Vanderbilt University

About: Vanderbilt University is a education organization based out in Nashville, Tennessee, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 45066 authors who have published 106528 publications receiving 5435039 citations. The organization is also known as: Vandy.

Atypical hyperplastic lesions of the female breast. A long-term follow-up study.

Abstract: A total of 10,542 breast biopsy specimens obtained between 1950 and 1968 were studied. Examples of atypical "ductal" (ADH) and atypical lobular hyperplasia (ALH), defined as having only some features of carcinoma in situ (CIS), were diagnosed in 3.6% of these specimens. In the same series, CIS was diagnosed in 1.7% of biopsy specimens excluding those with invasive cancer. The subsequent risk of invasive breast carcinoma after ALH or ADH was 4-5 times that of the general population. Follow-up was 90% successful and extended 17 years after biopsy. History of breast cancer in a mother, sister, or daughter doubled the risk of subsequent invasive carcinoma development (to 8 times for ALH and 10 times for ADH). The authors conclude that among the epithelial hyperplastic lesions of the human breast, a minority may be recognized by their resemblance to CIS which have a clinically significant elevation of subsequent breast cancer risk. This risk is one-half that of CIS.

Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021.

Abstract: Background Sepsis poses a global threat to millions of lives. The Surviving Sepsis Campaign (SSC) guidelines provide evidence-based recommendations on the recognition and management of sepsis and its complications. Methods We formed a panel of 60 experts from 22 countries and 11 members of the public. The panel prioritized questions that are relevant to the recognition and management of sepsis and septic shock in adults. New questions and sections were addressed, relative to the previous guidelines. These questions were grouped under 6 subgroups (screening and early treatment, infection, hemodynamics, ventilation, additional therapies, and long-term outcomes and goals of care). With input from the panel and methodologists, professional medical librarians performed the search strategy tailored to either specific questions or a group of relevant questions. A dedicated systematic review team performed screening and data abstraction when indicated. For each question, the methodologists, with input from panel members, summarized the evidence assessed and graded the quality of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. The panel generated recommendations using the evidence-to-decision framework. Recommendations were either strong or weak, or in the form of best practice statements. When evidence was insufficient to support a recommendation, the panel was surveyed to generate “in our practice” statements. Results The SSC panel issued 93 statements: 15 best practice statements, 15 strong recommendations, and 54 weak recommendations and no recommendation was provided for 9 questions. The recommendations address several important clinical areas related to screening tools, acute resuscitation strategies, management of fluids and vasoactive agents, antimicrobials and diagnostic tests and the use of additional therapies, ventilation management, goals of care, and post sepsis care. Conclusion The SSC panel issued evidence-based recommendations to help support key stakeholders caring for adults with sepsis or septic shock and their families.

Implicit Active Contours Driven by Local Binary Fitting Energy

Abstract: Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piece-wise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising.