Trojan Horses: Nanoparticles sneak drugs into brain to battle cancer

Posted July 25, 2016 by Jennifer Huber
Categories: Health

Tags: , ,

I just read an interesting article in the Berkeley Science Review about using nanoparticles to make chemotherapy more effective against a type of brain cancer called glioblastoma. I was then surprised and proud when I realized one my former science-writing students, Dharsi Devendran, wrote it.

Although rare, glioblastoma is an invasive and deadly brain cancer with octopus-like tentacles that are difficult to completely remove with surgery. Even the standard combined treatment of surgery, radiation and chemotherapy isn’t very effective — people typically die within months of diagnosis. So researchers are actively searching for better treatments.

Devendran explains:

Although it is difficult to fight, glioblastoma also has a weakness. In its rush to feed itself, it accelerates the blood vessel formation process and creates hole-riddled blood vessels around the tumor. Because cancer drugs are small enough to slip through these holes, they can exploit this defect—but they also need a strategy to cross the blood-brain barrier in order to reach the tumor.

Acting like a security system for the brain, the blood-brain barrier is a network of blood vessels that allow essential nutrients to enter while blocking harmful molecules. Unfortunately, it also blocks life-saving chemotherapy drugs, unless researchers can find clever ways to sneak them through the barrier.

Ting Xu, PhD, professor of materials science at UC Berkeley, and her collaborators are developing tiny nanocarriers that can envelop and protect chemotherapy drugs as they move through the blood, across the blood-brain barrier, and into the brain to the glioblastoma tumor tissue.

The researchers designed a new nanocarrier, called a 3-helix micelle (3HM), out of proteins with molecules on the surface that fit into specific proteins found only on the surface of the tumor cells — like fitting a key into a lock. Once the 3HM nanocarriers access the tumor cells, they release their chemotherapy drugs to help destroy the glioblastoma.

Xu’s team has shown that their 3HM nanocarrier is twice as effective at reaching glioblastoma cells as liposomes, a nanocarrier made of fatty acids that is a standard in nanotechnology drug delivery. This is in part because the 3HM is five times smaller than liposomes.

“We still don’t understand the mechanism completely,” said JooChuan Ang, a graduate student in Xu’s group, in the article. “Size is definitely a factor, but there could be other factors that contribute…”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Quitting smoking: Best drug differs for men and women

Posted July 22, 2016 by Jennifer Huber
Categories: Health

Tags: ,

It’s tough to quit smoking. I’ve seen friends and family members struggle with nicotine withdrawal symptoms: cravings for tobacco, anxiety, anger, irritation, increased hunger and even trouble thinking.

One out of six adults in the United States currently smoke and about half of them are trying to quit, but the success rate remains low. What’s the best way to stop smoking? A new study may help point the way — for women.

The study found that a medication frequently used to help smokers quit is more effective for women than men. Philip Smith, PhD, assistant medical professor at the City College of New York, led the multi-institutional study: a network meta-analysis of 28 randomized clinical trials involving a total of 14,389 smokers (51 percent female).

The researchers did a head-to-head comparison between the three common types of medications used for smoking cessation: the nicotine patch, varenicline (sold as Chantix and Champix) and sustained-release bupropion (sold as Wellburtin or Zyban). The quit rate of the participants was based on biochemical verification of their abstinence after six months.

The authors reported in their new paper in Nicotine & Tobacco Research:

“Women treated with varenicline were 41 percent more likely to achieve 6-month abstinence compared to women treated with TN [transdermal nicotine patch], and were 38 percent more likely to achieve 6-month abstinence than women treated with bupropion. For men, the benefit of varenicline over TN and bupropion were smaller and were not statistically significant.”

“Before our study, research had shown that among the choices for medications for smokers who wanted to quit, varenicline was the clear winner when it came to promoting quitting,” said Smith in a recent news release. “Our study shows this is clearly the case for women. The story seems less clear among men, who showed less of a difference when taking any of the three medications.”

The research findings identify varenicline as a particularly potent first option treatment for women. However, the good news for all smokers is that all three medicines significantly improved quit rates for both men and women, when compared with placebo.

If you’re trying to quit smoking, a combination of counseling and medication has been shown to be an effective way to treat tobacco dependence — speak with your doctor or contact a smoking cessation program.

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

From art to surgery: Stanford alumna reconstructs new ears for children

Posted July 15, 2016 by Jennifer Huber
Categories: Health

Tags: ,
Dr Sheryl Lewin in the operating room (courtesy of Lewin)

Dr Sheryl Lewin in the operating room (courtesy of Lewin)

Some children are born with a missing or malformed small ear due to a rare congenital condition called microtia. In most cases, the child’s ear canal is also very small or absent, resulting in hearing loss.

The surgical procedures used to correct microtia require the skills of both a sculptor and surgeon — making it the perfect specialty for Sheryl Lewin, MD, a craniofacial plastic surgeon who began her training as an artist and architect.

Lewin’s career has been passionately devoted to treating microtia through her private medical practice and nonprofit organization called Earicles, which helps children born without ears through education, research and free or reduced-cost treatment. I recently spoke with Lewin about her work: 

As an architect major, what inspired you to become a physician?

“When I was in architecture school at UC Berkeley, I loved the challenge of design, where you can use your own creativity to solve visual and spatial problems. My program was heavily artistic — we drew, painted and sculpted. But what was missing was the ability to use those skills to directly affect someone’s life in a tangible and meaningful way.

During college, I lived across the street from an elementary school that served underprivileged kids, which inspired me to start a volunteer organization of Berkeley undergraduates that mentored disadvantaged children in the local community. I recognized that I really enjoyed working and helping kids, and medicine was a way to do that.

When I went to medical school at Stanford, I was drawn to surgery as it gave me the ability to work with my hands. I decided to pursue pediatric plastic surgery after I saw my first cleft lip surgery on a tiny infant, whose life was transformed in a couple of hours. I realized it absolutely used the same skill set that I was used to working in: design, thinking three-dimensionally and visualizing symmetry. It was very much like sculpture.

Years later in medical school, I saw my first surgery to correct a rare condition called microtia. Once I saw what was involved, there was no doubt that I would love the challenge of making ears, which is considered by many plastic surgeons to be one of the most technically difficult things we do. But what really sealed the deal was the intangible feeling you get taking care of these children and their families. I came home that day and told my husband, ‘I know what I want to do with the rest of my life.’”

What is microtia?

Microtia ear before and after surgery (Courtesy of Lewin)

Child’s ear before and after microtia reconstruction surgery (courtesy of Lewin)

“Microtia is a congenital condition in which the ear does not develop properly. The word microtia translates to “small ear.” It occurs in about one in 6,000 to 12,000 children worldwide, with a higher prevalence among Hispanics, Asians and Native Americans.

The cause of microtia is not well understood, particularly regarding the role of environmental and genetic factors. Some medications have been linked to microtia when ingested in the first trimester of pregnancy, including Thalidomide and Accutane. However, it’s important to understand that microtia is rarely caused by what a mother does during pregnancy.”

How do you treat microtia? 

“Ninety-five percent of the world treats microtia by removing rib cartilage from the chest, carving it into an ear framework and then slipping it under the skin. In order to have enough cartilage, surgery must be delayed until children are six to ten years old. Three to four surgeries are required with this technique, and the ability to match the opposite ear is limited.

Several colleagues and I use a different technique. In an eight to ten hour outpatient surgery, I customize a porous polyethylene implant into a three-dimensional ear shape that matches the opposite ear. This biocompatible implant is then covered with vascular tissue. This allows for a symmetric and natural appearing ear to be created in just one operation as early as three years of age.

Children with microtia almost always have conductive hearing loss — since the ear canal is missing but the auditory nerve is functional. During the ear reconstruction surgery, I can do an additional scarless procedure to help restore hearing. I implant a titanium device in the skull that is connected to a bone conduction hearing processor, commonly referred to as a BAHA. The hearing processor captures sound and transmits these vibrations to the skull through the implant, which stimulates the auditory nerve that processes it as sound.”

What is most rewarding about your work?

“This surgery not only helps provide functionality, such as giving children the ability to wear eyeglasses, but it’s often about helping children attain the simplest human need: to feel the same as everyone else.

One of my favorite moments involved a four-year old boy named Davin, who had microtia of both ears. I was sitting next to him as he was about to see his second ear for the first time. He looked me in the eyes and said, “Dr. Lewin, do I have two big boy ears now?” I said, “Yes Davin, two beautiful ears.” Then, out of nowhere, he leaned over and planted a big kiss right on my lips and said, “Dr. Lewin, I love you.” For a moment, I was speechless, and then managed to say, “Davin, I love you too.” It really can’t get any better than that in my book.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Neuroscience camp: Teens learn about mental health

Posted July 12, 2016 by Jennifer Huber
Categories: Health, Science Education

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Photo by Norbert von der Groeben

As a teenager, I spent summers swimming and sunbathing at the community pool. However, many teens from around the country found something more interesting to do this summer: neuroscience summer camp at Stanford.

Over 100 high school students attended Clinical Neuroscience Internship Experience (CNI-X) 2016 — an intensive, weeklong summer program that introduced them to the breadth of work underway by researchers from the Department of Psychiatry and Behavioral Sciences. Students came from throughout the Bay Area and as far away as Georgia and New York.

Several dozen department faculty members taught 90-minute classes, ranging from introductory seminars to hands-on workshops and laboratory tours.

For example, in one session, the teens constructed brains out of Play-Doh, shown above. In another, Kate Hardy, DClinPsy, clinical assistant professor of psychiatry and behavioral sciences, taught a group exercise designed to build empathy for people that hear voices, such as schizophrenics. During the exercise, two students conversed while a third whispered in one’s ear. Hardy described the results in a recent news story:

“Some students said they found it hard to concentrate; others said the experience was scary or threatening. When I do this exercise with adults, it’s difficult to get them to respond. The teens got right into it. There’s a great benefit to exposing people at that age to the prevailing preconceptions of psychosis and reduce the stigma, even at a small scale.”

The goals of the CNI-X program are to identify promising students interested in mental health and to destigmatize mental illness through education.

“With CNI-X, our faculty are taking the most direct route to the future — by introducing incredibly bright, motivated young people to the excitement and diversity of clinical neuroscience,” said CNI-X program co-director Laura Roberts, MD, MA, professor and chair of psychiatry and behavioral sciences, and chief of the psychiatry service at Stanford Health Care. “We introduce novel science to the interns…. My guess is that in several years we will see some of these students in our medical school classrooms.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Intermittent fasting: Fad or science-based diet?

Posted July 7, 2016 by Jennifer Huber
Categories: Health, Nutrition

Tags: , ,
Photo by Jean Fortunet

Photo by Jean Fortunet

The diet regime of intermittent fasting recently caught my attention when listening to an episode of  This American Life on my car radio. And then a close friend told me he’s planning to switch from a low-carbohydrate diet to some form of intermittent fasting.

I got to wondering, though: Are the health-benefit claims from intermittent fasting backed up by scientific evidence?

Research studies have shown that reducing your daily caloric intake by 20 to 40 percent is an effective way to lose weight and improve cardiovascular and metabolic health. However, it’s very difficult to eat less every day for a long time. So people are looking for more manageable ways to improve their health, and many are turning to intermittent fasting — short periods of eating little to no energy-containing food and drink.

To learn more about intermittent fasting, I turned to fasting expert John Trepanowski, PhD, postdoctoral research fellow at the Stanford Prevention Research Center for answers:

What are the health benefits of a calorie-restricted diet?

Calorie restriction is probably the most scientifically established diet regimen for improving health. The main benefits include improvements in risk indicators for cardiovascular disease and type 2 diabetes, which include reductions in total cholesterol level, blood triglycerides, blood pressure, carotid intima-media thickness, insulin and fasting glucose. The biggest limitation is that most people find it incredibly challenging, and some find it impossible, to follow a calorie-deprived diet for any notable length of time.

Why has intermittent fasting become increasingly popular?

Michael Mosley’s “Eat, Fast and Live Longer” documentary on the BBC introduced millions of people to intermittent fasting. Beyond that, I think intermittent fasting is appealing to many people, because they can lose weight on the diet but still have guilt-free days of eating what they want on a regular basis.

There is an increasing number of studies that suggest that intermittent fasting is a viable approach to weight loss for some. But you will have to wait until the results of my doctoral thesis are published to see if intermittent fasting is as effective for weight loss as daily calorie restriction (shameless plug!). And no study to date has examined whether intermittent fasting is effective in people who previously tried and were unsuccessful at calorie restriction.

Can you give examples of different types of intermittent fasting?

The 5:2 diet is a particular form of intermittent fasting, with five consecutive “normal” days of no restriction followed by two consecutive days of eating only 25 percent of your energy needs. I believe there have been two studies on the 5:2 diet in humans, and both studies found that the benefits were mostly the same as calorie restriction, such as weight loss and decreases in insulin.

Time-restricted feeding involves reducing the window of time to anywhere between four to twelve hours that someone takes in calories each day. The theory behind this dietary plan is that we have a circadian rhythm that calls for food intake at times and no food intake at other times in order to experience optimal health. Continuously eating, without periods of no food intake, disrupts the circadian clock and leads to metabolic derangements — such as lowered energy expenditure and elevated glucose and insulin.

Time-restricted feeding could lead to weight loss by harmonizing our eating pattern with our circadian rhythm, or it could be simply due to the fact that there are fewer “opportunities” to take in energy. And some people will lose weight due to following any type of structured eating plan, regardless of the specifics.

It’s very hard to do an accurate intermittent fasting study in humans, because it’s really difficult to get an accurate measurement of what people eat at any particular time of day. The main disadvantage of time-restricted feeding is resisting the temptations that come from our 24-hour-access-to-food environment, but that disadvantage exists with all dietary plans.

What inspired you to study different diets?

I met a very inspirational professor, Richard Bloomer, PhD, at the University of Memphis. I helped him run some studies on the Daniel Fast, which is a more stringent form of veganism based on the biblical book of Daniel. From there I wrote some review articles on fasting and calorie restriction, and I decided to study a form of intermittent fasting called alternate-day fasting for my PhD.

As a postdoctoral research fellow at the Stanford Prevention Research Center, I’m now studying factors that predict weight loss success on low-fat and low-carbohydrate diets. I am also doing meta-research — basically “research on research” to find ways to do science better.

Have you ever fasted?

I have done the Daniel Fast. It’s pretty tough. If you want to expand your cooking skills, I suggest doing the Daniel Fast. There’s no way to eat anything on this diet that is both warm and appetizing without following good cooking principles.

A cautionary note: In his review of fasting studies, Trepanowski said daily calorie restriction and alternate-day fasting do not appear to increase eating and mood disturbances among research participants who did not have an eating disorder. However, it’s best to speak with your physician before starting an intermittent fasting regimen, particularly for those with a history of or at risk for eating disorders.

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine

What color is your cloud? Study finds large variability in resident workloads

Posted June 30, 2016 by Jennifer Huber
Categories: Health, Science Education

Tags: ,

For decades medical residents have put themselves into two camps: “black clouds” and “white clouds.” Black-cloud residents carry with them the bad luck of consistently getting a patient load that requires more work; the perceived workload intensity and stress may keep them pacing the halls at night, while their white-cloud counterparts are likely to sleep peacefully while on call.

Does this cloud status actually exist, though? Adam Was, MD, fourth year Stanford resident of pediatrics and anesthesia, decided to find out. The results of his study were just published in Pediatrics.

“The study was inspired by my late-night argument with other interns about our workloads,” said Was. “We commonly discuss what type of cloud we have, meaning what kind of workload. So one of the interns said his workload was really high, but someone else argued that we all have the same workload and he was just complaining about it more. I realized that we could do an objective, rigorous study of actual workloads to get a real answer.”

With the help of KT Park, MD, assistant professor of pediatric gastroenterology and senior author of the study, Was measured the workload of twenty-six pediatric residents during the six core inpatient rotations of their intern year — to make sure they were comparing “like to like.” Using the Stanford Children’s Health research database, they quantified the workload intensity of each of the residents based on the number of electronic notes and orders that they wrote while in the hospital. Was explained:

“ We wanted to focus on objective data that described the work done at the hospital, as opposed to just the number of hours spent there. Residents do a lot of things that aren’t captured in electronic notes and orders, but we found this data to be the most robust and representative.”

And the outcome? The differences are real. The researchers found a very significant variability of workload intensity between the residents. High-workload residents wrote 91 percent more orders and 19 percent more notes than low-workload residents. Here’s Park:

“I really thought that we were going to conclusively lay to rest this idea that there is statistically significant workload variability between residents. I was very surprised. We did sophisticated mathematical models and there is no way around it — there are high-workload and low-workload residents. There is no ethological explanation right now, and it remains a big question mark especially for program directors.”

Thinking through the study’s implications from a program director’s point of view was the main role of the third author, Becky Blankenburg, MD, clinical associate professor of pediatrics and pediatric residency program director, who thinks the results can guide residency directors. “This data provides more information for resident assessments and will allow us to better individualize the residents’ curriculum based on what they’ve really been exposed to,” she said.

Determining the root causes behind this workload variability is beyond the scope of their study. However, the authors have a few of their own theories.

One belief: high-workload or black cloud residents behave differently than their white cloud colleagues. For example, some black cloud residents may be inefficient, while others may create extra work for themselves. And some white cloud residents may need to be more vigilant.

“I would like to get into the heads of the residents in real time,” Park said. “As they put in that note or order or take that phone call, what is the impetuous? From my observation, anxiety and perfectionistic tendencies drive them to do more than what’s necessary for effective patient care.”

Blankenburg agreed, “Some residents early on learn to look at the big picture and some see only the trees without seeing the forest. Another important factor is how comfortable people are with ambiguity. If you’re able to deal with ambiguity better, you might not order as many tests.”

The researchers are contemplating how best to use this information and how to design a follow-up study to understand the root causes of resident workload variability. One idea is to somehow incorporate peer evaluations, since their study found self-assessments to be inaccurate. “I think peers would do the best job of picking up on cloud status or workload intensity,” Blankenburg said.

Although successful, did the study settle the late night argument that inspired it?

“The study data was annonymized, so we don’t know who was who,” said Was. “So I never got to settle my original argument of whether I was doing more or less work. Before the study, I thought I was a black cloud. Afterwards, I feel like I’m a confused and possibly grey cloud.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Enlisting artificial intelligence to assist radiologists

Posted June 24, 2016 by Jennifer Huber
Categories: Health

Tags: ,
Photo by Gerd Leonhard

Photo by Gerd Leonhard

Specialized electronic circuits called graphic processing units, or GPUs, are at the heart of modern mobile phones, personal computers and gaming consoles. By combining multiple GPUs in concert, researchers can now solve previously elusive image processing problems. For example, Google and Facebook have both developed extremely accurate facial recognition software using these new techniques.

GPUs are also crucial to radiologists, because they can rapidly process large medical imaging datasets from CT, MRI, ultrasound and even conventional x-rays.

Now some radiology groups and technology companies are combining multiple GPUs with artificial intelligence (AI) algorithms to help improve radiology care. Simply put, an AI computer program can do tasks normally performed by intelligent people. In this case, AI algorithms can be trained to recognize and interpret subtle differences in medical images.

Stanford researchers have used machine learning for many years to look at medical images and computationally extract the features used to predict something about the patient, much as a radiologist would. However, the use of artificial intelligence, or deep learning algorithms, is new. Sandy Napel, PhD, a professor of radiology, explained:

“These deep learning paradigms are a deeply layered set of connections, not unlike the human brain, that are trained by giving them a massive amount of data with known truth. They basically iterate on the strength of the connections until they are able to predict the known truth very accurately.”

“You can give it 10,000 images of colon cancer. It will find the common features across those images automatically,” said Garry Choy, MD, a staff radiologist and assistant chief medical information officer at Massachusetts General Hospital, in a recent Diagnostic Imaging article. “If there are large data sets, it can teach itself what to look for.”

A major challenge is that these AI algorithms may require thousands of annotated radiology images to train them. So Stanford researchers are creating a database containing millions of de-identified radiology studies, including billions of images, totaling about a half million gigabytes. Each study in the database is associated with the de‐identified report that was created by the radiologist when the images were originally used for patient care.

“To enable our deep learning research, we are also applying machine learning methods to our large database of narrative radiology reports,” said Curtis Langlotz, MD, PhD, a Stanford professor of radiology and biomedical informatics. “We use natural language processing methods to extract discrete concepts, such as anatomy and pathology, from the radiology reports. This discrete data can then be used to train AI systems to recognize the abnormalities shown on the images themselves.”

Potential applications include using AI systems to help radiologists more quickly identify intracranial hemorrhages or more effectively detect malignant lung nodules. Deep learning systems are also being developed to perform triage — looking through all incoming cases and prioritizing the most critical ones to the top of the radiologist’s work queue.

However, the potential clinical applications have not been validated yet, according to Langlotz:

“We’re cautious about automated detection of abnormalities like lung nodules and colon polyps. Even with high sensitivity, these systems can distract radiologists with numerous false positives. And radiology images are significantly more complex than photos from the web or even other medical images. Few deep learning results of clinical relevance have been published or peer-reviewed yet.”

Researchers say the goal is to improve patient care and workflow, not replace doctors with intelligent computers.

“Reading about these advances in the news, and seeing demonstrations at meetings, some radiologists have become concerned that their jobs are at risk,” said Langlotz. “I disagree. Instead, radiologists will benefit from even more sophisticated electronic tools that focus on assistance with repetitive tasks, rare conditions, or meticulous exhaustive search — things that most humans aren’t very good at anyway.”

Napel concluded:

“At the end of the day, what matters to physicians is whether or not they can trust the information a diagnostic device, whether it be based in AI or something else, gives them. It doesn’t matter whether the opinion comes from a human or a machine. … Some day we may believe in the accuracy of these deep learning algorithms, when given the right kind of data, to create useful information for patient management. We’re just not there yet.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.


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