Category: English

Source: The Conversation – USA – By Saif Khan, Ph.D. Candidate in Biology, University of Southern California

Naloxone, also known by the brand name Narcan, is one of the most important drugs in the United States’ fight against the opioid crisis. It reverses an opioid overdose nearly instantly, restarting breathing in a person who was unresponsive moments before and on the brink of death. To bystanders witnessing it being administered, naloxone can appear almost supernatural.

Although the Food and Drug Administration approved naloxone for medical use in 1971 and for over-the-counter purchase in 2023, exactly how it works is still unclear. Researchers know naloxone acts on opioid receptors, a family of proteins responsible for the body’s response to pain. When opioids such as morphine and fentanyl bind to these receptors, they produce not only pain relief and euphoria but also dangerous side effects. Naloxone competes with opioids for access to these receptors, preventing the drugs from triggering effects in the body. How it does this at the molecular level, however, has been an ongoing question.

In our recently published research in the journal Nature, my team and I were able to provide some definitive evidence of how naloxone works by capturing images of it in action for the first time.

Biology of opioids

To better grasp how naloxone works, it’s helpful to first zoom in on the biology behind opioids.

One member of the family of opioid receptors, MOR – short for µ-opioid receptor – is a central player in regulating the body’s response to pain. It sits on the surface of neurons, mostly in the brain and spinal cord, and acts as a communication hub.

When an opioid – such as an endorphin, the body’s natural painkillers – interacts with MOR, it changes the structure of the receptor. This change in shape allows what’s called a G protein to bind to the receptor and trigger a signal to the rest of the body to reduce pain, induce pleasure, or – in the case of overdose – dangerously slow breathing and heart rate.

In everyday terms, MOR is like a lock on the outside of the cell. The G protein is the mechanism inside the lock that turns when the correct key – in this case, an endorphin or a drug like fentanyl – goes in. For decades, scientists believed that an opioid’s ability to enable this signaling cascade was linked to how effectively it reshaped the structure of the receptor – essentially, whether the lock could open wide enough for the internal mechanism of the G-protein to engage.

Yet, recent research – including our work – has revealed that the critical step to how opioids work is not how wide they open the lock but how well the mechanism works. G proteins act like a switch, releasing one molecule in exchange for another molecule that triggers the protein to send the signal that sets off opioid effects.

In essence, drugs like fentanyl, by acting on the receptor, transmit physical changes to G proteins that result in the switch flipping more rapidly. What we now see is that naloxone jams the mechanism, preventing the switch from flipping and sending the signal.

Capturing the switch

Researchers know that the effects of opioids are triggered when the G protein switch is flipped. But what does this process look like?

For years, attempts to visualize this mechanism were largely limited to two states – before the G protein binds to the µ-opioid receptor, and after the molecule was released from the G protein. The states in between were considered too unstable to isolate. My team and I wanted to capture these unseen states moment by moment as the switch flips and the molecule is released.

To do this, we used a technique called cryo-electron microscopy, which freezes molecules in motion to visualize them at near-atomic resolution. For both naloxone and the opioid drug loperamide (Imodium), we trapped the G protein bound to the opioid receptor right before it released the molecule.

We captured four distinct structural states leading up to the release of the molecule from the G protein.

The first of these, which we call the latent state, is the earliest form of the opioid receptor and G protein after they make contact. We found that both the opioid receptor and the G protein are inactive at this point. Moreover, naloxone stabilizes this latent state. What this means is that naloxone effectively jams the mechanism right at the start, preventing all subsequent steps required for activation.

In the absence of naloxone, an opioid drug promotes a transition to the remaining three states: The G-protein rotates and aligns itself with the receptor (engaged), swings open the door blocking the molecule that would trigger the switch from flipping (unlatched), and holds that door open so the molecule can be released (primed) and send the signal to carry out the drug’s effects.

To confirm that our snapshots reflect what’s really happening, we performed extensive computational simulations to watch these four states change over time. Together, these findings point to the molecular root of naloxone’s therapeutic effects: By stalling the opioid receptor and G protein at a latent state, it shuts down opioid signaling, reversing an opioid overdose within minutes.

Visualizing new drugs

Designing a new key for a lock is most successfully done when you know exactly what that lock looks like. By mapping the exact sequence of how opioids interact with opioid receptors and pinpointing where different drugs can intervene in this process, our findings provide a blueprint for engineering the next generation of opioid medicines and overdose antidotes.

For example, one of the persistent challenges with naloxone is that it must often be administered repeatedly during an overdose. This is especially the case for fentanyl overdoses, where the opioid can outcompete or outlast the effects of the treatment.

Knowing that naloxone works by stalling the µ-opioid receptor in an early, latent state suggests that molecules that can bind more tightly or more selectively to this form of the receptor could be more effective at stabilizing this inactive state and thus preventing an opioid’s effects.

By uncovering the structure of molecules involved in opioid signaling, researchers may be able to develop drugs that provide longer-lasting protection against overdose.

This research was supported by the National Institutes of Health.

– ref. How does Narcan work? Mapping how it reverses opioid overdose can provide a molecular blueprint for more effective drugs – https://theconversation.com/how-does-narcan-work-mapping-how-it-reverses-opioid-overdose-can-provide-a-molecular-blueprint-for-more-effective-drugs-269706

Source: The Conversation – USA – By Mark Louie Ramos, Assistant Research Professor of Health Policy and Administration, Penn State

Should you believe the findings of scientific studies? Amid current concerns about the public’s trust in science, old arguments are resurfacing that can sow confusion.

As a statistician involved in research for many years, I know the care that goes into designing a good study capable of coming up with meaningful results. Understanding what the results of a particular study are and are not saying can help you sift through what you see in the news or on social media.

Let me walk you through the scientific process, from investigation to publication. The research results you hear about crucially depend on the way scientists formulate the questions they’re investigating.

The scientific method and the null hypothesis

Researchers in all kinds of fields use the scientific method to investigate the questions they’re interested in.

First, a scientist formulates a new claim – what’s called a hypothesis. For example, is having some genetic mutations in BRCA genes related to a higher risk of breast cancer? Then they gather data relevant to the hypothesis and decide, based on the data, whether that initial claim was correct or not.

It’s intuitive to think that this decision is cleanly dichotomous – that the researcher decides the hypothesis is either true or false. But of course, just because you decide something doesn’t mean you’re right.

If the claim is really false but the researcher decides, based on the evidence, it’s true – a false positive – they commit what’s called a Type 1 error. If the claim is really true but the researcher fails to see that – a false-negative conclusion – then they commit a Type 2 error.

Moreover, in the real world, it gets a little messier. It’s really hard to decide about the truth or falsity of a claim just based on what’s observed.

For that reason, most scientists employ what is called the null hypothesis significance testing framework. Here’s how it works: A researcher first states a “null hypothesis,” something that’s contrary to what they want to prove. For instance, in our example the null hypothesis is that BRCA genetic mutations are not associated with increased breast cancer occurrence.

The scientist still gathers data and makes a decision, but the decision is not about whether the null is true. Instead, a researcher decides whether there’s enough evidence to reject the null hypothesis or not.

What rejecting the null does and doesn’t mean

Understanding this distinction is crucial. Rejecting the null is equivalent in practice to acting as though it is false – in the example, rejecting the null means claiming that those with some BRCA gene mutations do have a higher risk of breast cancer. Along with other evidence, such as the size of the increased risk, this outcome can justify recommending early breast cancer screening for people with the identified BRCA mutations.

But failing to reject the null hypothesis doesn’t imply that it’s true – in this case, it doesn’t mean there is no association between the BRCA mutations and breast cancer. Rather, such a result is inconclusive; there’s not enough evidence to claim there is an association. A negative result – inadequate evidence to say the null is false – does not necessarily invite the researcher to believe the null is true.

This is because null hypothesis significance testing is set up to control for Type 1 error (false positive) at a level defined in advance by the researcher but at the cost of having less control over Type 2 error (false negative).

A researcher’s chances of correctly rejecting the null if there is increased risk can depend on how much data they have, how complex the design of the study is and, most importantly, how large the effect actually is. It’s much easier to reject the null if BRCA mutations truly increase cancer risk many times than it is if the risk is only slightly elevated. A researcher can end up with a result that is not statistically significant but cannot rule out the possibility of an increased risk that is too small for the study to detect.

Which results are more often publicized

Once they have their result and the researchers want to disseminate their work, they typically do so through peer-reviewed publication. Journal publishers consider a researcher’s write-up of their study, send it out for other scientists to review, and then decide whether to publish it.

In this process, the publishers tend to favor studies that rejected their null hypothesis over those that failed to reject it. This is called positive publication bias.

It is natural for publishers to prefer studies that support new claims since they objectively carry more information than studies that failed to reject their null hypothesis. Journals want to publish something new and noteworthy.

Many sources flag this phenomenon as “bad science,” but is it really? Remember, the framework used to make decisions about scientific claims is intentionally only capable of either rejecting the null hypothesis – in other words, supporting the claim – or alternatively declaring inconclusive results.

The framework isn’t designed to be able to prove the null hypothesis. That said, researchers can reverse the design of a scientific investigation so that a previous claim becomes the null hypothesis in a new study with fresh data.

For instance, rather than a null hypothesis that there is no association between BRCA mutations and breast cancer, the null hypothesis becomes that the increased breast cancer risk from BRCA mutations is equal to or greater than some value the researcher settles on before gathering fresh data.

Rejecting the null this time would mean the increased risk is smaller than that set value, thus supporting the claim consistent with what had previously been the null hypothesis on prior data. In the example, rejecting the null means the effect of BRCA genes is small enough to be practically negligible in terms of developing breast cancer.

It’s critical for a researcher to structure their study so that what they’re interested in proving is aligned with the rejection of the null. Publishers are naturally less inclined to consider studies that failed to reject their null hypothesis, not because they do not want to publish studies that support negative statements but because null hypothesis significance testing does not actually support negative statements. Failure to reject the null just means your results are inconclusive – and may perhaps seem less newsworthy.

What positive publication bias does

So what does the practice of preferring to publish studies that reject their null hypothesis do?

While we can’t know for certain, we can see how this plays out under different circumstances. You can explore the scenarios in this app I made.

If scientists are acting in good faith, using null hypothesis significance testing appropriately, it turns out that positive publication bias on the part of scientific journal publishers will increase the proportion of true discoveries in their pages much more than it will increase the proportion of false positives.

If editors did not exercise any positive publication bias, journals would be almost entirely full of studies with inconclusive results.

Of course, if scientists are not acting in good faith and are just interested in getting published while ignoring proper use of statistical tests, that can lead to false-positive rates being as high or higher than the rate of true discoveries. But this possibility is true even without positive publication bias.

Mark Louie Ramos does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Absence of evidence is not evidence of absence – and that affects what scientific journals choose to publish – https://theconversation.com/absence-of-evidence-is-not-evidence-of-absence-and-that-affects-what-scientific-journals-choose-to-publish-264854

Source: The Conversation – USA – By Gregory A. Daddis, Professor and Melbern G. Glasscock Endowed Chair in American History, Texas A&M University

In an unprecedented step, the Department of Defense announced online on Nov. 24, 2025, that it was reviewing statements by U.S. Sen. Mark Kelly, a Democrat, who is a retired Navy captain, decorated combat veteran and former NASA astronaut.

Kelly and five other members of Congress with military or intelligence backgrounds told members of the armed forces “You can refuse illegal orders” in a video released on Nov. 18, reiterating oaths that members of the military and the intelligence community swear to uphold and defend the Constitution. The legislators said they acted in response to concerns expressed by troops currently serving on active duty.

President Donald Trump called the video “seditious behavior, punishable by death.”

Retired senior officers like Kelly can be recalled to duty at any time, which would make it possible for the Pentagon to put Kelly on trial under the Uniform Code of Military Justice, although the Defense Department announcement did not specify possible charges. Defense Secretary Pete Hegseth wrote online that “Kelly’s conduct brings discredit upon the armed forces and will be addressed appropriately.”

This threat to punish Kelly is just the latest move by the Trump administration against perceived enemies at home. By branding critics and opponents as disloyal, traitorous or worse, Trump and his supporters are resurrecting a playbook that hearkens back to Sen. Joseph McCarthy’s crusade against people he portrayed as domestic threats to the U.S. in the 1950s.

As a historian who studies national security and the Cold War era, I know that McCarthyism wrought devastating social and cultural harm across our nation. In my view, repeating what I believe constitutes social and political fratricide could be just as harmful today, perhaps even more so.

Targeting homegrown enemies

In the late 1940s and early 1950s, many Americans believed the United States was a nation under siege. Despite their victory in World War II, Americans saw a dangerous world confronting them.

The communist-run Soviet Union held Eastern Europe in an iron grip. In 1949, Mao Zedong’s communist troops triumphed in the bloody Chinese civil war. One year later, the Korean peninsula descended into full-scale conflict, raising the prospect of World War III – a frightening possibility in the atomic era.

Anti-communist zealots in the U.S., most notably Wisconsin Republican Sen. McCarthy, argued that treasonous Americans were weakening the nation at home. During a February 1950 speech in Wheeling, West Virginia, McCarthy asserted that “the traitorous actions of those who have been treated so well by this nation” were undermining the United States during its “final, all-out battle” against communism.

When communist forces toppled China’s government, critics such as political activist Freda Utley lambasted President Harry Truman’s administration for what they cast as its timidity, blundering and, worse, “treason in high places.” Conflating foreign and domestic threats, McCarthy claimed without evidence that homegrown enemies “within our borders have been more responsible for the success of communism abroad than Soviet Russia.”

As ostensible proof, the senator pointed to American lives being lost in Korea and argued that it was possible to “fully fight a war abroad and at the same time … dispose of the traitorous filth and the Red vermin which have accumulated at home.”

Political opponents might disparage McCarthy for his “dishonest and cowardly use of fractional fact and innuendo,” but the Wisconsinite knew how to play to the press. Time and again, McCarthy would bombastically lash out against his critics as he did with columnist Drew Pearson, calling him “an unprincipled liar,” “a fake” and the owner of a “twisted perverted mentality.”

While McCarthy focused on allegedly disloyal government officials and media journalists, other self-pronounced protectors of the nation sought to warn naive members of the public. Defense Department pamphlets like “Know Your Communist Enemy” alerted Americans against being duped by Communist Party members skilled in deception and manipulation.

Virulent anti-communists denounced what they viewed as inherent weaknesses of postwar American society, with a clearly political bent. Republicans asserted that cowardly, effeminate liberals were weakening the nation’s defense by minimizing threats both home and abroad.

Censure and worse

In such an anxiety-ridden environment, “red-baiting” – discrediting political opponents by linking them to communism – spread across the country, leaving a trail of wrecked lives. From teachers to public officials, anyone deemed un-American by McCarthyites faced public censure, loss of employment or even imprisonment.

Under the 1940 Smith Act, which criminalized promoting the overthrow of the U.S. government, hundreds of Americans were prosecuted during the Cold War simply for having been members of the Communist Party of the United States. The act also authorized the “deportation of aliens,” reflecting fears that communist ideas had seeped into nearly all facets of American society.

The 1950 Internal Security Act, widely known as the McCarran Act, further emphasized existential threats from within. “Disloyal aliens,” a term the law left purposefully vague, could have their citizenship revoked. Communist Party members were required to register with the government, a step that made them susceptible to prosecution under the Smith Act.

Immigrants could be detained or deported if the president declared an “internal security emergency.” Advocates called this policy “preventive detention,” while critics derided the act as a “Concentration Camp Law,” in the words of historian Masumi Izumi.

Scapegoating outsiders

The scaremongering wasn’t just about people’s political views: Vulnerable groups, such as gay people, were also targeted. McCarthy warned of links between “communists and queers,” asserting that “sexual perverts” had infested the U.S. government, especially the State Department, and posed “dangerous security risks.” Closeted gay or lesbian employees, the argument went, were vulnerable to blackmail by foreign governments.

Fearmongering also took on a decidedly racist tone. South Carolina Governor George Bell Timmerman, Jr., for instance, argued in 1957 that enforcing “Negro voting rights” would promote the “cause of communism.”

Three years later, a comic book titled “The Red Iceberg” insinuated that communists were exploiting the “tragic plight” of Black families and that the NAACP, a leading U.S. civil rights advocacy group, had been infiltrated by the Kremlin. Conservatives like Arizona Sen. Barry Goldwater criticized the growing practice of using federal power to enforce civil rights, calling it communist-style social engineering.

A new McCarthyism

While it’s never simple to draw neat historical parallels from past eras to the present, it appears McCarthy-like actions are recurring widely today. During the Red Scare, the focus was on alleged communists. Today, the focus is on straightforward dissent. Critics, both past and present, of President Donald Trump’s actions and policies are being targeted.

At the national level, Trump has called for using military force against “the enemy from within.” On Sept. 30, 2025, Trump told hundreds of generals and admirals who had been called to Quantico, Virginia, from posts around the world that the National Guard should view America’s “dangerous cities as training grounds.”

The Trump administration is making expansive use of the McCarran Act to crack down on immigrants in U.S. cities. White House adviser Stephen Miller has proposed suspending the constitutionally protected writ of habeas corpus, which entitles prisoners to challenge their detentions in court, in order to deport “illegal aliens,” alleging that the U.S. is “under invasion.”

In my home state of Texas, political fearmongering has taken on an equally McCarthyesque tone, with the Legislature directing the State Board of Education to adopt mandatory instruction on “atrocities attributable to communist regimes.”

Perhaps it is unsurprising, then, that right-wing activist Laura Loomer has unapologetically called for “making McCarthy great again.”

Disagreement is democratic

The history of McCarthyism shows where this kind of action can lead. Charging political opponents with treason and calling the media an “enemy of the people,” all without evidence, undercuts democratic principles.

These actions cast certain groups as different and dehumanize them. Portraying political rivals as existential threats, simply for disagreeing with their fellow citizens or political leaders, promotes forced consensus. This diminishes debate and can lead to bad policies.

Americans live in an insecure world today, but as I see it, demonizing enemies won’t make the United States a safer place. Instead, it only will lead to the kind of harm that was brought to pass by the very worst tendencies of McCarthyism.

Gregory A. Daddis does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Pentagon investigation of Sen. Mark Kelly revives Cold War persecution of Americans with supposedly disloyal views – https://theconversation.com/pentagon-investigation-of-sen-mark-kelly-revives-cold-war-persecution-of-americans-with-supposedly-disloyal-views-265964

Source: The Conversation – USA – By Andrew Cowell, Professor of Linguistics, University of Colorado Boulder

I was hired at the University of Colorado Boulder in 1995 as a language professor. I relocated from Hawaii, where I had learned the Hawaiian language.

When I arrived in Colorado, I decided I needed to learn about the Indigenous language of the Boulder and Denver area, Arapaho. The Arapaho people had occupied the area for many years until they were forced to leave in the 1860s.

I first visited the Northern Arapaho people on the Wind River Reservation in Wyoming in 1999. At that time, there were hundreds of speakers of the Arapaho language.

Today, there are less than 100, and all are over the age of 70.

The Arapaho people in Wyoming and Colorado believe their language can still survive, and so do I. That’s why I am working to combine decades of language documentation with new technological approaches in order to help revive the language.

Loss of Native languages

Many Native American languages currently have few Native speakers, and the speakers are typically the oldest members of the community. The languages of the Wichita and Kansa people, for example, are among many that are no longer spoken at all.

Native American languages have been in decline in the face of Euro-American pressure for centuries.

On the Great Plains, this decline accelerated after World War II when Native soldiers came home after seeing prosperity off the reservation.

Arapaho elders tell me that bilingual parents decided to speak only English to their children to improve their chances of success in life. They were certain the tribal languages would come “later.”

But “later” didn’t happen. Boarding schools had already been suppressing the language, and now economic improvements brought cars, radios and televisions to Wind River, further promoting the use of English. Without language exposure in the home, children were not able to acquire good speaking abilities.

Today, however, tribal communities around the country increasingly want to maintain or reacquire their languages. Efforts to do this have been going on for several decades, with some successes, such as the Mohawk language of New York and Canada, Cherokee in Oklahoma and North Carolina and the Blackfoot language of northern Montana.

In most places however, numbers of Native speakers continue to decline, while learning among younger speakers progresses slowly.

Uses of data for curriculum

My early work focused on documenting the Arapaho language. Past linguists working with Native languages typically focused on traditional storytelling, as well as audio-recorded data. But my interest in anthropology led me to focus on conversation and everyday interaction. I also recorded on video to capture social settings, gestures and sign language. And to better understand the role of the language in daily use, I worked to become a good speaker myself.

I have compiled my documentation into a database that contains over 100,000 sentences of natural Arapaho speech. All of this has been transcribed, translated into English and accompanied by detailed linguistic analysis.

The database is further supported by an online learning site and an online dictionary of around 25,000 entries. They are among the largest such resources for an Indigenous language, though resources do exist for other languages, such as Yurok.

From documentation to curriculum

In response to the Arapaho people’s goal of language revitalization, my own work has shifted from documentation to assisting teachers, students and curriculum developers. The database turns out to have great value in this area.

Adult learners can watch the videos along with the Arapaho transcriptions or English translations, or both, and review the detailed grammatical analysis.

However, it is quite difficult for young learners to immediately benefit from listening to natural discourse. That’s why carefully graded curricula are crucial. Unlike for commonly taught languages such as French or Spanish, materials for most Native American languages are just being developed.

Arapaho can be challenging to learn because its structure is quite different from English. Many small chunks of meaning are combined to produce long, complex words. For example, an English speaker can start with “happy” and produce “un-happi-ness.” Arapaho speakers typically add three, four or even five prefixes, and multiple suffixes as well. A speaker can say the word “niibeetwonwoteekoohunoo” – which has six separate meaningful chunks. This translates to English, “I want to go and drive to town.”

There is little value in memorizing such complex words, just as English learners don’t memorize entire sentences. Instead, Arapaho learners need to understand the separate parts, and how they combine.

Previous efforts have succeeded in teaching children to speak basic Arapaho. The challenge now is to keep improving their Arapaho language abilities, using a graded curriculum that continues through all school levels.

The database can identify and label the individual chunks of words, and assign meanings to each chunk. A beginner’s dictionary of 1,300 entries has been created by calculating the overall frequency of base words in the 100,000 sentences, and then selecting only the most common ones.

The list has been broken down further to produce target vocabulary for each grade level. Smaller chunks of prefixes and suffixes are also measured, and sequential grammar-learning goals can be produced based on frequency and complexity.

A draft Arapaho learning sequence has been created, with 44 stages. It is now possible for the first time to produce a full, progressive language curriculum for Arapaho. The next step is to develop more curricular materials and train teachers to use them.

The sequence of 44 stages is now being introduced at Wyoming Indian Elementary School, the first school on the Wind River Reservation to pioneer dual-language classrooms.

Limitations of technology

Technology is not a magic bullet, however. Only Native people can save their languages, by choosing to learn and speak them.

Because artificial intelligence works using large language models, it needs billions of words of discourse to be trained effectively in a language. No Indigenous language has nearly that amount of data, so the capacity of AI to address Native language endangerment is limited. Moreover, many Indigenous communities are wary of AI due to concerns over data sovereignty and cultural property rights.

My own old-fashioned experience as a learner and teacher has proved crucial. I can see where difficulties lie for learners, and how to fine-tune computational measurements and predictions. I’ve learned that success in helping revitalize Native languages depends on researchers building long-term relationships with Native peoples and, ideally, speaking Native languages. Only then can new technologies be applied most productively.

Andrew Cowell currently receives funding from National Science Foundation. Past funding related to the work described here has come from the American Council of Learned Societies and Hans Rausing Endangered Language Documentation Programme.

He has received compensation from elements of the Northern Arapaho Tribe and the Southern Cheyenne and Arapaho Tribe for some of his assistance and consultation.

– ref. A database could help revive the Arapaho language before its last speakers are gone – https://theconversation.com/a-database-could-help-revive-the-arapaho-language-before-its-last-speakers-are-gone-269592