How to Find Out If Microplastics Are Actually Destroying Our Health
While the answer to that question might seem like a no-brainer—and certainly no one is crazy enough to theorize that microplastics in breast milk are a good thing—there haven't been any human trials to confirm that microplastics are detrimental to human health. Some research has simply linked microplastics to health complications, which isn't nearly definitive enough.
So what are we waiting for? To be clear, it's not simply a matter of getting it done. To understand how or if micro- and nanoplastics (MNPs) are toxic for human health, we first need to quantify and analyze their concentration and composition in samples from living organisms. Spoiler alert—there is no guidebook on how to do this. After surveying the existing scientific literature on the matter, however, a team of researchers has outlined some best practices that could finally get us started in the right direction.
'Most detection techniques are better suited for microplastic and nanoplastic (MNP) identification in ideal media (such as water) and face limitations when analysing biological samples,' the researchers wrote in a study published last month in the journal Nature Reviews Bioengineering.
Part of the problem is that different biological samples have different compositions. Apples, for example, are fibrous, while our bodies also have fats and proteins, and trees and plants have lignin, Baoshan Xing, an environmental and soil chemistry professor at UMass Amherst and lead author of the study, said in a university statement.
'Strategies for digestion [preparation], separation, enrichment and detection of MNPs need to be optimized depending on the category of organism under investigation,' the researchers explained in the study. Currently, there is no standard approach for this, an unfortunate fact that Xing described as a 'headache' in the statement.
You Don't Want to Know Where Scientists Just Found 27 Million Tons of Plastic
Another complication is that most studies in this context presume MNPs to be spherical-shaped. That might not be the case, which carries important implications, given that particle shape can impact how MNPs travel through a system. Plus, toxic substances might collect in tiny niches or cavities. As such, the team argues that researchers need protocols for analyzing the polymer types and shapes, as well as MNPs' surface characteristics.
If that sounds like a whole lot of features to analyze, you're not wrong. Luckily, 'machine learning algorithms can greatly reduce the labour time and cost of MNP identification and characterization,' the researchers pointed out in the study.
'The day is not far off when we'll be able to accurately detect, characterize and quantify MNPs in biological samples,' Xing concluded in the statement.
In the meantime, though, stop chewing gum. Just in case.
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Forbes
41 minutes ago
- Forbes
Psychology Aims For A Unified Theory Of Cognition And AI Will Be A Big Help To Get There
In today's column, I examine the ongoing pursuit by psychology to devise a unified theory of cognition. The deal is this. There have been numerous attempts that have been floated regarding proposed unified theories or models of cognition. Subsequently, by and large, those theories or models have been sharply criticized as being at times incomplete, illogical, unfounded, and otherwise not yet fully developed. The desire and need for a true and comprehensive unified theory of cognition persists and remains exasperatingly elusive. Into this pursuit comes the use of AI, especially modern-era AI such as generative AI and large language models (LLMs). Can we make a substantive forward leap on devising a unified theory of cognition via leaning into contemporary AI and LLMs? Some say abundantly yes, others wonder if doing so will be a distraction and lead us down a primrose path. Let's talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). AI And Psychology As a quick background, I've been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that entails the field of psychology, such as providing AI-driven mental health advice and performing AI-based therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For a quick summary of some of my posted columns on this evolving topic, see the link here, which briefly recaps about forty of the over one hundred column postings that I've made on the subject. There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors too. I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS's 60 Minutes, see the link here. You might find of keen interest that AI and psychology have had a longstanding relationship with each other. There is a duality at play. AI can be applied to the field of psychology, as exemplified by the advent of AI-powered mental health apps. Meanwhile, psychology can be applied to AI, such as aiding us in exploring better ways to devise AI that more closely approaches the human mind and how we think. See my in-depth analysis of this duality encompassing AI-psychology and psychology-AI at the link here. The Enigma Of Human Cognition The American Psychological Association (APA) defines cognition this way: One nagging mystery underlies how it is that we can think and embody cognition. All sorts of biochemical elements in our brain seem to work in a manner that gives rise to our minds and our ability to think. But we still haven't cracked the case on how those neurons and other elements in our noggin allow us to do so. Sure, you can trace aspects at a base level, yet explaining how that produces everyday cognition is a puzzle that won't seem to readily be solved. This certainly hasn't stopped researchers from trying dearly to figure things out. Hope springs eternal that the mysteries of cognition will be unraveled and we will one day know precisely the means by which cognition happens. Nobel prizes are bound to be awarded. Fame and fortune are in the cards. And imagine what else we might do to help and overcome cognitive disorders, along with potentially enhancing cognition to nearly unimaginably heightened levels. This is undoubtedly one of the most baffling mysteries of all time, and there is a purist sense of absolute joy and satisfaction in solving it. Various Types Of Models When seeking to come up with a unified theory of cognition, the route taken usually entails these four major paths: You can use only one of those approaches, or you can use two or more. If you opt to use two or more, your best bet is to make sure each model aligns with the other models being utilized. Any misalignment will indubitably bring criticism and skepticism raining down upon you. For example, if you propose a conceptual model and a mathematical model, but those two don't sync up, it becomes an easy line of attack to suggest that your theory is hogwash. AI And Computational Models A tempting avenue for cognition modeling these days is to rely upon an AI-based computational model that leverages the latest generative AI and LLMs. You can essentially repurpose a popular LLM, i.e., OpenAI's ChatGPT, which garners 400 million weekly active users, or Anthropic Claude, Google Gemini, Meta Llama, and so on. Those off-the-shelf LLMs are ready-made for experimenting on psychology-based premises. I recently explained how contemporary generative AI is devised to react to psychological ploys and techniques, an intriguing facet that is both helpful and potentially hurtful, see my coverage at the link here. One monumental wrinkle is whether a conventional LLM is suitable for representing a semblance of human cognition. Allow me to elaborate on this vital point. The mainstay of LLMs makes use of an artificial neural network (ANN). This is a series of mathematical functions that are computationally rendered in a computer system. I refer to this as an artificial neural network to try and distinguish it from a true neural network (NN) or wetware that is inside your head. Please be aware that ANNs are an exceedingly loosely contrived variation of true NNs. They are not the same. An ANN is quite far from real NNs and, in contrast, is many magnitudes simpler. For my detailed explanation about ANNs versus NNs, see the link here. The bottom line is that an instant criticism of any cognition research that dovetails into LLMs is that you are starting at a recognized point of heated contention. Namely, a cogent argument is that since ANNs are not the same as true NNs, you are building your cognition hopes on somewhat of a house of cards. The counterargument is to acknowledge that ANNs are indeed not an isomorphic match, and instead, you are merely engaging them to aid in a broad-based simulation that doesn't have to be a resolute match. In any case, I stridently support using LLMs as insightful exploratory vehicles and assert that we can gain a great deal of progress about cognition in doing so, assuming we proceed mindfully and alertly. LLMs And Intrinsic Human Behavior Suppose you decide to use an off-the-shelf LLM to perform a cognitive modeling investigation. There is something important that you need to be thinking about. I shall unpack the weighty consideration. First, be aware that LLMs are developed via pattern-matching on human writing that is scanned across the Internet. That's how the fluency of LLMs comes about. The ANN is used to pattern-match on how we use words. In turn, when you enter a prompt into generative AI, the generated response produces words composed into sentences that appear to be on par with human writing. They reflect the computational mimicry of extensive computational pattern-matching based on words (actually, it is based on tokens, see the details in my discussion at the link here). You can't especially declare that the LLM is thinking like humans. The AI is using words and patterns about the usage of words. That's not necessarily a direct embodiment of human thinking per se, and more so, presumably, the indirect outcomes of human thinking. One clever idea is to augment an off-the-shelf LLM by aiming to further data-train the AI on veritable traces of human thinking (well, kind of, as you'll see momentarily). Perhaps that will enable the LLM to be more closely aligned with what human cognition consists of. For example, I fed transcripts of therapist-patient sessions into a major LLM to see if it might be feasible to augment its data training and guide the AI toward behaving more like a versed human therapist, see my experiment at the link here. Psych Experimental Results As Rich Data What other kinds of data could we potentially use to perform augmented data training of an LLM so that it can be more readily suited for cognition experimentation? Easy-peasy, tap into the vast tome of psychology experiments that have been performed endlessly on all sorts of people for many decades. Here are the steps. Collect together that data. Work the data into a readable and usable shape. Feed it into an existing LLM, doing so via a method such as RAG (retrieval-augmented generation), see my RAG elicitation at the link here. Voila, perhaps you've tuned up conventional generative AI to better simulate human behavior. A recent research study took that innovative approach. In an article entitled 'A Foundation Model To Predict And Capture Human Cognition' by Marcel Binz et al, Nature, July 2, 2025, the paper made these key points (excerpts): Details Of The Approach The researchers chose to use Meta Llama as their base LLM. The data augmentation was done via the use of the increasingly popular technique known as QLoRA (quantized low-rank adaptation), a distant cousin of RAG. They transcribed 160 experiments into natural language data. It was publicly available data. The types of experiments included many of the classics in psychology, such as memory recall, supervised learning, decision-making, multi-armed bandits, Markov decision processes, and others. To give you a sense of what those experiments are like, consider these two examples: Handily, the researchers have opted to make the dataset available, known as Psych-101, and can be accessed freely on Huggingface. In addition, they have nicely made available the augmented Meta Llama model, which they refer to as Centaur, and which is also freely available on Huggingface. It is a welcome touch because other researchers can now come along and do not need to begin from scratch. They can reuse the arduous and time-consuming work that went into devising Psych-101 and Centaur. Thus, the dataset and the model are ready-made for launching new investigations and serve as a springboard accordingly. The Results In Brief A commonly utilized means of validating an LLM consists of holding back some of the training data so that you can use the holdback for testing purposes. This is a longstanding technique that has been used for statistical model validations. You might use, say, 90% of the data to do the augmented data training and keep the remaining 10% in reserve. When you are ready to test the LLM, you give it the data that was aside to see if the AI can adequately predict the presumed unseen data. They did this and indicated that their Centaur LLM did a bang-up job on the hold-out data. The next step typically undertaken is to employ a make-or-break test when aiming to devise a generalizable model. You give the LLM data that is considered outside the initial scope of the augmentation. The handwringing question is whether the LLM will generalize sufficiently to contend with so-called out-of-distribution (OOD) circumstances. The researchers opted to select a handful of OOD settings, including economic games, deep sequential decision tasks, reward learning, etc. Their reported results indicate that Centaur LLM did quite well at making predictions associated with those previously unseen experimental transcripts. Overall, kudos to the researchers for thinking outside the box on AI and psychology. Some Thoughts To Ponder I'd like to cover a few quick thoughts overall. First, one agonizing difficulty with gauging an off-the-shelf pre-cooked LLM for any kind of newly encountered circumstances is that it is challenging to know whether such data or similar data might have been scanned during the initial setup of the LLM. Usually, only the AI maker knows precisely what data was initially scanned. Ergo, it is worthwhile to be mindful in interpreting generalizability since an LLM might have already had an unknown leg-up previously. Second, and perhaps more importantly, the desire to push toward a semblance of cognitive realism by further data training of an LLM is a laudable idea. Will the AI be more human-like in its reasoning patterns? Maybe, maybe not. One important determinant is whether the AI is still resorting to human-like language and not necessarily patterning on human reasoning. There is a huge debate going on regarding LLM foundational models that are claimed to be using 'reasoning' versus whether they are still potentially doing heads-down next-token prediction, see my coverage on the lively dispute at the link here. Taking Next Steps The overarching aim to see if we can properly ground cognitive computer-based computational simulations in a more psychologically plausible way is exciting. No doubt about that. The researchers also noted that there might be entirely different AI architectural approaches that might be better for us to pursue, beyond the somewhat conventional infrastructures currently dominating the AI realm right now. As a heads-up, some ardently believe that our prevailing LLMs and AI architecture are not going to get us to artificial general intelligence (AGI) or artificial superintelligence (ASI). You see, the trend right now is to mainly power up prevailing designs with faster hardware and more computational running time. But the incremental benefits could be misleadingly tying us to a road that leads to a dead-end. Could the desire to attain a unified model of cognition be the kick in the pants to the AI field to look beyond the groupthink of today's AI and LLMs? I certainly hope so. As General George S. Patton once proclaimed: 'If everyone is thinking alike, then somebody isn't thinking.'
Yahoo
an hour ago
- Yahoo
RealESALetter Launches Fast and Fully Online ESA Letter Service Across the US
SURFSIDE BEACH, S.C., Aug. 15, 2025 (GLOBE NEWSWIRE) -- is excited to announce its nationwide service that makes getting an emotional support animal letter easier than ever. People across America can now skip the stressful paperwork and long waits and instead get a fully legitimate ESA letter from the comfort of their home in as little as 24 hours. With more Americans relying on emotional support animals to cope with conditions like anxiety, depression, PTSD, ADHD and other emotional challenges the need for a quick and reliable ESA letter service has never been greater. RealESALetter is stepping up to meet that need with a process designed for speed simplicity and complete legal compliance. What is an ESA Letter and Why It Matters An ESA letter is an official document written by a licensed mental health professional confirming that you need an emotional support animal as part of your treatment plan. 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Yahoo
2 hours ago
- Yahoo
Doctors Warn These 11 Everyday Foods May Wreck Your Gut Health
"Hearst Magazines and Yahoo may earn commission or revenue on some items through these links." Watery stools, a.k.a. diarrhea, are annoying at best and terribly unpleasant at worst. While most bouts are caused by a virus or bacteria, per the National Institutes of Health (NIH), and aren't typically a cause for concern, there are also many common foods that can cause diarrhea. If you suspect that food, rather than an illness, is causing your diarrhea (or other bothersome digestive woes), there's an easy way to identify the culprit. 'The best way to investigate which foods are making your symptoms worse is to keep a food diary,' said gastroenterologist Shilpa Ravella, M.D.. Simply write down everything you eat each day, how you feel after eating, and if and when you have any unpleasant trips to the bathroom. Meet the experts: Gastroenterologist Shilpa Ravella, M.D.; Bhavesh Shah, M.D., gastroenterologist and Director of Advanced Endoscopy at MetroHealth Medical Center in Cleveland; Anne Roland Lee, Ed.D, R.D.N., L.D., assistant professor of nutritional medicine in the celiac disease center at Columbia University. To help streamline the process, we narrowed down the most likely foods that cause diarrhea. Ahead, GI doctors share the most common culprits to watch out Substitutes Diet sodas and sugar-free snacks and chewing gum may help satisfy your sweet tooth, but many contain sugar substitutes that can also act as a laxative. 'Sweeteners such as aspartame, sucralose, and sorbitol can contribute to diarrhea and bloating based on how your body metabolizes them in the gut,' said gastroenterologist Bhavesh Shah, M.D., the Director of Advanced Endoscopy at MetroHealth Medical Center in Cleveland. Recent research in iScience also found that artificial sweeteners can alter the composition of the gut microbiome, disrupting the balance of healthy bacteria and further contributing to issues like Is your morning cup promptly followed by a rush to the bathroom? That's pretty normal. In fact, up to 29% of people report feeling the urge to go after sipping a cup of coffee, according to the Cleveland Clinic. 'Coffee can cause diarrhea in some people,' Dr. Ravella said. 'The caffeine can stimulate the gut to contract more quickly than it normally does, so food moves through faster and isn't absorbed as well.' The acidity of coffee can also worsen the symptoms of some digestive disorders, such as acid reflux. Other caffeinated foods and drinks, including tea and energy drinks, could have the same If you have a few too many during a night out (or in!), you may wake up with an upset stomach the next morning. 'This is often a symptom that accompanies a hangover,' Dr. Shah said. 'Alcohol is an irritant. Your gut may not agree with drinking a large amount.' Booze also speeds up digestion and pulls water into your digestive tract, per the Cleveland Clinic—hence the liquid stools when you're According to the NIH, up to 50 million American adults may be lactose intolerant and could benefit from limiting their consumption of dairy or cutting it out altogether. If your stomach goes rogue when you eat milk, cheese, or yogurt, you may be one of them. 'Common symptoms, which begin about 30 minutes to two hours after consuming foods containing lactose, may be diarrhea, nausea, cramps, gas, and bloating,' Dr. Shah Rye, and Barley Gluten—a mix of proteins found in wheat, rye, and barley—can cause diarrhea and bloating for up to 15% of people, according to Northwestern Medicine. Keeping track of any GI symptoms you experience after eating bread, pasta, and grains or cutting out these foods to see if your symptoms disappear will help you find out if you're one of them. 'If you think you have gluten intolerance, it's important to see a gastroenterologist to make sure you don't have celiac disease,' Dr. Ravella added. For people with this disorder, gluten can do serious damage by causing the body to attack the lining of the small and Salad Dressings Store-bought condiments, dressings, and sauces might contain traces of gluten in the form of ingredients like malt vinegar or wheat starch to help thicken the texture and add flavor, said Anne Roland Lee, Ed.D, R.D.N., L.D., assistant professor of nutritional medicine in the Celiac Disease Center at Columbia University. Try preparing your own dressings and sauces at home if you have a gluten intolerance, or carefully read the ingredient labels at the store to avoid any hidden Foods FODMAP stands for fermentable oligosaccharides, disaccharides, monosaccharides, and polyols. They're a group of carbs and sugar alcohols that can be tough to digest, especially for people with irritable bowel syndrome (IBS), Dr. Ravella said, leading to gas, bloating, and diarrhea. A low-FODMAP diet may help improve symptoms, but should be monitored by a dietitian, as it requires you to cut out certain foods rich in vital nutrients. According to Johns Hopkins Medicine, High-FODMAP foods include dairy, wheat, beans, lentils, artichokes, asparagus, broccoli, cauliflower, onions, garlic, apples, cherries, and peaches, while foods low in FODMAPs include eggs, meat, rice, quinoa, potatoes, eggplant, tomatoes, cucumbers, grapes, pineapple, and Foods Fiber gets your digestive system moving, which is a good thing—but sometimes it's a little too effective, Dr. Shah warned. Fiber binds with water, which can help prevent constipation but may also have a laxative effect if you consume too much at once. Plus, according to UCSF Health, eating a lot of insoluble fiber (the type found in nuts, seeds, dried fruits, and whole grains) can speed up digestion, leading to watery stool. That's not to say you shouldn't be eating plenty of high-fiber foods—just be sure to increase your fiber intake gradually until your stomach Foods Some people can eat jalapeños like they're candy, while others feel their stomach churn at the mere sight of a chili pepper. 'Each individual is unique when it comes to tolerating spicy foods,' Dr. Ravella said. 'Spicy foods can irritate the lining of the stomach and intestines, causing food to move more quickly through the gastrointestinal tract, which results in loose stools.' If you don't eat spicy food often, she added, you're more likely to feel the burn (and the potential digestive side effects) when you Food If you need motivation to skip the drive-thru window, consider that greasy takeout grub can be hard on your gut. 'In general, all fats can be harder to digest, but the worst culprits are the fats in greasy, fried foods typically found at fast food restaurants,' Dr. Ravella said. 'You're less likely to have issues when eating healthy fats from whole foods, like avocados.' Indeed, a recent study published in the journal Nutrients found that fast food consumption was associated with an increased risk of having inflammatory bowel diseases like ulcerative colitis and Crohn's disease, making issues like diarrhea that much more likely to Foods Like fast food, processed and packaged foods from the grocery store often contain lots of the hard-to-digest ingredients listed above. Be sure to read nutrition labels and ingredient lists carefully, and be on the lookout for gut-irritating and hard-to-digest ingredients like sugar substitutes, dairy, gluten, or FODMAPs. According to research in Nature Reviews Gastroenterology & Hepatology, all these ingredients in processed foods can increase your risk of diarrhea-causing illnesses like inflammatory bowel disease. You Might Also Like Can Apple Cider Vinegar Lead to Weight Loss? Bobbi Brown Shares Her Top Face-Transforming Makeup Tips for Women Over 50
