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Sure, I mainly just want to make a large dataset with as many studies as possible so it can be shared and analyzed by anyone in whatever way they want, including the method you said.

Sure yeah that'd be awesome. I can DM later to share access. I'm still not completely sure what I want to do. It'd be nice to create a huge dataset with fold change and significance values for every protein from lots of studies, to do more in depth analysis like what Murph and chillier were...

The senior author has an MEpedia page. Also quite a lot of ME/CFS papers on there from him.

Immune Cell Exhaustion and Apoptotic Markers in Major Depressive Disorder: Effects of in Vitro Cannabidiol Administration. Muanpetch Rachayon, Ketsupar Jirakran, Pimpayao Sodsai, Chawit Tunvirachaisakul, Atapol Sughondhabirom, Yingqian Zhang, Jing Li, Michael Maes Background Immune...

Excellent write up! One suggestion is adding the oxaloacetate trial, since I think news on that has been eagerly anticipated by many in the community. And sorry if this is insignificant, but one letter didn't get italicized in the MAGENTA trial section:

So I think it makes more sense to just go all in on all proteins instead of just complement then coming back to everything later. I'd have to go over pretty much every paper twice. So here's what I've got of my half-thought out plan so far. I got all the findings I could from this study so...

Oh yeah maybe something following the trend of putting ME at the end of a word, like SolveME, CureME, DecodeME, SequenceME.

Just chipping in that I also initially thought it was a blog denying ME/CFS is real. Especially since the first post I saw was about whether POTS diagnosis makes sense. But I've come to like the name. Yeah people might get the same first impression, but if other people they trust are sharing...

Cool idea @Murph! I just yesterday started something in the same vein. Complement keeps popping up, so I thought it could be useful to look at every study that has tested any complement proteins, compile them into one dataset, and visualize which ones are consistently higher or lower across...

Oh, one of the "symptoms" listed is "Symptomatic". There are 8 not filled in for this. None of them have any long COVID symptoms. 3 have boxes filled in for medications or unrelated symptoms like high cholesterol.

A multi-omics based anti-inflammatory immune signature characterizes long COVID-19 syndrome, 2022, Kovarik et al And this one in LC, again on plasma, shows the opposite. More downregulated than upregulated. So maybe it's nothing.

Data-independent LC-MS/MS analysis of ME/CFS plasma reveals a dysregulated coagulation system, endothelial dysfunction, downregulation of complement machinery, 2024, Nunes, Kell, Pretorius et al This one did proteomics on plasma, like the first three studies, but on ME/CFS. Slightly more...

This study did untargeted proteomics, but on extracellular vesicles not plasma: Dysregulation of extracellular vesicle protein cargo in female ME/CFS cases & sedentary controls in response to maximal exercise, 2023 Giloteaux et al Split evenly at baseline, most proteins downregulated at 15...

Proteomic and metabolomic profiling of plasma uncovers immune responses in patients with Long COVID-19, 2024, Wei et al This preprint did untargeted proteomics as well. Not as striking as the previous two studies I referenced, but still more proteins are upregulated than downregulated when...

Proteomic and metabolomic profiling of plasma uncovers immune responses in patients with Long COVID-19 Yulin Wei, Hongyan Gu, Jun Ma, Xiaojuan Mao, Bing Wang, Weiyan Wu, Shimin Yu, Jinyuan Wang, Huan Zhao, yanbin he [Preprint] Abstract Long COVID is an often-debilitating condition with...

Probably because ChatGPT partly wrote it, as they disclose. I also was very confused. I just assumed they meant recovered, but I'm not sure. It might mean low number of symptoms. Figure 3C has the reported symptoms of each patient: Hard to read the symptoms, but there are some participants...

I'm not quite sure how to interpret the volcano plot based on clustering. Whether it's possible the reason so many are upregulated and so few are downregulated is based on clustering, like the algorithm decided something like "all the people with low proteins go in cluster 1, all the high ones...

I think the volcano plots are comparing the two clusters from the unsupervised clustering, not symptomatic vs asymptomatic, which I don't really understand the reason for. Edit: I guess maybe they're assuming the clustering reveals a subtype of LC?

I don't know what pellet is. There's not much information online. Maybe this paper has some information: From this thread's paper. I think it says RBCs are overrepresented in pellet compared to plasma. --------- ---- "Volcano plot of differentially expressed proteins in cluster 2 (mostly...

Proteomic analysis of post-COVID condition: Insights from plasma and pellet blood fractions Alejandro Seco-González, Paula Antelo-Riveiro, Susana B. Bravo, P.F. Garrido, M.J. Domínguez-Santalla, E. Rodríguez-Ruiz, Á. Piñeiro, R. Garcia-Fandino Background Persistent symptoms extending beyond...