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COVID and the brain: researchers zero in on how damage occurs

Posted in Cancer Researchers Fighting COVID-19, COVID-19, COVID-19, Human Antibody Response, Immunology, memory loss, Neurological Diseases, SARS-CoV-2, SARS-CoV-2 Viral Variants, Stroke, Treatment Protocols for COVID-19, tagged brain, brain development, Clinical trial, COVID-19, damage, health, medicine, memory loss, SARS-CoV-2 on July 8, 2021| Leave a Comment »

COVID and the brain: researchers zero in on how damage occurs

Reporter: Danielle Smolyar

Research Assistant 3 – Text Analysis for 2.0 LPBI Group’s TNS #1 – 2020/2021 Academic Internship in Medical Text Analysis (MTA)

Recent evidence has indicated that coronavirus can cause brain fog and also lead to different neurological symptoms. 

Since the beginning of the pandemic, researchers have been trying to understand how the coronavirus SARS-CoV-2 affects the brain

Image Credit: Stanislav Krasilnikov/TASS/Getty

image source:https://www.nature.com/articles/d41586-021-01693-6?utm_source=Nature+Briefing

New evidence has shown how coronavirus has caused much damage to the brain. There is a new evidence that shows that COVID-19 assault on the brain I has the power to be multipronged. What this means is that it can attack on certain Brain cells such as reduce the amount of blood flow that the brain needs to the brain tissue.

Along with brain damage COVID-19 has also caused strokes and memory loss. A neurologist at yell University Serena Spudich says, “Can we intervene early to address these abnormalities so that people don’t have long-term problems?”

We’re on 80% of the people who have been hospitalized due to COVID-19 have showed brain symptoms which seem to be correlated to coronavirus.

At the start of the pandemic a group of researchers speculated that coronavirus they can damage the brain by infecting the neurons in the cells which are important in the process of transmitting information. After further studies they found out that coronavirus has a harder time getting past the brains defense system and the brain barrier and that it does not affect the neurons in anyway.

An expert in this study indicated that a way in which SARS-CoV-2 may be able to get to the brain is by going through the olfactory mucosa which is the lining of the nasal cavity. It is found that this virus can be found in the nasal cavity which is why we swab the nose one getting tested for COVID-19.

Spudich quotes, “there’s not a tonne of virus in the brain”.

Recent studies indicate that SARS-CoV-2 have ability to infect astrocytes which is a type of cell found in the brain. Astrocytes do quite a lot that supports normal brain function,” including providing nutrients to neurons to keep them working, says Arnold Kriegstein, a neurologist at the University of California, San Francisco.

Astrocytes are star-shaped cells in the central nervous system that perform many functions, including providing nutrients to neurons.

Image Credit: David Robertson, ICR/SPL

image source: https://www.nature.com/articles/d41586-021-01693-6?utm_source=Nature+Briefing

Kriegstein and his fellow colleagues have found that SARS-CoV-2 I mostly infects the astrocytes over any of the other brain cells present. In this research they expose brain organoids which is a miniature brain that are grown from stem cells into the virus.

As quoted in the article” a group including Daniel Martins-de-Souza, head of proteomics at the University of Campinas in Brazil, reported⁶ in a February preprint that it had analysed brain samples from 26 people who died with COVID-19. In the five whose brain cells showed evidence of SARS-CoV-2 infection, 66% of the affected cells were astrocytes.”

The infected astrocytes could indicate the reasoning behind some of the neurological symptoms that come with COVID-19. Specifically, depression, brain fog and fatigue. Kreigstein quotes, “Those kinds of symptoms may not be reflective of neuronal damage but could be reflective of dysfunctions of some sort. That could be consistent with astrocyte vulnerability.”

A study that was published on June 21 they compared eight different brands of deceased people who did have COVID-19 along with 14 brains as the control. The results of this research were that they found that there was no trace of coronavirus Brain infected but they found that the gene expression was affected in some of the astrocytes.

As a result of doing all this research and the findings the researchers want to know more about this topic and how many brain cells need to be infected for there to be neurological symptoms says Ricardo Costa.

Further evidence has also been done on how SARS-CoV-2 can affect the brain by reducing its blood flow which impairs the neurons’ function which ends up killing them.

Pericytes can be found on the small blood vessels which are called capillaries and are found all throughout the body and in the brain. In a February pre-print there was a report about how SARS-CoV-2 can infect the pericyte in the brain organoids. 

David Atwell, a neuroscientist at the University College London, along with his other colleagues had published a pre-print which has evidence to show that SARS-CoV-2 odes In fact pericytes behavior. I researchers saw that in the different part of the hamsters brain SARS-CoV-2 blocks the function of receptors on the pericytes which ultimately causes the capillaries found inside the tissues to constrict.

As stated in the article, It’s a “really cool” study, says Spudich. “It could be something that is determining some of the permanent injury we see — some of these small- vessel strokes.”

Attwell brought to the attention that the drugs that are used to treat high blood pressure may in fact be used in some cases of COVID-19. Currently there are two clinical trials that are being done to further investigate this idea.

There is further evidence showing that the neurological symptoms and damage could in fact be happening because of the bodies on immune system reacting or misfiring after having COVID-19.

Over the past 15 years it has become evident that people’s immune system’s make auto antibodies which attack their own tissues says Harald Prüss in the article who has a Neuroimmunologist at the German Center for neurogenerative Diseases in Berlin. This may cause neuromyelitis optica which is when you can experience loss of vision or weakness in limbs. Harald Prüss summarized that the autoantibodies can pass through the blood brain barrier and ultimately impact neurological disorders such as psychosis.

Prüss and his colleagues published a study last year that focused on them isolating antibodies against SARS-CoV-2 from people. They found that one was able to protect hamsters from lung damage and other infections. The purpose of this was to come up with and create new treatments. During this research they found that some of the antibodies from people. They found that one was able to protect hamsters from lung damage and other infections. The purpose of this was to come up with and create new treatments. During this research they found that some of the antibodies can bind to the brain tissue which can ultimately damage it. Prüss states, “We’re currently trying to prove that clinically and experimentally,” says Prüss.

Was published online in December including Prüss sorry the blood and cerebrospinal fluid of 11 people who were extremely sick with COVID-19. These 11 people had neurological symptoms as well. All these people were able to produce auto antibodies which combined to neurons. There is evidence that when the patients were given intravenous immunoglobin which is a type of antibody it was successful.

Astrocytes, pericytes and autoantibodies we’re not the only  pathways. However it is likely that people with COVID-19 experience article symptoms for many reasons. As stated, In the article, Prüss says a key question is what proportion of cases is caused by each of the pathways. “That will determine treatment,” he says.

SOURCE: https://www.nature.com/articles/d41586-021-01693-6?utm_source=Nature+Briefing

Original article: 

Marshall, M. (2021, July 7). COVID and the brain: researchers zero in on how damage occurs. Nature News. https://www.nature.com/articles/d41586-021-01693-6

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Covid-19 and its implications on pregnancy

Reporter and Curator: Mr. Srinjoy Chakraborty (Junior Research Felllow) and Dr. Sudipta Saha, Ph.D.

Covid-19 and its implications on pregnancy

Nir Hacohen and Marcia Goldberg, Researchers at MGH and the Broad Institute identify protein “signature” of severe COVID-19

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Identification of Novel genes in human that fight COVID-19 infection

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Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines

Reporter and Curator: 2012pharmaceutical

Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines

Early Details of Brain Damage in COVID-19 Patients

Early Details of Brain Damage in COVID-19 Patients

Reporter and Curator: Irina Robu, PhD

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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

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Gene Therapy could be a Boon to Alzheimer’s disease (AD): A first-in-human clinical trial proposed

Posted in Alzheimer's Disease, Drug Carrier Design, Gene Therapy & Gene Editing Development, Neurodegenerative Diseases, Neurological Diseases, tagged AAV2-BDNF pathway, Alzheimer’s disease, BDNF, blood brain barrier, gene therapy on March 22, 2021| Leave a Comment »

Gene Therapy could be a Boon to Alzheimer’s disease (AD): A first-in-human clinical trial proposed

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

A recent research work performed by the Researchers at the University of California San Diego School of Medicine has shared their first-in-human Phase I clinical trial to assess the safety and viability of gene therapy to deliver a key protein into the brains of persons with Alzheimer’s Disease (AD) or Mild Cognitive Impairment (MCI), a condition that often precedes full-blown dementia.  

Mark Tuszynski, M.D., Ph.D., Professor of Neuroscience and Director of the Translational Neuroscience Institute at UC San Diego and team predicted that Gene therapy could be a boon to potential treatments for the disorders like AD and MCI.

The study provides an insight into the genetic source of these mental diseases.

The roots of mental disorders have remained an enigma for so many years. Alzheimer’s disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. AD is a neurodegenerative condition. A buildup of plaques and tangles in the brain, along with cell death, causes memory loss and cognitive decline. In most people with the disease, those with the late-onset type – symptoms first appear in their mid-60s. Alzheimer’s disease is the mostly appearing type of dementia in patients.

What the study impart?

Despite decades of effort and billions of dollars of research investment, there are just mere two symptomatic treatments for AD. There is no cure or approved way to slow or stop the progression of the neurological disorder that afflicts more than 5 million Americans and is the sixth leading cause of death in the United States.

Prof. Tuszynski said gene therapy has been tested on multiple diseases and conditions, represents a different approach to a disease that requires new ways of thinking about the disease and new attempts at treatments.

The research team found that delivering the BDNF to the part of the brain that is affected earliest in Alzheimer’s disease; the entorhinal cortex and hippocampus – was able to protect from ongoing cell degeneration by reversing the loss of connections. “These trials were observed in aged rats, amyloid mice, and aged monkeys.”

The protein, called Brain-Derived Neurotrophic Factor or BDNF, a family of growth factors found in the Brain and Central Nervous System that support the survival of existing neurons and promote growth and differentiation of new neurons and synapses. BDNF is especially important in brain regions susceptible to degeneration in AD. It is normally produced throughout life in the entorhinal cortex, an important memory center in the brain and one of the first places where the effects of AD typically appear in the form of short-term memory loss. Persons with AD have diminished levels of BDNF.

However, BDNF is a large molecule and cannot pass through the Blood-Brain Barrier. As a solution, researchers will use gene therapy in which a harmless Adeno-Associated Virus (AAV2) is modified to carry the BDNF gene and injected directly into targeted regions of the brain, where researchers hope it will prompt the production of therapeutic BDNF in nearby cells.

Precautions were taken precisely in injecting the patient to avoid exposure to surrounding degenerating neurons since freely circulating BDNF can cause adverse effects, such as seizures or epileptic conditions.

The recent research and study speculate a safe and feasible assessment of the AAV2-BDNF pathway in humans. A previous gene therapy trial from 2001 to 2012 using AAV2 and a different protein called Nerve Growth Factor (NGF) was carried out by Prof. Tuszynski and team where they observed immense growth, axonal sprouting, and activation of functional markers in the brains of participants.

He also shared that “The BDNF gene therapy trial in AD represents an advancement over the earlier NGF trial, BDNF is a more potent growth factor than NGF for neural circuits that degenerate in AD. Besides, new methods for delivering BDNF will more effectively deliver and distribute it into the entorhinal cortex and hippocampus.”

The research team hopes that the three-year-long trial will recruit 12 participants with either diagnosed AD or MCI to receive AAV2-BDNF treatment, with another 12 persons serving as comparative controls over that period.

The researchers have plans to build on recent successes of gene therapy in other diseases, including a breakthrough success in the treatment of congenital weakness in infants (spinal muscular atrophy) and blindness (Leber Hereditary Optic Neuropathy, a form of retinitis pigmentosa).”

Main Source

https://www.universityofcalifornia.edu/news/could-gene-therapy-halt-progression-alzheimers-disease-first-human-clinical-trial-will-seek?utm_source=fiat-lux

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Dysregulation of ncRNAs in association with Neurodegenerative Disorders

Posted in Alzheimer's Disease, Biomarkers & Medical Diagnostics, Cerebrovascular and Neurodegenerative Diseases, Etiology, Molecular Genetics & Pharmaceutical, mRNA, mRNA Therapeutics, Neurodegenerative Diseases, Neurological Diseases, RNA Biology, RNA interference (RNAi) therapeutic, tagged Alzheimers Disease, neurodegenerative disorders, Neurological Disorders, Non-coding RNA, Parkinsons disease, RNA Biology, RNA interference (RNAi) therapeutic on January 11, 2021| Leave a Comment »

Dysregulation of ncRNAs in association with Neurodegenerative Disorders

Curator: Amandeep Kaur

Research over the years has added evidences to the hypothesis of “RNA world” which explains the evolution of DNA and protein from a simple RNA molecule. Our understanding of RNA biology has dramatically changed over the last 50 years and rendered the scientists with the conclusion that apart from coding for protein synthesis, RNA also plays an important role in regulation of gene expression.

The universe of non-coding RNAs (ncRNAs) is transcending the margins of preconception and altered the traditional thought that the coding RNAs or messenger RNAs (mRNAs) are more prevalent in our cells. Research on the potential use of ncRNAs in therapeutic relevance increased greatly after the discovery of RNA interference (RNAi) and provided important insights into our further understanding of etiology of complex disorders.

Latest research on neurodegenerative disorders has shown the perturbed expression of ncRNAs which provides the functional association between neurodegeneration and ncRNAs dysfunction. Due to the diversity of functions and abundance of ncRNAs, they are classified into Housekeeping RNAs and Regulatory ncRNAs.

The best known classes of ncRNAs are the microRNAs (miRNAs) which are extensively studied and are of research focus. miRNAs are present in both intronic and exonic regions of matured RNA (mRNA) and are crucial for development of CNS. The reduction of Dicer-1, a miRNA biogenesis-related protein affects neural development and the elimination of Dicer in specifically dopaminergic neurons causes progressive degeneration of these neuronal cells in striatum of mice.

A new class of regulatory ncRNAs, tRNAs-derived fragments (tRFs) is superabundantly present in brain cells. tRFs are considered as risk factors in conditions of neural degeneration because of accumulation with aging. tRFs have heterogenous functions with regulation of gene expression at multiple layers including regulation of mRNA processing and translation, inducing the activity of silencing of target genes, controlling cell growth and differentiation processes.

The existence of long non-coding RNAs (lncRNAs) was comfirmed by the ENCODE project. Numerous studies reported that approximately 40% of lncRNAs are involved in gene expression, imprinting and pluripotency regulation in the CNS. lncRNA H19 is of paramount significance in neural viability and contribute in epilepsy condition by activating glial cells. Other lncRNAs are highly bountiful in neurons including Evf2 and MALAT1 which play important function in regulating neural differentiation and synapse formation and development of dendritic cells respectively.

Recently, a review article in Nature mentioned about the complex mechanisms of ncRNAs contributing to neurodegenerative conditions. The ncRNA-mediated mechanisms of regulation are as follows:

• Epigenetic regulation: Various lncRNAs such as BDNF-AS, TUG1, MEG3, NEAT1 and TUNA are differentially expressed in brain tissue and act as epigenetic regulators.

• RNAi: RNA interference includes post-transcriptional repression by small-interfering RNAs (siRNAs) and binding of miRNAs to target genes. In a wide spectrum of neurodegenerative diseases such as Alzheimer’s disease, Parkinson disease, Huntington’s disease, Amyotrophic lateral sclerosis, Fragile X syndrome, Frontotemporal dementia, and Spinocerebellar ataxia, have shown perturbed expression of miRNA.

• Alternative splicing: Variation in splicing of transcripts of ncRNAs has shown adverse affects in neuropathology of degenerative diseases.

• mRNA stability: The stability of mRNA may be affected by RNA-RNA duplex formation which leads to the degradation of sense mRNA or blocking the access to proteins involved in RNA turnover and modify the progression of neurodegenerative disorders.

• Translational regulation: Numerous ncRNAs including BC200 directly control the translational process of transcripts of mRNAs and effect human brain of Alzheimer’s disease.

• Molecular decoys: Non-coding RNAs (ncRNAs) dilute the expression of other RNAs by molecular trapping, also known as competing endogenous RNAs (ceRNAs) which hinder the normal functioning of RNAs. The ceRNAs proportion must be equivalent to the number of target miRNAs that can be sequestered by each ncRNAs in order to induce consequential de-repression of the target molecules.

The unknown functions of numerous annotated ncRNAs may explain the underlying complexity in neurodegenerative disorders. The profiling of ncRNAs of patients suffering from neurodevelopmental and neurodegenerative conditions are required to outline the changes in ncRNAs and their role in specific regions of brain and cells. Analysis of Large-scale gene expression and functional studies of ncRNAs may contribute to our understanding of these diseases and their remarkable connections. Therefore, targeting ncRNAs may provide effective therapeutic perspective for the treatment of neurodegenerative diseases.

References https://www.nature.com/scitable/topicpage/rna-functions-352/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6035743/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7695195/ https://link.springer.com/article/10.1007/s13670-012-0023-4 https://www.nature.com/articles/nrn.2017.90

 

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https://pharmaceuticalintelligence.com/2016/05/28/recent-progress-in-neurodegenerative-diseases-and-gliomas/

Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

Reporter and writer: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2013/02/19/genomic-promise-for-neurodegenerative-diseases-dementias-autism-spectrum-schizophrenia-and-serious-depression/

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