiving supportive measures) (32.7%) when compared to patients being treated with curative intent (18.2%, p=0.004).This study is one of the largest studies to date which uses CT analysis to accurately identify reduced muscle mass and confirms that the GLIM criteria can be used to predict overall survival in a large mixed-cancer cohort. These findings suggest that malnutrition, regardless of GLIM severity ranking has a significant impact on overall survival. Future research should focus on determining oncology specific cut-points for the GLIM criteria.