66 episodes

This podcast might not actually kill you, but it covers so many things that can. Each episode tackles a different disease, from its history, to its biology, and finally, how scared you need to be. Ecologists and epidemiologists Erin Welsh and Erin Allmann Updyke make infectious diseases acceptable fodder for dinner party conversation and provide the perfect cocktail recipe to match

This Podcast Will Kill You Exactly Right

    • Life Sciences

This podcast might not actually kill you, but it covers so many things that can. Each episode tackles a different disease, from its history, to its biology, and finally, how scared you need to be. Ecologists and epidemiologists Erin Welsh and Erin Allmann Updyke make infectious diseases acceptable fodder for dinner party conversation and provide the perfect cocktail recipe to match

    Ep 53 Radiation: X-Ray Marks the Spot

    Ep 53 Radiation: X-Ray Marks the Spot

    “I have discovered something interesting, but I do not know whether or not my observations are correct.” With these words, Wilhelm Röntgen introduced the world to an invisible power, a power which would in turn be used to both harm and heal. This week, we take a tour of the wide world of radiation, starting with a primer on what radiation actually is and how it works, courtesy of Dr. Timothy Jorgensen, Associate Professor of Radiation Medicine and Director of the Health Physics and Radiation Protection Graduate Program, Georgetown University. Then we discuss the nitty gritty on what radiation does to you on a cellular level. We follow that up with a stroll through some of the major moments in the history of radiation - from X-rays to atomic bombs and from radioluminescent paint to cancer treatments. Finally we wrap things up by chatting about the many amazing medical applications of radiation therapy and how you can assess the risk/benefit of that X-ray or mammogram.


    To read Dr. Jorgensen’s incredible book Strange Glow: The Story of Radiation, check out his website or head to our website for our full list of sources.

    • 1 hr 53 min
    Ep 52 Rinderpest: Moo Cows, Moo Problems

    Ep 52 Rinderpest: Moo Cows, Moo Problems

    The second disease ever to be eradicated, rinderpest could be the most devastating and notorious infection you never knew existed. Though its name means “cattle plague”, the deadly rinderpest virus infected hundreds of species of animals during its long reign, and outbreaks of rinderpest left nothing but famine and ruin in their wake. In this episode, we start by taking you through the biology of one of the biggest killers we’ve ever faced. We then trace the long history of this feared disease, from fire festival rituals in Russia to the imperialist exploitation of the Great African Rinderpest Panzootic of the 1890s that paved the way for European colonial rule over a large part of the continent. Fortunately, this story ends happily as only one other has done so far - with complete and total eradication. You may have started this episode not knowing about rinderpest, but when you’re done, you won’t be able to stop talking about it. Trust us.

    • 1 hr 20 min
    Ep 51 The Path of Most (Antibiotic) Resistance

    Ep 51 The Path of Most (Antibiotic) Resistance

    No story of antibiotics would be complete without the rise of resistance. As promised in our last episode, this week we dive into what the WHO calls ‘one of the biggest threats to global health, food security, and development today’ - antibiotic resistance. In the decades since their development, misuse and overuse of antibiotics has led to many becoming all but useless, and our world seems on the verge of plunging into a post-antibiotic era. How does resistance work? Where did it come from? Why did it spread so far so rapidly? Is there any hope? In this episode, we answer all these questions and more. First, we explore the many ways bacteria evade the weaponry of antibiotic compounds. Then we trace the global spread of these resistant bugs by examining the major contributors to their misuse and overuse. And finally we assess the current global status of antibiotic resistant infections (spoiler: it’s very bad) and search for any good news (spoiler: there’s a lot!). To chat about one super cool and innovative alternative to antibiotics, we are joined by the amazing Dr. Steffanie Strathdee (Twitter: @chngin_the_wrld), Associate Dean of Global Health Sciences, Harold Simon Professor at the University of California San Diego School of Medicine and Co-Director at the Center for Innovative Phage Applications and Therapeutics. Dr. Strathdee provides a firsthand account of helping her husband, Dr. Tom Patterson, fight off a deadly superbug infection by calling on a long-forgotten method of treating bacterial infections: phage therapy.

     

    To read more about phage therapy and Dr. Strathdee’s incredible experiences, check out The Perfect Predator: A Scientist's Race to Save Her Husband from a Deadly Superbug: A Memoir. 

    • 1 hr 50 min
    Ep 50 Antibiotics: We owe it all to chemistry!

    Ep 50 Antibiotics: We owe it all to chemistry!

    Fifty episodes. That’s fifty (sometimes) deadly viruses, bacteria, protozoa, parasites, and poisons. And don’t forget the fifty quarantinis to accompany each! What better way to celebrate this momentous occasion than talking about something that may actually save you: antibiotics. In this, our golden anniversary episode, our ambition tempts us to tackle the massive world of these bacteria-fighting drugs. We explore the various ways that antibiotics duel with their bacterial enemies to deliver us from infection, and we trace their history, from the early years of Fleming and Florey to the drama-laden labs of some soil microbiologists. Finally, we end, as we always do, with discussing where we stand with antibiotics today. Dr. Jonathan Stokes (@ItsJonStokes), postdoctoral fellow in Dr. Jim Collins’ lab at MIT, joins us to talk about some of his lab’s amazing research on using machine learning to discover new antibiotics, which prompts us to repeat “that is SO COOL” and “we are truly living in the future.” We think you’ll agree.

     

    To read more about using machine learning to uncover antibiotic compounds, head to the Collins’ lab website, the Audacious Project site, or check out Dr. Stokes’ paper: 

    Stokes, Jonathan M., et al. "A deep learning approach to antibiotic discovery." Cell 180.4 (2020): 688-702.

    • 1 hr 59 min
    COVID-19 Ch 11: Modeling

    COVID-19 Ch 11: Modeling

    The eleventh episode of our Anatomy of a Pandemic series has arrived, and just in time. Have you found yourself trying to sift through headlines claiming “this model predicts that” and “that model predicts this”, but you’re not sure where the truth really lies? Then this episode is for you. With the help of Dr. Mike Famulare from the Institute for Disease Modeling (interview recorded April 29, 2020), we walk through the basics of mathematical modeling of infectious disease, explore some of the current projections for this pandemic, and discuss some guidelines for evaluating these headline-making models. As always, we wrap up the episode by discussing the top five things we learned from our expert. To help you get a better idea of the topics covered in this episode, we’ve listed the questions below:
    What is a math model and what are some of the goals of mathematical modeling?
    So talking specifically now about infectious disease models, can you walk us through what the basic components are of an infectious disease model, like an SIR model?
    Where do you get the data that you use to estimate the parameters in an SIR model - what is based on actual data and what has to be estimated?
    Infectious disease outbreaks often have a curve-like shape, with the number of infected individuals on the y-axis and time on the x-axis. Can you explain why infectious disease epidemics tend to follow a curve?
    Can you talk us through some of the assumptions that you have to make when you're constructing one of these models and how that kind of relates to the uncertainty inherent within models? How might that uncertainty affect interpretation?
    What are some examples of the various ways we use infectious disease models in public health policy? Can you talk about how models might be used at various stages of a pandemic to guide public health measures? How might our use of models early on in a pandemic be different from the middle of one?
    Speaking specifically about COVID-19 now, can you talk about what a basic model for this pandemic might look like? 
    Are models for COVID-19 using only lab-confirmed cases of the disease or clinical-confirmed cases as well?
    Looking back on these earlier models of COVID-19, what can we take away from the performance of these models?
    Is there any agreement among models as to what policies might be the best in terms of keeping cases and deaths as low as possible? 
    For those of us who have no background in mathematical or statistical modeling, are there guidelines that we should use to evaluate these models or compare them? What should we (as in the general public) be taking away from these models?
    Are there any positive changes you hope to see come out of this pandemic, either as a member of the community or as a math modeler?

    For a deeper dive into the wonderful world of infectious disease models, we recommend checking out this recent video from Robin Thompson, PhD of Oxford Mathematics titled “How do mathematicians model infectious disease outbreaks?” The video was posted on April 8, 2020.

     

    • 1 hr 21 min
    Ep 49 Eastern Equine Encephalitis: Triple EEEk!

    Ep 49 Eastern Equine Encephalitis: Triple EEEk!

    In 2019, eastern equine encephalitis (EEE), a viral disease transmitted by mosquitoes, made headlines in much of the US as cases skyrocketed compared to previous years. But why is this disease so feared and even more importantly, why is it on the rise? Those are just a couple of the questions we seek to answer on this week’s episode. From the nitty gritty on what this virus does to your body to centuries-long forest dynamics in Massachusetts, we connect the disease ecology dots of EEE. We promise, the biology and history of eastern equine encephalitis is much more exciting than its etymology.

    • 1 hr 9 min

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