Research points to potential new medical therapy for Lyme disease

 A medical therapy that inhibits the growth of cancer cells may one day be effective at treating Lyme disease, according to new research by a University of Massachusetts Amherst team at the New England Regional Center of Vector-borne Diseases (NEWVEC). "It's a long way from something you're going to pick up at CVS, but these early findings are very encouraging," says vector-borne disease expert Stephen Rich, professor of microbiology, executive director of NEWVEC and senior author of the study published in the journal Pathogens. Lyme disease is the most common vector-borne disease in the U.S., spread by infected deer ticks. The potentially debilitating illness, which is diagnosed in about 476,000 people each year in the U.S., doesn't always respond to antibiotics. "There are people who have cases of Lyme disease that go on and on," Rich says. "So there's always interest in finding new therapies or new ways to inhibit the growth of the bacterium. And based on what we're seeing in the lab, this may be one of those ways."

The discovery began with an "aha" moment by then-Ph.D. candidate Patrick Pearson, who was working in Rich's lab, along with graduate student Adam Lynch. Pearson, co-author of the paper, is now a NEWVEC post-doctoral researcher at UMass Amherst. Lynch, lead author, is now a research fellow in the Department of Veterinary and Animal Sciences.

Tumor cells and Borrelia burgdorferi, the corkscrew-shaped bacterium that causes Lyme disease, share an unusual feature about the way they grow, Pearson noted and pondered. "It turns out that cancer cells and Borrelia both rely solely on glycolysis for their metabolism," Rich explains.

Glycolysis, in turn, is dependent on one molecule called lactate dehydrogenase, or LDH. Pearson wondered whether LDH inhibitors, which are used as drug therapies to target certain cancers, might also be an effective strategy against Lyme disease.

"It was a very clever idea," Rich says. "In principle, we thought these LDH inhibitors should work well to inhibit the growth of Lyme disease bacteria."

And in fact, in in vitro experiments, they did. "…a range of commercially available LDH inhibitors with various mechanisms of action and origins were tested on Borrelia in Culture," the paper states. "Of these inhibitors, gossypol, AT-101, and oxamate substantially impacted B. burgdorferigrowth in vitro and represent promising candidates against Borrelia infections in vivo."

Rich says the research will continue at NEWVEC, which was funded by the Centers for Disease Control and Prevention last year with a $10 million award to prevent and reduce tick- and mosquito-borne diseases in New England. NEWVEC aims to bring together academic communities, public health practitioners, residents and visitors across the Northeast, where Lyme infections are concentrated.

"These experiments were done outside of hosts. Now we need to carry this out in mouse models and, eventually, in people," Rich says.

The researchers note that this drug therapy may also be effective against another tick-borne disease, babesiosis, a malaria-like infection. "This has the potential to kill two birds with one stone," Rich says. "And that makes this discovery even more tantalizing."

sources-science daily

Scientists propose revolution in complex systems modelling with quantum technologies

 Scientists have made a significant advancement with quantum technologies that could transform complex systems modelling with an accurate and effective approach that requires significantly reduced memory.

Complex systems play a vital role in our daily lives, whether that be predicting traffic patterns, weather forecasts, or understanding financial markets. However, accurately predicting these behaviours and making informed decisions relies on storing and tracking vast information from events in the distant past -- a process which presents huge challenges.

Current models using artificial intelligence see their memory requirements increase by more than a hundredfold every two years and can often involve optimisation over billions -- or even trillions -- of parameters. Such immense amounts of information lead to a bottleneck where we must trade-off memory cost against predictive accuracy.

A collaborative team of researchers from The University of Manchester, the University of Science and Technology of China (USTC), the Centre for Quantum Technologies (CQT) at the National University of Singapore and Nanyang Technological University (NTU) propose that quantum technologies could provide a way to mitigate this trade-off.

The team have successfully implemented quantum models that can simulate a family of complex processes with only a single qubit of memory -- the basic unit of quantum information -- offering substantially reduced memory requirements.

Unlike classical models that rely on increasing memory capacity as more data from past events are added, these quantum models will only ever need one qubit of memory.

The development, published in the journal Nature Communications, represents a significant advancement in the application of quantum technologies in complex system modelling.

Dr Thomas Elliott, project leader and Dame Kathleen Ollerenshaw Fellow at The University of Manchester, said: "Many proposals for quantum advantage focus on using quantum computers to calculate things faster. We take a complementary approach and instead look at how quantum computers can help us reduce the size of the memory we require for our calculations.

"One of the benefits of this approach is that by using as few qubits as possible for the memory, we get closer to what is practical with near-future quantum technologies. Moreover, we can use any extra qubits we free up to help mitigate against errors in our quantum simulators."

The project builds on an earlier theoretical proposal by Dr Elliott and the Singapore team. To test the feasibility of the approach, they joined forces with USTC, who used a photon-based quantum simulator to implement the proposed quantum models.

The team achieved higher accuracy than is possible with any classical simulator equipped with the same amount of memory. The approach can be adapted to simulate other complex processes with different behaviours.

Dr Wu Kang-Da, post-doctoral researcher at USTC and joint first author of the research, said: "Quantum photonics represents one of the least error-prone architectures that has been proposed for quantum computing, particularly at smaller scales. Moreover, because we are configuring our quantum simulator to model a particular process, we are able to finely-tune our optical components and achieve smaller errors than typical of current universal quantum computers."

Dr Chengran Yang, Research Fellow at CQT and also joint first author of the research, added: "This is the first realisation of a quantum stochastic simulator where the propagation of information through the memory over time is conclusively demonstrated, together with proof of greater accuracy than possible with any classical simulator of the same memory size."

sources :science daily

New technique in error-prone quantum computing makes classical computers sweat

 Despite steady improvements in quantum computers, they're still noisy and error prone, which leads to questionable or wrong answers. Scientists predict that they won't truly outcompete today's "classical" supercomputers for at least five or 10 years, until researchers can adequately correct the errors that bedevil entangled quantum bits, or qubits.

But a new study shows that, even lacking good error correction, there are ways to mitigate errors that could make quantum computers useful today.

Researchers at IBM Quantum in New York and their collaborators at the University of California, Berkeley, and Lawrence Berkeley National Laboratory report today (June 14) in the journal Nature that they pitted a 127-qubit quantum computer against a state-of-the-art supercomputer and, for at least one type of calculation, bested the supercomputer.

The calculation wasn't chosen because it was difficult for classical computers, the researchers say, but because it's similar to ones that physicists make all the time. Crucially, the calculation could be made increasingly complex in order to test whether today's noisy, error-prone quantum computers can produce accurate results for certain types of common calculations.

The fact that the quantum computer produced the verifiably correct solution as the calculation became more complex, while the supercomputer algorithm produced an incorrect answer, provides hope that quantum computing algorithms with error mitigation, instead of the more difficult error correction, could tackle cutting-edge physics problems, such as understanding the quantum properties of superconductors and novel electronic materials.

"We're entering the regime where the quantum computer might be able to do things that current algorithms on classical computers cannot do," said UC Berkeley graduate student and study co-author Sajant Anand.

"We can start to think of quantum computers as a tool for studying problems that we wouldn't be able to study otherwise," added Sarah Sheldon, senior manager for Quantum Theory and Capabilities at IBM Quantum.

Conversely, the quantum computer's trouncing of the classical computer could also spark new ideas to improve the quantum algorithms now used on classical computers, according to co-author Michael Zaletel, UC Berkeley associate professor of physics and holder of the Thomas and Alison Schneider Chair in Physics.

"Going into it, I was pretty sure that the classical method would do better than the quantum one," he said. "So, I had mixed emotions when IBM's zero-noise extrapolated version did better than the classical method. But thinking about how the quantum system is working might actually help us figure out the right classical way to approach the problem. While the quantum computer did something that the standard classical algorithm couldn't, we think it's an inspiration for making the classical algorithm better so that the classical computer performs just as well as the quantum computer in the future."

Boost the noise to suppress the noise

One key to the seeming advantage of IBM's quantum computer is quantum error mitigation, a novel technique for dealing with the noise that accompanies a quantum computation. Paradoxically, IBM researchers controllably increased the noise in their quantum circuit to get even noisier, less accurate answers and then extrapolated backward to estimate the answer the computer would have gotten if there were no noise. This relies on having a good understanding of the noise that affects quantum circuits and predicting how it affects the output.

The problem of noise comes about because IBM's qubits are sensitive superconducting circuits that represent the zeros and ones of a binary computation. When the qubits are entangled for a calculation, unavoidable annoyances, such as heat and vibration, can alter the entanglement, introducing errors. The greater the entanglement, the worse the effects of noise.

In addition, computations that act on one set of qubits can introduce random errors in other, uninvolved qubits. Additional computations then compound these errors. Scientists hope to use extra qubits to monitor such errors so they can be corrected -- so-called fault-tolerant error correction. But achieving scalable fault-tolerance is a huge engineering challenge, and whether it will work in practice for ever greater numbers of qubits remains to be shown, Zaletel said.

Instead, IBM engineers came up with a strategy of error mitigation they called zero noise extrapolation (ZNE), which uses probabilistic methods to controllably increase the noise on the quantum device. Based on a recommendation from a former intern, IBM researchers approached Anand, postdoctoral researcher Yantao Wu and Zaletel to ask their help in assessing the accuracy of the results obtained using this error mitigation strategy. Zaletel develops supercomputer algorithms to solve difficult calculations involving quantum systems, such as the electronic interactions in new materials. These algorithms, which employ tensor network simulations, can be directly applied to simulate interacting qubits in a quantum computer.

Over a period of several weeks, Youngseok Kim and Andrew Eddins at IBM Quantum ran increasingly complex quantum calculations on the advanced IBM Quantum Eagle processor, and then Anand attempted the same calculations using state-of-the-art classical methods on the Cori supercomputer and Lawrencium cluster at Berkeley Lab and the Anvil supercomputer at Purdue University. When Quantum Eagle was rolled out in 2021, it had the highest number of high-quality qubits of any quantum computer, seemingly beyond the ability of classical computers to simulate.

In fact, exactly simulating all 127 entangled qubits on a classical computer would require an astronomical amount of memory. The quantum state would need to be represented by 2 to the power of 127 separate numbers. That's 1 followed by 38 zeros; typical computers can store around 100 billion numbers, 27 orders of magnitude too small. To simplify the problem, Anand, Wu and Zaletel used approximation techniques that allowed them to solve the problem on a classical computer in a reasonable amount of time, and at a reasonable cost. These methods are somewhat like jpeg image compression, in that they get rid of less important information and keep only what's required to achieve accurate answers within the limits of the memory available.

Anand confirmed the accuracy of the quantum computer's results for the less complex calculations, but as the depth of the calculations grew, the results of the quantum computer diverged from those of the classical computer. For certain specific parameters, Anand was able to simplify the problem and calculate exact solutions that verified the quantum calculations over the classical computer calculations. At the largest depths considered, exact solutions were not available, yet the quantum and classical results disagreed.

sources: science daily

Hybrid AI-powered computer vision combines physics and big data

 Researchers from UCLA and the United States Army Research Laboratory have laid out a new approach to enhance artificial intelligence-powered computer vision technologies by adding physics-based awareness to data-driven techniques.

Published in Nature Machine Intelligence, the study offered an overview of a hybrid methodology designed to improve how AI-based machinery sense, interact and respond to its environment in real time -- as in how autonomous vehicles move and maneuver, or how robots use the improved technology to carry out precision actions.

Computer vision allows AIs to see and make sense of their surroundings by decoding data and inferring properties of the physical world from images. While such images are formed through the physics of light and mechanics, traditional computer vision techniques have predominantly focused on data-based machine learning to drive performance. Physics-based research has, on a separate track, been developed to explore the various physical principles behind many computer vision challenges.

It has been a challenge to incorporate an understanding of physics -- the laws that govern mass, motion and more -- into the development of neural networks, where AIs modeled after the human brain with billions of nodes to crunch massive image data sets until they gain an understanding of what they "see." But there are now a few promising lines of research that seek to add elements of physics-awareness into already robust data-driven networks.

The UCLA study aims to harness the power of both the deep knowledge from data and the real-world know-how of physics to create a hybrid AI with enhanced capabilities.

"Visual machines -- cars, robots, or health instruments that use images to perceive the world -- are ultimately doing tasks in our physical world," said the study's corresponding author Achuta Kadambi, an assistant professor of electrical and computer engineering at the UCLA Samueli School of Engineering. "Physics-aware forms of inference can enable cars to drive more safely or surgical robots to be more precise."

The research team outlined three ways in which physics and data are starting to be combined into computer vision artificial intelligence:

sources:science daily

Metamaterials with built-in frustration have mechanical memory

 Researchers from the UvA Institute of Physics and ENS de Lyon have discovered how to design materials that necessarily have a point or line where the material doesn't deform under stress, and that even remember how they have been poked or squeezed in the past. These results could be used in robotics and mechanical computers, while similar design principles could be used in quantum computers.

The outcome is a breakthrough in the field of metamaterials: designer materials whose responses are determined by their structure rather than their chemical composition. To construct a metamaterial with mechanical memory, physicists Xiaofei Guo, Marcelo Guzmán, David Carpentier, Denis Bartolo and Corentin Coulais realised that its design needs to be 'frustrated', and that this frustration corresponds to a new type of order, which they call non-orientable order.

Physics with a twist

A simple example of a non-orientable object is a Möbius strip, made by taking a strip of material, adding half a twist to it and then gluing its ends together. You can try this at home with a strip of paper. Following the surface of a Möbius strip with your finger, you'll find that when you get back to your starting point, your finger will be on the other side of the paper.

A Möbius strip is non-orientable because there is no way to label the two sides of the strip in a consistent manner; the twist makes the entire surface one and the same. This is in contrast to a simple cylinder (a strip without any twists whose ends are glued together), which has a distinct inner and outer surface.

Guo and her colleagues realised that this non-orientability strongly affects how an object or metamaterial responds to being pushed or squeezed. If you place a simple cylinder and a Möbius strip on a flat surface and press down on them from above, you'll find that the sides of the cylinder will all bulge out (or in), while the sides of the Möbius strip cannot do the same. Instead, the non-orientability of the latter ensures that there is always a point along the strip where it does not deform under pressure.

Frustration is not always a bad thing

Excitingly, this behaviour extends far beyond Möbius strips. 'We discovered that the behaviour of non-orientable objects such as Möbius strips allows us to describe any material that is globally frustrated. These materials naturally want to be ordered, but something in their structure forbids the order to span the whole system and forces the ordered pattern to vanish at one point or line in space. There is no way to get rid of that vanishing point without cutting the structure, so it has to be there no matter what,' explains Coulais, who leads the Machine Materials Laboratory at the University of Amsterdam.

The research team designed and 3D-printed their own mechanical metamaterial structures which exhibit the same frustrated and non-orientable behaviour as Möbius strips. Their designs are based on rings of squares connected by hinges at their corners. When these rings are squeezed, neighbouring squares will rotate in opposite directions so that their edges move closer together. The opposite rotation of neighbours makes the system's response analogous to the anti-ferromagnetic ordering that occurs in certain magnetic materials.

Rings composed of an odd number of squares are frustrated, because there is no way for all neighbouring squares to rotate in opposite directions. Squeezed odd-numbered rings therefore exhibit non-orientable order, in which the rotation angle at one point along the ring must go to zero.

Being a feature of the overall shape of the material makes this a robust topological property. By connecting multiple metarings together, it is even possible to emulate the mechanics of higher-dimensional topological structures such as the Klein bottle.

Mechanical memory

Having an enforced point or line of zero deformation is key to endowing materials with mechanical memory. Instead of squeezing a metamaterial ring from all sides, you can press the ring at distinct points. Doing so, the order in which you press different points determines where the zero deformation point or line ends up.

This is a form of storing information. It can even be used to execute certain types of logic gates, the basis of any computer algorithm. A simple metamaterial ring can thus function as a mechanical computer.

sources :science daily

Open-source software to speed up quantum research

 Quantum technology is expected to fundamentally change many key areas of society. Researchers are convinced that there are many more useful quantum properties and applications to explore than those we know today. A team of researchers at Chalmers University of Technology in Sweden have now developed open-source, freely available software that will pave the way for new discoveries in the field and accelerate quantum research significantly.

Within a few decades, quantum technology is expected to become a key technology in areas such as health, communication, defence and energy. The power and potential of the technology lie in the odd and very special properties of quantum particles. Of particular interest to researchers in the field are the superconducting properties of quantum particles that give components perfect conductivity with unique magnetic properties. These superconducting properties are considered conventional today and have already paved the way for entirely new technologies used in applications such as magnetic resonance imaging equipment, maglev trains and quantum computer components. However, years of research and development remain before a quantum computer can be expected to solve real computing problems in practice, for example. The research community is convinced that there are many more revolutionary discoveries to be made in quantum technology than those we know today.

Open-source code to explore new superconducting properties

Basic research in quantum materials is the foundation of all quantum technology innovation, from the birth of the transistor in 1947, through the laser in the 1960s to the quantum computers of today. However, experiments on quantum materials are often very resource-intensive to develop and conduct, take many years to prepare and mostly produce results that are difficult to interpret. Now, however, a team of researchers at Chalmers have developed the open-source software SuperConga, which is free for everyone to use, and specifically designed to perform advanced simulations and analyses of quantum components. The programme operates at the mesoscopic level, which means that it can carry out simulations that are capable of 'picking up' the strange properties of quantum particles, and also apply them in practice. The open-source code is the first of its kind in the world and is expected to be able to explore completely new superconducting properties and eventually pave the way for quantum computers that can use advanced computing to tackle societal challenges in several areas.

"We are specifically interested in unconventional superconductors, which are an enigma in terms of how they even work and what their properties are. We know that they have some desirable properties that allow quantum information to be protected from interference and fluctuations. Interference is what currently limits us from having a quantum computer that can be used in practice. And this is where basic research into quantum materials is crucial if we are to make any progress," says Mikael Fogelström, Professor of Theoretical Physics at Chalmers.

These new superconductors continue to be highly enigmatic materials -- just as their conventional siblings once were when they were discovered in a laboratory more than a hundred years ago. After that discovery, it would be more than 40 years before researchers could describe them in theory. The Chalmers researchers now hope that their open-source code can contribute to completely new findings and areas of application.

"We want to find out about all the other exciting properties of unconventional superconductors. Our software is powerful, educational and user-friendly, and we hope that it will help generate new understanding and suggest entirely new applications for these unexplored superconductors," says Patric Holmvall, postdoctoral researcher in condensed matter physics at Uppsala University.

Desire to make life easier for quantum researchers and students

To be able to explore revolutionary new discoveries, tools are needed that can study and utilise the extraordinary quantum properties at the minimal particle level, and can also be scaled up large enough to be used in practice. Researchers need to work at mesoscopic scale. This lies at the interface between the microscopic scale, i.e. the atomic level at which the quantum properties of the particles can still be utilised, and the macroscopic scale which measures everyday objects in our world which, unlike quantum particles, are subject to the laws of classical physics. On account of the software's ability to work at this mesoscopic level, the Chalmers researchers now hope to make life easier for researchers and students working with quantum physics.

"Extremely simplified models based on either the microscopic or macroscopic scale are often used at present. This means that they do not manage to identify all the important physics or that they cannot be used in practice. With this free software, we want to make it easier for others to accelerate and improve their quantum research without having to reinvent the wheel every time," says Tomas Löfwander, Professor of Applied Quantum Physics at

sources:science daily

AI reveals hidden traits about our planet's flora to help save species

 In a world-first, scientists from UNSW and Botanic Gardens of Sydney, have trained AI to unlock data from millions of plant specimens kept in herbaria around the world, to study and combat the impacts of climate change on flora.

"Herbarium collections are amazing time capsules of plant specimens," says lead author on the study, Associate Professor Will Cornwell. "Each year over 8000 specimens are added to the National Herbarium of New South Wales alone, so it's not possible to go through things manually anymore."

Using a new machine learning algorithm to process over 3000 leaf samples, the team discovered that contrary to frequently observed interspecies patterns, leaf size doesn't increase in warmer climates within a single species.

Published in the American Journal of Botany, this research not only reveals that factors other than climate have a strong effect on leaf size within a plant species, but demonstrates how AI can be used to transform static specimen collections and to quickly and effectively document climate change effects.

Herbarium collections move to the digital world

Herbaria are scientific libraries of plant specimens that have existed since at least the 16th century.

"Historically, a valuable scientific effort was to go out, collect plants, and then keep them in a herbarium. Every record has a time and a place and a collector and a putative species ID," says A/Prof. Cornwell, a researcher at the School of BEES and a member of UNSW Data Science Hub.

A couple of years ago, to help facilitate scientific collaboration, there was a movement to transfer these collections online.

"The herbarium collections were locked in small boxes in particular places, but the world is very digital now. So to get the information about all of the incredible specimens to the scientists who are now scattered across the world, there was an effort to scan the specimens to produce high resolution digital copies of them."

The largest herbarium imaging project was undertaken at the Botanic Gardens of Sydney when over 1 million plant specimens at the National Herbarium of New South Wales were transformed into high-resolution digital images.

sources:science daily

A backpack full of multiple sclerosis therapy

 Multiple sclerosis (MS) is a devastating autoimmune disease that destroys the protective myelin covering around nerves, disrupting communication between the brain and body, and causing patients' ability to move and function to progressively decline. The MS atlas reported in 2020 that someone is diagnosed with MS every five minutes around the world, adding to about 2.8 million individuals that currently have to live with the disease. Alarmingly, since 2013, the world-wide prevalence of MS has risen by 30%.

A key driver of MS is the sudden inflammation of nerves caused by so-called myeloid cells of the "innate" immune system in vulnerable regions of the brain and spinal cord, which together form the central nervous system (CNS). These "acute inflammatory lesions" then attract other myeloid cells, as well as self-reactive T and B cells that belong to the immune system's second arm, known as the "adaptive immune system" and directly attack the myelin covering. While no cure is available for MS, existing disease-modifying therapies in the form of small molecule and protein drugs either directly target the self-reactive immune cells or broadly dampen inflammation. However, many of those therapies cause severe side effects in different parts of the body, including the immune system itself, and thus carry significant health risks.

Now, a research team at the Wyss Institute for Biologically Inspired Engineering at Harvard University and Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has developed a cell therapy as a strong alternative to existing small molecule and protein therapies that leverages myeloid cells, the very type of immune cells that cause the MS-triggering nerve inflammation in patients.

To transform potentially inflammatory myeloid cells into therapeutic cells, they isolated and cultured monocytes (a type of myeloid cell) from the bone marrow of donor mice and stably attached tiny microparticles, termed "backpacks," to the cells' surfaces. These backpacks are loaded with anti-inflammatory molecules that direct the carrier cells' differentiation into anti-inflammatory cells in vivo. When infused back into a mouse model of MS, the backpack-laden monocytes were able to affect MS-specific immune responses, and partially reverse hind limb paralysis and improve motor functions. The results are published in the Proceedings of the National Academy of Sciences (PNAS).

"Current MS therapies do not specifically target myeloid cells. These are very plastic cells that can toggle between different states and are thus hard to control. Our biomaterial-based backpack approach is a highly effective way to keep them locked into their anti-inflammatory state," said senior author Samir Mitragotri, Ph.D., who is a Core Faculty member at the Wyss Institute. "In many ways simpler than other cell therapies, myeloid cells can be easily obtained from patients' peripheral blood, modified with backpacks in a short culture step, and reinfused back into the original donor, where they find their way to inflammatory lesions and affect the MS-specific immune response not only locally, but more broadly." Mitragotri is also the Hiller Professor of Bioengineering and Hansjörg Wyss Professor of Biologically Inspired Engineering at SEAS.

Many cell therapies, such as the famed CAR-T cell therapies, require the mobilization of immune cells from specific tissue compartments in the body with drugs, genetic modification, and then amplification over weeks outside of the body. Myeloid cells can be directly retrieved using established methods and modified with backpacks within hours, making the therapy more easily translatable. In addition, some myeloid cell types possess the ability to traverse the blood-brain barrier, which makes them particularly suitable for treating CNS diseases.

Source: ScienceDaily

Is alcohol good for you?

 While drinking alcohol moderately comes with both risks and possible benefits, a person should exercise caution. The risks of drinking alcohol excessively may outweigh any possible benefits.

This article will explore drinking habits, the potential health benefits of drinking in moderation, the risks, and other effects of alcohol on the body. We will also look at ways to reduce risks and alcohol consumption.

According to data from the Substance Abuse and Mental Health Services Administration (SAMHSA), as of 2019, 85.6% of people over age 18 in the United States reported that they had drunk alcohol at some point in life.

Meanwhile, 54.9% of people reported that they had consumed alcohol in the past month.

Drinking in moderation

According to the Dietary Guidelines for AmericansTrusted Source, moderate drinking consists of two drinks or less per day for men of legal drinking age and one drink or less per day for women of legal drinking age.

One drink is equivalentTrusted Source to:

• a bottle of regular beer — 12 ounces (oz)
• a glass of wine — 5 oz
• a shot of liquor such as vodka — 1.5 oz

The guidelines do not recommendTrusted Source that individuals who currently do not drink start drinking for any reason. They also mention that drinking less is better for a person’s health than drinking more.

Possible benefits of alcohol

Moderate alcohol consumption has some potential benefits for the body, but these do not outweigh the risks of alcohol consumption.

People should consult a doctor to discuss ways to reduce the risk and treat the effects of certain health conditions.

Alcohol may offer protective effects for certain body systems and may reduce the risk of developing some health conditions, including the following.

Cardiovascular diseases

According to a 2020 reviewTrusted Source, alcohol consumption at low and moderate levels may help protect against cardiovascular diseases.

Fermented alcoholic beverages, such as beer and wine, contain polyphenols such as resveratrol.

Polyphenols have antioxidant and anti-inflammatory effects, and experts associate these with a decrease in the incidence of some diseases, including cardiovascular diseases.

While the 2020 review suggests that consuming small amounts of alcohol may carry some benefit, consuming large amounts, even occasionally, remains detrimental.

To better reflect short-term and habitual alcohol consumption and its effects, future studies need to use more reliable measurements of alcohol exposure rather than self-reported intake.

A 2018 animal studyTrusted Source found that resveratrol had protective effects on cardiovascular function in diabetic rats.

A 2017 study also found an association between moderate alcohol consumption and a lower incidence of heart failure.

Considerations

Consuming alcohol in excess may cause other heart-related conditions, such as cardiomyopathy — damage to the heart muscle — or arrhythmias, which are abnormal heart rhythms. These conditions may also increase the risk of a stroke.

Type 2 diabetes

Type 2 diabetes causes the body’s cells to take in less glucose, or sugar, from the blood as a result of insulin resistance. When the body’s cells do not respond to insulin and take up glucose, a person will have highTrusted Source blood sugar levels.

According to the American Diabetes Association, moderate alcohol consumption may improve blood glucose management and sensitivity to insulin.

A 2015 review associates moderate alcohol consumption with a reduced risk of type 2 diabetes. Additionally, the authors state that moderate alcohol consumption may improve insulin sensitivity for some people but not for all.

The researchers also suggest that alcohol may reduce hemoglobin A1c (HbA1c) concentrations, or blood glucose levels. A person with diabetes is likely to have an HbA1c level of 6.5%Trusted Source or higher.

Overall, the studies had small sample sizes and short durations. More research is necessary to explore any further associations across bigger samples and longer time periods.

Considerations

Excess alcohol intake has an association with an increased risk of type 2 diabetes.

Drinking alcohol can also reduce the body’s ability to recover when blood sugar levels drop. A person with diabetes should discuss with their doctor any effects that alcohol may have on their condition or medications.

Dementia

A 2017 meta-analysisTrusted Source found an association between moderate alcohol consumption — 12.5 grams or less per day — and a reduced risk of dementia.

However, the same analysis states that excessive drinking — 38 grams or more per day — may increase the risk of developing dementia.

The risks of drinking alcohol

While moderate alcohol consumption may have some potential benefits, the negative effects of long-term or excessive alcohol drinking outweigh these benefits.

According to SAMHSA, in 2019, 25.8% of adults over age 18 reported that they had consumed alcohol excessively in the past month.

The Centers for Disease Control and Prevention (CDC)Trusted Source classifies this as drinking five or more drinks on one occasion for men and drinking four or more drinks on one occasion for women.

Heavy drinking has an association with various health issues, such as:

• alcohol poisoning
• various cancer types, including cancer of the:
  • breast
  • liver
  • colon
  • rectum
  • mouth
  • throat
  • esophagus
• memory and learning difficulties

Long-term heavy alcohol drinking has an association with an increased riskTrusted Source of:

• hypertension
• atrial fibrillation
• liver disease and digestive issuesTrusted Source
• immune system problemsTrusted Source
• depression and anxietyTrusted Source

Long-term alcohol drinking may also lead to alcohol use disorderTrusted Source, which involves difficulty stopping or regulating alcohol consumption despite negative social and health consequences.

Other effects of alcohol

Alcohol can also affect other parts of the body in both the short and the long term.

Skin

Alcohol has a diuretic effect on the body. This means that it increasesTrusted Source urine output, which can affect the body’s hydration.

Dehydration can resultTrusted Source in:

• dry skin and lips
• sunken eyes
• poor skin elasticity
• blue lips

Alcohol can also cause facial flushingTrusted Source, a red appearance in the face.

Chronic overconsumption of alcohol may lead toTrusted Source:

• yellowing of the skin, or jaundice
• an increased risk of skin cancer and skin infections
• itchy skin

Learn more about the effects of alcohol on the skin.

Hair

Drinking an excessive amount of alcohol can have a negative impact on the hair.

A 2022 reviewTrusted Source suggests that alcohol consumption may have an association with the immunological risk of alopecia areata. Alopecia areata is a type of hair loss that occurs when the immune system attacks the hair follicles.

Heavy alcohol drinking can affectTrusted Source the absorption of or increase the loss of zinc and other nutrients.

According to the American Academy of Dermatology Association, people may experience hair loss if they do not consume enough of nutrients such as:

• iron
• protein
• biotin
• zinc

Teeth

A 2017 studyTrusted Source found that people who had a dependence on alcohol had a higher prevalence of dental issues — such as dental caries and periodontitis — than those who did not have a dependence on alcohol.

Similarly, another 2017 studyTrusted Source found that participants with a dependence on alcohol had lower oral hygiene scores and a higher risk of dental and periodontal diseases.

Ways to reduce risks

A person can choose not to drinkTrusted Source or to drink in moderation, as the Dietary Guidelines for Americans suggest. To reduce drinking, a person may plan to have several drink-free days each week.

People can also reduce their risk of developing certain health conditions by making other lifestyle changes. For example, people can consume antioxidants through foods and nutrients such asTrusted Source:

• vegetables and fruits
• vitamin C and vitamin E
• selenium
• carotenoids such as:
  • beta carotene
  • lycopene
  • lutein

To reduce health risks on any single occasion, a person may try:

• limiting drinks
• drinking more slowly
• consuming food while drinking
• alternating alcoholic drinks with water or other nonalcoholic drinks

• Summary

  Drinking in moderation may have some protective effects for the cardiovascular system. It may also increase insulin sensitivity in type 2 diabetes and reduce the risk of dementia.

  However, the potential benefits do not outweigh the risks of alcohol consumption. Consuming alcohol can increase a person’s risk of developing other health conditions, such as cancer. It can also negatively affect the skin, hair, and teeth.

  People can reduce their risk of certain conditions by modifying their diet and changing their drinking habits.

• Source - Medical News Today