The present study aimed at clarifying the effect of coffee consumption on gut pathways implicated in non-alcoholic fatty liver disease (NAFLD) development such as intestinal and liver lipid metabolism, gut barrier functions and gut microbiota. To this purpose an animal study with mice fed an HFD and drinking water or a coffee extract as well as mice fed with a standard diet (SD) and drinking water for 12 weeks was implemented. Serum samples and liver histology were assessed in parallel with gene expression of molecular mediators of fat oxidation, cholesterol efflux, lipid digestion and energy metabolism regulation, gut permeability and composition of the gut microbiota.

Vitaglione P et al. (2019) Coffee prevents fatty liver disease induced by a high-fat diet by modulating pathways of the gut–liver axis. J Nutr Sci. 8: e15. doi: 10.1017/jns.2019.10.

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Sections of the Abstract/Introduction and Discussion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6477661/

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labeling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labeling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work describes the technical aspects of the platform, thereby covering the functionalities at its current state and exploring its future use cases and extensions.

Müller MM et al. (2019) Crowdbreaks: Tracking Health Trends Using Public Social Media Data and Crowdsourcing. Front Public Health. 7: 81. Published online 2019 Apr 12. doi: 10.3389/fpubh.2019.00081.

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Sections of the Abstract, Introduction, and Results are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6476276/

Despite the plentiful evidence of the positive effects of physical activity on both physical health and general well-being, a significant share of the world’s population is not active enough. In an attempt to reach a high share of the population, public health authorities encourage employers to promote physical activity at the workplace, where most adults spend a significant amount of time. This research is highly relevant for companies that consider implementing a workplace health promotion programs (WHPP) as it provides insights on how to design such a program in a way that maximizes participation levels. Furthermore, it contributes to the advancement of research, as we are the first to analyze data of a fitness platform company that is interacting as intermediary between members and a variety of fitness outlets – a business model that is currently disrupting the fitness industry.

Lier LM et al. (2019) Organizational-level determinants of participation in workplace health promotion programs: a cross-company study. BMC Public Health. 19: 268. doi: 10.1186/s12889-019-6578-7.

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Sections of the Background and Conclusion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427860/

The abilities of certain microorganisms to be transferred across the food production chain, persist in the final product and, potentially, colonize the human gut are poorly understood. Here, we provide strain-level evidence supporting that dairy cattle-associated bacteria can be transferred to the human gut via consumption of Parmesan cheese. We characterize the microbial communities in samples taken from five different locations across the Parmesan cheese production chain, confirming that the final product contains microorganisms derived from cattle gut, milk, and the nearby environment. In addition, we carry out a human pilot study showing that Bifidobacterium mongoliense strains from cheese can transiently colonize the human gut, a process that can be enhanced by cow milk consumption.

Milani C et al. (2019) Colonization of the human gut by bovine bacteria present in Parmesan cheese. Nat Commun. 10: 1286.doi: 10.1038/s41467-019-09303-w.

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Sections of the Introduction and Conclusion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426854/

Miscarriage is a common outcome of pregnancy, with most studies reporting 12% to 15% loss among recognised pregnancies by 20 weeks of gestation. Quantifying the full burden of miscarriage is challenging because rates of pregnancy loss are high around the time that pregnancies are clinically recognised. As a result, the total rate of recognised loss is sensitive to how early women recognise their pregnancies. There are also differences across countries and studies in distinguishing between miscarriage and stillbirth. Furthermore, the observed miscarriage rate is affected by the competing risk of induced abortion. A general lack of data on induced abortions has made it difficult to determine how seriously this competing risk distorts the estimation of miscarriage rates. Based on national registries or population based cohort studies, the reported risk of miscarriage in Sweden, Finland, and Denmark was between 12.9% and 13.5%. A previous Norwegian study included all women treated at one of the main hospitals in Oslo between 2000 and 2002, and estimated a miscarriage rate of 12% when taking into account induced abortions.

The aim of the current study was to estimate the rate of miscarriage among Norwegian women and to evaluate the association with age and pregnancy history.

Wilcox A et al. Role of maternal age and pregnancy history in risk of miscarriage: prospective register based study. (2019). BMJ. 364: l869. doi: 10.1136/bmj.l869.

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Sections of the Introduction and Conclusion are presented in the Podcast. Access the full-text article here:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425455/

How similar is the human mind to the machines that can behave like it? After decades spent lagging behind the recognitional capabilities of even a young child, machine-vision systems can now classify natural images with accuracy rates that match adult humans. The success of such models, especially biologically inspired Convolutional Neural Networks (CNN's), has been exciting not only for the practical purpose of developing new technologies (for example screening baggage at airports, reading street signs in autonomous vehicles, or diagnosing radiological scans), but also for better understanding the human mind itself. Recent work, for example, has found that CNN's can be used to predict the behavior of humans and non-human primates, large-scale activation of brain regions, and even the firing patterns of individual neurons — leading to speculation that the mechanisms and computational principles underlying CNN's may resemble those of our own brains.

To address this question, we introduce a “machine-theory-of-mind” task that asks whether humans can infer the classification that a machine-vision system would assign to a given image. We acquired images produced by several prominent adversarial attacks, and displayed them to human subjects who were told that a machine had classified them as familiar objects. The human’s task was to “think like a machine” and determine which label was generated for each image. We conducted eight experiments using this task, probing human understanding of five different adversarial image sets. Importantly, none of these images was created with human vision in mind —they were simply generated to fool a machine-vision system into misclassifying an image.

Zhou Z et al. (2019) Humans can decipher adversarial images. Nat Commun. 10: 1334. doi: 10.1038/s41467-019-08931-6

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Sections of the Introduction and Discussion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430776

Kidney transplantation is the first choice treatment for end-stage renal disease patients. The main obstacle in offering this treatment to everyone who needs it is an organ deficiency. There is still a discrepancy between the number of patients on the waiting list for kidney transplantation and the number of performed transplants. The needs exceed the capabilities of supply. Strategies for increasing transplant availability include using expanded criteria donor (ECD) organs, donor after cardiac death organs, dual kidney transplantation, living organ donation, and experiments with artificial organs.

This study examined organ donors presented acute kidney injury (AKI) as a potentially valuable origin of kidneys for transplantation. The aim of this study is to present, that renal transplants from donors with AKI may have adequate renal function 5 years after transplantation.

Domagala P et al. (2019) Successful transplantation of kidneys from deceased donors with terminal acute kidney injury. Ren Fail. 41(1): 167–174. doi: 10.1080 0886022X.2019.1590209BMC.

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Sections of the Background and Discussion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001022/

Each year, thousands of people die from an adverse drug reaction, defined as an undesirable health effect that occurs when medication is used as prescribed. Adverse drug reactions can vary from a simple rash to more severe effects, such as heart failure, acute liver injury, arrhythmias, and even death. These events have a significant impact on both patients and the health care system in terms of cost and health service utilization (for example frequent visits to physicians and emergency departments, hospitalizations). Post-marketing adverse drug reaction surveillance in most countries is suboptimal and consists largely of spontaneous reporting.

As this is a rapidly evolving field, we conducted a comprehensive scoping review to assess the utility of social media data for detecting adverse events related to health products, including pharmaceuticals, medical devices, and natural health products.

Tricco AC et al. (2018) Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review. BMC Med Inform Decis Mak. 18: 38. doi: 10.1186/s12911-018-0621-y.

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Sections of the Background, Discussion, and Conclusion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001022/

Methamphetamine (METH) is an illicit psychostimulant that is subject to abuse worldwide. While the modulatory effects of METH on dopamine neurotransmission and its neurotoxicity in the central nervous system are well studied, METH’s effects on modulating microglial neuroimmune functions and on eliciting neuroinflammation to affect dopaminergic neurotoxicity has attracted considerable attention in recent years. The primary goal of the current review is to re-evaluate this neurotoxicity from the perspective of reactive microglial cell changes, as neuroinflammatory reactions have been reported to occur following METH administration and are believed to causally contribute to METH-induced neurotoxicity.

Fatemeh S et al. (2018) Methamphetamine neurotoxicity, microglia, and neuroinflammation. J Neuroinflammation. 15: 341. doi: 10.1186/s12974-018-1385-0.

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Sections of the Background, METH neurotoxicity and reactive gliosis, and Conclusion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6292109/

Eating foods prepared away from home is a popular behavior in the U S population. In 2005–2008, over a third of the daily energy intake in the U S came from foods prepared away from home. Restaurant prepared foods are known to be energy-dense, and higher in fat and sodium, but lower in protective nutrients. To our knowledge there are no published prospective studies of the association of restaurant meal exposure with the risk of cardiometabolic and all-cause mortality in the U S population. To fill these gaps, we examined the prospective association of frequency of eating restaurant prepared meals and risk of all-cause and cardiometabolic mortality in a representative sample of the U S population. Given some prior reports of adverse cardiometabolic risk biomarker profiles of frequent consumers of restaurant meals, we also examined cross-sectional associations of cardiometabolic biomarkers with frequency of eating away from home meals.

Kant AK et al. (2018) A prospective study of frequency of eating restaurant prepared meals and subsequent 9-year risk of all-cause and cardiometabolic mortality in U S adults. PLoS One. 13(1): e0191584. doi: 10.1371/journal.pone.0191584.

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Sections of the Introduction and Discussion are presented in the Podcast. Access the full-text article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5779659/