Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
“Basically we’re considering blood as another vital organ,” he said. “Why that is important is because now we look at a patient’s blood system as an organ that needs to be assessed and treated for the sake of that organ and not simply to decide when or when not to transfuse.”
Recommendations span the entire spectrum from preoperative assessment of bleeding risk and anemia to intraoperative perfusion and blood salvage practices to postoperative treatment with human albumin for volume replacement.
“Most hospitals around the U.S. are acutely aware of patient blood management and, to some degree or another, are implementing many of the things we are talking about,” noted Tibi, who is immediate past president of SABM. Nationwide, the amount of blood transfused in cardiac surgery has dropped 45% in the past 10 to 15 years but still ranges widely from center to center.
Reporter: Danielle Smolyar, Research Assistant 3 – Text Analysis for 2.0 LPBI Group’s TNS #1 – 2020/2021 Academic Internship in Medical Test Analysis (MTA)
Recently, researchers have found many ways to manipulate and alter gene activity in specific cells. As a result of seeing this alteration, it has caused much development and progress in understanding cancer, brain function, and immunity.
IMAGE SOURCE: 3D-model of DNA. Credit: Michael Ströck/Wikimedia/ GNU Free Documentation Lic
Tissues and Organs are composed of cells that look the same but have different roles. For example, single-cell analysis allows us to research and test the cells within an organ or cancerous tumor. However, the single-cell study has its boundaries and limits in trying a more significant number of cells. This result is not an accurate data and analysis of the cells.
Mulqueen, R. M., Pokholok, D., O’Connell, B. L., Thornton, C. A., Zhang, F., O’Roak, B. J., Link, J., Yardımcı, G. G., Sears, R. C., Steemers, F. J., & Adey, A. C. (2021, July 5). High-content single-cell combinatorial indexing. Nature News. https://www.nature.com/articles/s41587-021-00962-z
states that the new method gives us the ability to have a ten-fold improvement in the amount of DNA produced from a single DNA sequence. A DNA sequence is composed of units which are called bases. The sequence puts the bases in chronological order for it to code correctly.
To understand cancer better, single-cell studies are a crucial factor in doing so. Different cells catch on to other mutations in the DNA sequence in a cancerous tumor, which ultimately alters the DNA sequence. This results in tumor cells with new alterations, which could eventually spread to the rest of the body.
Adey and his team provided evidence that the method they had created can show DNA alterations that have come from cells present in tumor samples from patients with pancreatic cancer. Adey stated,
quote “For example, you can potentially identify rare cell subtypes within a tumor that are resistant to therapy.”
Abey and his team have been working with OHSU Knight Cancer Institute, and with them, they are testing a single-cell method to see if patients’ tumors have changed by doing chemo or drug therapy.
This new method allows itself to create DNA libraries and fragments of DNA that helps analyze the different genes and mutations within the sequence. This method uses something called an enzymatic reaction that attaches primers to the end of each DNA fragment. For the cells to be analyzed, each primer must be present on both ends of the fragment.
As a result of this new method, all library fragments present must-have primers on both ends of the fragments. At the same time, it improves efficiency by reducing its sequencing price overall, that these adapters can be used instead of the regular custom workflows.
4.1.2 The race to map the human body — one cell at a time, A host of detailed cell atlases could revolutionize understanding of cancer and other diseases
4.1.3 Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute
4.1.7 Norwich Single-Cell Symposium 2019, Earlham Institute, single-cell genomics technologies and their application in microbial, plant, animal and human health and disease, October 16-17, 2019, 10AM-5PM
4.2.1 How to build a human cell atlas – Aviv Regev is a maven of hard-core biological analyses. Now she is part of an effort to map every cell in the human body.
4.2.2 Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab
4.3.2 eProceedings 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA
4.4.1 iBioChips integrate diagnostic assays and cellular engineering into miniaturized chips that achieve cutting-edge sensitivity and high-throughput. We have resolved traditional biotech challenges with innovative biochip approaches
4.4.2 Targeted Single-Cell Solutions for High Impact Applications – Mission Bio’s Tapestri® Platform is the only technology that provides single-cell targeted DNA sequencing at single-base resolution.
Here we are at the “end” of the road. End here between quotation, because just like any other technology-related field, there’s no end. The field is developing rapidly because new algorithms and techniques are under research as I type this article.
So, being a data scientist means you will be in a continuous learning stage. You will be developing your knowledge and your style as you go. You will probably feel more attracted to a specific sub-field than another and dig even deeper and maybe specialize in that sub-field.
The most important thing to know as you embark on this journey is, you can do it. You need to be open-minded and dedicate enough time and effort to achieve your end goals.
Ramatroban, a Thromboxane A2/TPr and PGD2/DPr2 receptor antagonist for Acute and Long haul COVID-19
Author: Ajay Gupta, MD
From: “Gupta, Ajay” <ajayg1@hs.uci.edu> Date: Wednesday, July 7, 2021 at 1:10 PM To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu> Cc: “Dr. Saul Yedgar” <saulye@ekmd.huji.ac.il> Subject: Ramatroban, a Thromboxane A2/TPr and PGD2/DPr2 receptor antagonist for Acute and Long haul COVID-19
While corticosteroids may have a role in about 5% of hospitalized patients who have the cytokine storm, currently there is no effective treatment for mild or moderate COVID and long haul COVID. Massive increase in respiratory and plasma thromboxane A2 (TxA2) plays a key role in thromboinflammation and microvascular thrombosis, while an increase in respiratory and plasma PGD2 potentially suppresses innate interferon response, and acquired Th1 anti-viral response, while promoting a maladaptive type 2, anti-helminthic like immune response. Ramatroban is a potent dual receptor antagonist of Thromboxane A2/TPr and PGD2/DPr2 that has been used in Japan for the treatment of allergic rhinitis for past 20 years (Baynas®, Bayer Japan). We first disclosed use of ramatroban for COVID in a provisional patent application filed on 31st March, 2020; followed by the publication Gupta et al, J Mol Genet Med, 2020
Several experts, as outlined below in yellow highlighted text, have supported the idea of using ramatroban as an anti-thrombotic and immunomodulator in COVID-19.
1. Prof. Louis Flamand, Nicolas Flamand, Eric Boilard Laval Univ. Quebec, Canada: There is a lipid-mediator storm in COVID-19 characterized by massive increases in thromboxane A2 and PGD2 in the lungs and plasma. “Blocking the deleterious effects of PGD2 and TxA2 with the dual DPr2/TPr antagonist Ramatroban might be beneficial in COVID-19Archambault et al, FASEB, June 2021, doi: https://doi.org/10.1096/fj.202100540R
2. Prof. Garret A FitzGerald, Univ. Of Pennsylvania, Member National Academy of Sciences.https://en.wikipedia.org/wiki/Garret_A._FitzGerald “In the current pandemic there may be utility in targeting eicosanoids with existing drugs. These approaches would likely be most effective early in the disease before the development of ARDS, where cytokines and chemokines dominate. Dexamethasone limits COX-2 expression and might diminish COVID-19 severity and mortality at least in part, by diminishing COX metabolites… Dexamethasone might improve severe COVID-19 by diminishing the prostaglandins / thromboxane storm in the lungs”. “Treatment with a PGD2/DPr2 inhibitor decreased viral load and improved morbidity by upregulating IFN-lambda expression. ….. Antagonism of the thromboxane receptor (TPr) prevents ARDS…. Early administration of well-tolerated TPr antagonists may limit progress to severe COVID-19 (Theken and FitzGerald, Science, 2021)
4. Prof. Simon Phipps, Univ. of Queensland, Brisbane Australia “It has been hypothesized that DP2 antagonists be repurposed as a novel immunotherapy for the treatment of COVID-19, and this may be appropriate in mild to moderate cases where Th1 immunity is impaired.” (Ullah et al, Mucosal Immunology, 2021)
5. Prof. Bruce D. Hammock, Distinguished Professor, Univ of California Davis, Member US National Academy of Sciences and National Academy of Inventors; April 25, 2021. https://www.entsoc.org/fellows/hammock “I find your idea of blocking specific thromboxane receptors in preventing or reducing some of the devastating co-morbidity of COVID-19 very compelling. … A DPr2 receptor blocker is conceptually attractive in offering the potential of effective therapy and low risk due to a high therapeutic index.” E mail dated April 25, 2021.(https://ajp.amjpathol.org/action/showPdf?pii=S0002-9440%2820%2930332-1 and http://ucanr.edu/sites/hammocklab/files/328012.pdf)
6. Ann E Eakin, PhD, Senior Scientific Officer, NIH-NIAID “very compelling data supporting potential benefits of ramatroban in both reducing viral load as well as modulating host responses” E Mail dated Nov 20, 2020
Ramatroban is expected to reduce lung fibrosis in COVID-19 and therefore diminish clinical manifestations of Long haul COVID. Pang et al, 2021 “examined the effect of Ramatroban, a clinical antagonist of both PGD2 and TXA2 receptors, on treating silicosis using a mouse model. The results showed that Ramatroban significantly alleviated silica-induced pulmonary inflammation, fibrosis, and cardiopulmonary dysfunction compared with the control group.” https://www.thno.org/v11p2381.htm
Unfortunately, the animal models of COVID-19 are harsh, lack microvascular thrombosis and immune perturbations characteristic of human disease. These models may be good for testing antivirals but not for testing immunomodulators or anti-thrombotics. There is highly positive anecdotal experience with use of ramatroban in moderately severe COVID-19 (https://www.researchsquare.com/article/rs-474882/v1
Twitter: @GilPress I write about technology, entrepreneurs and innovation.
Intuition Robotics announced today that it is expanding its mission of improving the lives of older adults to include enhancing their interactions with their physicians. The Israeli startup has developed the AI-based, award-winning proactive social robot ElliQ which has spent over 30,000 days in older adults’ homes over the past two years. Now ElliQ will help increase patient engagement while offering primary care providers continuous actionable data and insights for early detection and intervention.
The very big challenge Intuition Robotics set up to solve was to “understand how to create a relationship between a human and a machine,” says co-founder and CEO Dor Skuler. Unlike a number of unsuccessful high-profile social robots (e.g., Pepper) that tried to perform multiple functions in multiple settings, ElliQ has focused exclusively on older adults living alone. Understanding empathy and how to grow a trusting relationship were the key objectives of Intuition Robotics’ research project, as well as how to continuously learn the specific (and changing) behavioral characteristics, habits, and preferences of the older adults participating in the experiment.
The results are impressive: 90% of users engage with ElliQ every day, without deterioration in engagement over time. When ElliQ proactively initiates deep conversational interactions with its users, there’s 70% response rate. Most important, the participants share something personal with ElliQ almost every day. “She has picked up my attitude… she’s figured me out,” says Deanna Dezern, an ElliQ user who describes her robot companion as “my sister from another mother.”
The very big challenge Intuition Robotics set up to solve was to “understand how to create a relationship between a human and a machine,” says co-founder and CEO Dor Skuler. Unlike a number of unsuccessful high-profile social robots (e.g., Pepper) that tried to perform multiple functions in multiple settings, ElliQ has focused exclusively on older adults living alone. Understanding empathy and how to grow a trusting relationship were the key objectives of Intuition Robotics’ research project, as well as how to continuously learn the specific (and changing) behavioral characteristics, habits, and preferences of the older adults participating in the experiment.
The results are impressive: 90% of users engage with ElliQ every day, without deterioration in engagement over time. When ElliQ proactively initiates deep conversational interactions with its users, there’s 70% response rate. Most important, the participants share something personal with ElliQ almost every day. “She has picked up my attitude… she’s figured me out,” says Deanna Dezern, an ElliQ user who describes her robot companion as “my sister from another mother.”
Higher patient engagement leads to lower costs of delivering care and the quality of the physician-patient relationship is positively associated with improved functional health, studies have found. Typically, however, primary care physicians see their patients anywhere from once a month to once a year, even though about 85% of seniors in the U.S. have at least one chronic health condition. ElliQ, with the consent of its users, can provide data on the status of patients in between office visits and facilitate timely and consistent communications between physicians and their patients.
Supporting the notion of a home-based physician assistant robot is the transformation of healthcare delivery in the U.S. More and more primary care physicians are moving from a fee-for-service business model, where doctors are paid according to the procedures used to treat a patient, to “capitation,” where doctors are paid a set amount for each patient they see. This shift in how doctors are compensated is gaining momentum as a key solution for reducing the skyrocketing costs of healthcare: “…inadequate, unnecessary, uncoordinated, and inefficient care and suboptimal business processes eat up at least 35%—and maybe over 50%—of the more than $3 trillion that the country spends annually on health care. That suggests more than $1 trillion is being squandered,” states “The Case for Capitation,” a Harvard Business Review article.
Under this new business model, physicians have a strong incentive to reduce or eliminate visits to the ER and hospitalization, so ElliQ’s assistance in early intervention and support of proactive and preventative healthcare is highly valuable. ElliQ’s “new capabilities provide physicians with visibility into the patient’s condition at home while allowing seamless communication… can assist me and my team in early detection and mitigation of health issues, and it increases patients’ involvement in their care through more frequent engagement and communication,” says in a statement Dr. Peter Barker of Family Doctors, a Mass General Brigham-affiliated practice in Swampscott, MA, that is working with Intuition Robotics.
With the new stage in its evolution, ElliQ becomes “a conversational agent for self-reported data on how people are doing based on what the doctor is telling us to look for and, at the same time, a super-simple communication channel between the physician and the patient,” says Skuler. As only 20% of the individual’s health has to do with the administration of healthcare, Skuler says the balance is already taken care of by ElliQ—encouraging exercise, watching nutrition, keeping mentally active, connecting to the outside world, and promoting a sense of purpose.
A recent article in The Communication of the ACM pointed out that “usability concerns have for too long overshadowed questions about the usefulness and acceptability of digital technologies for older adults.” Specifically, the authors challenge the long-held assumption that accessibility and aging research “fall under the same umbrella despite the fact that aging is neither an illness nor a disability.”
For Skuler, a “pyramid of value” is represented in Intuition Robotics offering. At the foundation is the physical product, easy to use and operate and doing what it is expected to do. Then there is the layer of “building relationships based on trust and empathy,” with a lot of humor and social interaction and activities for the users. On top are specific areas of value to older adults, and the first one is healthcare. There will be more in the future, anything that could help older adults live better lives, such as direct connections to the local community. ”Healthcare is an interesting experiment and I’m very much looking forward to see what else the future holds for ElliQ,” says Skuler.
Yet another Success Story: Machine Learning to predict immunotherapy response
Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc
Immune-checkpoint blockers(ICBs) immunotherapy appears promising for various cancer types, offering a durable therapeutic advantage. Only a number of cases with cancer respond to this therapy. Biomarkers are required to adequately predict the responses of patients. This article evaluates this issue utilizing a system method to characterize the immune response of the anti-tumor based on the entire tumor environment. Researchers build mechanical biomarkers and cancer-specific response models using interpretable machine learning that predict the response of patients to ICB.
The lymphatic and immunological systems help the body defend itself by combating. The immune system functions as the body’s own personal police force, hunting down and eliminating pathogenic baddies.
According to Federica Eduati, Department of Biomedical Engineering at TU/e, “The immune system of the body is quite adept at detecting abnormally behaving cells. Cells that potentially grow into tumors or cancer in the future are included in this category. Once identified, the immune system attacks and destroys the cells.”
Immunotherapy and machine learning are combining to assist the immune system solve one of its most vexing problems: detecting hidden tumorous cells in the human body.
It is the fundamental responsibility of our immune system to identify and remove alien invaders like bacteria or viruses, but also to identify risks within the body, such as cancer. However, cancer cells have sophisticated ways of escaping death by shutting off immune cells. Immunotherapy can reverse the process, but not for all patients and types of cancer. To unravel the mystery, Eindhoven University of Technology researchers used machine learning. They developed a model to predict whether immunotherapy will be effective for a patient using a simple trick. Even better, the model outperforms conventional clinical approaches.
“Tumor also contains multiple types of immune and fibroblast cells which can play a role in favor of or anti-tumor, and communicates among themselves,” said Oscar Lapuente-Santana, a researcher doctoral student in the computational biology group. “We had to learn how complicated regulatory mechanisms in the micro-environment of the tumor affect the ICB response. We have used RNA sequencing datasets to depict numerous components of the Tumor Microenvironment (TME) in a high-level illustration.”
Using computational algorithms and datasets from previous clinical patient care, the researchers investigated the TME.
Eduati explained
While RNA-sequencing databases are publically available, information on which patients responded to ICB therapy is only available for a limited group of patients and cancer types. So, to tackle the data problem, we used a trick.
All 100 models learned in the randomized cross-validation were included in the EaSIeR tool. For each validation dataset, we used the corresponding cancer-type-specific model: SKCM for the melanoma Gide, Auslander, Riaz, and Liu cohorts; STAD for the gastric cancer Kim cohort; BLCA for the bladder cancer Mariathasan cohort; and GBM for the glioblastoma Cloughesy cohort. To make predictions for each job, the average of the 100 cancer-type-specific models was employed. The predictions of each dataset’s cancer-type-specific models were also compared to models generated for the remaining 17 cancer types.
From the same datasets, the researchers selected several surrogate immunological responses to be used as a measure of ICB effectiveness.
Lapuente-Santana stated
One of the most difficult aspects of our job was properly training the machine learning models. We were able to fix this by looking at alternative immune responses during the training process.
DREAM is an organization that carries out crowd-based tasks with biomedical algorithms. “We were the first to compete in one of the sub-challenges under the name cSysImmunoOnco team,” Eduati remarks.
The researchers noted,
We applied machine learning to seek for connections between the obtained system-based attributes and the immune response, estimated using 14 predictors (proxies) derived from previous publications. We treated these proxies as individual tasks to be predicted by our machine learning models, and we employed multi-task learning algorithms to jointly learn all tasks.
The researchers discovered that their machine learning model surpasses biomarkers that are already utilized in clinical settings to evaluate ICB therapies.
But why are Eduati, Lapuente-Santana, and their colleagues using mathematical models to tackle a medical treatment problem? Is this going to take the place of the doctor?
Eduati explains
Mathematical models can provide an overview of the interconnection between individual molecules and cells and at the same time predicting a particular patient’s tumor behavior. This implies that immunotherapy with ICB can be personalized in a patient’s clinical setting. The models can aid physicians with their decisions about optimum therapy, it is vital to note that they will not replace them.
Furthermore, the model aids in determining which biological mechanisms are relevant for the biological response.
The researchers noted
Another advantage of our concept is that it does not need a dataset with known patient responses to immunotherapy for model training.
Further testing is required before these findings may be implemented in clinical settings.
Main Source:
Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns, 100293. https://www.cell.com/patterns/pdfExtended/S2666-3899(21)00126-4
Other Related Articles published in this Open Access Online Scientific Journal include the following:
Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis
Deep Learning for In-silico Drug Discovery and Drug Repurposing: Artificial Intelligence to search for molecules boosting response rates in Cancer Immunotherapy: Insilico Medicine @John Hopkins University
Some people who have been treated for breast cancer or lymphoma have a higher risk of developing congestive heart failure than people who haven’t had cancer, results from a new study show.
The study researchers retrospectively compared heart failure rates in people who were diagnosed with breast cancer or lymphoma with those in people who did not have cancer. Although the risk of developing heart failure was relatively low overall, people who had been treated for cancer had more than twice the risk of developing heart failure than those who had never had cancer, they found, and the risk was evident as early as one year after their cancer diagnosis. The increased risk persisted for at least 20 years.
“As more cancer patients live longer, they are living long enough to manifest the long-term cardiac effects of cancer treatment,” said Lori Minasian, M.D., of NCI’s Division of Cancer Prevention, who was not involved in the study. “Increasingly, cardiologists and cardiovascular investigators have seen the need to evaluate the short- and long-term cardiac effects of cancer treatment.”
The bottom line, said study investigator Carolyn Larsen, M.D., of the Mayo Clinic, is that people who have been treated for breast cancer or lymphoma and their physicians should be aware of these risks, and patients should be assessed annually for signs of heart failure.
Congestive heart failure (also referred to as heart failure) is a condition in which weakened or damaged heart muscles are unable to effectively pump blood to the rest of the body. Heart disease, diabetes, and high blood pressure are all risk factors for heart failure, as are some cancer treatments such as chemotherapy, chest radiation, immunotherapy, and some targeted therapies.
To assess the long-term risk of heart failure in people with cancer, Mayo Clinic researchers analyzed data from the Rochester Epidemiology Project. They focused on participants who were diagnosed with breast cancer or lymphoma from 1985 to 2010 and compared them with matched controls—people without cancer who were the same age and sex, and who had similar risk factors for heart disease.
Some people with breast cancer or lymphoma are “treated with therapies that can be toxic to the heart, particularly anthracyclines,” explained Dr. Larsen. Among the patients with cancer included in the analysis, nearly all had been treated with chemotherapy and 84% had received an anthracycline.
Within 5 years of their cancer diagnosis, the risk of heart failure was three times higher in people treated for breast cancer or lymphoma than in people without cancer, the researchers found. Within 20 years, 10% of the cancer survivors had developed heart failure, compared with 6% of control subjects.
The risk of heart failure was even higher for certain people with cancer. For example, people who were diagnosed with cancer at age 80 or older had three times the risk of heart failure as those who were diagnosed at a younger age. And heart failure risk was twice as high for survivors who had diabetes compared with those without diabetes.
In addition, they found that the risk of heart failure was two times higher for patients who were treated with doxorubicin (an anthracycline-based chemotherapy drug) compared with patients who received other cancer treatments.
What the Results Mean for People with Cancer
The study findings “add more information about the long-term risk after chemotherapy to the existing knowledge base and provide that data in an epidemiology study rather than a clinical trial—so the findings may be more applicable to a general population of breast cancer and lymphoma patients,” Dr. Larsen said.
Many clinical trials exclude patients with heart disease from participating, Dr. Minasian explained. Consequently, data from clinical trials may not reveal the extent to which heart failure risks are increased in those with pre-existing risk factors.
Nevertheless, Dr. Larsen stressed that “not every breast cancer or lymphoma patient is going to develop heart failure.”
Overall, 7% of those in the study treated for cancer developed heart failure, compared with approximately 3% of those in the control group. “It’s the minority” of people who develop heart failure, she said.
The researchers’ main goal, she added, “is to raise awareness of the risk of heart failure and to encourage a heart-healthy lifestyle in cancer survivors.” A heart-healthy lifestyle includes healthy eating, managing weight and stress, maintaining physical activity, and quitting smoking.
In addition, breast cancer and lymphoma survivors should be assessed for signs or symptoms of heart failure and for additional risk factors such as high blood pressure, diabetes, and smoking, Dr. Larsen said. Treating or controlling those risk factors may mitigate heart failure risk
Patients should also “be mindful that the risk of heart failure doesn’t end when they finish their cancer treatment,” Dr. Minasian added.
Ongoing Cardiotoxicity Research
Researchers are actively investigating approaches to lessen or prevent heart damage from cancer treatments. One trial—sponsored by NCI and the National Heart, Lung, and Blood Institute—is testing the cholesterol-lowering medication atorvastatin for reducing heart damage in women with breast cancer who are receiving anthracycline treatment.
Along the same lines, two studies presented at the ACC conference found that cardiac drugs may protect women with breast cancer from cardiotoxicity of cancer treatment.
Organizations such as the ACC are also helping to better educate cardiologists and oncologists about heart failure risk factors in people with cancer “so we’re better able to take care of these patients,” Dr. Minasian said.
In it, the organization stressed the importance of managing cardiac risk factors in older women who have been treated for breast cancer, “because [cardiovascular disease], if not recognized and treated, can pose a greater health risk than the cancer itself.”
Ending a global pandemic demands a global response. I am thrilled that a novel vaccine adjuvant developed in the United States with NIAID support is now included in an effective COVID-19 vaccine that is available to individuals in India.”
Adjuvants are components that are created as part of a vaccine to improve immune responses and increase the efficiency of the vaccine. COVAXIN was developed and is manufactured in India, which is currently experiencing a terrible health catastrophe as a result of COVID-19. An adjuvant designed with NIH funding has contributed to the success of the extremely effective COVAXIN-COVID-19 vaccine, which has been administered to about 25 million individuals in India and internationally.
Alhydroxiquim-II is the adjuvant utilized in COVAXIN, was discovered and validated in the laboratory by the biotech company ViroVax LLCof Lawrence, Kansas, with funding provided solely by the NIAID Adjuvant Development Program. The adjuvant is formed of a small molecule that is uniquely bonded to Alhydrogel, often known as alum and the most regularly used adjuvant in human vaccines. Alhydroxiquim-II enters lymph nodes, where it detaches from alum and triggers two cellular receptors. TLR7 and TLR8 receptors are essential in the immunological response to viruses. Alhydroxiquim-II is the first adjuvant to activate TLR7 and TLR8 in an approved vaccine against an infectious disease. Additionally, the alum in Alhydroxiquim-II activates the immune system to look for an infiltrating pathogen.
Although molecules that activate TLR receptors strongly stimulate the immune system, the adverse effects of Alhydroxiquim-II are modest. This is due to the fact that after COVAXIN is injected, the adjuvant travels directly to adjacent lymph nodes, which contain white blood cells that are crucial in recognizing pathogens and combating infections. As a result, just a minimal amount of Alhydroxiquim-II is required in each vaccination dosage, and the adjuvant does not circulate throughout the body, avoiding more widespread inflammation and unwanted side effects.
This scanning electron microscope image shows SARS-CoV-2 (round gold particles) emerging from the surface of a cell cultured in the lab. SARS-CoV-2, also known as 2019-nCoV, is the virus that causes COVID-19. Image Source: NIAID
COVAXIN is made up of a crippled version of SARS-CoV-2 that cannot replicate but yet encourages the immune system to produce antibodies against the virus. The NIH stated that COVAXIN is “safe and well tolerated,” citing the results of a phase 2 clinical investigation. COVAXIN safety results from a Phase 3 trial with 25,800 participants in India will be released later this year. Meanwhile, unpublished interim data from the Phase 3 trial show that the vaccine is 78% effective against symptomatic sickness, 100% effective against severe COVID-19, including hospitalization, and 70% effective against asymptomatic infection with SARS-CoV-2, the virus that causes COVID-19. Two tests of blood serum from persons who had received COVAXIN suggest that the vaccine creates antibodies that efficiently neutralize the SARS-CoV-2 B.1.1.7 (Alpha) and B.1.617 (Delta) variants (1) and (2), which were originally identified in the United Kingdom and India, respectively.
Since 2009, the NIAID Adjuvant Program has supported the research of ViroVax’s founder and CEO,Sunil David, M.D., Ph.D. His research has focused on the emergence of new compounds that activate innate immune receptors and their application as vaccination adjuvants.
Dr. David’s engagement with Bharat Biotech International Ltd. of Hyderabad, which manufactures COVAXIN, began during a 2019 meeting in India organized by the NIAID Office of Global Research under the auspices of the NIAID’s Indo-US Vaccine Action Program. Five NIAID-funded adjuvant investigators, including Dr. David, two representatives of the NIAID Division of Allergy, Immunology, and Transplantation, and the NIAID India representative, visited 4 top biotechnology companies to learn about their work and discuss future collaborations. The delegation also attended a consultation in New Delhi, which was co-organized by the NIAID and India’s Department of Biotechnology and hosted by the National Institute of Immunology.
Among the scientific collaborations spawned by these endeavors was a licensing deal between Bharat Biotech and Dr. David to use Alhydroxiquim-II in their candidate vaccines. During the COVID-19 outbreak, this license was expanded to cover COVAXIN, which has Emergency Use Authorization in India and more than a dozen additional countries. COVAXIN was developed by Bharat Biotech in partnership with the Indian Council of Medical Research’sNational Institute of Virology. The company conducted thorough safety research on Alhydroxiquim-II and undertook the arduous process of scaling up production of the adjuvant in accordance with Good Manufacturing Practice standards. Bharat Biotech aims to generate 700 million doses of COVAXIN by the end of 2021.
NIAID conducts and supports research at the National Institutes of Health, across the United States, and across the world to better understand the causes of infectious and immune-mediated diseases and to develop better methods of preventing, detecting, and treating these illnesses. The NIAID website contains news releases, info sheets, and other NIAID-related materials.
Other Related Articles published in this Open Access Online Scientific Journal include the following:
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
The Future of Speech-Based Human-Computer Interaction
Reporter: Ethan Coomber, Research Assistant III
2021 LPBI Summer Internship in Data Science and Podcast Library Development This article reports on a research conducted by the Tokyo Institute of Technology, published on 9 June 2021.
As technology continues to advance, the human-computer relationship develops alongside with it. As researchers and developers find new ways to improve a computer’s ability to recognize the distinct pitches that compose a human’s voice, the potential of technology begins to push back what people previously thought was possible. This constant improvement in technology has allowed us to identify new potential challenges in voice-based technological interaction.
When humans interact with one another, we do not convey our message with only our voices. There are a multitude of complexities to our emotional states and personality that cannot be obtained simply through the sound coming out of our mouths. Aspects of our communication such as rhythm, tone, and pitch are essential in our understanding of one another. This presents a challenge to artificial intelligence as technology is not able to pick up on these cues.
In the modern day, our interactions with voice-based devices and services continue to increase. In this light, researchers at Tokyo Institute of Technology and RIKEN, Japan, have performed a meta-synthesis to understand how we perceive and interact with the voice (and the body) of various machines. Their findings have generated insights into human preferences, and can be used by engineers and designers to develop future vocal technologies.
– Kate Seaborn
While it will always be difficult for technology to perfectly replicate a human interaction, the inclusion of filler terms such as “I mean…”, “um” and “like…” have been shown to improve human’s interaction and comfort when communicating with technology. Humans prefer communicating with agents that match their personality and overall communication style. The illusion of making the artificial intelligence appear human has a dramatic affect on the overall comfort of the person interacting with the technology. Several factors that have been proven to improve communication are when the artificial intelligence comes across as happy or empathetic with a higher pitched voice.
Using machine learning, computers are able to recognize patterns within human speech rather than requiring programming for specific patterns. This allows for the technology to adapt to human tendencies as they continue to see them. Over time, humans develop nuances in the way they speak and communicate which frequently results in a tendency to shorten certain words. One of the more common examples is the expression “I don’t know”. This expression is frequently reduced to the phrase “dunno”. Using machine learning, computers would be able to recognize this pattern and realize what the human’s intention is.
With advances in technology and the development of voice assistance in our lives, we are expanding our interactions to include computer interfaces and environments. While there are still many advances that need to be made in order to achieve the desirable level of communication, developers have identified the necessary steps to achieve the desirable human-computer interaction.
Sources:
Tokyo Institute of Technology. “The role of computer voice in the future of speech-based human-computer interaction.” ScienceDaily. ScienceDaily, 9 June 2021.
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