
Happy 2021! @MBVanElzakker and I are excited to share our new article published in #Immunometabolism: “Pathogens Hijack Host Cell Metabolism: Intracellular Infection as a Driver of the Warburg Effect in Cancer and Other Chronic Inflammatory Conditions”:





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https://t.co/hXlo8qgkD0
Look like that they got a classical case of PCR Cross-Contamination.
They had 2 fabricated samples (SRX9714436 and SRX9714921) on the same PCR run. Alongside with Lung07. They did not perform metagenomic sequencing on the “feces” and they did not get
A positive oral or anal swab from anywhere in their sampling. Feces came from anus and if these were positive the anal swabs must also be positive. Clearly it got there after the NA have been extracted and were from the very low-level degraded RNA which were mutagenized from
The Taq. https://t.co/yKXCgiT29w to see SRX9714921 and SRX9714436.
Human+Mouse in the positive SRA, human in both of them. Seeing human+mouse in identical proportions across 3 different sequencers (PRJNA573298, A22, SEX9714436) are pretty straight indication that the originals
Were already contaminated with Human and mouse from the very beginning, and that this contamination is due to dishonesty in the sample handling process which prescribe a spiking of samples in ACE2-HEK293T/A549, VERO E6 and Human lung xenograft mouse.
The “lineages” they claimed to have found aren’t mutational lineages at all—all the mutations they see on these sequences were unique to that specific sequence, and are the result of RNA degradation and from the Taq polymerase errors accumulated from the nested PCR process
Look like that they got a classical case of PCR Cross-Contamination.
They had 2 fabricated samples (SRX9714436 and SRX9714921) on the same PCR run. Alongside with Lung07. They did not perform metagenomic sequencing on the “feces” and they did not get

A positive oral or anal swab from anywhere in their sampling. Feces came from anus and if these were positive the anal swabs must also be positive. Clearly it got there after the NA have been extracted and were from the very low-level degraded RNA which were mutagenized from
The Taq. https://t.co/yKXCgiT29w to see SRX9714921 and SRX9714436.
Human+Mouse in the positive SRA, human in both of them. Seeing human+mouse in identical proportions across 3 different sequencers (PRJNA573298, A22, SEX9714436) are pretty straight indication that the originals
Were already contaminated with Human and mouse from the very beginning, and that this contamination is due to dishonesty in the sample handling process which prescribe a spiking of samples in ACE2-HEK293T/A549, VERO E6 and Human lung xenograft mouse.
The “lineages” they claimed to have found aren’t mutational lineages at all—all the mutations they see on these sequences were unique to that specific sequence, and are the result of RNA degradation and from the Taq polymerase errors accumulated from the nested PCR process

1/ Automobiles and Intake Fraction. Since cars are back in the news I thought I would retweet this model result I offered in early April 2020. I focused only on 1 micron particles & accounted for windows completely closed & cracked slightly open.
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.
Simulation: Riding in car for 120 min w/ infected passenger who seems fine other than a cough every few mins. (1) a lot of SARS-CoV-2 virus (in fine aerosol particles) accumulation in car cabin w/ windows closed; (2) cracking window open slightly = dramatic reduction. #COVID19 pic.twitter.com/bCmrmnLUPG
— Dr. Richard Corsi (@CorsIAQ) April 4, 2020
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.
It was great to talk about reproducible workflows for @riotscienceclub @riotscience_wlv. You can watch the recording below, but if you don't want to listen to me talk for 40 minutes, I thought I would summarise my talk in a thread:
My inspiration was making open science accessible. I wanted to outline the mistakes I've made along the way so people would feel empowered to give it a go. Increased accountability is seen as a barrier to adopting open science practices as an ECR
It also comes across as all or nothing. You are either fully open science or your research won't get anywhere. However, that can be quite intimidating, so I wanted to emphasise this incremental approach to adapting your workflow
There are two sides to why you should work towards reproducibility. The first is communal. It's going to help the field if you or someone else can reproduce your whole pipeline.
There is also the selfish element of it's just going to help you do your work. If you can't remember what your work means after a lunch break, you're not going to remember months or years down the line
Thank you again @JamesEBartlett for a fantastic talk (with a really nice personal touch) on reproducible workflows!
— RIOT Science Club Wolverhampton (@riotscience_wlv) February 16, 2021
Thanks especially for the co-leads @IMLahart for co-hosting and @DrManiBhogal for nabbing James!
Slides: https://t.co/CNqxzOhch1
Video: https://t.co/YjHEHuRJlz
My inspiration was making open science accessible. I wanted to outline the mistakes I've made along the way so people would feel empowered to give it a go. Increased accountability is seen as a barrier to adopting open science practices as an ECR
It also comes across as all or nothing. You are either fully open science or your research won't get anywhere. However, that can be quite intimidating, so I wanted to emphasise this incremental approach to adapting your workflow
There are two sides to why you should work towards reproducibility. The first is communal. It's going to help the field if you or someone else can reproduce your whole pipeline.

There is also the selfish element of it's just going to help you do your work. If you can't remember what your work means after a lunch break, you're not going to remember months or years down the line
Why are lunch breaks important for #code?
— Dr Rebecca Hirst (@HirstRj) February 11, 2021
If you can't remember what your variable names refer to after lunch, you sure as hell won't remember in 3 months.
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