NPs’ New Role Delivering Tx for Opioid Addiction

Congress recently passed, and President Trump signed “a comprehensive package of anti-opioid bills into law with a key provision permanently authorizing NPs [nurse practitioners] to prescribe medication-assisted treatments (MATs), further expanding patient access to these critical treatments,” according to Joyce Knestrick, PhD, C-FNP, APRN, the president of the American Association of Nurse Practitioners (AANP).

This passage is extremely important to those fighting opioid addictions as well as those healthcare workers who are treating them. “Recognizing the ongoing impact of the opioid crisis, Congress and the president moved quickly to get this critical legislation across the finish line. We applaud their actions, which acknowledge the vital role NPs play in treating patients with opioid use disorder with MATs,” Knestrick said.

What does this mean for the healthcare community? Knestrick explains.

Why is this law important for NPs and for opioid addicts?

Knestrick: First and foremost, for the millions of American families struggling with addiction today, passage of this legislation ensures patients continuity of care, knowing that their NPs can continue to provide their loved ones access to MAT treatment.

Second, knowing that NPs are now permanently authorized to prescribe MAT, we anticipate significant growth in the number of America’s NPs who will become waivered to prescribe MATs, which will help turn the tide of opioid addiction in communities nationwide.

How will this help more opioid addicts?

Knestrick: As primary care professionals, NPs really are on the front lines of combating the opioid epidemic. Tragically, 80% of patients addicted to opioids don’t receive the treatment they need, due in part to healthcare access challenges, stigma, cost, and other factors. Thanks to advances in MAT, which combines medications that temper cravings with counseling and therapy, and this new law granting NPs permanent authority to prescribe MATs, the opportunities to reach and treat patients struggling with addiction are better than ever before.

In addition to helping addicts, will this make the process more cost-effective? If not, how else will it be beneficial to healthcare facilities and/or treatment centers?

Knestrick: We do know that treating people with addiction to opioids and other substances is costly, in part because of the need for in-patient treatment and more frequent hospitalizations. Yet most people in need of treatment simply don’t receive it. As a nation, we are facing significant shortages of specialty treatment facilities for addiction, and this makes it all the more important to ensure that NPs and other primary care providers have the tools to meet the patient need for MATs.

AANP has formed a collaboration with the American Society of Addiction Medicine and the American Association of Physician Assistants to provide the 24-hour waiver training for NPs and physician assistants. We invite NPs to visit AANP’s CE Center for more information.

https://www.medpagetoday.com/nursing/nursing/76916

Sensorion’s vertigo drug hits tolerability endpoint in phase 2

A phase 2a trial of Sensorion’s vertigo seliforant has met its primary tolerability endpoint. The trial found the histamine type 4 receptor antagonist had no effect on vigilance and cognitive performance, suggesting it may have an edge over existing vertigo treatments such as meclizine.

Meclizine, a histamine H1 receptor antagonist, is used to treat acute vertigo but can cause sedation, impaired motor function and other side effects. These adverse events are particularly problematic in the treatment of vertigo as sedated patients are unable to start the vestibular rehabilitation therapy that could help alleviate their disorders.

France’s Sensorion thinks seliforant, a small molecule formerly known as SENS-111, can inhibit vestibular neuron activity and thereby treat vertigo without causing the sedation associated with the off-label use of meclizine.

To test that idea, Sensorion enrolled 32 people in a crossover trial and randomized them to receive one of two doses of seliforant, meclizine or placebo one week apart in a random order. The subjects were healthy volunteers who were exposed to “experimental motion.” Sensorion assessed the effect of the drugs on people using a psychomotor test battery and a vigilance and tracking test.

Sensorion is yet to share data from the trial but has disclosed that the study “met its tolerability primary endpoint in a statistically significant manner.” Unlike meclizine, seliforant was not linked to negative CNS side effects.

The finding is small, but given Sensorion’s positioning of seliforant, it’s an important step in the advance of the candidate. CNS side effects would have undermined a key plank of Sensorion’s pitch to get seliforant established in the vertigo market.

None of that will matter if seliforant falls short on the efficacy front, though. The big test of that aspect of the drug will come in the second half of next year when Sensorion releases data from an ongoing phase 2 proof-of-concept clinical trial.

https://www.fiercebiotech.com/biotech/sensorion-s-vertigo-drug-hits-tolerability-endpoint-phase-2

Aleksander Madry on building trustworthy artificial intelligence

Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we’re done typing. But as artificial intelligence becomes further embedded in daily life, expectations have risen. Before autonomous systems fully gain our confidence, we need to know they are reliable in most situations and can withstand outside interference; in engineering terms, that they are robust. We also need to understand the reasoning behind their decisions; that they are interpretable.

Aleksander Madry, an associate professor of computer science at MIT and a lead faculty member of the Computer Science and Artificial Intelligence Lab (CSAIL)’s Trustworthy AI initiative, compares AI to a sharp knife, a useful but potentially-hazardous tool that society must learn to weild properly. Madry recently spoke at MIT’s Symposium on Robust, Interpretable AI, an event co-sponsored by the MIT Quest for Intelligence and CSAIL, and held Nov. 20 in Singleton Auditorium. The symposium was designed to showcase new MIT work in the area of building guarantees into AI, which has almost become a branch of machine learning in its own right. Six faculty members spoke about their research, 40 students presented posters, and Madry opened the symposium with a talk the aptly titled, “Robustness and Interpretability.” We spoke with Madry, a leader in this emerging field, about some of the key ideas raised during the event.

Q: AI owes much of its recent progress to deep learning, a branch of machine learning that has significantly improved the ability of algorithms to pick out patterns in text, images and sounds, giving us automated assistants like Siri and Alexa, among other things. But deep learning systems remain vulnerable in surprising ways: stumbling when they encounter slightly unfamiliar examples in the real world or when a malicious attacker feeds it subtly-altered images. How are you and others trying to make AI more robust?

A: Until recently, AI researchers focused simply on getting machine-learning algorithms to accomplish basic tasks. Achieving even average-case performance was a major challenge. Now that performance has improved, attention has shifted to the next hurdle: improving the worst-case performance. Most of my research is focused on meeting this challenge. Specifically, I work on developing next-generation machine-learning systems that will be reliable and secure enough for mission-critical applications like self-driving cars and software that filters malicious content. We’re currently building tools to train object-recognition systems to identify what’s happening in a scene or picture, even if the images fed to the model have been manipulated. We are also studying the limits of systems that offer security and reliability guarantees. How much reliability and security can we build into machine-learning models, and what other features might we need to sacrifice to get there?

My colleague Luca Daniel, who also spoke, is working on an important aspect of this problem: developing a way to measure the resilience of a deep learning system in key situations. Decisions made by deep learning systems have major consequences, and thus it’s essential that end-users be able to measure the reliability of each of the model’s outputs. Another way to make a system more robust is during the training process. In her talk, “Robustness in GANs and in Black-box Optimization,” Stefanie Jegelka showed how the learner in a generative adversarial network, or GAN, can be made to withstand manipulations to its input, leading to much better performance.

Q: The neural networks that power deep learning seem to learn almost effortlessly: Feed them enough data and they can outperform humans at many tasks. And yet, we’ve also seen how easily they can fail, with at least three widely publicized cases of self-driving cars crashing and killing someone. AI applications in health care are not yet under the same level of scrutiny but the stakes are just as high. David Sontag focused his talk on the often life-or-death consequences when an AI system lacks robustness. What are some of the red flags when training an AI on patient medical records and other observational data?

A: This goes back to the nature of guarantees and the underlying assumptions that we build into our models. We often assume that our training datasets are representative of the real-world data we test our models on—an assumption that tends to be too optimistic. Sontag gave two examples of flawed assumptions baked into the training process that could lead an AI to give the wrong diagnosis or recommend a harmful treatment. The first focused on a massive database of patient X-rays released last year by the National Institutes of Health. The dataset was expected to bring big improvements to the automated diagnosis of lung disease until a skeptical radiologist took a closer look and found widespread errors in the scans’ diagnostic labels. An AI trained on chest scans with a lot of incorrect labels is going to have a hard time generating accurate diagnoses.

A second problem Sontag cited is the failure to correct for gaps and irregularities in the data due to system glitches or changes in how hospitals and health care providers report patient data. For example, a major disaster could limit the amount of data available for emergency room patients. If a machine-learning model failed to take that shift into account its predictions would not be very reliable.

Q: You’ve covered some of the techniques for making AI more reliable and secure. What about interpretability? What makes neural networks so hard to interpret, and how are engineers developing ways to peer beneath the hood?

A: Understanding neural-network predictions is notoriously difficult. Each prediction arises from a web of decisions made by hundreds to thousands of individual nodes. We are trying to develop new methods to make this process more transparent. In the field of computer vision one of the pioneers is Antonio Torralba, director of The Quest. In his talk, he demonstrated a new tool developed in his lab that highlights the features that a neural network is focusing on as it interprets a scene. The tool lets you identify the nodes in the network responsible for recognizing, say, a door, from a set of windows or a stand of trees. Visualizing the object-recognition process allows software developers to get a more fine-grained understanding of how the network learns.

Another way to achieve interpretability is to precisely define the properties that make the model understandable, and then train the model to find that type of solution. Tommi Jaakkola showed in his talk, “Interpretability and Functional Transparency,” that models can be trained to be linear or have other desired qualities locally while maintaining the network’s overall flexibility. Explanations are needed at different levels of resolution much as they are in interpreting physical phenomena. Of course, there’s a cost to building guarantees into machine-learning systems—this is a theme that carried through all the talks. But those guarantees are necessary and not insurmountable. The beauty of human intelligence is that while we can’t perform most tasks perfectly, as a machine might, we have the ability and flexibility to learn in a remarkable range of environments.

 Explore further: Training artificial intelligence with artificial X-rays

Provided by: Massachusetts Institute of Technology

https://phys.org/news/2018-12-aleksander-madry-trustworthy-artificial-intelligence.html#jCp

New discovery about how a baby’s sex is determined

Medical researchers at Melbourne’s Murdoch Children’s Research Institute have made a new discovery about how a baby’s sex is determined—it’s not just about the X-Y chromosomes, but involves a ‘regulator’ that increases or decreases the activity of genes which decide if we become male or female.

The study, ‘Human Sex Reversal is caused by Duplication or Deletion of Core Enhancers Upstream of SOX9’ has been published in the journal Nature Communications. MCRI researcher and Hudson Institute Ph.D. student, Brittany Croft, is the first author.

“The sex of a baby is determined by its chromosome make-up at conception. An embryo with two X chromosomes will become a girl, while an embryo with an X-Y combination results in a boy,” Ms Croft said.

“The Y chromosome carries a critical gene, called SRY, which acts on another gene called SOX9 to start the development of testes in the embryo. High levels of the SOX9 gene are needed for normal testis development.

“However, if there is some disruption to SOX9 activity and only low levels are present, a testis will not develop resulting in a baby with a disorder of sex development.”

Lead author of the study, Professor Andrew Sinclair, said that 90 percent of human DNA is made up of so called ‘junk DNA or dark matter’ which contains no genes but does carry important regulators that increase or decrease gene activity.

“These regulatory segments of DNA are called enhancers,” he said. If these enhancers that control testis genes are disrupted it may lead to a baby being born with a disorder of sex development.”

Professor Sinclair, who is also a member of the Paediatrics Department of the University of Melbourne, said this study sought to understand how the SOX9 gene was regulated by enhancers and whether disruption of the enhancers would result in disorders of sex development.

“We discovered three enhancers that, together ensure the SOX9 gene is turned on to a high level in an XY embryo, leading to normal testis and male development,” he said.

“Importantly, we identified XX patients who would normally have ovaries and be female but carried extra copies of these enhancers, (high levels of SOX9) and instead developed testes. In addition, we found XY patients who had lost these SOX9 enhancers, (low levels of SOX9) and developed ovaries instead of testes.”

Ms Croft said human sex reversal such as seen in these cases is caused by gain or loss of these vital enhancers that regulate the SOX9 gene; consequently, these three enhancers are required for normal testes and male development.”

“This study is significant because in the past researchers have only looked at genes to diagnose these patients, but we have shown you need to look outside the genes to the enhancers,” Ms Croft said.

Professor Sinclair said that across the human genome there were about one million enhancers controlling about 22,000 genes.

“These enhancers lie on the DNA but outside genes, in regions previously referred to as junk DNA or dark matter,” he said. “The key to diagnosing many disorders may be found in these enhancers which hide in the poorly understood dark matter of our DNA.”

 Explore further: Non-coding DNA changes the genitals you’re born with

More information: Brittany Croft et al, Human sex reversal is caused by duplication or deletion of core enhancers upstream of SOX9, Nature Communications (2018). DOI: 10.1038/s41467-018-07784-9

https://medicalxpress.com/news/2018-12-geneticists-discovery-baby-sex.html

Novartis May Team With Reinsurers to Cut Drug Costs

Swiss drugmaker Novartis AG is considering working with reinsurers to provide alternative financing options for life-saving drugs, potentially saving substantial health-care costs, Financial Times reported on Sunday.

Among the options proposed includes a “reinsurance model in which a third party underwrites the catastrophic case of a child having one of these conditions,” Novartis Chief Executive Officer Vas Narasimhan told the FT in an interview.

Reinsurers, which already provide a backstop to employer health insurance plans, could benefit from this new revenue amid increasing competition from rival sources of risk capital, the FT said. Reinsurers could pool the costs of treatments by different drug companies across countries.

Discussions about funding the next generation of therapies — which may require a single infusion but at a high cost — are at the concept stage and the “reinsurance option” could work for state-funded health systems such as in the U.K., as well as private insurance-based systems, the newspaper reported.

https://www.bloomberg.com//news/articles/2018-12-16/novartis-may-team-with-reinsurers-to-cut-drug-costs-ft-reports?srnd=markets-vp

Insurance Companies That Are Most Likely To Refuse To Pay Doctors

Healthcare reimbursement in the U.S. is frighteningly complex. We have federal payers, like Medicare; state/federal payers, like Medicaid; private, for-profit insurance companies, like Aetna; private, not for profit insurers, like many local Blue Cross Blue Shield networks. Oh yes, and we have private insurance companies managing reimbursement for many Medicare and Medicaid recipients.

This complexity comes with costs. Doctors and hospitals need to hire armies of people to process bills for all those different payers. By one estimate, in fact, physician offices spend 30 billion dollars a year on billing-related costs.

A recent study shows which payers are most likely to reject the bills submitted to them by physicians’ offices. The winner, in case you haven’t guessed it already, is Medicaid, regardless of whether the program is run by the government or private insurers.

Here is a picture of that finding. In orange, it shows how often payers challenged bills; in blue(ish), it shows how often they rejected the bills. The highest proportion of challenges and rejections come from fee-for-service (FFS) Medicaid programs; that’s the traditional Medicaid programs run by each state. Next highest rates of challenges and rejections come from Medicaid managed care programs, which typically involve private insurance companies managing Medicaid recipients’ healthcare costs. The picture below shows the proportion of bills challenged and the proportion of those denied:

Measures of billing complexity, by insurance type, with patient characteristics and physician identity controlled forHEALTH AFFAIRS

These data were reported in Health Affairs and come from a database capturing billing information from around 70,000 physicians, during 2015. The data don’t have enough information to show whether these payment denials were appropriate or inappropriate. But they do give a sense of how burdensome it is for physicians’ offices to receive payment for their services. Responding to payment denials costs effort and money, sometimes requiring clinicians to take time away from patient care so they can explain to an insurance company why a specific service was indicated.

As the above figure shows, private insurers are far less likely to challenge bills than government payers. Add to that the higher reimbursement offered by most private insurers, and it is easy to see why so many doctors limit the number of Medicaid and Medicare patients they see.

To make matters worse, even when Medicaid does reimburse for physician services, the program takes longer to do so. Here is a picture of that result:

Changes in time to payment over time, by insurance type, with patient characteristics and physician identity controlled forHEALTH AFFAIRS

Americans deserve healthcare coverage, whether from private or public coffers. But American physicians also deserve timely payment when they provide needed care to their patients.

https://www.forbes.com/sites/peterubel/2018/11/30/the-insurance-companies-that-are-most-likely-to-refuse-to-pay-doctors/#468f12e97f77

NIH report scrutinizes role of China in theft of U.S. scientific research

Institutions across the U.S. may have fallen victim to a tiny fraction of foreign researchers who worked to feed American intellectual property to their home countries, an advisory committee to the National Institutes of Health found in a report issued Thursday.

The report zeroed in on China’s “Talents Recruitment Program,” which the Pentagon has previously identified as an effort “to facilitate the legal and illicit transfer of US technology, intellectual property and know-how” to China.

A key qualification for becoming part of the Chinese program, also known as “Thousand Talents,” is access to intellectual property, said M. Roy Wilson, the co-chair of the advisory committee to the NIH director and the president of Wayne State University.

While only a small fraction of foreign researchers in the U.S. are part of the Chinese program, many of the recruits have received U.S. federal funding from institutions including the NIH, the report said.

The report represents NIH’s most concrete public action to date to combat the threat of American research being transported overseas to countries attempting to compete with the U.S. scientifically. While the report focused on China, it stressed that NIH has encountered similar problems with a small number of researchers from other countries as well.

In August, NIH announced it had launched an investigation into foreign threats to research, saying then that it had identified “undisclosed foreign financial conflicts,” along with undisclosed affiliations with other research institutions and violations in the peer-review process at roughly a half-dozen institutions.

At the investigation’s outset, Collins sent a letter to roughly 10,000 grantee institutions recommending they set up briefings with local FBI offices to learn about how to protect U.S. intellectual property.

Thursday’s report specified that some information had been inappropriately shared by NIH peer reviewers with individuals not authorized to participate in the peer-review process.

Wilson declined to specify specific monetary values for the intellectual property lost to illicit transfer, but he said the severity was impossible to ignore.

“It’s not just random here or there,” Wilson said of foreign threats to U.S. research. “It is significant.”

The report makes several recommendations to NIH Director Francis Collins, including that his agency begin a broad education campaign about need for grantee institutions to require that investigators disclose foreign support, international interests, and overseas collaborations guiding their research.

Institutions should also work to develop enhanced cybersecurity protocols and routines for hosting foreign scholars on research visits, which can sometimes be “potential entry points for unwanted information gathering,” the report said.

Much of the debate stems from media coverage this year featuring Liu Ruopeng, a billionaire Chinese researcher working at Duke University who was accused of stealing information used to develop a so-called “invisibility cloak” on behalf of the Chinese government between 2006 and 2009.

“In retrospect, it may be that he targeted this particular lab,” Wilson said of Liu, before alluding to situations in which foreign researchers at Wayne State and at Duke had run “shadow labs” in their home country while conducting government-funded research in the U.S.

At institutions with multiple infractions, the committee recommended, NIH should pursue an institution-wide assessment to determine the extent of the compromise.

The advisory committee, however, echoed previous statements from Collins by stressing the importance of making foreign researchers welcome in the United States, while ensuring appropriate safeguards for the few bad actors identified.

Twenty-four percent of U.S. Nobel prizes, the report noted, have been awarded to American scientists born abroad.

https://www.statnews.com/2018/12/13/nih-report-scrutinizes-role-of-china-in-theft-of-u-s-scientific-research/