Artificial Intelligenсe to Improve Cancer Diagnosis in NHS

British Prime Minister Theresa May is expected to challenge the National Health Service, health charities and artificial intelligence developers to work to together in order to transform how chronic diseases are diagnosed. Commentary by Tim Sandle.

Artificial Intelligenсe to Improve Cancer Diagnosis in NHS by Tim Sandle on Scribd

3-D structure of 1918 influenza virus

Virus-like particles (VLPs) are protein-based structures that mimic viruses and bind to antibodies. Because VLPs are not infectious, they show considerable promise as vaccine platforms for many viral diseases, including influenza. Realizing that fine details about influenza VLPs were scant, a team of researchers who specialize in visualizing molecular structures developed a 3D model based on the 1918 H1 pandemic influenza virus. They say their research, which appears online in Scientific Reports, could benefit VLP vaccine projects, targeting a range of viruses from HIV to Ebola and SARS coronavirus. The research was conducted by scientists at the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health.

Other researchers had produced VLPs for 1918 H1 influenza that successfully protected animals from different influenza viruses. The NIAID group prepared hundreds of such VLP samples and analyzed their structure with a technique called cryo-electron microscopy, which quick-freezes samples with glass-like clarity. They then sliced through those VLP 3D structures -- like slicing through a loaf of bread -- to analyze their internal structure, using computers to document the size and placement of key molecules. After averaging all their data, the group then created a 3D 1918 influenza VLP model.

The scientists found that about 90 percent of the VLPs are hemagglutinin (HA) proteins (by weight) found on the VLP surface. In contrast, HAs comprise less than half of the viral proteins of natural influenza viruses. The number and location of HA molecules may influence the efficacy of VLP vaccines, influencing the binding of antibodies to specific epitopes on the HA protein. Those antibodies can similarly bind live influenza viruses, preventing them from infecting cells.

The research group, in NIAID's Laboratory of Infectious Diseases, is continuing its work by comparing its VLP data to data from other natural influenza viruses. They believe the more that is understood about the molecular organization of influenza VLPs, the better scientists will be able to develop effective seasonal and universal influenza vaccines.

See: Dustin M. McCraw, John R. Gallagher, Udana Torian, Mallory L. Myers, Michael T. Conlon, Neetu M. Gulati, Audray K. Harris. Structural analysis of influenza vaccine virus-like particles reveals a multicomponent organization. Scientific Reports, 2018; 8 (1) DOI: 10.1038/s41598-018-28700-7
Posted by Dr. Tim Sandle, Pharmaceutical Microbiology

Diagnostic connectivity to combat antimicrobial resistance

The British government has entered into a partnership with FIND, a global non-profit dedicated to accelerating the development of diagnostic tests for diseases. This is to introduce digital technologies to combat the antimicrobial resistance problem.

The partnership, following the signing of a Memorandum of Understanding, establishes a three-year project focusing on connecting data from patients’ diagnostic test results into various national antimicrobial resistance surveillance program in low- and middle-income countries. This digital information will be to help address the rising problem of drug resistant infections.

The announcement about the collaboration was made on May, 22 2018 at the 71st World Health Assembly, which took place in Geneva, Switzerland. The project is being funded from U.K. Government’s Global Antimicrobial Resistance Innovation Fund. Here the British state will work with the Foundation for Innovative New Diagnostics (FIND).

The aim of the project, for the connectivity for diagnostics, is to improve worldwide surveillance of antimicrobial resistance. Here FIND and partner bodies will produce alternative tools and new solutions to connect information from antimicrobial resistant-related diagnostic testing of patients and to input the analyzed information into national surveillance programs operating in low- and middle-income countries. The aim is to greatly extend the scope of existing programs so they include routine hospital and community data.

The growing menace of antibiotic resistance presents the single most significant threat faced by the global population. The urgency is with profiling patterns of resistance, so that epidemiological patterns can be assessed, and with the development of new antibiotics. The risk is very real: human populations face the very real risk of a future without antibiotics. The implications of this are that life expectancy could fall due to people dying from diseases that are readily treatable today. For example, around 700,000 deaths each year are caused by drug-resistant pathogens worldwide.

The FIND strategy is that diagnostics plays an important role in helping to minimize the proliferation of drug-resistant bacteria, viruses, parasites and fungi. By using appropriate diagnostic tests, medical professionals can identify disease-causing pathogens and use this information to determine the presence of drug resistance. This is only possible through comprehensive databases, assessed using big data analytics.

An example is with drug resistant tuberculosis, here FIND are helping to develop better tests for case detection & drug susceptibility testing (sputum); improved tests for detection and triage (non-sputum); and latent-to-active prediction tests. These are often alternatives to lengthy culture based methods, offering rapid microbiological alternatives aimed at improved accuracy and faster time-to-result.

According to Catharina Boehme, who is the CEO of FIND: “Diagnostics are critical to tracking and monitoring diseases and the spread of drug resistance…Connecting diagnostics to surveillance systems at various levels from local to global will allow surveillance to be strengthened in LMICs – where the burden of infectious diseases is highest but data are currently limited.”

Posted by Dr. Tim Sandle, Pharmaceutical Microbiology