The "Hypertension" dataset is complete! Here's the full report...

by Egle (seplute)

Fanfares 🎺 Another datase is complete in Stall Catchers! This time we are looking to find out if high blood pressure affects the rate of stalls in Alzheimer's mice.

See the full dataset report below!
P.S. the official research result will come later, after the researchers have had a chance to look at the data.

What was the research question?

The research question of the Hypertension dataset, otherwise knows as the Megathon dataset 👏, (see early peek at the result here), was whether stalls occur more in the brain of mice that have high blood pressure, and whether that stalling can be reversed. If true, this could eventually lead to a preventive treatment for people that are at a higher risk of developing Alzheimer's.

Cornell's preliminary blood flow studies (using a different and less precise technique than visualizing stalls) seemed to confirm those hypotheses. To be absolutely sure though, and to look in detail at what's happening in the blood vessels under those conditions, we have to look at stalls.

When will we have the answers?

Now that the dataset is fully analyzed, validated and verified, we can be 95% confident that we have lab-grade crowd answers. Next, we need our biomedical collaborators at Cornell to look at the data analyzed by catchers, compare the different experimental groups (stalls in mice with normal and with increasing blood pressure, with and without Alzheimer's disease, before and after treatment), and draw the final conclusions. Stay tuned for those answers here on the blog. In the meantime, we will probably post a preliminary result as well!

How much work did we do with this dataset? The numbers...

Hypertension dataset

Total movies: 66143
Average views per movie: 4.3655
Min views on movie: 3, Max views: 110*
* normally it would never go up higher than ~7 views per movie! Read about the Megathon tech issues that led to that here.

Time dataset active: 48 days, 1.6 months
Research time accomplished: ~11 months ~7 times faster research!

Crowd answers:

  • at least 1 user identified a stall: 8922 movies
  • crowd-verified stalls: 541
  • movies where nobody identified a stall: 57221 movies
  • crowd-verified flowing movies: 65602
If you'd like to learn more about how Stall Catchers crowd answers are generated, read up here.

Total catchers participated: still checking, come back later! :)

❗If you have questions about any of these numbers, the dataset, the research question or anything else, by all means, please ask it on our forum here! :)

How did individual catchers do?
Here's the list of top 100 users by movies analyzed (left table) by rank, and crowd-verified stalls found (right table) in alphabetical order:
Top 100 users by movies analyzed Stalls found, A-Z (top 100)
Rank Username Movies Username Stalls found
1 Badstallsbadbad 57562 ababbie 39
2 caprarom 23349 AIL 105
3 christiane 19200 AlyW 3
4 sachambers 13577 annelindsey54 4
5 jkaufbenefits 13497 annettei 10
6 gcalkins 10877 azpeg85 3
7 Cicero 9962 Badstallsbadbad 420
8 AIL 9948 Baylor-_-Evans 3
9 lscatcher 8818 bloomicy 3
10 Carol_aka_Mema 6940 bmchatham 3
11 MsFidich 6768 BraveBrad 3
12 mamaanne 6476 burkycs53 6
13 Plenum 5342 C.Eis 4
14 glol 4914 Camk2005 4
15 grampa1387 4529 caprarom 153
16 sean4046 3863 Carol Wagner 3
17 ababbie 3835 Carol_aka_Mema 72
18 WendyH 3694 chairstar 12
19 garageguy1939 2378 Chip 3
20 Unstaller 2016 christiane 171
21 ziziji3 1911 Cicero 116
22 Lybiw 1655 CowboyYeehaw 5
23 chairstar 1651 Drkev1 2
24 monkey 1651 elAmber 3
25 Uganalandia 1544 emilydapurpleecouch 8
26 th0mms3n 1449 Estelle_Angelinas 2
27 GENBOZ 1335 evanfunky 4
28 Isabella_Cavaliere 1289 Falkenfisch 7
29 Kathie_Henderson 1205 garageguy1939 20
30 orbithunter 1008 gcalkins 120
31 Jurjen 1003 GENBOZ 10
32 sweatpants 872 glol 34
33 Tara 856 grampa1387 50
34 stall_catcher 839 Greenvirgo 2
35 TammyElliott 833 grovermatic 9
36 CowboyYeehaw 824 happycamelindigo 2
37 MaisyMoo92 794 Heath 2
38 emilydapurpleecouch 791 IcySpicy1018 4
39 elAmber 773 Isabella_Cavaliere 22
40 annettei 731 Jacqueline R Aviss 3
41 C.Eis 721 Jamie_Lin 2
42 Michael_Landau 685 jkaufbenefits 130
43 grovermatic 675 Jonathan_Killebrew 2
44 LisaH33 657 Judy_Molnar 4
45 burkycs53 646 Jurjen 8
46 Yeet 615 Karli 5
47 Thomas_Adams 549 Kathie_Henderson 13
48 Karli 546 kenzielee 6
49 Christie_Lyon 532 KetoKaja 3
50 seethruw 502 LAYTON_CORDELL 2
51 Oldsettler1949 488 LisaH33 7
52 michaelcolombo 461 LivSlaughter 3
53 Tpad 456 lorb 3
54 annelindsey54 443 LR070801 6
55 evanfunky 440 lscatcher 81
56 Oofergodric 439 Lybiw 15
57 Jacqueline R Aviss 437 MaisyMoo92 5
58 Wildkarrde55 427 mamaanne 64
59 Camk2005 398 mampela01 4
60 rickdeckard 389 memeboy99 4
61 No_one_goingto_beat_zach 379 Michael_Landau 7
62 Zinnykal 376 michaelcolombo 5
63 Mr 364 monkey 10
64 lanita 354 Moxie 2
65 bmchatham 336 MrsArrr 3
66 Falkenfisch 333 Ms_H 3
67 lorb 330 MsFidich 74
68 nitori 329 nkrue 2
69 KetoKaja 320 Oldsettler1949 4
70 LR070801 317 Oofergodric 3
71 Bryson 317 orbithunter 7
72 Moxie 311 patch 3
73 Quazimoto 303 Plenum 32
74 azpeg85 285 RadioactiveHepcat 5
75 Annie25 284 rchawla 5
76 fadderguido 281 rfitzgibbons 4
77 RadioactiveHepcat 281 rickdeckard 5
78 ruthseid 280 Ryan_Strom 3
79 2footgiraffe 278 sachambers 61
80 badjokeshop 276 Sandimcc 3
81 TatiJen 275 sean4046 29
82 bloomicy 271 seethruw 13
83 Autumn2 268 siddys 3
84 Baylor-_-Evans 260 stall_catcher 9
85 kenzielee 254 sweatpants 12
86 BraveBrad 252 TammyElliott 7
87 908205 248 Tara 6
88 treehouselibrarian 238 TatiJen 6
89 Drkev1 231 th0mms3n 14
90 Mark_Hymel 230 Thomas_Adams 2
91 mampela01 228 Tryston 3
92 Tryston 221 tsquare 3
93 meatloafcat 218 Uganalandia 17
94 Meandmay 217 Unstaller 21
95 IcySpicy1018 215 wendygirl71 2
96 LAYTON_CORDELL 208 WendyH 29
97 patch 207 Wildkarrde55 2
98 Jamie_Lin 206 Yeet 3
99 maggy 205 Zinnykal 2
100 tsquare 203 ziziji3 19

Thank you everybody who helped us analyze this exciting dataset, which could lead to cues about preventing Alzheimer's in high risk groups and help us learn more about factors involved in the development of stalls.

❗If you have questions about any of these numbers, the dataset, the research question or anything else, by all means, please ask it on our forum here! :)

What's next?
Next in [Stall Catchers](https://stallcatchers.com) we are working on **==the "Long Term" dataset==**.

With the "Long Term" dataset we are seeking to understand how late into disease development an increase in brain blood flow, due to a decrease in capillary stalling, still leads to an improvement in performance on short-term memory tasks.

Our collaborators at Cornell University have already demonstrated that an increase in blood flow by reducing the number of stalls in the brain of Alzheimer's disease mice leads to improved congnitive functions and reduces symptoms of Alzheimer's. Read more about their research, recently published in Nature Neuroscience, here. Therefore, as the next step, it will be fascinating to know how far into disease development can an intervention targeting stalls still have a positive effect.

The dataset is already over 70% analyzed at the time of writing (that's because it was already active for a little bit before the Megathon!). To fully complete it, though, and get reliable crowd answers we will have to validate and verify it after the analysis stage is done, so there's plenty of work to go around! ;)

👉 Head straight to Stall Catchers and start analyzing!

Amazing work everybody, as always! 💜 Stay tuned for the research result, and let's go conquer the next dataset!