Don’t take this the wrong way because I’m a scientist that does cancer research. I was driving home today and I was captivated by this story on NPR So crazy it might workhttp://goo.gl/cAGXf
Dr. John Brody (http://goo.gl/Djbb5), an expert in pancreatic cancer, goes home to give a speech at his alma mater about thinking outside the box. After his talk, his old music teacher, Anthony Holland, approaches him about an idea. Anthony had been working in his garage on an idea from Royal Rife to use electromagnetic waves to make cells burst like a wine glass when the resonant frequency is used. What really captivated me is when Dr. Brody tried to explain to his friend that his results were meaningless without proper controls. He also tried to explain that when Mr. Holland got support from other scientists, it was also tainted because he had not explained that the control experiments had failed so far.
mary Zeman via Melissa Bryan posted about an accident where a student nurse in training (3rd day on the job) administered a feeding bag of coffee and milk instead of a unit of blood. http://goo.gl/otI4e For starters, I don’t think coffee mixed with milk looks like a unit of blood. Nevertheless, in Mary’s post I explained what I think likely happened, e.g., COD.
Nutritional specialist Dr. Armando Carreir told the network that Ribeiro’s death “would have been as if [she] was suffocating.” from HuffPuff: http://goo.gl/euhoM
So how can the patient be suffocating? It was likely due to pulmonary edema (fluid in the lungs). So how did the patient get fluid in the lungs? Not knowing the exact mixture of coffee and milk, I’m guessing that the mixture was hypertonic. Let me back up and talk briefly about intravenous (IV) pharmaceuticals.
When you adminster a large volume of fluids IV, it has to be at physiologic pH (7.4), isotonic, and iso-oncotic. There are cases where you can give a fluid that is not one of these three properties to correct for the patients condition.
Tonicity
From the Wiki:
Tonicity is a measure of the osmotic pressure gradient (as defined by the water potential of the two solutions) of two solutions separated by a semipermeable membrane. It is commonly used when describing the response of cells immersed in an external solution. Like osmotic pressure, tonicity is influenced only by solutes that cannot cross the membrane, as only these exert an osmotic pressure. Solutes able to freely cross the membrane do not affect tonicity because they will always be in equal concentrations on both sides of the membrane.
There are three classifications of tonicity that one solution can have relative to another. The three are hypertonic, hypotonic, and isotonic. There’s a good figure in the Wiki that shows what happens to the blood cells in the three types of tonicity.
Colloid osmotic pressure (COP) or Oncotic pressure
COP is an osmotic pressure caused by protiens in the vasculature which opposes the hydrostatic pressure. The normal COP of plasma is between 20-25 tor. An increase in COP above normal levels will lead to water leaving the interstitium and entering the vascular space. http://en.wikipedia.org/wiki/Oncotic_pressure
Thanks Richard Smith for this timely reminder about science and science communication. In addition to the events you mentioned, the recent legal action in Italy involving scientist and the earthquake also warrants some edification.
Part of the mission of ScienceSunday is to reach out to the general population on G+ and explain what is real science. Of course we have fun with jokes, puns, and memes. However, we want people to have a place where they can ask questions and learn about real science. We want a place where we can explain why something might be pseudoscience. Finally, we want to share our passion for science and hope to encourage others to join our passion for science.
Here are a few of my posts that are related to either correcting pseudoscience or bad science related journalism.
If you are interested in science, circle ScienceSunday and watch for #ScienceEveryday when it isn’t #ScienceSunday
#Anti_anti_intellectualism
Originally shared by Richard Smith-Unna
Science as a candle in the dark; our responsibility as scientists
Today for ScienceSunday I want to take a moment to talk about something serious.
Anti-science and irrationality have a strong hold in the modern world. Political, religious and cultural values often conflict with what science tells us, and lead to situations which are not just intellectually frustrating, but in the worst cases lead to people’s lives being put at risk.
As an example, the NHS in the UK currently funds four homeopathic hospitals (http://goo.gl/8qSzX, to learn why homeopathy is a problem: http://1023.org.uk). It’s not just sad, it tears at the fabric of my intellectual being to see my country treating people with such distain.
At the same time, we see the scientific method being abused to oppose GM agriculture; a group of technologies which have the potential to avert future food crises and eventually provide food security for the whole world (http://goo.gl/Q0J4o).
And even within the scientific community, we have recently seen that chauvinism and discrimination are serious problems (http://goo.gl/OdHFW).
In each case, we as members of the science-supporting public or the scientific community can do something to address the problem. More than that – it’s our responsibility to do so.
We can make our voices heard, invest our time and effort in expelling mysticism and ignorance. When we see abuses, we can expose them. We can collectively discuss and hone our methods of communication and argumentation. We can join forces to have a greater impact, and to support each other when the incessant battle gets demoralising. I don’t have the solution, but we do.
The problem is that it’s difficult and demoralising to talk to someone who is anti-science. How do you deal with irrationality? How do you debate with someone whose world view rejects evidence for dogma? Please discuss.
This was inspired by Buddhini Samarasinghe’s post: http://goo.gl/o9bC5. ScienceSunday is curated by Rajini Rao, Chad Haney, Robby Bowles, and Allison Sekuler.
Buzz about when natural remedies become real medicine
Buddhini Samarasinghe ,others, and I often mention how alternative medicine becomes medicine when it’s rigorously tested. Here’s an example from the The University of Chicago . Chih-Pin Chuu et al, in Dr. Richard Jones’ lab report how Caffeic Acid Phenethyl Ester (CAFE) suppress cell proliferation of prostate cancer cells. CAPE is the active ingredient in beehive propolis (see below). They started with cancer cells in vitro and then moved to a mouse model.
But if CAPE were to truly make the crossover from holistic remedy to clinical option, the scientists would also have to demonstrate how the compound freezes cancer cells in a non-proliferative state. Enter the micro-western array, the innovative proteomics technique first described in 2010 by Jones and colleagues.
Once they found their target pathway from the micro-western array, they over-express components of those pathways to block the effect of CAPE. This kind of follows my diet post earlier today (http://goo.gl/vlUWT). CAPE makes the cancer cells think there is no nutrients available. There is mention of patent issues in the article, i.e., it would be hard to get a drug company to pay for the clinical trials on this because propolis cannot be patented. For more on that issue, check out my Bench to Bedside post. http://goo.gl/xpu7W
Caffeic acid phenethyl ester suppresses the proliferation of human prostate cancer cells through inhibition of p70S6K and Akt signaling networks.
Chuu CP, Lin HP, Ciaccio MF, Kokontis JM, Hause RJ Jr, Hiipakka RA, Liao S, Jones RB.
In searching for a good eye catching photo to go along with the article, I realized that the image in the news blurb is a stock image from Wikipedia. It was also used in this interesting article.
Image of propolis, the sticky resin that bees line their hive with. In a PLosONE article Michael Simone-Finstrom and Marla Spivak report it’s anti-fungal properties. http://goo.gl/duvSC
Dwarf species of fanged dinosaur emerges from southern Africa
The link below is from The University of Chicago it’s slightly different than the other news blurbs you’ve probably seen already. Also the full article is Open Access. Be warned the PDF is 125 MB. There is a 25 MB version.
Paul C. Sereno, “Taxonomy, Morphology, Masticatory Function and Phylogeny of Heterodontosaurid Dinosaurs,” ZooKeys online, Oct. 3, 2012.
I’ve done some imaging for Paul. Leave a comment if you would like Dr. Paul Sereno at The University of Chicago to discuss Pegomastax africanus on a #SSHOw I will try to tempt Paul into a HO if there is enough interest.
Round 3 of the #SSHOw (ScienceSunday HO-woot) will be about the upcoming “Birds of Egypt” exhibit at the Oriental Institute, focusing on how medical imaging helped.
#ScienceEveryday when it isn’t #ScienceSunday
Originally shared by ScienceSunday
Join us this Sunday where JP Brown from the The Field Museum Christian Wietholt from VSG, Rozenn Bailleul-LeSuer from the Oriental Institute (OI), and Chad Haney from the University of Chicago (ScienceSunday co-curator) preview the upcoming Birds of Egypt exhibit. They will be discussing their contributions to the project, mainly focusing on how computed tomography (CT) was helpful in examining the artifacts, non-destructively.
We hopefully guided you through the maze of the GMO Corn hysteria in the media. Here’s a link to some of the statistical issues. http://goo.gl/epcnr Next weeks #SSHOw will be on the mummy bird that I posted a while ago (http://goo.gl/sbzJq).
Today’s #SSHOw (http://goo.gl/0eGrh) will be discussing GMO corn and in particular a poorly done experiment/publication that sparked the media storm (Séralini et al Food and Toxicology 2012). http://goo.gl/5GOWa
Orac dissects the paper quite nicely, although I think he repeatedly says mice when he means rats. http://goo.gl/SSE2F
The two areas I will comment on are the tumor rat model and a statistical issue dealing with multiple comparisons.
Spontaneous Tumors in Rats
Orac points to a study in 1979 where 81% of the Sprague-Dawley rats, the same strain used in the Séralini paper, develop tumors. When you use an animal model looking at tumor development, you need to know the prevalence of spontaneous tumor development. The control group(s) have to be designed such that you can differentiate between “normal” spontaneous tumor development in the control groups vs. the experimental groups. Part of that design is having sufficient number of animals to have statistical power. Using previously published data, the authors could have done a power analysis to determine the proper sample size. For these types of studies, where you are not doing intricate daily or weekly interventions/experiments, i.e., just keeping the animals for long periods while looking for mortality, it is not uncommon to have 3-5 times the number of animals used in the Séralini study.
As an example, I had the privilege to collaborate with Prof Morris Pollard at Notre Dame who developed the Lobund-Wistar rat model. Lobund-Wistar rats spontaneously develop prostate adenocarcinoma (PA) at a mean age of 26 months. In the publication of the model, out of 72 L-W rats, 19 (26%) developed large PAs. Imagine if Prof Pollard only used 10 male rats as in the Séralini study.
We use a transgenic mouse model for spontaneous ductal carcinoma in situ. Invasive carcinomas develop in 100% of the mice. The mice are called SV40-Tag mice based on the C3(1)/Tag mice. SV40-Tag stands for Simian virus 40 T-antigen, a trans-activating protein, which are essential for viral gene expression.
The point is you have to know the tumor prevalence in the rodent model you are using and plan the control groups accordingly.
Multiple Comparisons/Sample Size
The study mentions that they use Discriminant Analysis (DA) to partition groups, i.e., you lump all the variables (factors) together and use DA to flesh out which factors influence the outcome, e.g., tumor size, biochemical markers, etc. In image analysis we use Linear Discriminant Analysis (LDA,http://en.wikipedia.org/wiki/Discriminant_analysis, http://goo.gl/oyNzh) to segment (classify) pixels. Say you want to automatically segment tumor from normal tissue using several image types of the same sample. You have thousands of pixels to work with, not 10. The method isn’t robust with 10 or less samples in the 20 groups used (note I separated the male and female groups in the Séralini study). Also, in the context of machine learning, you have to have a training set. In my example you give the program a set of pixels that you know belong to each group before testing the pixels you want to classify.
A quick review on null hypothesis testing. A type I error is when the null hypothesis is true but is rejected, i.e., a false positive. A type II error is when the null hypothesis is false but is incorrectly accepted as true, a false negative. Remember the null hypothesis can never be proven.
Here are examples from the Wiki:
Suppose the treatment is a new way of teaching writing to students, and the control is the standard way of teaching writing. Students in the two groups can be compared in terms of grammar, spelling, organization, content, and so on. As more attributes are compared, it becomes more likely that the treatment and control groups will appear to differ on at least one attribute by random chance alone.
Suppose we consider the efficacy of a drug in terms of the reduction of any one of a number of disease symptoms. As more symptoms are considered, it becomes more likely that the drug will appear to be an improvement over existing drugs in terms of at least one symptom.
Suppose we consider the safety of a drug in terms of the occurrences of different types of side effects. As more types of side effects are considered, it becomes more likely that the new drug will appear to be less safe than existing drugs in terms of at least one side effect.
Statistical power is the probability of committing a type II error, false negative. Prof Pollard’s study has a statistical power of around 97% while the Séralini study is probably closer to 45%.
In Memoriam
I the process of digging up the study by Prof Pollard, I realized he had passed away. I met him when he was in his late 80s, to do an experiment for him at the University of Chicago. He was an impressive man and scientist. It is really a shame he is most often known for his son.
Pollard worked at all of these things until his very last days. “I can’t imagine doing anything else,” he said recently. “I think if you are doing something meaningful and important and you stop doing it, you’ll always look back with regret.”
Here’s an article by an anti-quack blogger extraordinaire. http://goo.gl/DRpQk It says a lot of what I said in my comments about the Telegraph article discussed here and the linked discussion within:
Of course Orac does a much better job of taking the article apart.
Attached image:
A scanning electrom microscopic image of HIV. The glycoprotein complex on its surface enables the virus to attach to and fuse with target cells to initiate the infectious cycle.
Gaythia Weis asked me to comment on her post about ta blog post about he study. http://goo.gl/rerki
Sorry to disappoint you all, but I haven’t had enough time to dig into this framing debate. It really isn’t my area anyway. However, I use statistics all of the time in my research. So I’m going to introduce what is meta-analysis and hopefully return to the articles later.
Meta-analysis is a method of contrasting and combining results from a large group of studies. That sounds like an advantage and it is, i.e., strength in numbers. However, there are disadvantages and meta-analyses can be done wrong. The biggest disadvantage is that each study has a purpose and study design that is most likely not identical. So the key is how do you decide which studies to include and which studies to exclude. In today’s #ScienceSunday SSHOw, I hope to discuss this in a little bit more detail. The Wiki is actually quite good.
Getting back to the pesticide story here’s the abstract from the article.
Background: The health benefits of organic foods are unclear.
Purpose: To review evidence comparing the health effects of organic and conventional foods.
Data Sources: MEDLINE (January 1966 to May 2011), EMBASE, CAB Direct, Agricola, TOXNET, Cochrane Library (January 1966 to May 2009), and bibliographies of retrieved articles.
Study Selection: English-language reports of comparisons of organically and conventionally grown food or of populations consuming these foods.
Data Extraction: 2 independent investigators extracted data on methods, health outcomes, and nutrient and contaminant levels.
Data Synthesis: 17 studies in humans and 223 studies of nutrient and contaminant levels in foods met inclusion criteria. Only 3 of the human studies examined clinical outcomes, finding no significant differences between populations by food type for allergic outcomes (eczema, wheeze, atopic sensitization) or symptomatic Campylobacter infection. Two studies reported significantly lower urinary pesticide levels among children consuming organic versus conventional diets, but studies of biomarker and nutrient levels in serum, urine, breast milk, and semen in adults did not identify clinically meaningful differences. All estimates of differences in nutrient and contaminant levels in foods were highly heterogeneous except for the estimate for phosphorus; phosphorus levels were significantly higher than in conventional produce, although this difference is not clinically significant. The risk for contamination with detectable pesticide residues was lower among organic than conventional produce (risk difference, 30% [CI, -37% to -23%]), but differences in risk for exceeding maximum allowed limits were small. Escherichia coli contamination risk did not differ between organic and conventional produce. Bacterial contamination of retail chicken and pork was common but unrelated to farming method. However, the risk for isolating bacteria resistant to 3 or more antibiotics was higher in conventional than in organic chicken and pork (risk difference, 33% [CI, 21% to 45%]).
Limitation: Studies were heterogeneous and limited in number, and publication bias may be present.
Conclusion: The published literature lacks strong evidence that organic foods are significantly more nutritious than conventional foods. Consumption of organic foods may reduce exposure to pesticide residues and antibiotic-resistant bacteria.
I don’t really have a problem with the description of their methods. Again, I hope to return to this and talk in more detail.
