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NIH to address unmet clinical needs in testing, monitoring, and treatment technologies

The National Institutes of Health will advance the development of home-based and point-of-care health technologies with awards to six technology research…

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The National Institutes of Health will advance the development of home-based and point-of-care health technologies with awards to six technology research and development centers around the country. The centers comprise the Point of Care Technology Research Network (POCTRN) and will parlay the momentum of the original network established in 2007 by the National Institute of Biomedical Imaging and Bioengineering (NIBIB). In the first year of the new five-year grant period, these six centers will share $9.6 million in total awards. 

Credit: CIMIT

The National Institutes of Health will advance the development of home-based and point-of-care health technologies with awards to six technology research and development centers around the country. The centers comprise the Point of Care Technology Research Network (POCTRN) and will parlay the momentum of the original network established in 2007 by the National Institute of Biomedical Imaging and Bioengineering (NIBIB). In the first year of the new five-year grant period, these six centers will share $9.6 million in total awards. 

Through technological advances, health care can be delivered closer to the patient, leading to more timely and convenient care and ultimately better outcomes. The POCTRN model builds multidisciplinary partnerships that drive the development and application of technologies needed to achieve this goal. Awarded centers focus on the development of innovative point-of-care devices for unmet medical needs in the United States and worldwide. 

“NIH and NIBIB have long played an important role in guiding and supporting innovative health technologies,” said Bruce J. Tromberg, Ph.D., director of NIBIB. “As POCTRN demonstrated during the COVID-19 pandemic, point-of-care and home-based technologies are more accessible to populations in low-resource settings and, therefore, serve a democratizing function in the health care sector.”  

POCTRN technology research and development centers address a range of unmet testing, monitoring, and treatment areas, such as heart disease, cancer and HIV/AIDS, in a spectrum of settings, from child health to global health. Key to this approach is incorporating clinical and user needs in the technology development process while addressing early barriers to commercialization and implementation. In an extraordinary adaptation of its charge during the COVID-19 pandemic, POCTRN was expanded to create NIBIB’s Rapid Acceleration of Diagnostics (RADx®) Tech program, which delivered 8 billion COVID-19 tests to the nation and shifted testing from central labs to the home and point of care. Having facilitated the transformation in at-home and point-of-care diagnostics for COVID-19, POCTRN will apply this experience and expertise to a broad range of health care needs. 

“The POCTRN network has become a standout award mechanism for NIBIB and several other NIH institutes that recognize the empowering nature of point-of-care technologies,” said Tiffani Bailey-Lash, Ph.D., director of NIBIB’s POCTRN program. “Centers within the network are known for impactful technology designs, and supported projects receive specialized expert guidance to overcome a variety of tech development pitfalls, giving them the best chance for success.” 

The following centers comprise the fourth cycle of POCTRN awards: 

  

Center for Advancing Point of Care in Heart, Lung, Blood and Sleep Diseases (CAPCaT), University of Massachusetts Chan Medical School, Worcester, and University of Massachusetts at Lowell 

Principal investigators: Bryan Buchholz, Ph.D.; Nate Hafer, Ph.D.; and David McManus, M.D. 

CAPCaT will develop and optimize novel point-of-care and home-based technologies to improve the diagnosis and management of heart, lung, blood and sleep disorders.  

  

Center for Innovation and Translation of Point of Care Technologies for Equitable Cancer Care (CITEC), Rice University, Houston 

Principal investigators: Sharmila Anandasabapathy, M.D.; Rebecca Richards-Kortum, Ph.D.; and Tomasz Tkaczyk, Ph.D. 

CITEC will accelerate the development and adoption of new technologies that can be used to improve the early detection of cancer in low-resource settings in the United States and globally.  

  

Point of Care Technologies for Nutrition, Infection, and Cancer for Global Health (PORTENT), Cornell University, Ithaca, New York 

Principal investigators: David Erickson, Ph.D.; and Saurabh Mehta, Sc.D. 

PORTENT will focus on primary health care globally, address the needs of the most vulnerable in the United States and internationally, and enable a broad range of diagnostic technologies to be validated on a global scale while simultaneously developing expertise and building testing capacity worldwide. 

  

Center for Innovative Diagnostics for Infectious Diseases, Johns Hopkins University, Baltimore 

Principal investigators: Yukari Manabe, M.D. 

 This center will accelerate innovation and access to infectious disease diagnostic point-of-care technology to impact global public health.  

  

Center for Innovation in Point-of-Care Technologies for HIV/AIDS and Emerging Infectious Diseases at Northwestern University (C-THAN), Evanston, Illinois 

Principal investigators: Chad Achenbach, M.D.; Sally McFall, Ph.D.; and Robert Murphy, M.D. 

C-THAN’s technologies include point-of-care devices for detection of infection and monitoring of HIV/AIDS and its common potentially fatal comorbidities, including tuberculosis, non-tuberculous mycobacterium, hepatitis B, hepatitis C, and HIV-associated malignancies.  

  

Atlanta Center for Microsystems Engineered Point-of-Care Technologies (ACME POCT), Emory University  

Principal investigators: Wilbur Lam, M.D., Ph.D.; and Gregory Martin, M.D. 

ACME POCT assists and enables inventors from across the country who have developed microsystems-based point-of-care technologies in defining their specific clinical needs, conducting clinical validation, and refining their technology with the objective of accelerating the path to translation and clinical adoption.  

 

Coordination among all the centers is led by CIMIT under a contract with NIBIB. Each center will use a milestone-driven approach to focus resources on the development of technologies that demonstrate the highest chance of success.  

This year’s POCTRN awards represent an expansion of the program with support from multiple NIH Institutes, Centers, and Offices. Besides NIBIB, NIH components that support POCTRN centers include the National Heart, Lung, and Blood Institute, the National Center for Complimentary and Integrative Health, the National Institute of Allergy and Infectious Diseases, Fogarty International Center, the Office of AIDS Research, the Office of Behavioral and Social Science Research, and the Office of Disease Prevention.  

# # # 

About the National Institute of Biomedical Imaging and Bioengineering (NIBIB): NIBIB’s mission is to improve health by leading the development and accelerating the application of biomedical technologies. The institute is committed to integrating the physical and engineering sciences with the life sciences to advance basic research and medical care. NIBIB supports emerging technology research and development within its internal laboratories and through grants, collaborations, and training. More information is available at the NIBIB website: https://www.nibib.nih.gov. 

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit https://www.nih.gov/. 

NIH…Turning Discovery Into Health® 

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Gwangju Institute of Science and Technology scientists develop deep learning-based biosensing platform to count viral particles better

Rapid and on-site diagnostic technologies for identifying and quantifying viruses are essential for planning treatment strategies for infected patients…

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October 17, 2023

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Rapid and on-site diagnostic technologies for identifying and quantifying viruses are essential for planning treatment strategies for infected patients and preventing further spread of the infection. The COVID-19 pandemic has highlighted the need for accurate yet decentralized diagnostic tests that do not involve complex and time-consuming processes needed for conventional laboratory-based tests.

Credit: Professor Young Min Song from GIST, Korea

Rapid and on-site diagnostic technologies for identifying and quantifying viruses are essential for planning treatment strategies for infected patients and preventing further spread of the infection. The COVID-19 pandemic has highlighted the need for accurate yet decentralized diagnostic tests that do not involve complex and time-consuming processes needed for conventional laboratory-based tests.

A popular point-of-care diagnostic tool for quantifying viral loads is bright-field microscopic imaging. However, the small size (~ 100 nm) and low refractive index (~ 1.5, same as that of a microscope slide) of bioparticles such as viruses often makes their accurate estimation difficult and increases the limit of detection (the lowest concentration of viral load that can be reliably detected). Recent studies have found that Gires-Tournois (GT) biosensors, a type of nanophotonic resonators, can detect minuscule virus particles and produce colorful micrographs (images taken through a microscope) of viral loads. But they suffer from visual artifacts and non-reproducibility, limiting their utilization.

In a recent breakthrough, an international team of researchers, led by Professor Young Min Song from the School of Electrical Engineering and Computer Science at Gwangju Institute of Science and Technology in Korea, has leveraged artificial intelligence (AI) to overcome this problem. Their work was made available online on August 24, 2023 and will be published in Volume 52 of the journal Nano Today in October 01, 2023.

The team proposed a synergistic biosensing tool called “DeepGT,” which can harness the advantages of GT sensing platforms and merge them with deep learning-based algorithms to accurately quantify nanoscale bioparticles, including viruses, without the need for complex sample preparation methods.

“We designed DeepGT to objectively assess the severity of an infection or disease. This means that we will no longer have to rely solely on subjective assessments for diagnosis and healthcare but will instead have a more accurate and data-driven approach to guide therapeutic strategies,” explains Prof. Song, revealing the motivation behind their study.

The team designed a GT biosensor with a trilayered thin-film configuration and biofunctionalized it to enable colorimetric sensing upon interaction with target analytes. The sensing abilities were verified by simulating the binding mechanism between host cells and the virus using specially prepared bioparticles that mimicked SARS-CoV-2—the coronavirus strain that caused the COVID-19 pandemic.

Next, the researchers trained a convolutional neural network (CNN) using over a thousand optical and scanning electron micrographs of the GT biosensor surface with different types of nanoparticles. They found that DeepGT was able to refine visual artifacts associated with bright-field microscopy and extract relevant information, even at viral concentrations as low as 138 pg ml–1. Moreover, it determined the bioparticle count with a high accuracy, characterized by a mean absolute error of 2.37 across 1,596 images compared to 13.47 for rule-based algorithms, in under a second. Boosted by the performance of CNNs, the biosensing system can also indicate the severity of the infection from asymptomatic to severe based on the viral load.

DeepGT thus presents an efficient and precise way of screening viruses across a broad size range without being hindered by the minimum diffraction limit in visible light. “Our approach provides a practical solution for the swift detection and management of emerging viral threats as well as the improvement of public health preparedness by potentially reducing the overall burden of costs associated with diagnostics,” concludes Prof. Song.

We too hope that this study will enable new AI-powered healthcare technologies that will improve the quality of life of patients across the globe!

 

***

 

Reference

DOI: https://doi.org/10.1016/j.nantod.2023.101968

 

About the Gwangju Institute of Science and Technology (GIST)

The Gwangju Institute of Science and Technology (GIST) was founded in 1993 by the Korean government as a research-oriented graduate school to help ensure Korea’s continued economic growth and prosperity by developing advanced science and technology with an emphasis on collaboration with the international community. Since that time, GIST has pioneered a highly regarded undergraduate science curriculum in 2010 that has become a model for other science universities in Korea. To learn more about GIST and its exciting opportunities for researchers and students alike, please visit: http://www.gist.ac.kr/.

 

About the Author

Young Min Song is a distinguished professor at the School of Electrical Engineering and Computer Science at Gwangju Institute of Science and Technology (GIST). His research interests encompass advanced optoelectronic sensors, multifunctional nanophotonics, and semiconductor devices. He has published more than 150 peer-reviewed research articles, including contributions to prestigious journals such as Nature, Science, and Nature Electronics. His extensive impact is reflected in his Google Scholar profile, which boasts over 10,000 citations and an h-index of 40. He is also an active editorial board member for several respected journals, including IEEE Photonics Journal and Electronics (MDPI), Micromachines (MDPI), and IJPEM (Springer).

Journal

Nano Today

DOI

10.1016/j.nantod.2023.101968

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Deep learning-based quantification of nanosized bioparticles in bright-field micrographs of Gires-Tournois biosensor

Article Publication Date

24-Aug-2023

COI Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

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Treasuries Pain Can Get Much Worse, Term Premium Dynamics Show

Treasuries Pain Can Get Much Worse, Term Premium Dynamics Show

By Garfield Reynolds, Bloomberg Markets Live reporter and strategist

Treasuries’…

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October 17, 2023

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Treasuries Pain Can Get Much Worse, Term Premium Dynamics Show

By Garfield Reynolds, Bloomberg Markets Live reporter and strategist

Treasuries’ recent slump owed plenty to the return of the so-called term premium as investors became more concerned about the risks of holding longer-dated debt. Even as US bonds get some help from geopolitical uncertainty, there’s plenty of scope for yields to march considerably higher on the same dynamics that helped drive September’s spike.
 
For one thing there’s little chance that the supply outlook is going to improve noticeably, no matter how the Middle East conflict and the US House speaker situation are resolved. For another, an examination of the relative yields for Australian and US debt signals there’s potential that US term premiums have further to go to.

Australia’s 10-year term premium has tended to align closely with the US gauge, but it’s been going through a relatively rare period since the pandemic with the two diverging. At first, it was the US term premium that swelled, perhaps representing the impact of extreme QE or lingering liquidity concerns after Treasuries froze as the pandemic broke out. That script flipped from early 2022 as the Fed started what would prove to be a far more aggressive hiking cycle than the RBA.

Still, as inflation slows in both economies and traders anticipate and end to rate hikes, that term premium gap closed dramatically even as September’s selloff drove steep losses for both Treasuries and Aussie bonds. Term premiums are tough enough to measure, let alone predict, but there’s a case to be made that one potential guide for the way for this to develop would be for the US term premium to close much of the remaining spread to Australia, which stood at about 60bps at the end of last month.

Tyler Durden Tue, 10/17/2023 - 07:45

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How Has Treasury Market Liquidity Evolved in 2023?

In a 2022 post, we showed how liquidity conditions in the U.S. Treasury securities market had worsened as supply disruptions, high inflation, and geopolitical…

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3 hours ago

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October 17, 2023

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Michael Fleming

In a 2022 post, we showed how liquidity conditions in the U.S. Treasury securities market had worsened as supply disruptions, high inflation, and geopolitical conflict increased uncertainty about the expected path of interest rates. In this post, we revisit some commonly used metrics to assess how market liquidity has evolved since. We find that liquidity worsened abruptly In March 2023 after the failures of Silicon Valley Bank and Signature Bank, but then quickly improved to levels close to those of the preceding year. As in 2022, liquidity in 2023 continues to closely track the level that would be expected by the path of interest rate volatility.

Importance of Treasury Market Liquidity

The U.S. Treasury securities market is the largest and most liquid government securities market in the world, with more than $25 trillion in marketable debt outstanding (as of August 31, 2023). The securities are used by the Treasury Department to finance the U.S. government, by countless financial institutions to manage interest rate risk and price other financial instruments, and by the Federal Reserve in implementing monetary policy. Having a liquid market is important for all of these purposes and thus of concern to market participants and policymakers alike.

Measuring Liquidity

Liquidity often refers to the cost of quickly converting an asset into cash (or vice versa) and is measured in various ways. We look at three commonly used measures, estimated using high-frequency data from the interdealer market: the bid-ask spread, order book depth, and price impact. The measures are estimated for the most recently auctioned (on-the-run) two-, five-, and ten-year notes (the three most actively traded Treasury securities, as shown in this Liberty Street Economics post), and are calculated for New York trading hours (defined as 7 a.m. to 5 p.m.).

Market Liquidity Worsened in March 2023

The bid-ask spread—the difference between the lowest ask price and the highest bid price for a security—is one of the most popular liquidity measures. As shown in the chart below, bid-ask spreads widened abruptly after the failures of Silicon Valley Bank (March 10) and Signature Bank (March 13), suggesting reduced liquidity.  For the two-year note, spreads exceeded those observed during the COVID-related disruptions of March 2020 (examined in this Liberty Street Economics post). Spreads then narrowed over the subsequent month or so to levels close to those of the preceding year but remained somewhat elevated for the two-year note.

Bid-Ask Spreads Widened in March 2023

Source: Author’s calculations, based on data from BrokerTec.
Notes: The chart plots five-day moving averages of average daily bid-ask spreads for the on-the-run two-, five-, and ten-year notes in the interdealer market from September 1, 2019 to September 30, 2023. Spreads are measured in 32nds of a point, where a point equals one percent of par.

The next chart plots order book depth, measured as the average quantity of securities available for sale or purchase at the best bid and offer prices. This metric again points to relatively poor liquidity in March 2023, as the available depth declined precipitously. Depth in the five-year note was at levels commensurate with those of March 2020, whereas depth in the two-year note was appreciably lower—and depth in the ten-year note appreciably higher—than the levels of March 2020. Within about a month, depth for all three notes was back to levels similar to those of the preceding year.

Order Book Depth Plunged in March 2023

Source: Author’s calculations, based on data from BrokerTec.
Notes: This chart plots five-day moving averages of average daily depth for the on-the-run two-, five-, and ten-year notes in the interdealer market from September 1, 2019 to September 30, 2023. Data are for order book depth at the inside tier, averaged across the bid and offer sides. Depth is measured in millions of U.S. dollars par and plotted on a logarithmic scale.

Measures of the price impact of trades also suggest a notable deterioration of liquidity. The next chart plots the estimated price impact per $100 million in net order flow (defined as buyer-initiated trading volume less seller-initiated trading volume). A higher price impact suggests reduced liquidity. Price impact for the two-year note rose sharply in March 2023 to a level about twice as high as at its March 2020 peak, and then within a month or so returned to levels comparable to those of the preceding year.  Price impact for the five-and ten-year notes rose more modestly in March.

Price Impact Rose in March 2023

Source: Author’s calculations, based on data from BrokerTec.
Notes: The chart plots five-day moving averages of slope coefficients from daily regressions of one-minute price changes on one-minute net order flow (buyer-initiated trading volume less seller-initiated trading volume) for the on-the-run two-, five-, and ten-year notes in the interdealer market from September 1, 2019 to September 30, 2023. Price impact is measured in 32nds of a point per $100 million, where a point equals one percent of par.

Volatility Spiked in March 2023

The failures of Silicon Valley Bank and Signature Bank increased uncertainty about the economic outlook and expected path of interest rates. Interest rate volatility increased sharply as a result, as shown in the next chart, with two-year note volatility in particular reaching levels more than twice as high as in March 2020. Volatility causes market makers to widen their bid-ask spreads and post less depth at any given price to manage the increased risk of taking on positions, producing a negative relationship between volatility and liquidity. The sharp rise in volatility and its subsequent decline hence help explain the observed patterns in the liquidity measures.

Price Volatility Spiked in March 2023

Source: Author’s calculations, based on data from BrokerTec.
Notes: The chart plots five-day moving averages of price volatility for the on-the-run two-, five-, and ten-year notes in the interdealer market from September 1, 2019 to September 30, 2023. Price volatility is calculated for each day by summing squared one-minute returns (log changes in midpoint prices) from 7 a.m. to 5 p.m., annualizing by multiplying by 252, and then taking the square root. It is reported in percent.

Liquidity Continues to Track Volatility

As in “How Liquid Has the Treasury Market Been in 2022?,” we assess whether liquidity has been unusual given the level of volatility by examining scatter plots of price impact against volatility. The chart below provides such a plot for the five-year note, showing that the 2023 observations (in gray) fall in line with the historical relationship. That is, the association between liquidity and volatility in 2023 has been consistent with the past association between these two variables. This is true for the ten-year note as well, whereas for the two-year note the evidence points to somewhat higher-than-expected price impact given the volatility (as also occurred in fall 2008, March 2020, and 2022).

Liquidity in Line with Historical Relationship with Volatility

Source: Author’s calculations, based on data from BrokerTec.
Notes: This chart plots price impact against price volatility by week for the on-the-run five-year note from January 1, 2005, to September 30, 2023. The weekly measures for both series are averages of the daily measures plotted in the preceding two charts. Fall 2008 points are for September 21, 2008–January 3, 2009, March 2020 points are for March 1, 2020–March 28, 2020, 2022 points are for January 1, 2022–December 31, 2022, and 2023 points are for January 1, 2023–September 30, 2023.

The preceding analysis is based on realized price volatility—that is, on how much prices are actually changing. We repeated the analysis with implied (or expected) interest rate volatility, as measured by the ICE BofAML MOVE Index, and found similar results for 2023. That is, liquidity for the five- and ten-year notes is in line with the historical relationship between liquidity and expected volatility, whereas liquidity is somewhat worse for the two-year note.

Continued Vigilance

While Treasury market liquidity has not been unusually poor given the level of interest rate volatility, continued vigilance by policymakers and market participants is appropriate. The market’s capacity to smoothly handle large trading flows has been of concern since March 2020, as discussed in this Brookings paper. Moreover, new empirical work shows how constraints on intermediation capacity can exacerbate illiquidity. Careful monitoring of Treasury market liquidity, and continued efforts to enhance the market’s resilience, are warranted.

Photo: portrait of Michael Fleming

Michael J. Fleming is the head of Capital Markets Studies in the Federal Reserve Bank of New York’s Research and Statistics Group. 

How to cite this post:
Michael Fleming, “How Has Treasury Market Liquidity Evolved in 2023?,” Federal Reserve Bank of New York Liberty Street Economics, October 17, 2023, https://libertystreeteconomics.newyorkfed.org/2023/10/how-has-treasury-market-liquidity-evolved-in-2023/.

You may also be interested in:

The 2023 U.S. Treasury Market Conference (Nov.16)

Breaking Down TRACE Volumes Further

Treasury Market Liquidity and the Federal Reserve during the COVID-19 Pandemic

Staff Reports

Dealer Capacity and U.S. Treasury Market Functionality

Disclaimer
The views expressed in this post are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author(s).

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