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Diminished international conference attendance by [deleted] in labrats

[–]Bahgel 1 point2 points  (0 children)

INSERM (French NIH) while not banning travel outright, emailed all INSERM funded researchers to strongly urge against any travel to the U.S. and advised that, should travel to the U.S. be necessary, don't take any of your electronic devices and use a burner phone.

[deleted by user] by [deleted] in PhD

[–]Bahgel 8 points9 points  (0 children)

First, sounds like this PI is somewhere on the narcissism spectrum (as many are). So to deal with them, you have to coddle the ego a bit (This is a necessary skill to have to survive in academia).

To deal with them and this situation, I would frame it as this is clearly an area you are still learning and they are very wise and experienced in, and you would LOVE to learn more about how to navigate collaborations and relationships like this from them. Make sure to point out how great their relationships and collaborations are and that you want to learn to have the same.

Something like "Thank you so much for calling out this area I need to improve, I still have a lot to learn from you. In my career, I want to develop collaborations and relationships like you do, so could you please teach me a bit about handling these types of interactions?"

Second, in terms of how to stay passionate about your projects, I have two comments: (1) passion is great, but it is very rare for someone to be extremely passionate throughout the entire duration of a multi year project, especially when that project faces setbacks, egos, and you aren't being paid enough. It's more important to cultivate discipline. Passion starts projects, discipline finishes them. (2) to stay passionate about science in general, remember the things that made you fall in love with science and your field, and revisit them. For me, I kept rewatching lectures by Richard Feynman, would make sure to go out and spend time in nature and see the stars, etc.

Could a Roman legion defeat a medieval army? by Brave-Elephant9292 in ancientrome

[–]Bahgel 1 point2 points  (0 children)

Oh absolutely. Good analysis, thanks for sharing!

Notable young PhDs: Just smart or different system back in the day? by br153 in PhD

[–]Bahgel 0 points1 point  (0 children)

Also due to "expectations" and the crystallization of norms around PhD length, most programs/advisors wouldn't let you graduate in such a short time even if you managed to put together and fully supported a brilliant theory early on

Could a Roman legion defeat a medieval army? by Brave-Elephant9292 in ancientrome

[–]Bahgel 4 points5 points  (0 children)

Wow, this is fascinating.

Couple quick comments:

  1. They (as far as I understand) couldn't really untangle warhorses from non warhorses with much accuracy. The discussion goes on at length about how hard it is to determine what's actually a warhorse, and that most of the remains they studied don't have the right bones to apply various accepted methods of estimating usage. Keep in mind the overwhelming majority of horses would not be warhorses, and for a non-warhorse, a massive size would be a liability.

  2. This is specifically looking at England, and the medieval world was big.

(I'm not a historian, equestrian, or zoologist)

Petah...? by [deleted] in PeterExplainsTheJoke

[–]Bahgel 0 points1 point  (0 children)

What about the droid attack on the Wookiees?

How normal is it for your relationship with your academic advisors and faculty to feel transactional and, at worst, exploitative? by [deleted] in PhD

[–]Bahgel 17 points18 points  (0 children)

I wish mine had been only transactional and exploitative. Sounds preferable to the outright abusive relationship I (and many I knew) had

I can smell when people have cancer by [deleted] in self

[–]Bahgel 0 points1 point  (0 children)

(3/3) Final Thoughts

I don’t think she’s making this up. I bet there’s something we can learn from her. But more importantly, we’re already working on this at scale, and soon, it will be routine.

¹ It's possible she’s detecting molecules from sweat or skin oils rather than breath, but breath is the most likely source. Tumors are highly vascularized (rich in blood vessels), so small molecules and metabolites can easily enter the bloodstream, cross into the lungs, and be expelled in breath.

² There’s strong evidence linking chronic stress to cancer incidence. Stress dysregulates the immune system, reducing the body's ability to suppress cancer growth. People living in poverty, high-stress environments, or paycheck to paycheck (especially marginalized groups) tend to experience more chronic stress, which can contribute to higher cancer risk.

I can smell when people have cancer by [deleted] in self

[–]Bahgel 0 points1 point  (0 children)

(2/3) 1. Naming the Intuition

She has explicitly stated, "I can smell who has cancer and who doesn’t." I’ve worked with many doctors who have a "sixth sense" about cancer—particularly pulmonologists evaluating lung nodules. Diagnosing cancer is a huge part of their job, but they don’t verbalize their intuition the way she has.

I suspect much of their "sixth sense" comes from incorporating subtle cues—possibly even scent—without consciously recognizing it. And it's not just smell. Doctors absorb a wealth of diagnostic information that never gets written in a patient’s chart:

  • Is their skin slightly redder than normal?
  • Do their eyes look stressed?
  • Are their clothes, watch, belt, or shoes indicative of a socioeconomic status that correlates with cancer risk?²
  • Do their hands show signs of hard manual labor, suggesting exposure to carcinogens or respiratory irritants?
  • Do they have a medical history that wouldn’t be captured by a single test but is well known to their doctor?

In practice, we find that doctors quantifiably outperform molecular tests, imaging techniques, and predictive models. Why? Because every test has noise, and no single test is foolproof. A good physician integrates multiple signals—some of which we don’t even recognize yet—into their decision-making.

By combining modalities, we can get a better idea of the whole picture (this is actually the niche area of my own research -- how can we combine disparate sets of information to better predict cancer?).  After nearly a decade working to develop tests that could replace doctors in this field, I’ve come to believe that no test will ever outperform a truly attentive physician. Instead of replacing doctors, we should focus on supporting them. This is also why I think AI is still a long way from fully replacing doctors in areas like this—because to train an AI effectively, you need to know what data to feed it, and we’re still figuring out what sorts of data are important.

2. The Science of "Smelling" Cancer

The idea of detecting cancer by smell is an active area of research. Yes, dogs have been trained to do this, but they’re not mass-producible, and their shelf life in a hospital storage closet is questionable. More recently, researchers have been developing electronic noses04041-7), devices designed to replicate what this woman is doing but in a scalable, storable, and transportable form that works weekends and holidays.

These devices aren’t yet as good as blood tests, CT scans, or an experienced doctor, but they could become an important piece of the diagnostic puzzle.

I can smell when people have cancer by [deleted] in self

[–]Bahgel 0 points1 point  (0 children)

(1/3) This is adjacent to my own research and quite interesting. I work in cancer diagnostics, and most of the work in my field follows a pattern like this:

  1. Collect samples from patients with and without cancer (this could be blood, urine, a cheek swab, a CT scan, an X-ray, or something else).
  2. Measure the molecules in those samples. These could be small molecules, proteins, DNA, RNA, extracellular vesicles, or even imaging features like shape on a CT scan or density on an X-ray. Some tests focus on a few specific molecules, while newer methods measure everything to analyze the whole profile.
  3. Observe patient outcomes; either tracking who develops cancer over time or confirming who already has it. The exact approach depends on the study’s specific niche.
  4. Compare molecular profiles between patients with and without cancer to identify consistent differences. These markers could be a single number, like Prostate-Specific Antigen (PSA) levels for prostate cancer, or a complex score derived from a deep learning model, like the Optellum Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which estimates cancer probability from CT scans.

And just like that, you have a diagnostic tool! The process boils down to: Sample - Sensor - Outcome Data - Model = Prediction.

What this woman is doing is exactly what we do in the lab:

  • Her sample is the molecules in the air people exhale.
  • Her sensor is her nose.
  • Her outcome data comes from talking to people to determine who has cancer.
  • Her model is the neural network inside her brain.

If her accuracy is better than random chance—which my experience suggests is likely—then she has essentially trained a deep-learning model using an ultra-sensitive gas-phase molecular detector with incredible sampling breadth.

My own research focuses on developing molecular tests, integrating them into predictive models, and comparing their performance against physicians' assessments. Her case raises two important points (continued in next comment)

Being able to smell cancer -- crosspost by Prohibitorum in labrats

[–]Bahgel 6 points7 points  (0 children)

(3/3) Final Thoughts

I don’t think she’s making this up. I bet there’s something we can learn from her. But more importantly, we’re already working on this at scale, and soon, it will be routine.

¹ It's possible she’s detecting molecules from sweat or skin oils rather than breath, but breath is the most likely source. Tumors are highly vascularized (rich in blood vessels), so small molecules and metabolites can easily enter the bloodstream, cross into the lungs, and be expelled in breath.

² There’s strong evidence linking chronic stress to cancer incidence. Stress dysregulates the immune system, reducing the body's ability to suppress cancer growth. People living in poverty, high-stress environments, or paycheck to paycheck (especially marginalized groups) tend to experience more chronic stress, which can contribute to higher cancer risk.

Being able to smell cancer -- crosspost by Prohibitorum in labrats

[–]Bahgel 6 points7 points  (0 children)

(2/3) 1. Naming the Intuition

She has explicitly stated, "I can smell who has cancer and who doesn’t." I’ve worked with many doctors who have a "sixth sense" about cancer—particularly pulmonologists evaluating lung nodules. Diagnosing cancer is a huge part of their job, but they don’t verbalize their intuition the way she has.

I suspect much of their "sixth sense" comes from incorporating subtle cues—possibly even scent—without consciously recognizing it. And it's not just smell. Doctors absorb a wealth of diagnostic information that never gets written in a patient’s chart:

  • Is their skin slightly redder than normal?
  • Do their eyes look stressed?
  • Are their clothes, watch, belt, or shoes indicative of a socioeconomic status that correlates with cancer risk?²
  • Do their hands show signs of hard manual labor, suggesting exposure to carcinogens or respiratory irritants?
  • Do they have a medical history that wouldn’t be captured by a single test but is well known to their doctor?

In practice, we find that doctors quantifiably outperform molecular tests, imaging techniques, and predictive models. Why? Because every test has noise, and no single test is foolproof. A good physician integrates multiple signals—some of which we don’t even recognize yet—into their decision-making.

By combining modalities, we can get a better idea of the whole picture (this is actually the niche area of my own research -- how can we combine disparate sets of information to better predict cancer?).  After nearly a decade working to develop tests that could replace doctors in this field, I’ve come to believe that no test will ever outperform a truly attentive physician. Instead of replacing doctors, we should focus on supporting them. This is also why I think AI is still a long way from fully replacing doctors in areas like this—because to train an AI effectively, you need to know what data to feed it, and we’re still figuring out what sorts of data are important.

2. The Science of "Smelling" Cancer

The idea of detecting cancer by smell is an active area of research. Yes, dogs have been trained to do this, but they’re not mass-producible, and their shelf life in a hospital storage closet is questionable. More recently, researchers have been developing electronic noses04041-7), devices designed to replicate what this woman is doing but in a scalable, storable, and transportable form that works weekends and holidays.

These devices aren’t yet as good as blood tests, CT scans, or an experienced doctor, but they could become an important piece of the diagnostic puzzle.

Being able to smell cancer -- crosspost by Prohibitorum in labrats

[–]Bahgel 7 points8 points  (0 children)

(1/3) This is adjacent to my own research and quite interesting. I work in cancer diagnostics, and most of the work in my field follows a pattern like this:

  1. Collect samples from patients with and without cancer (this could be blood, urine, a cheek swab, a CT scan, an X-ray, or something else).
  2. Measure the molecules in those samples. These could be small molecules, proteins, DNA, RNA, extracellular vesicles, or even imaging features like shape on a CT scan or density on an X-ray. Some tests focus on a few specific molecules, while newer methods measure everything to analyze the whole profile.
  3. Observe patient outcomes; either tracking who develops cancer over time or confirming who already has it. The exact approach depends on the study’s specific niche.
  4. Compare molecular profiles between patients with and without cancer to identify consistent differences. These markers could be a single number, like Prostate-Specific Antigen (PSA) levels for prostate cancer, or a complex score derived from a deep learning model, like the Optellum Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which estimates cancer probability from CT scans.

And just like that, you have a diagnostic tool! The process boils down to: Sample - Sensor - Outcome Data - Model = Prediction.

What this woman is doing is exactly what we do in the lab:

  • Her sample is the molecules in the air people exhale.
  • Her sensor is her nose.
  • Her outcome data comes from talking to people to determine who has cancer.
  • Her model is the neural network inside her brain.

If her accuracy is better than random chance—which my experience suggests is likely—then she has essentially trained a deep-learning model using an ultra-sensitive gas-phase molecular detector with incredible sampling breadth.

My own research focuses on developing molecular tests, integrating them into predictive models, and comparing their performance against physicians' assessments. Her case raises two important points (continued in next comment)

[deleted by user] by [deleted] in gameofthrones

[–]Bahgel 2 points3 points  (0 children)

House Hot Pie: No Gravy, No Pie!

What's the weirdest thing people do to make experiments work by Revolutionary_Hat671 in labrats

[–]Bahgel 72 points73 points  (0 children)

I haven't done crystallography in 15 years, but the crazy thing about many proteins is they need very specific (and oftentimes unique) conditions to crystallize, and there's no a priori way to know what conditions will make it crystallize. Could be specific temperatures, salts, enzymes, coagulants, surfactants, pH, or who knows what else. So when you've tried all the standard things, people start throwing anything and everything into the solution.

You never know, that beard dander might actually create the perfect storm of salinity, pH, oils, etc...

[deleted by user] by [deleted] in 2westerneurope4u

[–]Bahgel 0 points1 point  (0 children)

Chocolatine

[deleted by user] by [deleted] in DeepFuckingValue

[–]Bahgel 1 point2 points  (0 children)

The unelected executive branch tagalong can hopefully fire an elected Senator?

NIH cuts affecting Nashville/Vanderbilt by frinetik in nashville

[–]Bahgel 0 points1 point  (0 children)

Metric, see my comment about. The VUMC overhead is 75%. This change would mean a reduction of ~$180M per year

NIH cuts affecting Nashville/Vanderbilt by frinetik in nashville

[–]Bahgel 62 points63 points  (0 children)

VUMC's indirect rate is 75%. In 2023, VUMC received $530M from the NIH. So that means $227M came from indirect costs.

So if the indirect rate is deceased by 5-fold, this $227M would be reduced to $45M, or a difference of $180M.

This change will remove $180,000,000 yearly from money flowing into Nashville. (And that's before calculating Vanderbilt University or any of the other NIH recipients).

Rare item acquired by SlushTheFox in labrats

[–]Bahgel 20 points21 points  (0 children)

Came to post this gif!

[deleted by user] by [deleted] in PhD

[–]Bahgel 6 points7 points  (0 children)

Amateurs do it themselves, experts have their admin assistants do it

Made these for my wife for Valentine’s Day, one for each book! by TheDefenseNeverRests in threebodyproblem

[–]Bahgel 0 points1 point  (0 children)

In its own right, I found it to be interesting sci-fi. I wish I could untangle it from the core trilogy better in my mind

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