And a special shout out to Minh Nguyen and @LabRaleigh @NeurosurgUCSF for the co-publication in @NatureComms! A ton of fun working alongside them, check their paper out as well for an in depth analysis on meningioma CNAs. https://t.co/SkKo1hIAVu

Excited to see where this leads as we uncover the mechanism of CNAs in meningioma - all to guide therapeutic options and prognostication for patients. Too many people to thank, but this would not be possible without the support of @wenyalindabi and @MGBneurosurgery

Focusing on CNA size and location also enabled us to begin probing paired primary-recurrent meningiomas from the same patient as well as multifocal sampled tumors, holding insights CNA size stability versus fluctuation temporally and spatially.

We also can see how these CNAs distribute across the genome, providing clues in the sea of CNAs in meningioma on specific genomic regions that could be influencing tumor phenotype.

By looking at the size and distribution of CNAs, we can begin to understand why this is happening. Mapping the relationship between each chromosome CNA and meningioma recurrence shows which arms are salient and most susceptible to varying thresholds.

CNAs, which vary in size and location, are often translated into binary calls (arm gained/lost or unaffected). The threshold used to generate these calls matter, influencing meningioma molecular grade assignment, and prediction of tumor recurrence.

Taking a minute to share more info on our latest from @wenyalindabi lab and @MGBneurosurgery out now @NatureComms We used whole genome microarray data in #meningioma to dissect the role of CNAs, their phenotype, and implications on risk stratification. https://t.co/6QnVUfXuwA

Excited to share collaborative effort from CTNS recently published in Nature Communications! Dr. R. Patel, Dr. @wenyalindabi, Dr. Claus, Dr. A. Ligon, Dr. Santagata, and team explores how chromosome copy number alteration thresholds impact molecular grading and recurrence

Surreal, a dream come true, can’t wait to join this team!!

Great to work with @lilin_tong on this, congrats to the team!

Joins some exciting work applying computational pipelines with @wenyalindabi and @SHUNYAO11! Exploring unbiased meningioma mapping in @NeurosurgeryCNS and developing a deep learning model for MRI to CT translation in @TheJNS https://t.co/dxVaF7ADLW https://t.co/ZGQqD8AMYN

Thrilled to see this out, looking at gliomas across TCGA, GENIE, and DFCI/BWH - congrats to the whole team!

Thank you to all the research day organizers for the chance to share some of our work! Excited to see the project come together.

Grateful to have the chance to present some of our work this weekend at #AANS24, only possible because of the mentorship and support - thank you so much!

Our @CochraneLibrary #SystematicReview is finally here! 76,000 abstracts screened. 580 full-texts reviewed for potential inclusion. What did we learn about non-pharma interventions to promote #sleep for kids in 🏥? https://t.co/Gnxi0NMWIk #ICURehab #PedsICU #tweetiatrician

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And of course, thank you to @SapnaKmd for giving me the chance to turn this project into reality - this would not have been possible without your mentorship, support, and vision 😊 #PedsICU 8/8

Grateful for the incredible group of people that helped get this over the finish line, and all the staff at @HopkinsPICU that took time out of their schedules to help us better understand the impact of @PICU_Up 7/8

3. Room for innovation to facilitate early mobility exists (and is needed), through improved communication, documentation, simulation. We hope many of these themes will be helpful for units looking to create PICU early mobility initiatives or grow existing programs! 6/8

2. Barries to consistent mobility remain but have also evolved with program growth - challenges in resource management, sedation, and developmentally appropriate mobility. 5/8