My trick, I just use bootstrap, ask Claude for a custom Styles following a style, palete etc. Much better experience than buying and adapting and existing bootstrap theme
> This article is misleading because it does not mention Trump or Musk or Doge
The article doesn’t mention those things because you’re wrong about both the facts and the timeline, and you’d know that, had you read the article.
> Mexican cattle imports were banned in the US in 2024 because of the screw worm. Then trump allowed mexican cattle imports in February 2025 even though the screw worm situation was not resolved.
True, but a red herring. The first cases of Mexican screwworm were in late 2024 / early 2025 [1]. The current circumstances began long before the current presidential administration (at least 2020), as TFA correctly notes.
> Then, in March 2025, Musk's DOGE cut funding for COPEG, the organization that suppresses the screw worm in Panama.
No. The funding cut was for an unrelated UN agency (FAO) not COPEG [2]. FAO does not implement the fly eradication program, per their own website [3], but partisan critics have purposely confused the two issues, which you can see an example of at [4]. They mention COPEG, then talk about the FAO issue, then don’t mention that the one is unrelated to the other, because they want the reader to confuse the two.
In fact, the administration did not cut funding to COPEG, funding $165M in FY2025, with a supplemental grant of $21M the same year [5].
I have my problems with the current administration, and certainly don’t think they’re innocent here, but this kind of fact-free political backbiting that actively confuses the issue drives me batty.
I read the article and I am right about everything other than one not very significant detail. I am probably right about that too.
It is not a red herring that Trump allowed cattle imports from mexico when it was widely known that the screw worm was in mexico. It was a very serious lapse in safety and disease prevention.
Furthermore, DOGE did cut federal funding for programs for monitoring and curtailment of the screw worm in Central America in February 2025, and your own links show that. For example your link [2] says the above sentence almost verbatim. The FAO issue is not unrelated to the to the COPEG issue. The FAO also funded programs for detection and curtailment of the screw worm in central america just like COPEG. They were probably complimentary programs.
I could not find any link that COPEG funding was cut, but then again you showed no evidence that it was not cut. DOGE insisted on secrecy and was very vindictive, so a lot of DOGE cuts are not known and there is no definite public list of DOGE cuts. Furthermore, federal employees are scared. Any cuts to FAO programs would be made public because the FAO is an international organization and their employees are not at risk of being fired by Trump. But COPEG is an US organization and everyone in there will be scared to mention funding cuts to the media.
By the way, your statement that the Tump administration funded COPEG with 165 million in 2025 and a supplemental grant of 21 million is an outright falsehood and it is a falsehood proven by your own link [5]. Your own link [5] says that the 165 million funding came in 2024, not 2025 which would make it something done by the Biden administration. The supplemental 21 million funding came in 2025 but that was for fruit flies, not screw worm producing flies, so it did not go to COPEG.
I wonder, did you not read your own links, or did you know you were saying lies and hope that nobody else will read your links.
So in summary, DOGE did cut funding for screw worm detection and prevention in early 2025. Trump did allow Mexican cattle in the US in early 2025, even though it was known that the screw worm is in Mexico. It is not entirely clear whether DOGE cut funding for COPEG exactly or whether it only cut funding for other non-COPEG screw worm detection and prevention programs, but funding for screw worm detection and prevention was cut.
> Your own link [5] says that the 165 million funding came in 2024, not 2025 which would make it something done by the Biden administration.
From the link:
> APHIS received emergency funding of $109.8 million in 2023 and $165 million in 2024 from the Commodity Credit Corporation (CCC) for screwworm response activities. In May 2025, USDA announced an additional $21 million transfer of CCC funds to convert an existing fruit-fly-rearing facility in Metapa, Mexico, into a sterile fly-rearing facility.
The Trump administration didn’t start until January of 2025.
If you don’t want to give them credit for funding the project in the current financial year, fine, but then it’s especially dishonest to blame them for “defunding” during the same time period. (Particularly when that’s not true - I cited that explicitly to show that funding was not stopped.)
> I could not find any link that COPEG funding was cut, but then again you showed no evidence that it was not cut.
Other than the part I just quoted for you? The part you obviously read, since you cited it in your comment?
Nobody is arguing with you that the current administration cut funding for FAO. They did. What I’ve shown you is that this is is not the same thing as COPEG, FAO is not the prevention program, and even if it were, the cuts were far too late to have caused a problem that began six years ago.
> I heard there was one keeping this under control. It involved transgenic flies, which sounds close to transgender. DOGE ended it.
No.
First, the problem here predates DOGE by an entire presidential administration or more. If you had read the article, you’d know that the northward migration of flies started at least in 2020.
Second, the thing you’re misremembering has no relation to this program. It was funding for transgenic animals in a completely different area of research.
You are repeating hearsay that sounds like it should be the reason. It confirms your political priors while ironically making you feel smug about your scientific knowledge. I’m not a fan of the current administration, but seeing so many people here bend over backwards to blame this on their political enemies, without even a hint of intellectual curiosity is more depressing than anything the politicians have done. Your political team can be incompetent too — and probably is!
> It confirms your political priors while ironically making you feel smug about your scientific knowledge.
You are making the same error. Other Republicans (if not Trump) indeed criticized government-funded research into the screwworm lifecycle. Denying that the party of the president has been opposed to both science and regulation on principle -- including with this particular crisis -- makes you look naive at best, dishonest at worst.
I'm not arguing that one political team is innocent. When it comes to correctly managing complex scientific problems, all politicians are reliably incompetent.
It's right there, linked in TFA. The press release provided by the GP is instead discussing funding for the "UN Food and Agriculture Organization", which is different. Apparently they also do some unspecified amount of work on the issue.
That aid money went, in part, to preventing the spread of screwworms in Central America. As of 2024, the flies were mostly eradicated in Mexico and efforts were on-going in Panama to wipe them out down to the Darien Gap. In less than 2 years we've gone from them being almost entirely eradicated in North America to infections observed in the United States.
…and the current administration is using money to do that. It’s in the article, and I’ve posted links to evidence on multiple sub threads of this thread, which you had to ignore to make this comment.
Y’all are just absolutely fixated on blaming one side for this.
Two things can be true at once: one group cut funding early 2025, and another group added funding later. The former group, DOGE, was less responsible, and the latter group, USDA, is more responsible. I do not know why I have to ignore the former group to be fair to the latter.
Yes, the screwworm problem predates the funding cut. Surely that should prompt an increase or at least a maintenance of existing funding for monitoring programs though, certainly not a decrease.
I think atoav is saying the /stupid consequence/ is the cut in funding itself, not the screwworm resurgence.
Pointing out legitimate failure of an administration is not partisan -- denying or deflecting that criticism is partisan. The current regime has slashed so many programs based on the flimsiest reasoning (including "my predecessor supported this so therefore I hate it").
I'm more than happy to acknowledge any failures by Dem leadership because I'm not a party member and even if I were I would not let that blind me to the reality of that failure.
An interesting aspect of speaking with republican family members is that they assume democrats are monolithic and will revert to that assumption once enough time has passed. Like, unable to process being told that nobody in the room watches CNN or likes the Clintons.
I think conservative's brains are wired differently, and there's studies that back that up. They tend to lack empathy, which implies they can't walk in other people's shoes so therefor their assumptions about others are based upon how they themselves think.
I don't write that to demean them, I'd like it if it wasn't so -- this comment is in no way intended to be deragotory.
That said, I think this substantiates the notion that with conservatives "every accusation is a confession", because they can only see the world through their eyes they assume everybody else thinks like them.
A problem happening eventually is expected. The point of a good program is a layered approach that admits no layer is perfect so you have backups that kick in to minimize the impact of problems. So the problem was emerging in 2022, not great but not a tragedy. Cutting monitoring means we reacted slower and our inability to play with our neighbors well means that we can't coordinate a response quickly or as effectively. Destroying our layered, nuanced policies has real consequences and this is one of them.
Screw worms existing before Trump doesn't make it a bipartisan issue. Trump cut the funding, did Democrats do too? So then no only one party ignored and actively defunded it, making it exactly a partisan issue. Good job trying to cover for trump, it's extra pathetic here
> It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
No it isn’t. The things that were hard are now harder. The things that were comparatively easy are now easier. But if you build another piece of vibe-coded crap in a world awash in vibe-coded crap, you will not stand out. Nobody cares about your unpolished, one-shot prototype, so cranking them out faster is not really helpful.
Differentiation is always a problem of effort and care, and this isn’t going to change.
In fact, when you see someone in the art world claiming that X is a "defining" anything, it usually means that they have a big collection of X for sale.
In this case, I imagine it's submarine marketing for the movie that's out.
hacker news is, though. I'd be interested to see if the same article has been posted to two dozen subreddits etc (though not so interested as to actually do anything about it lol)
It's so weird to open a page on HN and see a photo of a place that I went to all the time as a child, but as some kind of abandoned-space porn for Zoomers (Century III mall).
I randomly drove by there back in about 2018 or so when I happened to be in Pittsburgh for a weekend. The parking lot was empty, so it must have closed by that time, but it was still intact.
Very intense memories of going there with my grandparents as a little kid, riding the holiday train, seeing Santa, etc. Even met the handyman from Mr Rogers Neighborhood one time!
Before you can investigate the causes of an illness, you have to define it. Otherwise, you’re chasing an ever-shifting cloud of ambiguous symptoms, any of which could have different causes. The article opens with this admission, so I’m not stating anything new here.
The problem with “Long Covid” as it exists today is that there’s no such definition. Literally anyone who had Covid once and feels bad today (and quite a few people who never had a confirmed case at all) includes their set of symptoms in the communal diagnosis. Thus, if you dig into these studies, you always find that the syndrome is a wide-ranging and variable constellation of symptoms, making it impossible for a study to have any systematic legitimacy. Moreover, the results of any particular study are more strongly influenced by the inclusion criterion (if there even is one) than by any other factor.
It’s perfectly possible to evaluate treatments in this situation, and would be a better use of resources - pick symptoms, make an inclusion criteria, and run a randomized trial of existing drugs or therapies. But this is likely to fail, and it’s much, much easier to write papers with unprovable theories and retrospective analysis.
Sometimes the symptoms are so ambiguous that it is hard to nail anything down. It’s the same thing with Lymes disease, which is definitely a real thing, but there aren’t good, reliable tests for it. It takes a long time to manifest and the symptoms vary wildly from person to person.
> It’s the same thing with Lymes disease, which is definitely a real thing, but there aren’t good, reliable tests for it
There are actually good, reliable tests for it. However Lyme disease (not Lymes disease) became an alternative medicine explanation for everything vague and many people became obsessed with thinking they had it based on vague symptoms like fatigue. When they couldn’t get positive test results to confirm their belief, the Lyme disease online communities established the idea that the tests cannot detect their version of the disease. It’s a belief that allows anyone to diagnose with Lyme disease in a completely unprovable way.
> and the symptoms vary wildly from person to person.
This belief is an unfortunate result of the online Lyme communities encouraging everyone with any unexplained symptoms to believe it’s caused by Lyme disease that can’t be detected. When the disease becomes redefined as being untestable and causing wildly different symptoms in everyone, it becomes impossible to say that anyone doesn’t have it. If you have any vague symptoms like feeling tired, a Lyme disease community will encourage you do believe that it’s caused by an undetectable case of Lyme disease.
There is a lot of strong evidence that these patients do not have Lyme disease, but they’re always good at coming up with another reason why they have it but it can’t be detected in them specifically
There are increasingly positive markers - autonomic dysfunction in previously healthy people, measurable small fiber neuropathy, and auto immune dysfunction in largely unmapped parts of the immune system.
Interesting. Someone should (or maybe have?) run a cluster analysis on the symptoms to define more specific subgroups. But I suppose getting access to the required health data at that scale is nontrivial?
It’s not that hard to get a long list of symptoms for long covid. Just watch this thread as it grows, and you’ll easily find dozens. Things like this end up being a lint trap for people who just feel bad for whatever reason (which is all of us, at various points in our lives!) Nobody likes to be told that their symptoms are idiopathic.
Massaging this kind of data (clustering, etc.) is much lower value than finding fixed criteria that define a consistent group of patients who have objectively defined symptoms that cannot be more readily explained by another diagnosis. This is a pre-requisite for any further study. It can be done, but it’s hard, and it tends to lead to criticisms because you end up excluding a large number of people who fervently believe they have the illness, but don’t fit the objective standards.
Just for example: it’s not enough to claim that you have “brain fog”. A more valid endpoint might instead attempt to classify people based on standardized tests of thinking. Even that has problems, of course, but if you can just claim that you are fatigued and unable to think clearly, there’s a huge problem of confounding (i.e. maybe your symptoms are caused by something else), let alone the unverified nature of the original claim.
Leading research into Long Covid is already doing this. You’re seeing neural and auto immune clusters gathering around certain immune dysfunction and previously rare diagnosis like Small Fiber Neuropathy. Autonomic dysfunction is being measured in young and healthy people also, and that has its own set of objective testing.
Everything you are saying is happening. But because the suspicion seems more and more that it’s an auto immune condition of some sort, and that we are only catching the downstream effects as some of the immune dysfunction isn’t mapped yet, we are seeing the clusters that you say emerge - overwhelming numbers of symptoms, relatively incoherent connection.
But autonomic dysfunction, small fiber neuropathic and detectable auto immune dysfunction are all known and increasingly mapped positive markers for the condition. Have you read the latest studies ?
> You’re seeing neural and auto immune clusters gathering around certain immune dysfunction and previously rare diagnosis like Small Fiber Neuropathy.
Everything I've personally seen in this space is exactly what I described: they start with a set of people who claim to have the illness, then go on a statistical fishing expedition to look for "signs of immune disfunction" (or whatever, but you're right that these researchers tend to focus on immune-related metrics), then use whatever signals they happen to find to create a class. This is not the same thing as what I'm talking about, and it isn't valid.
I'm not going to claim comprehensive knowledge of the space, but the papers I've read that make it into the high-profile journals are of this sort.
The papers cited by this Lowe article are better than most at least in the sense that they have control groups and are doing experiments. But let's be clear -- the first one is claiming to see "long covid" pain symptoms in mice who are injected with whole human IgG (a notoriously messy and subjective approach) [1], and the other is exactly the kind of fishing expedition I'm describing, where they indiscriminately look for "targets" of said antibodies [2]. The former is at least doing an experiment that I suppose could lead to some kind of claim of cause, but the latter (despite the exaggerated title) provides no evidence that the correlations they're seeing are meaningful in any disease process.
I guarantee that using the high-dimensional screening that the latter paper in particular is doing, I can take 1000 random people, split them into two arbitrary classes ("fooists" and "non-fooists"), and find some "statistically significant" difference in immune marker profile between them. That is the fundamental problem with the approach.
When I say that you have to start from an objective measurement of symptoms, it means literally that -- not starting from an assay result that is unlinked to any symptom.
Then you should fund it. The entire field is to my understanding absolutely starved of science funding.
There are two fairly strong clusters of findings that are objective, repeatable, and consistent. And that is the autonomic testing in long COVID patients is coherent in its dysfunction, and so is the Small Fiber Neuropathy testing that is now consistently showing abnormalities.
Lets go step by step.
Small Fiber Neuropathy. Nerve fiber density is a count with age/sex-normed reference ranges. In previously healthy post-COVID patients with no diabetes and no risk factor, then the test shows whether the nerves are there or they aren't.
If your argument is that people are showing up with abnormalities, then diagnosed with Long Covid, then spurious biomarkers are associated to it - you are just wrong. Wrong multiple times. Demonstrably so.
What we are seeing is more likely to be exactly what it looks like - an novel condition being captured by downstream effects of previously unknown or understudied mechanisms.
All of those are examples of exactly what I told you about: they take a group of people claiming to be sick, and go hunting for signals to claim as “significant”.
The MRI studies are particularly egregious examples of this. Just because you see a difference on an MRI does not mean that the difference is due to the thing you’re blaming. In fact, it almost never is.
> If your argument is that people are showing up with abnormalities, then diagnosed with Long Covid, then spurious biomarkers are associated to it - you are just wrong. Wrong multiple times. Demonstrably so.
I am? I have now followed every link. Literally every paper you posted is following this exact pattern. I don't know how you could possibly conclude otherwise, unless you just didn't read past the titles.
They each take a (typically small) cohort of people who self-identify as "long covid sufferers", they subject them to random combinations of tests, and report only what they find to be significant. It's literally the XKCD comic about jelly beans.
You are just ignoring the evidence, being unscientific, and unless you work for a top medical lab somewhere, plain arrogant.
The UK Biobank study scanned participants before and after infection with matched controls. The difference is measured against their own pre-infection brain. That is the opposite of what you're describing.
> You are just ignoring the evidence, being unscientific, and unless you work for a top medical lab somewhere, plain arrogant.
If you don't know how to interpret evidence, then I suppose it would sound like I am being overly critical. I didn't bother to pick on just one, but since you chose it [1]...
> The UK Biobank study scanned participants before and after infection with matched controls. The difference is measured against their own pre-infection brain. That is the opposite of what you're describing.
It is not. The longitudinal nature of the study is a distraction from the fundamental issues with the approach.
They did a longitudinal case-control study, one group of which had positive covid tests in the past, and the other one did not at the time of the second scan (2021). That's the entire evidence base that this study is built upon -- it has nothing to do with "long Covid", and it's only barely plausible that the control group is actually a control for the factors of interest.
Next, they took two scans for all participants - one from before the pandemic, and one made after (again, in 2021). They made over 6000 different images, and then cherry-picked the ones with differences for further analysis (~70). Ultimately only 6 of these fishing expeditions survived family-wise error correction:
> The main case-versus-control analysis between the 401 SARS-CoV-2 cases and 384 controls (Model 1) on 297 olfactory-related cerebral IDPs yielded 68 significant results after FDR correction for multiple comparisons, including 6 that survived FWE correction
So first off, no statistical correction can compensate for this fundamental bias. You cannot start with thousands of different samples - even if they're taken from the same people at different time points - and winnow that down to a handful by filtering on the outcome of interest, Applying a multiple-sample correction will not fix it. It's not even clear that there is such a correction that is valid for the underlying distribution of the data involved.
But setting that aside, the differences observed, even between longitudinal samples, do not have to be due to Covid! Even if they're not random (which we cannot grant; see previous paragraph) they could be due to everyone being locked inside during 2020. They could be due to factors completely unexamined by the study, like, say, increases in drinking or drug use, or lack of exercise. Or any of a million other things. We don't know. The authors don't know. They're just not intellectually honest enough to admit that they don't know.
I could go on, and point out more flaws (e.g. the "significant" results mostly disappear when you exclude hospitalized patients, yet oddly, the difference between "hostipitalized" and "control" cohorts is not itself significant, indicating inadequate statistics), but this post is already too long.
I'm sorry that you think this is arrogant, but this is how we actually read papers.
This seems to me like a performance at this point and not serious analysis.
It’s true I conflated this with long covid. It’s not a long covid study.
I am tired and done with this. You made several errors in this comment.
Your biggest error is the lockdown one.
This makes no sense whatsoever - the controls also lived through lockdown. If this is the rigorous analysis you’re bringing to the studies you read, I’m not surprised none of them pass the muster.
“No correction can fix it” is wrong because the olfactory IDPs were pre-specified. “Could be lockdown” is wrong because controls lived through the same lockdown. “Results disappear excluding hospitalized” is wrong because the paper says they persisted.
The statistical weaknesses you describe are in the papers own limitations section. You just read them back as limitations that can’t be surpassed while evidence based researchers in the field disclose them as meaningful but not exclusionary.
Unless you want to continue with debunking every other strong paper I’ve posted with similar limited and likely to be demonstrably wrong takedowns, then I can’t help you. You have unfalsifiable priors, are constantly ignoring evidence and seem to believe you know better than the top researchers in the field - people who are saving lives - because you catch some statistical limitations and imply that they debunk the entire thing, instead of accepting them as limits of incomplete research into a real condition that’s crippling millions of people.
> the controls also lived through lockdown. If this is the rigorous analysis you’re bringing to the studies you read, I’m not surprised none of them pass the muster.
You've missed the point. I'm not suggesting that the other factor or factors has to be "lockdown". I'm just giving examples that illustrate the idea: even if you assume that the differences between the control and the experimental group are non-random and significant, you still cannot attribute the longitudinal difference to the one factor alone. If you don't like my theory, it's easy to find another, if you're even a little bit imaginative.
> “Results disappear excluding hospitalized” is wrong because the paper says they persisted.
No. They lose all but one. The final "significant" result is teetering on the edge of insignificance. See table 4 [1]. Models 2-4.
> the statistical weaknesses you describe are in the papers own limitations section.
Yes, because they're real. It's great that they wrote them in the paper, but they're fatal flaws.
"We openly disclosed the reason our study is nonsense!" is not the damning indictment you're suggesting that it is.
It’s lockdown and now no lockdown. Could be anything.
All observational studies are wrong. The stated limitations are fatal flaws. You heard it here first in HN. All medical research is fatally flawed, says user “timr”.
> All observational studies are wrong...You heard it here first in HN.
No, but most of them are wrong, and all of them need to be treated with an incredibly high degree of skepticism. This is critical review 101. When you push on this paper, even lightly, it falls over.
Not all papers are bad, but this one is bad, and while there are a great many well-done studies in the world, the subject of "long covid", to date, has essentially ~none of them.
I knew this would be the conclusion. Again - good luck. You are always right.
If you’re right and everyone else is wrong about hundreds if not thousands of studies, then you should be writing a book, not comments in HN.
We started at “some studies have errors” and we ended in “an entire field of research is wrong”.
You have already decided the field has no valid studies. Even when given dozens of examples you picked one and made up a series of points about one study. You made mistakes, never admitted it, and now are calling into question an entire field of medical research.
I’m not even sure you understand how evidence based medicine works.
Afaik evidence based medicine ranks mechanistic analysis near the bottom of the hierarchy — below controlled trials and systematic observation. I believe that ordering was a deliberate choice.
You seem obsessed with something that modern medical research often doesn’t focus on - by design. We still don’t know how lithium works 50 years past its introduction. We don’t know how the conditions that it treats - psychosis or bipolar - work either. Yet lithium is used all over the world- because the effects data and reports show that it works. Your mechanistic obsession isn’t just wrong - it’s directionally incorrect as far as a lot of medical research goes.
No, this isn’t true, and the article makes the same fundamental mistake.
While certainly deaths are a more reliable indicator than diagnoses, you always expect to see an increase in “deaths from X” when you more aggressively screen for X. The intervention of cancer treatment comes with serious risks, and screening sometimes finds cancers that would otherwise never be a problem.
We’re talking about very subtle differences in population-level trends, so these kinds of errors matter.
> The fact that they are canceling pending applications is simply evil.
Where have you seen this documented? I haven't, and the only government statement I've seen about this was fairly clear that the change is for new applications.
I am genuinely asking. I have friends who are going through the process.
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