In this article by Olson et al., we learn Wilson’s disease, also known as hepatolenticular degeneration, is an autosomal recessive disease characterized by impaired copper metabolism due to a defective ATPase. Patients with Wilson’s disease may present with chronic liver disease, acute liver failure, hemolysis, and psychiatric or neurologic manifestations. It has been previously noted that viral infection or drug toxicity may serve as a trigger for fulminant Wilson’s disease.
Does acute liver failure in the pediatric population always present with hepatic encephalopathy?
Acute liver failure in adults is characterized by a sudden loss of hepatic function without evidence of preexisting liver disease. Criteria for the diagnosis include the presence of coagulopathy (international normalized ratio [INR], >1.5), hepatic encephalopathy, and an illness of fewer than 24 weeks’ duration. However, in the pediatric population (which can be considered to include patients who are up to 21 years of age), up to 50% of patients who present with acute liver failure do not present with encephalopathy. Modified criteria for the diagnosis of acute liver failure in children include evidence of acute liver injury and severe coagulopathy (INR, >2.0) in the absence of encephalopathy.
What are the rapid diagnostic screening test criteria for Wilson’s disease?
Rapid diagnostic criteria for Wilson’s disease can be used in patients who present with acute liver failure. A screen that shows a ratio of alkaline phosphatase (IU per liter) to total bilirubin (mg per deciliter) of lower than 4.0 and then subsequently shows a ratio of aspartate aminotransferase (IU per liter) to alanine aminotransferase (IU per liter) of higher than 2.2 has been described as 100% sensitive and specific for the diagnosis of Wilson’s disease.
Morning Report Questions
Q: Is copper staining of liver tissue a reliable test for the pathological diagnosis of Wilson’s disease?
A: The pathological diagnosis of Wilson’s disease is generally based on the presence of compatible histomorphologic features and results of staining for copper, including a rhodanine stain. However, staining for copper in tissue is unreliable, since the presence of copper in the cytoplasm of hepatocytes might not be detected on a rhodanine stain. Therefore, in patients with suspected Wilson’s disease, copper quantification performed on either a dedicated core-biopsy specimen or a paraffin-embedded tissue sample is considered to be the best available diagnostic test.
Q: What is the prognosis of patients with acute liver failure due to Wilson’s disease?
A: In Wilson’s disease, acute liver failure develops in the setting of subclinical chronic liver disease. If liver transplantation is not performed, acute liver failure due to Wilson’s disease is fatal. In this country, the highest priority (United Network for Organ Sharing status 1A) is reserved for patients with liver failure who have a life expectancy of fewer than 7 days if they do not undergo transplantation. Wilson’s disease is the only cause of acute liver failure that allows a patient with the pre-existing liver disease to be listed as status 1A. The outcomes associated with liver transplantation for acute liver failure induced by Wilson’s disease are excellent if transplantation is performed prior to neurologic deterioration.
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Scientists have constructed a series of eco-friendly soft robots that can self-heal on demand, leaving almost no traces of weakness at the location of their “scars.”
Their self-healing power relies on the material they are composed of: a rubbery polymer that has all the classic characteristics of soft robotic bodies — flexibility, elasticity and overall durability — with the added benefits of self-repair and recyclability.
Similar materials have already been used in real-world situations, such as for shooting stands that seal after a bullet has passed through, or coatings of cars that can smooth out scratches. Incorporating these materials into robots — especially those made to interact with humans — is the next step for translating the skill of “self-healing” into the artificial realm.
“Our research opens up promising perspectives. Robots can not only be made lighter and safer but will also be able to work longer independently without requiring constant repairs,” said Bram Vanderborght, a professor at Vrije Universiteit Brussel and co-author of the study appearing in the 16 August issue of Science Robotics.
Vanderborght mentioned the scene in the Disney movie “Big Hero 6,” in which its protagonist, the soft robot Baymax, tries to seal its cuts with tape, as the basic essence of this project.
[Associated image credit: Terryn et al., Sci. Robot. 2, eaan4268 (2017)]
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Think back to the last time you stubbed your toe or burned your finger. The pain probably took over all of your senses and concentration — you took deep gasps of air and maybe cursed under your breath until the discomfort subsided and you could return to your life. This is a typical reaction to acute—or short-duration—pain.
For some though, the pain lasts for months or even years. Those who suffer from chronic—or long-lasting—pain are often unable to work, interact with friends and family, or think about anything except the pain. The sensation of pain seems so fundamental to human experience, yet scientists and physicians struggle to understand what causes chronic pain and how to eliminate it.
Chronic pain is most commonly managed at home or in the hospital with narcotics and over-the-counter drugs. Unfortunately, people commonly develop resistance or addiction to these drugs, partly fueling the current opioid epidemic in America. How can physicians balance the subjective pain experienced by their patients with the risks inherent to pain management drugs?
Our recent study merges mathematical modeling, statistics, and a mobile health application to assist physicians in answering this tough question. Our team of clinicians, mobile health application designers, statisticians, and math modelers developed a hybrid statistical and mechanistic model of chronic pain dynamics that uses patient demographics and history to predict future pain levels and offer optimal drug interventions in real time.
Both statistical and mechanistic modeling play an important role in predicting subjective pain. Statistical models require little a priori knowledge about the causes of pain and offer correlations between patient characteristics and expected pain. Mechanistic models exploit known system behavior and allow for validation or rejection of underlying pain drivers.
Though one can apply our hybrid statistical and mechanistic approach to the understanding and prediction of any chronic pain, our study focuses on pain caused by complications of sickle cell disease. This chronic illness is associated with frequent hospitalization due to unsuccessfully-managed pain. Recently, physicians have used mobile health applications to offer remote interventions meant to minimize pain and prevent hospitalization. Using patient data— such as demographic information, self-reported pain, and drug treatments—collected by our mobile health application, our hybrid modeling approach guides data-driven physician recommendations.
The hybrid model’s mechanistic component exploits knowledge that many human sensory systems function on a roughly return-to-setpoint basis. For instance, if you stub your toe, you will experience a rush of pain that will gradually subside to your pain-free setpoint. We assume that individuals with chronic pain have a constant level of pain as a setpoint if they are not on medication. If they take pain medication, the pain will decrease rapidly before returning slowly to the setpoint level as the body metabolizes and eliminates the drugs. We propose a simple mathematical model to capture these dynamics:
whereis the patient’s subjective pain level, is the amount of the th drug (out of three types) in the patient’s bloodstream, is the relaxation rate to setpoint pain without drugs, is the impact of the th drug on the relaxation rate, is the patient’s set point (unmitigated) pain, is the elimination rate of the th drug from the bloodstream, is the total number of the th drug doses taken, and are the times the patient takes the th drug.
We derive the parameters validated pharmacokinetic models, but we must estimate the patient-specific parameters like (unmitigated pain) and (reaction to drugs) from patient data. Unfortunately, estimating these parameters for each patient in real time is computationally expensive; this is where the statistical modeling component can help.from
Because we have several dozen patients in our clinical trials, we can use population-level statistics (on variables such as disease type, age, and long-term medication use) to guess patient parameters (likeand ). These estimates serve as initial conditions for our parameter search algorithm, which speeds up the computation. After fitting all the parameters for each patient, we can update the statistical model to reflect knowledge of previous pain and drug interventions. Iterating back and forth between the statistical and mechanistic model quickly yields a personalized set of parameters for each patient.
Once we compute parameters for each patient, we can use the model to predict future pain under various drug intervention protocols. To help physicians use this information when prescribing drug protocols, we packaged the model into a graphical tool. In the future, given enough physician recommendation data, a machine learning algorithm could make automated recommendations to patients.
Though simple, this hybrid statistical and mechanistic model for chronic pain dynamics shows great promise. Sparse data limited the complexity of the model, but the expected deluge of medical data from mobile health applications and wearables (including current clinical trial NCT02895841) will open the door for more sophisticated models in the future.
The author presented this research during a poster session at the 2017 SIAM Conference on Applications of Dynamical Systems, which took place this May in Snowbird, Utah.
Clifton, S.M., Kang, C., Li, J.J., Long, Q., Shah, N., & Abrams, D.M. (2017). Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain. Journal of Computational Biology, 24(7). Preprint accessible here.
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