Prof Jeffrey S Morris ✓
@jsm2334 · Philadelphia, PA · joined 11 Dec 2014
George S. Pepper Professor of Public Health & Preventive Medicine; Biostats, Stats & AI; lifelong learner & truth seeker; Views are my own, not my employer's
14 784Followers
2 395Following
52 242Posts total
482.4KViews on collected posts
โพสต์ล่าสุด
กำลังค้นหาโพสต์ใหม่…
Here is abstract (sadly full paper behind paywall): https://t.co/h9XEuQ8f1F
556 views · 18 likes
· 2 reposts · 3 replies
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@jsm2334 Jeffrey you're a statistician.
Who gave you this monologue?
This is so far outside your lane it's not funny.
1.3K views · 57 likes
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Link to paper: https://t.co/OXmTdwRTuR https://t.co/fWI1WrAo2n
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10/10
The paper still has real value.
It identifies a plausible pathway worth studying further: LNP formulation, dose, inflammatory intensity, timing, neutrophils, NETs, and whether there are specific clinical situations where any of this matters.
But it should be kept in
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· 1 reposts · 3 replies
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8/10
The timing was highly artificial and very important.
In several experiments, researchers injected large numbers of tumor cells directly into the bloodstream and gave LNP right around the same time.
That effectively lined up the circulating tumor cells with the peak
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· 1 reposts · 1 replies
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9/10
So what does the paper actually show?
It shows that, under certain conditions, sufficiently strong LNP-induced inflammation can transiently create a more favorable environment for metastatic seeding in mice.
What it does not show is that COVID vaccines cause cancer, that
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6/10
The dose is a major limitation.
The mice received 12 mg/kg of LNP, which roughly on the order of 1,000x higher per kilogram than the lipid exposure from a human mRNA vaccine dose.
High doses can be useful for uncovering potential mechanisms at action.
But it is well known
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7/10
Also important: none of these mice received a COVID spike-mRNA vaccine.
The study used empty LNPs and other mRNA constructs.
So it cannot directly tell us what Pfizer or Moderna COVID vaccination does to metastatic cancer.
Any claim specifically about spike-mRNA
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5/10
The mRNA payload mattered too.
Empty LNPs and LNPs carrying irrelevant GFP mRNA increased metastases in one model.
But an E6/E7 mRNA vaccine actually reduced metastases.
So it is plainly not true that the biological outcome is determined by the LNP alone and that “it
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4/10
The LNP effect was also not consistent across formulations.
Some LNP formulations increased metastases.
Another formulation using the Moderna ionizable lipid SM-102 did not.
That matters, because it means the paper itself does not support the blanket claim that “LNPs” as
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2/10
The proposed mechanism is biologically plausible.
In the mice, LNP-related muscle injury released mitochondrial DNA, which activated inflammatory pathways, recruited neutrophils, and promoted formation of NETs — neutrophil extracellular traps.
NETs can help circulating
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3/10
But this is a metastasis model, not a cancer-causing model.
The study does not show LNPs causing normal cells to become cancerous.
It asks whether already-existing cancer cells are more likely to establish or grow metastatic deposits under a particular inflammatory
792 views · 26 likes
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1/10
Interesting new paper presenting results from a mouse study on lipid nanoparticles (LNPs) and metastasis.
It is a mechanistic, hypothesis-generating animal study: under some experimental conditions, certain LNPs caused a short-lived inflammatory response that made it https
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https://t.co/0CWn1RITfV https://t.co/KfIXapqf7J
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@UnMaskd_Truth, cowardly misrepresenting both my purported “conflicts of interest” (I personally have none) and my positions on the Inconvenient Study from behind a block so I couldn’t respond
In her efforts to push her narratives, she shows no understanding of the scientific ht
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Nope.
This oft-repeated claim is based on a blatant misrepresentation of the clinical trial data.
And the failure to detect an all-cause mortality benefit was entirely expected given the trial’s size, duration, and very small number of deaths. https://t.co/5fmC8RIrqn
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Here is Dan Wilson aka “Debunking the Funk”’s video pointing out the fatal flaws of this paper and pointing out the misleading and even fallacious arguments made in the ICAN propaganda video
https://t.co/HiioTAn91i
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Dan Wilson (aka Debunking the Funk) put together a long video extensively debunking the Henry Ford study dubbed by ICAN as “An Inconvenient Study” and the propaganda video of the same name.
It’s a cutting critique of both the study, its purported conclusions, its framing in the
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@jsm2334 @ICANdecide So the study is flawed because unvacc'ed kids weren't taken to see the doctor nearly as often as vacc'ed kids?
Excuse me... ? !
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For those who like to abrogate their own analysis or check mine, here is what ChatGPT 5.0 says when simply asked to comment on strengths and weaknesses of the study without any prompting or skepticism communicated:
https://t.co/TJ61V8uA45
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In the linked thread, I unpack the study’s key methodological flaws and point to better ways the data could have been analyzed to more reliably evaluate any potential vaccine–chronic disease relationships.
https://t.co/6bEGkByTE3
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Tonight @ICANdecide is releasing “An Inconvenient Study,” a video built around an unpublished Henry Ford Medical Center analysis that it says shows vaccines cause widespread childhood chronic disease.
In reality, the "Inconvenient Study" has multiple “Inconvenient Flaws” that
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I’ve written a news article for The Conversation about the so-called “Inconvenient Study,” which its promoters describe as a landmark study demonstrating vaccines cause chronic health conditions in children, highlighting its major design flaws and statistical problems that https:
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During the recent Senate hearings, participants highlighted a Henry Ford Health System study, billed in a forthcoming documentary as ‘An Inconvenient Study,’ and portrayed it as conclusive proof that vaccines elevate childhood chronic-disease risk, claiming it was concealed by ht
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Just put up a website https://t.co/LOKEBcwd8z that aggregates and tries to provide a balanced discussion of Covid-19 research results, data sets, applications and models, and commentaries and linking to useful data sets and models
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เทียบกับบัญชีขนาดเดียวกัน
16 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงไปไกลเกินผู้ติดตามของตัวเอง และผู้ชมก็ตอบสนอง.
ยอดดูมัธยฐาน744บัญชีนี้999ค่ามัธยฐานของ 10K–100K
การเข้าถึง, %5.03%บัญชีนี้3.62%ค่ามัธยฐานของ 10K–100K
การมีส่วนร่วม, %4.07%บัญชีนี้1.92%ค่ามัธยฐานของ 10K–100K
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 10K–100K | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 744 | 999 | 0.74× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 5.03% | 3.62% | 1.39× |
| อัตราการมีส่วนร่วม | 4.07% | 1.92% | 2.13× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
79024 Aug
14.5K4 Sep
792
865
882
671
598
657
595
571
647
698
1.3K
556
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.51%24 Aug
1.22%4 Sep
3.54%
3.01%
3.63%
4.47%
4.85%
5.94%
6.22%
5.43%
5.56%
4.01%
4.90%
4.14%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.92%.
What the audience does
Likes63.0%1 696 in total
Reposts15.0%404 in total
Replies11.2%302 in total
Quotes4.1%111 in total
Bookmarks6.6%178 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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