Extremely excited to share my DPhil research on naked mole rats (a tiny rodent that lives for 30+ years and does not get much cancer) which has been published in @NatureComms. If you are interested in stem cell biology and longevity, here is a tweetorial for you! (1/14)
Hello everyone. I have decided not to use this platform anymore due to the Nazi-like behaviour of @elonmusk. I am now available on Bluesky. If you are interested, please connect with me: https://t.co/yRkN1J7zmu
A system for diagnosing obesity that goes beyond BMI, combining it with other methods such as measuring waist circumference, which is a proxy for adiposity, or body scans using low-level X-rays.
https://t.co/XQ850ez9tx
In a surprising paper published in Nature, scientists accomplished what sounds impossible: using genes from a single-celled organism to create mouse stem cells, which eventually developed into a living, breathing mouse.
The Tomlinson Laboratory, situated within Oxford Oncology, is seeking a highly motivated DPhil student to join an exciting cancer genetics research project. Candidates interested in this opportunity are kindly requested to refer to the link.
https://t.co/GNJg8321qH
My brilliant med student asked me to explain correlation, causation, confounding &collider bias. I used the following ex… so sharing here in case anyone finds it helpful!
PS -I have learned much from @dnunan79@Catalogofbias - a great resource for EBM. hopefully he approves😅
Understanding the difference between Standard Deviation (SD) and Standard Error (SE) is crucial for accurate data interpretation. SD measures the variability within your data, indicating how spread out the individual data points are from the mean.
In contrast, SE measures the uncertainty around the sample mean as an estimate of the population mean. It reflects the precision of the mean, with SE decreasing as the sample size increases, making your estimate more reliable.
The relationship between SD and SE is given by the formula: SE = SD / √(sample size). While SD remains relatively constant with larger samples, SE diminishes, highlighting the reduced uncertainty in the mean estimate.
A common mistake in research is using the “±” notation without specifying whether it refers to SD or SE, leading to potential misinterpretation of the data. Clear distinction is essential for transparency and accuracy in reporting.
Key Takeaways:
• Use SD to describe data variability.
• Use SE to indicate the precision of the mean.
• Always specify which measure you are reporting.
Free scientific illustrations for biologists! 😍
@NIH has released a library of 500+ free scientific illustrations to create figures, presentations, and illustrations!
all freely available in the public domain.
Retweet and spread the message!
https://t.co/p1bD1kxO7H
Come join the lab as a PhD starting October 2025
We have a project on "Therapeutic cancer prevention of hereditary cancers" 🧬+💊+🐁
funded by @AstraZeneca in collaboration Andrew Reynolds and Mark Albertella
Full details and eligibility: https://t.co/dmEtBtJy19
Please RT
Congrats to my friend and colleague Guosong Hong for his stunning and original discovery, published today in Science, on clearing tissues *in living animals* with a common food dye!
The dye is tartrazine, used in Doritos!
https://t.co/fqVivyH0kI
The value of ‘wasting time’ on deep thinking is often overlooked in a scientific ecosystem increasingly tainted by Wall Street’s productivity mindset.
https://t.co/hNTA74DNOr
It’s finally out! 🥳 Today @cellcellpress we report non-mineral fossils of ancient chromosomes in skin from a woolly mammoth that died in Siberia, 52,000 years ago.
🦣💨
Don’t miss our thread below! 🧵👇🏽