Antibody Treatment against Angiopoietin-Like 4 Reduces Pulmonary Edema and Injury in Secondary Pneumococcal Pneumonia

By Liang Li, Benjamin Jie Wei Foo, Ka Wai Kwok, Noriho Sakamoto, Hiroshi Mukae, Koichi Izumikawa, Stéphane Mandard, Jean-Pierre Quenot, Laurent Lagrost, Wooi Keong Teh, Gurjeet Singh Kohli, Pengcheng Zhu, Hyungwon Choi, Martin Lindsay Buist, Ju Ee Seet, Liang Yang, Fang He, Vincent Tak Kwong Chow, Nguan Soon Tan

mBio. June 4, 2019

 

Secondary bacterial lung infection by Streptococcus pneumoniae (S. pneumoniae) poses a serious health concern, especially in developing countries. We posit that the emergence of multiantibiotic-resistant strains will jeopardize current treatments in these regions. Deaths arising from secondary infections are more often associated with acute lung injury, a common consequence of hypercytokinemia, than with the infection per se. Given that secondary bacterial pneumonia often has a poor prognosis, newer approaches to improve treatment outcomes are urgently needed to reduce the high levels of morbidity and mortality. Using a sequential dual-infection mouse model of secondary bacterial lung infection, we show that host-directed therapy via immunoneutralization of the angiopoietin-like 4 c-isoform (cANGPTL4) reduced pulmonary edema and damage in infected mice. RNA sequencing analysis revealed that anti-cANGPTL4 treatment improved immune and coagulation functions and reduced internal bleeding and edema. Importantly, anti-cANGPTL4 antibody, when used concurrently with either conventional antibiotics or antipneumolysin antibody, prolonged the median survival of mice compared to monotherapy. Anti-cANGPTL4 treatment enhanced immune cell phagocytosis of bacteria while restricting excessive inflammation. This modification of immune responses improved the disease outcomes of secondary pneumococcal pneumonia. Taken together, our study emphasizes that host-directed therapeutic strategies are viable adjuncts to standard antimicrobial treatments.

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End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

 

By Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher, Lily Peng, Daniel Tse, Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich & Shravya Shetty

Nature Medicine, May 20, 2019

 

With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20–43% and is now included in US screening guidelines. Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.

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Machine Learning at the Clinical Bedside—The Ghost in the Machine

By Joseph J. Zorc, James M. Chamberlain, Lalit Bajaj

 

JAMA Pediatrics, May 13, 2019

In this issue of JAMA Pediatrics, Bertsimas et al1 describe a novel machine-learning approach to derive a revised version of the head injury prediction rule developed by the Pediatric Emergency Care Applied Research Network (PECARN). The PECARN rule was derived and validated using a prospectively collected data set of more than 42 000 patients to classify which children with head injury are at very low risk of clinically significant intracranial abnormalities.2 The ultimate goal of such a decision rule is to reduce unnecessary computed tomographic imaging and associated radiation. Bertsimas et al1 analyzed a public use data set from the PECARN study using a technique called optimal classification trees. The revised rule has improved specificity and predictive value, identifying 33% more children younger than 2 years, and 14% more children 2 years or older as having a very low risk for intracranial injury compared with the PECARN rule, without missing any additional cases of intracranial injury. Although this is good use of the public use data sets now required for federally funded research, interpreting machine-learning techniques may be challenging for clinicians to understand and apply as the techniques become increasingly complex. Although we live in an era of precision medicine, with the ability to tailor personalized recommendations, it is also an era emphasizing shared decision making between clinicians and patients. It may be difficult for clinicians to counsel patients about the implications of a rule that is perceived as a black box or ghost in the machine, which may provide recommendations for unclear reasons.

 

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Can health promotion videos ‘go viral’? A non-randomised, controlled, before-and-after pilot study to measure the spread and impact of local language mobile videos in Burkina Faso

By Tessa Swigart, Jennifer Hollowell ORCID Icon, Pieter Remes, Matthew Lavoie, Joanna Murray, Mireille Belem, Rita Lamoukri

Global Health Action, May 8, 2019

 

Mobile phones present a new health communications opportunity but use of mobile videos warrants more exploration. Our study tested a new idea: to produce health promotion videos in languages for which films have never previously been produced to see if they were widely shared.

To investigate whether the novelty of films in local languages focusing on health messages would be shared ‘virally’ among the target population.

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Infectious Diseases Society of America Position Statement on Telehealth and Telemedicine as Applied to the Practice of Infectious Diseases

By Jeremy D Young, Rima Abdel-Massih, Thomas Herchline, Lewis McCurdy, Kay J Moyer, John D Scott, Brian R Wood, Javeed Siddiqui

Clinical Infectious Diseases, May 2019

 

Over the last 2 decades, telemedicine has effectively demonstrated its ability to increase access to care. This access has the ability to deliver quality clinical care and offer potential savings to the healthcare system. With increasing frequency, physicians, clinics, and medical centers are harnessing modern telecommunications technologies to manage a multitude of acute and chronic conditions, as well as incorporating telehealth into teaching and research. The technologies spanning telehealth, telemedicine, and mobile health (mHealth) are rapidly evolving, and the Infectious Diseases Society of America (IDSA) has prepared this updated position statement to educate its membership on the use of telemedicine and telehealth technologies. IDSA supports the appropriate and evidence-based use of telehealth technologies to provide up-to-date, timely, cost-effective subspecialty care to resource-limited populations.

 

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