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Welcome to the Stojdl Lab Blog!


We blog about science from our own lab, and labs around the world!

By stojdllab, Jul 7 2015 07:19PM

A leading Ontario cancer researcher, Professor David Stojdl, has received $200,000 in funding from the BioCanRx network. This funding will support the clinical development of a new virus biotherapy to treat patients with the devastating brain cancer, glioblastoma multiforme (GBM).

Dr. Stojdl's lab, located at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, has developed a new approach to GBM therapy in collaboration with scientists at McMaster University and the Ottawa Hospital Research Institute. This approach uses cancer-killing "oncolytic" viruses as tools to harness a patient's own immune cells to fight their tumour. This immune activation is critically important, as patients whose tumours are packed with immune cells have a much better prognosis. These viruses have proven extremely safe in the brain and effective at dealing with issues that frustrate current GBM therapies.

Dr. Stojdl's project, funded by BioCanRx, will custom build a single virus designed to activate immune cell populations that are already established at unprecendented levels in the majority of individuals. Almost all GBM patients in Canada will be eligible for this therapy. The virus will also be designed to act as a beacon to guide these activated immune cells to the tumour site.

The BioCanRx program was established as a new Networks of Centres of Excellence (NCE) in December 2014, and is funded by the Government of Canada, and several industrial and charitable partners. BioCanRx is designed to incubate the top Canadian research in biotherapeutics, including virus-, antibody- and immune cell-based therapies, and is structured to ensure that the very best ideas funnel up through conception to testing to clinical trials in patients.

You can find out more about the Stojdl lab on Twitter, Facebook, Tumblr and YouTube.

By stojdllab, Jan 19 2015 07:23PM

Scientists took soil samples every 50m across the 3.4km2 of Central Park, and analysed the number and type of microbes lurking in the dirt, including bacteria, archaea and eukaryotes.

All told, they found 167,000 species! On average, each single soil sample had about 7000 bacteria and archaea, and 1250 eukaryote species. Insanity!

Read more here.

Image credit: Ramirez et al 2014

By stojdllab, Dec 10 2014 05:39PM

As an academic scientist, this question usually hits you right after you've written your results section, but haven't had a chance to square up to the pit of seething hell that is the discussion. It's a blissful point where you can stop and carve out a moment to joyfully delay the inevitable. There are a few ways to choose which journal will have the honour of taking on your magnificent manuscript:-

Nail, meet hammer

The bluntest approach is to go through your reference list manually, and compile a list of each journal as it appears, as well as the number of times it shows up. Shuffle these into the order of the most frequent, and you can easily pinpoint the top five journals in your manuscript. If you want to be extra fastidious, you can look up the impact factors of the journals in your list (or at least those of the top five), and factor those in, too.

Online tools to the rescue!

A slightly more elegant approach is to use online tools created to solve this exact dilemma. My favourite is probably JANE: the Journal/Author Name Estimator, made by Martijn Schuemie from the Biosemantics group at Erasmus MC in The Netherlands. Pop your abstract into the search box, hit Find Journals, and prepare to be presented with a heady list of journals that have published articles similar to yours. These potential journals are ranked in terms of 'confidence', or how tight the match is between your input abstract and the past article output of that journal. While bona fide impact factors aren't displayed, you do get provided with probably the next best thing: the Article Influence (AI) score. This measures the average influence of articles published from that journal based on how often they got cited within the first five years after publication. The AI score is weighted based on which journal is doing the citing - so a citation in a big impact factor journal, like Science, bolsters the AI score to a more significant degree.

If you don't like the look of JANE, the Virginia Tech University Library has also compiled this helpful list of other web-based tools that can help you find a nice good-looking journal to publish in.

Happy manuscript submissions!

By stojdllab, Nov 25 2014 04:39PM

Nature Reviews Immunology just published a great new opinion article on the new and emerging roles of the complement system.


In the image above, we can see the three potential ways that complement responses get activated: through classical, alternative and lectin pathways. All three of these pathways converge on generating C3 and C5 convertase enzyme complexes, which cleave C3 into the anaphylatoxin C3a and the opsonin C3b, and C5 into the anaphylatoxin C5a and into C5b, respectively. Deposition of C5b onto a target site initiates formation of the membrane attack complex (MAC) and blasts apart the target. Credit: Kolev et al 2014.

By stojdllab, Aug 27 2014 02:47PM

Scientists at Arizona State University have moved closer towards a universal immunosignature diagnosis platform for cancer.

Publishing in the journal PNAS, they describe the use of a platform that applies antibodies circulating in the blood of cancer patients to a large microarray of random peptides. Those antibodies that bind to one or more peptide are then characterised to create an “immunosignature” for each individual patient.

When the immunosignatures of multiple patient's are compared, it becomes possible to pick out common peptides that keep cropping up. These could form the basis of a diagnostic test for cancer that would only require a drop of a patient's blood. It may also be possible to use these peptide immunosignatures to inform the most effective type of cancer treatment.

In the figure above, the expression levels of two immunostimulatory peptides are determined for 1,516 cancer patients across 15 different cancer types. Peptide 1 shows high expression in 3 classes of cancer (10, 13 & 15), while peptide 1 is only expressed at high levels in 1 class of cancer (11).

You can read the original article here.

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