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Bipolar Disorder now understood by Markers Identified of the Gene Expression for this Diagnosis

Posted in Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Signaling & Cell Circuits, Cerebrovascular and Neurodegenerative Diseases, Chemical Biology and its relations to Metabolic Disease, Clinical Diagnostics, Clinical Genomics, Cognition, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Gene Regulation, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Innovations in Neurophysiology & Neuropsychology, Proteomics, Transcriptomics, Translational Science on March 8, 2022| Leave a Comment »

Bipolar Disorder now understood by Markers Identified of the Gene Expression for this Diagnosis

Reporter: Aviva Lev-Ari, PhD, RN

Published: 07 March 2022

Amygdala and anterior cingulate transcriptomes from individuals with bipolar disorder reveal downregulated neuroimmune and synaptic pathways

Nature Neuroscience volume 25, pages381–389 (2022)Cite this article

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Abstract

Recent genetic studies have identified variants associated with bipolar disorder (BD), but it remains unclear how brain gene expression is altered in BD and how genetic risk for BD may contribute to these alterations. Here, we obtained transcriptomes from subgenual anterior cingulate cortex and amygdala samples from post-mortem brains of individuals with BD and neurotypical controls, including 511 total samples from 295 unique donors. We examined differential gene expression between cases and controls and the transcriptional effects of BD-associated genetic variants. We found two coexpressed modules that were associated with transcriptional changes in BD: one enriched for immune and inflammatory genes and the other with genes related to the postsynaptic membrane. Over 50% of BD genome-wide significant loci contained significant expression quantitative trait loci (QTL) (eQTL), and these data converged on several individual genes, including SCN2A and GRIN2A. Thus, these data implicate specific genes and pathways that may contribute to the pathology of BP.

SOURCE

https://www.nature.com/articles/s41593-022-01024-6

Gene Expression Markers for Bipolar Disorder Pinpointed

The work was led by researchers at Johns Hopkins’ Lieber Institute for Brain Development. The findings, published this week in Nature Neuroscience, represent the first time that researchers have been able to apply large-scale genetic research to brain samples from hundreds of patients with bipolar disorder (BD). They used 511 total samples from 295 unique donors.

“This is the first deep dive into the molecular biology of the brain in people who died with bipolar disorder—studying actual genes, not urine, blood or skin samples,” said Thomas Hyde of the Lieber Institute and a lead author of the paper. “If we can figure out the mechanisms behind BD, if we can figure out what’s wrong in the brain, then we can begin to develop new targeted treatments of what has long been a mysterious condition.”

Bipolar disorder is characterized by extreme mood swings, with episodes of mania alternating with episodes of depression. It usually emerges in people in their 20s and 30s and remains with them for life. This condition affects approximately 2.8% of the adult American population, or about 7 million people. Patients face higher rates of suicide, poorer quality of life, and lower productivity than the general population. Some estimates put the annual cost of the condition in the U.S. alone at $219.1 billion.

While drugs can be useful in treating BD, many patients find they have bothersome side effects, and for some patients, current medications don’t work at all.

In this study, researchers measured levels of messenger RNA in the brain samples. They observed almost eight times more differentially expressed gene features in the sACC versus the amygdala, suggesting that the sACC may play an especially prominent role—both in mood regulation in general and BD specifically.

In patients who died with BD, the researchers found abnormalities in two families of genes: one containing genes related to the synapse and the second related to immune and inflammatory function.

“There finally is a study using modern technology and our current understanding of genetics to uncover how the brain is doing,” Hyde said. “We know that BD tends to run in families, and there is strong evidence that there are inherited genetic abnormalities that put an individual at risk for bipolar disorder. Unlike diseases such as sickle-cell anemia, bipolar disorder does not result from a single genetic abnormality. Rather, most patients have inherited a group of variants spread across a number of genes.”

“Bipolar disorder, also known as manic-depressive disorder, is a highly damaging and paradoxical condition,” said Daniel R. Weinberger, chief executive and director of the Lieber Institute and a co-author of the study. “It can make people very productive so they can lead countries and companies, but it can also hurl them into the meat grinder of dysfunction and depression. Patients with BD may live on two hours of sleep a night, saving the world with their abundance of energy, and then become so self-destructive that they spend their family’s fortune in a week and lose all friends as they spiral downward. Bipolar disorder also has some shared genetic links to other psychiatric disorders, such as schizophrenia, and is implicated in overuse of drugs and alcohol.”

SOURCE

http://nsideprecisionmedicine.com/topics/translational-research/biomarkers-topic/gene-expression/gene-expression-markers-for-bipolar-disorder-pinpointed/

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Developing Deep Learning Models (DL) for the Instant Prediction of Patients with Epilepsy

Posted in Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Innovations in Neurophysiology & Neuropsychology, Neurological Diseases, tagged AI, Conditions and Diseases, deep learning, EEG, epilepsy on June 22, 2021| Leave a Comment »

Developing Deep Learning Models (DL) for the Instant Prediction of Patients with Epilepsy

Reporter: Srinivas Sriram, Research Assistant I
Research Team: Srinivas Sriram, Abhisar Anand

2021 LPBI Summer Intern in Data Science and Website Construction
This article reports on a research study conducted from January 2021 to May 2021.
This Research was completed before the 2021 LPBI Summer Internship that began on 6/15/2021.

The main criterion of this study was to utilize the dataset (shown above) to develop a DL network that could accurately predict new seizures based on incoming data. To begin the study, our research group did some exploratory data analysis on the dataset and we recognized the key defining pattern of the data that allowed for the development of the DL model. This pattern of the data can be represented in the graph above, where the lines representing seizure data had major spikes in extreme hertz values, while the lines representing normal patient data remained stable without any spikes. We utilized this pattern as a baseline for our model.

Conclusions and Future Improvements:

Through our system, we were able to create a prototype solution that would predict when seizures happened in a potential patient using an accurate LSTM network and a reliable hardware system. This research can be implemented in hospitals with patients suffering from epilepsy in order to help them as soon as they experience a seizure to prevent damage. However, future improvements need to be made to this solution to allow it to be even more viable in the Healthcare Industry, which is listed below.

Needs to be implemented on a more reliable EEG headset (covers all neurons of the brain, less prone to electric disruptions shown in the prototype).
Needs to be tested on live patients to deem whether the solution is viable and provides a potential solution to the problem.
The network can always be fine-tuned to maximize performance.
A better alert system can be implemented to provide as much help as possible.

These improvements, when implemented, can help provide a real solution to one of the most common diseases faced in the world.

Background Information:

Epilepsy is described as a brain disorder diagnostic category for multiple occurrences of seizures that happen within recurrent and/or a brief timespan. According to the World Health Organization, seizure disorders, including epilepsy, are among the most common neurological diseases. Those who suffer seizures have a 3 times higher risk of premature death. Epilepsy is often treatable, especially when physicians can provide necessary treatment quickly. When untreated, however, seizures can cause physical, psychological, and emotional, including isolation from others. Quick diagnosis and treatment prevent suffering and save lives. The importance of a quick diagnosis of epilepsy has led to our research team developing Deep Learning (DL) algorithms for the sole purpose of detecting epileptic seizures as soon as they occur.

Throughout the years, one common means of detecting Epilepsy has emerged in the form of an electroencephalogram (EEG). EEGs can detect and compile “normal” and “abnormal “brain wave activity” and “indicate brain activity or inactivity that correlates with physical, emotional, and intellectual activities”. EEG waves are classified mainly by brain wave frequencies (EEG, 2020). The most commonly studied are delta, theta, alpha, sigma, and beta waves. Alpha waves, 8 to 12 hertz, are the key wave that occurs in normal awake people. They are the defining factor for the everyday function of the adult brain. Beta waves, 13 to 30 hertz, are the most common type of wave in both children and adults. They are found in the frontal and central areas of the brain and occur at a certain frequency which, if slow, is likely to cause dysfunction. Theta waves, 4 to 7 hertz, are also found in the front of the brain, but they slowly move backward as drowsiness increases and the brain enters the early stages of sleep. Theta waves are known as active during focal seizures. Delta waves, 0.5 to 4 hertz, are found in the frontal areas of the brain during deep sleep. Sigma waves, 12-16 hertz, are very slow frequency waves that occur during sleep. EEG detection of electrical brain wave frequencies can be used to detect and diagnose seizures based on their deviation from usual brain wave patterns.

In this particular research project, our research group hoped to develop a DL algorithm that when implemented on a live, portable EEG brain wave capturing device, could accurately predict when a particular patient was suffering from Epilepsy as soon as it occurred. This would be accomplished by creating a network that could detect when the brain frequencies deviated from the normal frequency ranges.

The Study:

Line Graph representing EEG Brain Waves from a Seizure versus EEG Brain Waves from a normal individual.

Source Dataset: https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition

To expand more on the dataset, it is an EEG data set compiled by Qiuyi Wu and Ernest Fokoue (2021) from the work of medical researchers R.Andrzejak, M.D. et al. (2001) which had been made public domain through the UCI Machine Learning Repository We also confirmed fair use permission with UCI. The dataset had been gathered by Andrzejak during examinations of 500 patients with a chronic seizure disorder. R.G.Andrzejak, et al. (2001) recorded each entry in the EEG dataset used for this project within 23.6 seconds in a time-series data structure. Each row in the dataset represented a patient recorded. The continuous variables in the dataset were single EEG data points at that specific point in time during the measuring period. At the end of the dataset, was a y-variable that indicated whether or not the patient had a seizure during the period the data was recorded. The continuous variables, or the EEG data, for each patient, varied widely based on whether the patient was experiencing a seizure at that time. The Wu & Fokoue Dataset (2021) consists of one file of 11,500 rows, each with 178 sequential data points concatenated from the original dataset of 5 data folders, each including 100 files of EEG recordings of 23.6 seconds and containing 4097 data points. Each folder contained a single, original subset. Subset A contained EEG data gathering during epileptic seizure…. Subset B contained EEG data from brain tumor sites. Subset 3, from a healthy site where tumors had been located. Subsets 4 and 5 from non-seizure patients at rest with eyes open and closed, respectively.

Based on the described data, our team recognized that a Recurrent Neural Network (RNN) was needed to input the sequential data and return an output of whether the sequential data was a seizure or not. However, we realized that RNN models are known to get substantially large over time, reducing computation speeds. To help provide a solution to this issue, our group decided to implement a long-short-term memory (LSTM) model. After deciding our model’s architecture, we proceeded to train our model in two different DL frameworks inside Python, TensorFlow, and PyTorch. Through various rounds of retesting and redesigning, we were able to train and develop two accurate models in each of the models that not only performed well while learning the data while training, but also could accurately predict new data in the testing set (98 percent accuracy on the unseen data). These LSTM networks could classify normal EEG data when the brain waves are normal, and then immediately predict the seizure data based on if a dramatic spike occurred in the data.

After training our model, we had to implement our model in a real-life prototype scenario in which we utilized a Single Board Computer (SBC) in the Raspberry Pi 4 and a live capturing EEG headset in the Muse 2 Headband. The two hardware components would sync up through Bluetooth and the headband would return EEG data to the Raspberry Pi, which would process the data. Through the Muselsl API in Python, we were able to retrieve this EEG data in a format similar to the manner implemented during training. This new input data would be fed into our LSTM network (TensorFlow was chosen for the prototype due to its better performance than the PyTorch network), which would then output the result of the live captured EEG data in small intervals. This constant cycle would be able to accurately predict a seizure as soon as it occurs through batches of EEG data being fed into the LSTM network. Part of the reason why our research group chose the Muse Headband, in particular, was not only due to its compatibility with Python but also due to the fact that it was able to represent seizure data. Because none of our members had epilepsy, we had to find a reliable way of testing our model to make sure it worked on the new data. Through electrical disruptions in the wearable Muse Headband, we were able to simulate these seizures that worked with our network’s predictions. In our program, we implemented an alert system that would email the patient’s doctor as soon as a seizure was detected.

Individual wearing the Muse 2 Headband

Image Source: https://www.techguide.com.au/reviews/gadgets-reviews/muse-2-review-device-help-achieve-calm-meditation/

Sources Cited:

Wu, Q. & Fokoue, E. (2021). Epileptic seizure recognition data set: Data folder & Data set description. UCI Machine Learning Repository: Epileptic Seizure Recognition. Jan. 30. Center for Machine Learning and Intelligent Systems, University of California Irvine.

Nayak, C. S. (2020). EEG normal waveforms.” StatPearls [Internet]. U.S. National Library of Medicine, 31 Jul. 2020, www.ncbi.nlm.nih.gov/books/NBK539805/#.

Epilepsy. (2019). World Health Organization Fact Sheet. Jun. https://www.who.int/ news-room/fact-sheet s/detail/epilepsy

Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves

Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Innovations in Neurophysiology & Neuropsychology, tagged AI, CNN, deep learning, EEG, Emotions, LSTM, neural networks on June 22, 2021| Leave a Comment »

Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves

Reporter: Abhisar Anand, Research Assistant I
Research Team: Abhisar Anand, Srinivas Sriram

2021 LPBI Summer Internship in Data Science and Website construction.
This article reports on a research study conducted till December 2020.
Research completed before the 2021 LPBI Summer Internship began in 6/15/2021.

As the field of Artificial Intelligence progresses, various algorithms have been implemented by researchers to classify emotions from EEG signals. Few researchers from China and Singapore released a paper (“An Investigation of Deep Learning Models from EEG-Based Emotion Recognition”) analyzing different types of DL model architectures such as deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid of CNN and LSTM (CNN-LSTM). The dataset used in this investigation was the DEAP Dataset which consisted of EEG signals of patients that watched 40 one-minute long music videos and then rated them in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. The result of the investigation presented that CNN (90.12%) and CNN-LSTM (94.7%) models had the highest performance out of the batch of DL models. On the other hand, the DNN model had a very fast training speed but was not able to perform as accurately as other other models. The LSTM model was also not able to perform accurately and the training speed was much slower as it was difficult to achieve convergence.

This research in the various model architectures provides a sense of what the future of Emotion Classification with AI holds. These Deep Learning models can be implemented in a variety of different scenarios across the world, all to help with detecting emotions in scenarios where it may be difficult to do so. However, there needs to be more research implemented in the model training aspect to ensure the accuracy of the classification is top-notch. Along with that, newer and more reliable hardware can be implemented in society to provide an easy-to-access and portable EEG collection device that can be used in any different scenario across the world. Overall, although future improvements need to be implemented, the future of making sure that emotions are accurately detected in all people is starting to look a lot brighter thanks to the innovation of AI in the neuroscience field.

Emotions are a key factor in any person’s day to day life. Most of the time, we as humans can detect these emotions through physical cues such as movements, facial expressions, and tone of voice. However, in certain individuals, it can be hard to identify their emotions through their visible physical cues. Recent studies in the Machine Learning and AI field provide a particular development in the ability to detect emotions through brainwaves, more specifically EEG brainwaves. These researchers from across the world utilize the same concept of EEG implemented in AI to help predict the state an individual is in at any given moment.

Emotion classification based on brain wave: a survey (Figure 4)

Image Source: https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0201-x

EEGs can detect and compile normal and abnormal brain wave activity and indicate brain activity or inactivity that correlates with physical, emotional, and intellectual activities. EEG signals are classified mainly by brain wave frequencies. The most commonly studied are delta, theta, alpha, sigma, and beta waves. Alpha waves, 8 to 12 hertz, are the key wave that occurs in normal awake people. They are the defining factor for the everyday function of the adult brain. Beta waves, 13 to 30 hertz, are the most common type of wave in both children and adults. They are found in the frontal and central areas of the brain and occur at a certain frequency which, if slowed, is likely to cause dysfunction. Theta waves, 4 to 7 hertz, are also found in the front of the brain, but they slowly move backward as drowsiness increases and the brain enters the early stages of sleep. Theta waves are known as active during focal seizures. Delta waves, 0.5 to 4 hertz, are found in the frontal areas of the brain during deep sleep. Sigma waves, 12-16 hertz, are very slow frequency waves that occur during sleep. These EEG signals can help for the detection of emotions based on the frequencies that the signals happen in and the activity of the signals (whether they are active or relatively calm).

Sources:

Zhang, Yaqing, et al. “An Investigation of Deep Learning Models for EEG-Based Emotion Recognition.” Frontiers in Neuroscience, vol. 14, 2020. Crossref, doi:10.3389/fnins.2020.622759.

Nayak, Anilkumar, Chetan, Arayamparambil. “EEG Normal Waveforms.” National Center for Biotechnology Information, StatPearls Publishing LLC., 4 May 2021, http://www.ncbi.nlm.nih.gov/books/NBK539805.

Contribution of Nervous System Functional Deterioration to late-life Mortality: The Role Neurofilament light chain (NfL) a Blood Biomarker for the Progression of Neurological Diseases and its Correlation to Age and Life Expectancy

Posted in Age and Life Expectancy, Alzheimer's Disease, Etiology, Innovations in Neurophysiology & Neuropsychology, Nervous System Function, tagged Age and Life Expectancy, Blood Biomarker for the Progression of Neurological Diseases, Neurofilament light chain (NfL) on February 19, 2021| Leave a Comment »

Contribution of Nervous System Functional Deterioration to late-life Mortality: The Role Neurofilament light chain (NfL) a Blood Biomarker for the Progression of Neurological Diseases and its Correlation to Age and Life Expectancy

Reporter: Aviva Lev-Ati, PhD, RN

A neuronal blood marker is associated with mortality in old age

Stephan A. Kaeser,

Benoit Lehallier,

Mikael Thinggaard,

Lisa M. Häsler,

Anja Apel,

Carina Bergmann,

Daniela Berdnik,

Bernard Jeune,

Kaare Christensen,

Sebastian Grönke,

Linda Partridge,

Tony Wyss-Coray,

Jonas Mengel-From &

Mathias Jucker

Nature Aging volume 1, pages218–225(2021)Cite this article

Abstract

Neurofilament light chain (NfL) has emerged as a promising blood biomarker for the progression of various neurological diseases. NfL is a structural protein of nerve cells, and elevated NfL levels in blood are thought to mirror damage to the nervous system. We find that plasma NfL levels increase in humans with age (n = 122; 21–107 years of age) and correlate with changes in other plasma proteins linked to neural pathways. In centenarians (n = 135), plasma NfL levels are associated with mortality equally or better than previously described multi-item scales of cognitive or physical functioning, and this observation was replicated in an independent cohort of nonagenarians (n = 180). Plasma NfL levels also increase in aging mice (n = 114; 2–30 months of age), and dietary restriction, a paradigm that extends lifespan in mice, attenuates the age-related increase in plasma NfL levels. These observations suggest a contribution of nervous system functional deterioration to late-life mortality.

SOURCE

https://www.nature.com/articles/s43587-021-00028-4

How long will a healthy older person live? A substance in blood may provide a clue

Bruce Goldman February 18, 2021

Levels of a substance in nonagenerians’ and centenarians’ blood accurately predict how much longer they’re going to live. The substance comes from the brain.

The findings, in a study published in Nature Aging, could prove useful in developing life-extending drugs. They also raise questions about the brain’s role in aging and longevity.

The study, conducted by Stanford investigators including neuroscientist Tony Wyss-Coray, PhD, in collaboration with researchers in Denmark and Germany, zeroed in on a substance whose technical name is neurofilament light chain (abbreviated NfL). A structural protein produced in the brain, NfL is found in trace amounts in cerebrospinal fluids and blood, where it’s an indicator of damage to long extensions of nerve cells called axons.

Axons convey signals from one nerve cell to the next and are critical to all brain function. You’d rather they remain intact.

Too much NfL (different from the NFL)

High NfL levels in the blood have previously been associated with Alzheimer’s disease, multiple sclerosis, Huntington’s disease, amyotrophic lateral sclerosis (Lou Gehrig’s disease) and other neurological disorders. But the people monitored in the new study were generally pretty healthy for their age.

The researchers first looked at 122 people whose ages ranged from 21 to 107, and found increasing blood levels of NfL — as well as increasing variation among individuals — with increasing age.

Next, the scientists followed the fates of 135 people age 100 or over for a four-year period. Most of those centenarians were in good shape to begin with, as shown by their performance on standard tests of mental ability and by a measure of their capacity to meet the routine demands of daily living.

Not unexpectedly, those whose mental tests indicated impairment had more NfL in their blood than those with the sharpest minds did. And those with low levels were substantially likelier to live longer than those with high levels.

A look at people in their 90s confirmed the findings in the over-100 group. Blood NfL levels among 180 93-year-olds not only predicted the duration of these folks’ survival, but did so better than mental or daily-coping test scores did.

The investigators showed that mice’s blood NfL levels, too, increase with age. But cutting their caloric intake, beginning in young adulthood — already known to prolong the lives of mice and numerous other species — chopped the little creatures’ blood levels of this substance in half in old age. (This new finding doesn’t prove that lowering NfL blood levels causes increased longevity, but it’s consistent with it.)

Tie to life expectancy?

At a minimum, NfL appears to accurately flag mortality’s approach. That means it might be possible to monitor it as a surrogate marker for remaining life expectancy, much as blood cholesterol levels are used as proxies for cardiovascular health. If so, it could someday help drug developers assess life-extending interventions’ efficacy.

Clinical trials of interventions believed to enhance longevity have been impractical, because it would almost certainly take so long to get a statistically significant result that such trials would be hugely expensive — a major hang-up for pharmas considering investment in longevity drugs. But monitoring a proxy such as NfL could cut years off of such trials’ duration, perhaps encouraging drug developers to dive into the clinical arena with life-prolonging pharmacological candidates.

Possibly most intriguing of all: The new findings hint that maintaining a healthy brain in old age is the best route to a long life.

“It will be interesting to see how and why the brain might be so important in counting down our final years and months,” Wyss-Coray told me.

Photo by Pablo Bendandi

SOURCE

https://scopeblog.stanford.edu/2021/02/18/how-long-will-a-healthy-older-person-live-a-substance-in-blood-may-provide-a-clue/?utm_source=Stanford+Report&utm_campaign=2bba6009da-EMAIL_CAMPAIGN_2021_02_18_04_16&utm_medium=email&utm_term=0_29ce9f751e-2bba6009da-52267013

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Acute Changes in Serotonin Detected by AI-designed Serotonin Sensor

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Behavior, Cell Biology, Cell Biology, Signaling & Cell Circuits, Circadian Rhythm and Sleep, Innovations in Neurophysiology & Neuropsychology, Neurological Diseases, Neuroscience, tagged acetyl choline sensor (iAChSnFR), Central Nervous System, Mutagenesis, periplasmic binding proteins (PBPs), reuptake inhibition of serotonin on December 30, 2020| Leave a Comment »

Reporter: Adina Hazan, PhD

Elizabeth Unger from the Tian group at UC Davis, Jacob Keller from the Looger lab from HHMI, Michael Altermatt from the Gradinaru group at California Institute of Technology, and colleagues did just this, by redesigned the binding pocket of periplasmic binding proteins (PBPs) using artificial intelligence, such that it became a fluorescent sensor specific for serotonin. Not only this, the group showed that it could express and use this molecule to detect serotonin on the cell, tissue, and whole animal level.

By starting with a microbial PBP and early version of an acetyl choline sensor (iAChSnFR), the scientists used machine learning and modeling to redesign the binding site to exhibit a higher affinity and specificity to serotonin. After three repeats of mutagenesis, modeling, and library readouts, they produced iSeroSnFR. This version harbors 19 mutations compared to iAChSnFR0.6 and a K_d of 310 µM. This results in an increase in fluorescence in HEK293T cells expressing the serotonin receptor of 800%. Of over 40 neurotransmitters, amino acids, and small molecules screened, only two endogenous molecules evoked some fluorescence, but at significantly higher concentrations.

To acutely test the ability of the sensor to detect rapid changes of serotonin in the environment, the researchers used caged serotonin, a technique in which the serotonin is rapidly released into the environment with light pulses, and showed that iSeroSnFR accurately and robustly produced a signal with each flash of light. With this tool, it was then possible to move to ex-vivo mouse brain slices and detect endogenous serotonin release patterns across the brain. Three weeks after targeted injection of iSeroSnFR to specifically deliver the receptor into the prefrontal cortex and dorsal striatum, strong fluorescent signal could be detected during perfusion of serotonin or electrical stimulation.

Most significantly, this molecule was also shown to be detected in freely moving mice, a tool which could offer critical insight into the acute role of serotonin regulation during important functions such as mood and alertness. Through optical fiber placements in the basolateral amygdala and prefrontal cortex, the team measured dynamic and real-time changes in serotonin release in fear-trained mice, social interactions, and sleep wake cycles. For example, while both areas of the brain have been established as relevant to the fear response, they reliably tracked that the PFC response was immediate, while the BSA displayed a delayed response. This additional temporal resolution of neuromodulation may have important implications in neurotransmitter pharmacology of the central nervous system.

This study provided the scientific community with several insights and tools. The serotonin sensor itself will be a critical tool in the study of the central nervous system and possibly beyond. Additionally, an AI approach to mutagenesis in order to redesign a binding pocket of a receptor opens new avenues to the development of pharmacological tools and may lead to many new designs in therapeutics and research.

SOURCE:

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Sleep Deprivation Death Linked Causally to the Gut

Posted in Circadian Rhythm and Sleep, tagged Gut, ROS, Sleep deprivation, Sleep disorder on June 30, 2020| Leave a Comment »

Sleep Deprivation Death Linked Causally to the Gut

Reporter : Irina Robu, PhD

Neuroscientists at Harvard Medical School identified an unexpected link between sleep deprivation and premature death. Their findings show that the possibility that animals might be able to survive without sleep, under certain circumstances. Their study with sleep-deprived fruit flies found that death was continuously by the accumulation of reactive oxidative species in the gut. And when the flies were given antioxidant compounds that neutralized and cleared ROS from the gut, the sleep-deprived animals remained active and had normal lifespans. Extra experiments in mice confirmed that ROS accumulated in the gut when they didn’t get enough sleep.

Yet, in spite of decades of study, researchers still haven’t revealed why animals die when they don’t sleep. In attempts to answer how sleep deprivation culminates in death, most research has focused on the brain, where sleep originates. However, studies have yet to yield conclusive results. In addition to impairing cognition, sleep loss leads to dysfunction of the gastrointestinal, immune, metabolic, and circulatory systems.

The Harvard Medical School team carried out a sequence of experiments in fruit flies to search throughout the body for signs of damage caused by sleep deprivation. Fruit flies share many sleep-regulating genes with humans. To screen sleep, the investigators used infrared beams to constantly track the movement of flies housed in individual tubes. Scientist show that flies can sleep through physical shaking, so they genetically manipulated fruit flies to express a heat-sensitive protein in specific neurons, the activity of which are known to suppress sleep. When flies were housed at 29°C the protein induced neurons to remain constantly active, thus preventing the flies from sleeping.

The scientists discovered that fruit fly mortality spiked after 10 days of temperature-induced sleep deprivation and all of the flies died by around day 20 and control flies that had normal sleep lived up to approximately 40 days in the same environmental conditions. Since mortality increased around day 10, the scientists looked for markers of cell damage on that and the preceding days. They noticed that the guts of sleep-deprived flies had a dramatic build-up of ROS. The buildup of ROS in the fruit fly guts peaked around day 10 of sleep deprivation, and when deprivation was stopped, ROS levels decreased.

To find out if ROS in the gut plays a causal role in sleep deprivation-induced death, the researchers next looked at whether preventing ROS accumulation could prolong survival. They tested dozens of compounds with antioxidant properties known to neutralize ROS and identified 11 that, when given as a food supplement, allowed sleep-deprived flies to have a normal or near-normal lifespan. These compounds, such as melatonin, lipoic acid, and NAD, were particularly effective at clearing ROS from the gut. Notably, the supplements did not extend the lifespan of non-sleep-deprived flies.

The role of ROS removal in preventing death was also confirmed by experiments in which flies were genetically manipulated to overproduce antioxidant enzymes in their guts. These flies had normal to near-normal lifespans when sleep deprived, but flies that overproduced antioxidant enzymes in their nervous systems weren’t protected from sleep-deprivation-related death. While the results demonstrated that ROS build up in the gut plays a central role in causing premature death from sleep deprivation, the researchers acknowledged that many questions are still without answers. At the same time, they found that insufficient sleep is identified to restrict with the body’s hunger signaling pathways, which lead to measure the fruit fly food intake to analyze whether there were potential associations between feeding and death. They found that some sleep-deprived flies ate more throughout the day compared with non-deprived controls.

The researchers are now working to identify the biological pathways that lead to ROS accumulation in the gut and subsequent physiological disruptions.

SOURCE

Death Due to Sleep Deprivation Linked Causally to the Gut, and is Preventable in Flies

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Human personalized autologous cell therapy for Parkinson’s Disease

Posted in Autologous Cell Therapy, Biological Engineering, Biological Networks, Biomarkers & Medical Diagnostics, Biotechnology, Cell Biology, Cell Biology, Signaling & Cell Circuits, Cell Processing System in Cell Therapy Process Development, cell-based therapy, Cerebrovascular and Neurodegenerative Diseases, Competition in regenerative stem cell science, Genomic Endocrinology, hNPCs, Innovations in Neurophysiology & Neuropsychology, Neurodegenerative Diseases, Neurohumoral Transmission, Neurological Diseases, Neuronal Circuits, Neuroscience, Parkinson's disease dyskinesia levodopa, Patient-centered Medicine, Personal Health Applications: Tech Innovations serves HealhCare, Personalized and Precision Medicine & Genomic Research, Signaling & Cell Circuits, stem cell biology and patient-specific, Stem Cells for Regenerative Medicine, tagged brain, dopamine, hiPSC, Parkinsons disease, Therapy on December 2, 2019| Leave a Comment »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Parkinson’s Disease (PD), characterized by both motor and non-motor system pathology, is a common neurodegenerative disorder affecting about 1% of the population over age 60. Its prevalence presents an increasing social burden as the population ages. Since its introduction in the 1960’s, dopamine (DA)-replacement therapy (e.g., L-DOPA) has remained the gold standard treatment. While improving PD patients’ quality of life, the effects of treatment fade with disease progression and prolonged usage of these medications often (>80%) results in side effects including dyskinesias and motor fluctuations. Since the selective degeneration of A9 mDA neurons (mDANs) in the substantia nigra (SN) is a key pathological feature of the disease and is directly associated with the cardinal motor symptoms, dopaminergic cell transplantation has been proposed as a therapeutic strategy.

Researchers showed that mammalian fibroblasts can be converted into embryonic stem cell (ESC)-like induced pluripotent stem cells (iPSCs) by introducing four transcription factors i.e., Oct4, Sox2, Klf4, and c-Myc. This was then accomplished with human somatic cells, reprogramming them into human iPSCs (hiPSCs), offering the possibility of generating patient-specific stem cells. There are several major barriers to implementation of hiPSC-based cell therapy for PD. First, probably due to the limited understanding of the reprogramming process, wide variability exists between the differentiation potential of individual hiPSC lines. Second, the safety of hiPSC-based cell therapy has yet to be fully established. In particular, since any hiPSCs that remain undifferentiated or bear sub-clonal tumorigenic mutations have neoplastic potential, it is critical to eliminate completely such cells from a therapeutic product.

In the present study the researchers established human induced pluripotent stem cell (hiPSC)-based autologous cell therapy. Researchers reported a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, a method was developed to more efficiently generate clinical grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, a “spotting”-based in vitro differentiation methodology was established to generate functional and healthy mDA cells in a scalable manner. Third, a chemical method was developed that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus produced can express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restored motor dysfunction and reinnervated host brain, while showing no evidence of tumor formation or redistribution of the implanted cells.

Together these results supported the promise of these techniques to provide clinically applicable personalized autologous cell therapy for PD. It was recognized by researchers that this methodology is likely to be more costly in dollars and manpower than techniques using off-the-shelf methods and allogenic cell lines. Nevertheless, the cost for autologous cell therapy may be expected to decrease steadily with technological refinement and automation. Given the significant advantages inherent in a cell source free of ethical concerns and with the potential to obviate the need for immunosuppression, with its attendant costs and dangers, it was proposed that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

References:

https://www.jci.org/articles/view/130767/pdf?elqTrackId=2fd7d0edee744f9cb6d70a686d7b273b

https://www.ncbi.nlm.nih.gov/pubmed/31714896

https://www.ncbi.nlm.nih.gov/pubmed/23666606

https://www.ncbi.nlm.nih.gov/pubmed/27343168

https://www.ncbi.nlm.nih.gov/pubmed/21495962

https://www.ncbi.nlm.nih.gov/pubmed/28083784

https://www.ncbi.nlm.nih.gov/pubmed/20336395

https://www.ncbi.nlm.nih.gov/pubmed/28585381

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Contributions to Neuronal Systems by University Professors Eve Marder and Irv Epstein at Brandeis University

Posted in Human Sensation and Cellular Transduction: Physiology and Therapeutics, Innovations in Neurophysiology & Neuropsychology, Neurodegenerative Diseases, Neurohumoral Transmission, Neurological Diseases, Neuroscience on November 12, 2019| Leave a Comment »

Contributions to Neuronal Systems by University Professors Eve Marder and Irv Epstein at Brandeis University

Reporter: Aviva Lev-Ari, PhD, RN

Oscillators: Chemicals, Neurons and People: A Celebration of Eve Marder, Irving Epstein and the Volen Center for Complex Systems
Sunday, Nov. 17, 2019, 1 – 5:30 p.m.

Location	Schwartz
Room	Auditorium
Event Sponsor(S)	Office of the Provost
Website	www.brandeis.edu…

A celebration for new university professors Eve Marder and Irving Epstein, and the 25th anniversary of the Volen Center!

Schedule of Events

1p.m. – Opening remarks: Leslie Griffith, Nancy Lurie Marks Professor of Neuroscience and Director of the Volen National Center for Complex Systems
1:30 p.m. – Panel Discussion: “Oscillators: Chemicals, Neurons and People”
- Moderator: Gina Turrigiano, Joseph Levitan Professor of Vision Science
- Panelist: Jorge Golowasch, Professor, Department of Biological Sciences, New Jersey Institute of Technology
- Panelist: Nancy Kopell, Professor of Mathematics, Boston University
- Panelist: Horacio Rotstein, Professor of Mathematical Biology & Computational Neuroscience, Department of Biological Sciences, New Jersey Institute of Technology
- Panelist: Frances Skinner, Senior Scientist, Krembil Research Institute and Professor, Division of Neurology, Department of Medicine and Department of Physiology, University of Toront
2:30 p.m. – Coffee
3 p.m. – Irving Epstein: “How I Wandered into an Oscillatory State”
4 p.m. – Eve Marder: “The Challenges Posed by Neuronal Oscillators that are both Stable and Plastic”
5 p.m. – Closing remarks: Leslie Griffith

Eve Marder and Irv Epstein recall the collaborations that started it all

The University Professors will lead a public symposium on Nov. 17

headshots of University Professors Eve Marder, left, and Irv Epstein Photo/Mike LovettUniversity professors Eve Marder ’69, Left, and Irving R. Epstein

By Laura Gardner

Nov. 11, 2019

University Professors Eve Marder ’69 and Irv Epstein will help celebrate the 25th anniversary of the Volen National Center for Complex Systems in a public symposium November 17.

Marder, the Victor and Gwendolyn Beinfield Professor of Neuroscience, and Epstein, the Henry F. Fischbach Professor of Chemistry, were named University Professors last spring in recognition of their pioneering interdisciplinary achievements. At the symposium, they will each deliver a lecture drawing on their collaborative research on oscillators. In the case of Epstein, these are oscillating chemical reactions. In Marder’s case, it is rhythmically active neurons and/or circuits.

But some 35 years ago, before they were leaders in their fields, Epstein and Marder saw the benefit of sharing ideas. A mutual colleague noticed that the chemical reactions recorded on the chart recorder Epstein was using looked intriguingly similar to the neuronal signals Marder was recording in her research.

“It’s relatively unusual behavior for chemistry, but it’s sort of the essence of what goes on in neuronal systems,” said Epstein, who soon learned some rudiments of neuroscience from Marder and began mathematically modeling groups of neurons. For her part, Marder “got an appreciation for what interacting with theorists could bring, to help her answer the kind of questions she wanted to answer,” said Epstein.

These days, Marder’s research on small neural circuits found in lobsters and crabs is credited with revolutionizing understanding of the fundamental nature of neuronal circuit operation, including how neuromodulators control behavioral outputs and how the stability of circuits is maintained over time. She has won many top prizes in neuroscience, including the Gruber Award in Neuroscience, the Kavli Prize in Neuroscience, and the National Academy of Sciences Neuroscience Prize. In March, she will receive the Carnegie Prize in Mind and Brain Science from Carnegie Mellon University.

Epstein, a Howard Hughes Medical Institute professor, pioneered the field of chemical oscillators. “When we got into the field of oscillating reactions, there were just three that were known, and they were all discovered accidentally,” said Epstein. “We decided that if we really understood these systems, we should be able to design them.”

Although it took him several years and three unsuccessful grant applications to secure funding for his ideas, Epstein and his lab ultimately won funding, and within a few months succeeded in developing their first novel chemical oscillating reaction.

Writ large, Marder and Epstein collaboratively demonstrate that the kinds of phenomena seen in neurons are also found in chemical and physical systems. “What you learn from modeling chemical reactions can help you understand how neurons work, and vice versa,” said Epstein.

“Volen is a place where you get all kinds of collaborations,” said Marder. “One of Brandeis’ strengths is its interactivity; those early days were quite catalytic.”

Categories: Research, Science and Technology

SOURCE

https://www.brandeis.edu/now/2019/november/eve-irv-volen.html

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NSPR1 and DEC2 genes: Survival on 4.5 hours of Sleep per night: A mutation in the β1-adrenergic receptor gene in humans who require fewer hours of sleep than most, ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness

Posted in Circadian Rhythm and Sleep, Gene Regulation, Genome Biology, Variation in human protein-coding regions on October 22, 2019| Leave a Comment »

NSPR1 and DEC2 genes: Survival on 4.5 hours of Sleep per night: A mutation in the β₁-adrenergic receptor gene in humans who require fewer hours of sleep than most, ADRB1 ⁺ neurons are active during rapid eye movement (REM) sleep and wakefulness

Reporter: Aviva Lev-Ari, PhD, RN

10/2019 RESEARCH ARTICLE SLEEP

Abstract

Sleep is a crucial physiological process for our survival and cognitive performance, yet the factors controlling human sleep regulation remain poorly understood. Here, we identified a missense mutation in a G protein–coupled neuropeptide S receptor 1 (NPSR1) that is associated with a natural short sleep phenotype in humans. Mice carrying the homologous mutation exhibited less sleep time despite increased sleep pressure. These animals were also resistant to contextual memory deficits associated with sleep deprivation. In vivo, the mutant receptors showed increased sensitivity to neuropeptide S exogenous activation. These results suggest that the NPS/NPSR1 pathway might play a critical role in regulating human sleep duration and in the link between sleep homeostasis and memory consolidation.

It is possible that drugs could be developed to target either the NSPR1 or DEC2 genes, as a treatment for insomnia or other sleep disorders. However, further understanding of exactly how these genes function would be required before this stage. Both are involved in brain function, so targeting them could lead to negative neural side effects.

Volume 103, Issue 6, 25 September 2019, Pages 1044-1055.e7

A Rare Mutation of β₁-Adrenergic Receptor Affects Sleep/Wake Behaviors

Published:August 28, 2019DOI:https://doi.org/10.1016/j.neuron.2019.07.026

https://www.cell.com/neuron/fulltext/S0896-6273(19)30652-X

Highlights

A mutation in ADRB1 leads to natural short sleep trait in humans

Mice engineered with same mutation have similar short sleep behavior as humans

Activity of dorsal pons ADRB1 ⁺ neurons associates with REM sleep and wakefulness

Mutation increases the population activity of dorsal pons ADRB1 ⁺ neurons

Summary

Sleep is crucial for our survival, and many diseases are linked to long-term poor sleep quality. Before we can use sleep to enhance our health and performance and alleviate diseases associated with poor sleep, a greater understanding of sleep regulation is necessary. We have identified a mutation in the β ₁-adrenergic receptor gene in humans who require fewer hours of sleep than most. In vitro, this mutation leads to decreased protein stability and dampened signaling in response to agonist treatment. In vivo, the mice carrying the same mutation demonstrated short sleep behavior. We found that this receptor is highly expressed in the dorsal pons and that these ADRB1 ⁺ neurons are active during rapid eye movement (REM) sleep and wakefulness. Activating these neurons can lead to wakefulness, and the activity of these neurons is affected by the mutation. These results highlight the important role of β ₁-adrenergic receptors in sleep/wake regulation.

Keywords

familial natural short sleep

ADRB1

sleep duration

dorsal pons

Additional SOURCES

http://www.frontlinegenomics.com/news/27962/second-gene-mutation-that-lets-people-survive-on-less-sleep/

Clinically Studied, Continuous Release and Absorption Melatonin, REMfresh, Designed to Give Patients Up to 7 Hours of Sleep Support

Posted in Circadian Rhythm and Sleep, Innovations in Neurophysiology & Neuropsychology, Melatonin & Circadian Regulation, Voices of Patients and Healthcare Providers, tagged Melatonin, REMfresh, SLEEP 2019, sleep maintenance, sleep onset, sleep quality on June 19, 2019| Leave a Comment »

Novel delivery system in REMfresh mimics the way the body naturally releases and maintains melatonin over a 7-hour period

Real-world evidence confirms previous clinical data on improved sleep duration and sleep quality with REMfresh

Reporter: Gail S. Thornton, M.A.

Chronic disorders of sleep and wakefulness affect an estimated 50-70 million adults in the United States.^[i] The cumulative long term effects of sleep loss have been associated with a wide range of damaging health consequences, including obesity, diabetes, impaired glucose tolerance, cardiovascular disease, hypertension, anxiety and depression.^[ii]In terms of preventing health consequences, sleeping 6-8 hours per night consistently may provide optimal health outcomes.^[iii]

This month, real-world evidence from two recently completed patient-reported outcomes (PRO) studies presented at SLEEP 2019 in San Antonio, Texas, confirms previous clinical data demonstrating statistically significant improvements in sleep onset, sleep duration, sleep maintenance and sleep quality with REMfresh®, the first and only continuous release and absorption melatonin (CRA-Melatonin™). This data supports and reinforces the benefits of REMfresh, which is designed to give patients up to 7 hours of sleep support. PRO studies of this kind, which more closely address real-world patient experience, are increasingly being recognized by regulatory authorities and academia in evaluating new therapies.

The REMfresh Duration Validation (REMVAL) study provides further evidence of a correlative relationship between the 7-hour pharmacokinetic profile observed in the earlier clinical study, REM Absorption Kinetics Trial (REMAKT), and the hypnotic effects of REMfresh, observed in subsequent studies, as demonstrated by improvements in sleep onset, sleep duration, sleep maintenance, sleep quality and patient satisfaction. This latest study further validates the findings of past studies that have been presented and undergone peer review at major sleep conferences:

REMfresh Patient Reported Outcomes DURation (REMDUR), the first, 500-patient, PRO study of this sleep brand, presented at the annual meeting for sleep specialists, SLEEP 2018, which demonstrated that more than 77 percent of patients achieved 6 or more hours of sleep compared to 23.6 percent who slept that duration prior to taking REMfresh (p<.0001)^[iv], and
REM Absorption Kinetics Trial (REMAKT), a pharmacokinetic study presented at SLEEP 2017 and 2018, which demonstrated that REMfresh mimics the body’s own seven-hour Mesa-Wave® release profile, a natural pattern of melatonin blood levels during a normal night’s sleep cycle.^[v]

An additional PRO subset study, part of REMVAL, called the REMfresh Short Sleep Cohort Assessment (REMSS), assessed improvements in sleep duration and sleep maintenance among 311 patients with morbid or extreme short sleep duration of 4 hours or less.

These two PRO studies (REMVAL and REMSS) were presented at SLEEP 2019, the 33^rd Annual Meeting of the Associated Professional Sleep Societies (APSS), which is a joint meeting of the American Academy of Sleep Medicine and the Sleep Research Society, held in San Antonio, Texas, from June 8-12.

“These latest findings provide further confirmation of the potential for nonprescription REMfresh to help address the public health issue of the cumulative effects of sleep loss,” said David C. Brodner, M.D., a leading sleep specialist who is Double Board-Certified in Otolaryngology — Head and Neck Surgery as well as Sleep Medicine, Founder and Principle Physician at the Center for Sinus, Allergy, and Sleep Wellness, in Palm Beach County, Florida, and Senior Medical Advisor for Physician’s Seal, LLC. “Based on a novel Ion Powered Pump® (IPP®) delivery system that provides a pharmacokinetic (PK) profile that more closely aligns with the body’s own natural sleep pattern, REMfresh has demonstrated once again promising results and high levels of satisfaction in a real-world population of patients who have had chronic difficulties sleeping, providing up to seven hours of sleep support,” said Dr. Brodner.

Topline findings of these studies are as follows:

The 1,116 patient-reported outcomes (PRO) study, REMfresh Duration Validation (REMVAL), found that after taking 99 percent ultra-pure, continuous release and absorption melatonin (REMfresh®, CRA-melatonin™), the majority (78.8 percent) of patients involved achieved a sleep duration of greater than or equal to 6 hours (p<.0001), while more than 91 percent of patients reported a major/moderate improvement in sleep onset, sleep maintenance and sleep quality (p<.0001). Of the 30.7 percent of patients (342 in total), who reported never having taken other brands of melatonin, 99.4 percent indicated they were likely or very likely to continue taking CRA-melatonin for their sleep issues (p<.0001).
REMVAL provides further real-world evidence of a correlative relationship between the originally observed 7-hour pharmacokinetic profile in the REM Absorption Kinetics Trial (REMAKT) and the strong observed hypnotic effects of CRA-melatonin, as demonstrated by improvements in sleep onset, sleep duration, sleep maintenance and sleep quality.
A second PRO subset study, REMfresh Short Sleep Cohort Assessment (REMSS), involving 311 patients who reported sleeping four hours or less nightly from the REMVAL study, found that 95.8 percent of patients who previously experienced daily, morbid short sleep duration of less than or equal to 4 hours reported an improvement in sleep duration (p<.0001), including more than 46 percent who achieved a sleep duration of greater than or equal to 6 hours (p<.0001). More than 93 percent of patients reported a major/moderate improvement in sleep onset, sleep maintenance and sleep quality (p<.0001).
REMVAL and REMSS also provides validation of the results from the previously peer-reviewed and presented clinical study, REMAKT, which demonstrated that REMfresh mimics the body’s own 7-hour Mesa Wave®, a natural pattern of melatonin blood levels during a normal night’s sleep cycle and the 500-patient, peer-reviewed and presented REMfresh^® Patient Reported Outcomes DURation (REMDUR) study, that demonstrated statistically significant improvements in sleep onset, sleep maintenance and sleep quality.

REMVAL Study Describes Improvements in Sleep Duration and Sleep Quality

The poster entitled, “Observed Hypnotic Effects with a Continuous-Release Ion Powered Pump Melatonin Delivery System: Self-Reported Patient Outcomes Study Results Demonstrating Improvement in Sleep Duration and Quality,” reported findings provides further real-world evidence of a correlative relationship between the originally observed 7-hour pharmacokinetic profile in the REM Absorption Kinetics Trial (REMAKT) and the strong hypnotic effects of CRA-melatonin observed in subsequent studies and may offer a new low-dose, drug-free alternative to prescription hypnotics to treat chronic sleep disturbances.

The 1,116-patient REMVAL study was designed to obtain clinically relevant information about patients’ past usage of melatonin and non-melatonin sleep aids, sleep patterns prior to taking CRA-melatonin, sleep duration before and after taking CRA-melatonin, frequency of CRA-melatonin usage, improvement in sleep onset, sleep maintenance and sleep quality after taking CRA-melatonin, and overall satisfaction with CRA-melatonin.

In the study, patients with sleep disturbances in the general population received a sample of REMfresh from their physicians and were invited to complete a 13-question online survey. After taking REMfresh, the majority (78.8 percent) of patients achieved a sleep duration of greater than or equal to 6 hours (p<.0001). More than 91 percent of patients reported a major/moderate improvement in sleep onset, sleep maintenance and sleep quality (p<.0001). Of the 30.7 percent of patients (342 in total), who reported never having taken other brands of melatonin, 99.4 percent indicated they were likely or very likely to continue taking REMfresh for their sleep issues (p<.0001).

REMSS Study Shows Improvement in Patients with Chronic, Extreme Short Sleep

The poster entitled, “Improvement in Sleep Duration and Maintenance with Ion Powered Continuous Release and Absorption Melatonin in a Cohort of Patients with Chronic Short Sleep Duration: Results from a Patient-Reported Outcomes Study,” highlighted findings from the REMfresh Short Sleep Cohort Assessment (REMSS), involving a cohort of 311 patients from the REMVAL study who reported sleeping four hours or less nightly. This cohort analysis was designed to obtain clinically relevant information from these patients experiencing morbid short sleep disturbances, including sleep patterns and melatonin usage before taking REMfresh, sleep duration before and after taking REMfresh, improvement in sleep onset, sleep maintenance and sleep quality after taking REMfresh, and overall product satisfaction.

Data from this cohort show that 95.8 percent of patients who previously experienced daily, morbid short sleep duration of less than or equal to 4 hours reported an improvement in sleep duration (p<.0001), including more than 46 percent who achieved a sleep duration of greater than or equal to 6 hours (p<.0001). This increase from less than or equal to 4 hours to greater than or equal to 6 hours represents a major sleep duration upgrade in this group facing morbid sleep disturbances. More than 93 percent of patients reported a major/moderate improvement in sleep onset, sleep maintenance and sleep quality (p<.0001). Ninety-nine percent of the patients suffering with morbid short sleep (27.2 percent of whom had never previously tried a melatonin brand) reported that they were very likely or likely to continue using CRA-melatonin. These results provide real-world evidence that CRA-melatonin with its extended 7-hour pharmacokinetic plateau time and benign safety-profile may be a practical baseline therapy to improve sleep duration and other key sleep parameters, including, sleep maintenance and sleep quality in this group of patients who have a higher risk of all-cause mortality.^[vi]˒^[vii]˒^[viii]˒^[ix]

Statistics & Data Corporation (SDC), a top-tier clinical data services provider, has independently determined that the number of participants in the study provides adequate power (>90%) to detect even small improvements in sleep outcomes. This high power, or probability of seeing statistically significant results if CRA-melatonin is truly working to improve sleep outcomes, applies to the overall study population (REMVAL) as well as the cohort of short sleepers (REMSS). SDC has subsequently independently validated the statistical results achieved, (e.g., p-values and statistical language).

The Increasing Appreciation of PRO Studies to Include Patient Experience
Increasingly, there is an appreciation by the U.S. Congress, regulatory authorities and academia, of the substantive value that real-world patient experience brings to assessing new therapies. In addition to the traditional randomized, placebo-controlled trial studies, regulatory authorities are now incorporating the patient perspective in their decision making, including PRO studies. A PRO study is a measurement based on a report that comes directly from the patient about the status or change in their health condition and without amendment or interpretation of the patient’s response by health-care intermediaries. PRO measures can be used to capture a patient’s everyday experience outside of the clinician’s office, and the effects of a treatment on the patient’s activities of daily living.^[x]˒^[xi]Together, clinical measures and PRO measures can provide a fuller picture of patient benefit.

REMAKT Clinical Study Presented at Past Medical Meetings
Pharmacokinetic data on REMfresh^® was peer-reviewed and then presented in 2017 and 2018 at SLEEP, the Annual Meeting of the Associated Professional Sleep Societies LLC (APSS), and a joint meeting of the American Academy of Sleep Medicine (AASM) and the Sleep Research Society (SRS).

The study, REM Absorption Kinetics Trial (REMAKT), was a U.S.-based randomized, crossover pharmacokinetic (PK) evaluation study in healthy, non-smoking adults that compared REMfresh (CRA-melatonin) with a market-leading, immediate-release melatonin (IR-melatonin). The study found that melatonin levels with REMfresh exceeded the targeted sleep maintenance threshold for a median of 6.7 hours, compared with 3.7 hours with the leading IR-melatonin. Conversely, the levels of the market-leading IR-melatonin formulation dramatically increased 23 times greater than the targeted levels of exogenous melatonin for sleep maintenance and then had a rapid decline in serum levels that did not allow melatonin levels to be maintained beyond 4 hours.

Analysis presented at SLEEP 2017 and 2018 showed that REMfresh builds upon the body of evidence from prolonged-release melatonin (PR-M), approved by the European Medicines Agency (EMA) in 2007 as a prescription drug for insomnia, which demonstrated in well-conducted, placebo-controlled studies, statistically significant improvement in sleep quality, morning alertness, sleep onset and quality of life in patients aged 55 years and older compared with placebo.^[xv] REMfresh was designed to overcome the challenges of continuous release and absorption in the intestines, thereby extending the continual and gradual release pattern of melatonin through the night (known as the Mesa Wave®, a flat-topped hill with steep sides).^[xvi] There was the desirable fast time to reach the sleep threshold level, which is anticipated to result in improved sleep onset, while the extended median plateau time to 6.7 hours and rapid fall-off in plasma levels at the end of the Mesa Wave may help to improve sleep maintenance and morning alertness.

Over 5,000 healthcare practitioners are estimated to have used REMfresh for their patients and about 320,000 patients are estimated to have purchased and used REMfresh. The continuing, rapid acceptance of REMfresh by patients is observable by several markers, including rapid sales growth and availability among major drug retailers.

###

Data Presented at SLEEP 2019 Poster Sessions:

Monday, June 10, 2019, 5:15-7:15pm

(Abstract 0398, Poster Board #135) Improvement in Sleep Duration and Maintenance with Ion Powered Continuous Release and Absorption Melatonin in a Cohort of Patients with Chronic Short Sleep Duration: Results from a Patient-Reported Outcomes Study
- David J. Seiden, M.D., FAASM, David Brodner, M.D., Syed M. Shah, Ph.D.

(Abstract 0399, Poster Board #136) Observed Hypnotic Effects with a Continuous-Release Ion Powered Pump Melatonin Delivery System: Self-Reported Patient Outcomes Study Results Demonstrating Improvement in Sleep Duration and Quality
- David J. Brodner, M.D., David J. Seiden, M.D. FAASM, Syed M. Shah, Ph.D.

The abstracts are published in an online supplement of the journal, Sleep, which is available at https://sleepmeeting.org/wp-content/uploads/2019/04/SLEEP_42_S1-Website-Final.pdf.

REFERENCES:

^[i] Colten, H.R., & Altevogt, B.M. (Eds). (2006). Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Institute of Medicine (US) Committee on Sleep Medicine and Research. Washington, DC: National Academies Press (US). doi: https://doi.org/10.17226/11617.

^[ii] Cappuccio, F.P., D’Elia, L., Strazzullo, P., & Miller, M.A. (2010). Sleep duration and all-cause mortality: A systemic review and meta-analysis of prospective studies. Sleep, 33(5):585-592.

^[iii] Watson, N.F., Badr, M.S., Belenky, G., et al. (2015). Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society on the Recommended Amount of Sleep for the Healthy Adult, Methodology and Discussion. Journal of Clinical Sleep Medicine, 11(6); 591-592.

^[iv] Seiden,D.J., Brodner, D.C., & Shah, S.M. (2018, June 2-6). Improvement in Sleep Maintenance and Sleep Quality with Ion-Powered Pump Continuous Release and Absorption Melatonin: Results from a Self-Reported Patient Outcomes Study (Abstract #0419). Poster presented at SLEEP 2018, Baltimore, Maryland.

^[v] Brodner, D.C., Shah, S.M. (2017, June 3-7). REM Absorption Kinetics Trial: A Randomized, Crossover, Pharmacokinetics Evaluation of a Novel Continuous Release and Absorption Melatonin Formulation versus a Same Strength Immediate-Release Formulation in Healthy Adults (Abstract #0396). Poster presented at: SLEEP 2017, Boston, Massachusetts.

^[vi] Knutsen, K.L., Turek,, F.W., Patel, S.R., et al (2006). The u-shaped association between sleep and health: the 2 peaks do not mean the same thing. Comment on Patel, SR, et al. Sleep, 29(7): 878-879.

^[vii] Lubetkin,, E.I., & Haomiao, J. (2018). Burden of disease due to sleep duration and sleep problems in the elderly. Sleep Health, 4; 182-187.

^[viii] Hafner M, et al. (2017). Why sleep matters-the economic costs of insufficient sleep: A cross-country comparative analysis, Rand Quarterly.

^[ix] Ikehara, S, et al. (2009). Association of Sleep Duration with Mortality from Cardiovascular Disease and other Causes for Japanese Men and Women: the JACC Study. Sleep, 32(3); 295-301.

^[x] U.S. Food and Drug Administration. Real World Evidence. Retrieved from https://www.fda.gov/scienceresearch/specialtopics/realworldevidence/default.htm

^[xi] U.S. Food and Drug Administration. 21^st Century Cures Act. Retrieved from https://www.fda.gov/regulatoryinformation/lawsenforcedbyfda/significantamendmentstothefdcact/21stcenturycuresact/default.htm.

^[xii] Colten, H.R., & Altevogt, B.M. (Eds). (2006). Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Institute of Medicine (US) Committee on Sleep Medicine and Research. Washington, DC: National Academies Press (US). doi: https://doi.org/10.17226/11617.

^[xiii] Cappuccio, F.P., D’Elia, L., Strazzullo, P., & Miller, M.A. (2010). Sleep duration and all-cause mortality: A systemic review and meta-analysis of prospective studies. Sleep, 33(5):585-592.

^[xiv] Watson, N.F., Badr, M.S., Belenky, G., et al. (2015). Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society on the Recommended Amount of Sleep for the Healthy Adult, Methodology and Discussion. Journal of Clinical Sleep Medicine, 11(6); 591-592.

^[xv] European Medicines Agency.(2007). Assessment Report for CIRCADIN.

^[xvi] Brodner, D.C. & Shah, S.M. (2017, June 3-7). A Continuous Release Ion Powered Pump Melatonin Delivery System that Overcomes Challenges of Release and Absorption in the Intestines (Abstract #0385). Poster presented at: SLEEP 2017, Boston, Massachusetts.

SOURCES:

https://finance.yahoo.com/news/significant-real-world-evidence-confirms-123000247.html