A 1889 “Flu” Similar to SARS-CoV-2 is Now Under Investigation

In May 1889, people living in Bukhara, a city that was then part of the Russian Empire, began sickening and dying. The respiratory virus that killed them became known as the Russian flu. It swept the world, overwhelming hospitals and killing the old with special ferocity.

Schools and factories were forced to close because so many students and workers were sick. Some of the infected described an odd symptom: a loss of smell and taste. And some of those who recovered reported a lingering exhaustion. The Russian flu finally ended a few years later, after at least three waves of infection.

Its patterns of infection and symptoms have led some virologists and historians of medicine to now wonder: Might the Russian flu actually have been a pandemic driven by a coronavirus? And could its course give us clues about how our pandemic will play out and wind down?

If a coronavirus caused the Russian flu, some believe that pathogen may still be around, its descendants circulating worldwide as one of the four coronaviruses that cause the common cold. If so, it would be different from flu pandemics whose viruses stick around for a while only to be replaced by new variants years later that cause a new pandemic.

Source: https://www.nytimes.com/

New Algorithm Predicts Alzheimer’s with 99% accuracy

Researchers from Kaunas universities in Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer’s disease from brain images with an accuracy of over 99 per cent. The method was developed while analysing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity and specificity than previously developed methods.

According to World Health Organisation, Alzheimer’s disease is the most frequent cause of dementia, contributing to up to 70 per cent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years. Owing to societal ageing, the disease will become a costly public health burden in the years to come.

Medical professionals all over the world attempt to raise awareness of an early Alzheimer’s diagnosis, which provides the affected with a better chance of benefiting from treatment. This was one of the most important issues for choosing a topic for Modupe Odusami, a PhD student from Nigeria”, says Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering, Faculty of Informatics, Kaunas University of Technology (KTU), Odusami’s PhD supervisor. One of the possible Alzheimer’s first signs is mild cognitive impairment (MCI), which is the stage between the expected cognitive decline of normal ageing and dementia. Based on the previous research, functional magnetic resonance imaging (fMRI) can be used to identify the regions in the brain which can be associated with the onset of Alzheimer’s disease, according to Maskeliūnas. The earliest stages of MCI often have almost no clear symptoms, but in quite a few cases can be detected by neuroimaging.

However, although theoretically possible, manual analysing of fMRI images attempting to identify the changes associated with Alzheimer’s not only requires specific knowledge but is also time-consuming – application of Deep learning and other AI methods can speed this up by a significant time margin. Finding MCI features does not necessarily mean the presence of illness, as it can also be a symptom of other related diseases, but it is more of an indicator and possible helper to steer toward an evaluation by a medical professional.

Modern signal processing allows delegating the image processing to the machine, which can complete it faster and accurately enough. Of course, we don’t dare to suggest that a medical professional should ever rely on any algorithm one-hundred-per cent. Think of a machine as a robot capable of doing the most tedious task of sorting the data and searching for features. In this scenario, after the computer algorithm selects potentially affected cases, the specialist can look into them more closely, and at the end, everybody benefits as the diagnosis and the treatment reaches the patient much faster”, says Maskeliūnas, who supervised the team working on the model.

Source: https://en.ktu.edu/

COVID-19 Can Cause Antibodies that Mistakenly Target your Own Tissues

An increasing body of research is pointing toward the possibility that COVID-19 causes the development of autoantibodies linked to other autoimmune diseases — and may be tied to the long-hauler symptoms associated with coronavirus.

In the latest preprint study (which means it has not yet undergone peer review) researchers analyzed the levels of 18 different autoantibodies between four groups:

  • 29 unexposed pre-pandemic individuals from the general population
  • 20 individuals hospitalized with moderate-to-severe COVID-19
  • 9 recovering COVID-19-infected individuals with asymptomatic to mild viral symptoms during the acute phase, with samples collected between 1.8 and 7.3 months after infection
  • 6 unexposed pre-pandemic subjects with lupus (an autoimmune disease that involves different kinds of autoantibodies)
  • Autoantibodies are antibodies that mistakenly target your own tissues or organs and are associated with diseases such as rheumatoid arthritis and lupus. Unsurprisingly, the researchers found that autoantibodies were detected in five out of the six lupus subjects, compared to just 11 of 29 non-lupus, pre-pandemic controls.

However, the researchers also found that autoantibodies were detected in seven out of nine patients recovering from SARS-CoV-2 and in 12 out of the 20 hospitalized individuals with moderate to severe COVID-19. In the first group, autoantibodies were detected in all patients with reported persistent symptoms and two of the four without any long-term symptoms.

The autoantibodies that set SARS-CoV-2  infected patients apart from the pre-pandemic subjects are widely associated with myopathies (neuromuscular disorders), vasculitis (inflammation of the blood vessels), and antiphospholipid syndromes (when your body creates antibodies that make your blood much more likely to clot), all of which are conditions that share some similarities with COVID-19. The researchers note that these results underscore the importance of further investigating autoimmunity during a COVID-19 infection, and the role of autoimmunity in lingering symptoms. That said, they do urge caution in interpreting the results, which still need to undergo peer review.

It’s a signal; it is not definitive,” lead researcher Nahid Bhadelia, MD, told the New York Times. We don’t know how prevalent it is, and whether or not it can be linked to long COVID.” (Long COVID is sometimes used to describe the syndrome that causes long-hauler symptoms in those who have recovered from COVID-19.)

Still, as many as one-third of COVID-19 survivors say they still experience symptoms — and determining the role autoimmunity may play after coronavirus infection is critical.

This is a real phenomenon,” Dr. Bhadelia said. “We’re looking at a second pandemic of people with ongoing potential disability who may not be able to return to work, and that’s a huge impact on the health symptoms.”

Source: https://creakyjoints.org/

The DNA Drug Revolution

Doctors have been treating the symptoms of most diseases, and not the source, for centuries. They have cut out tumors, unclogged arteries, injected insulin and soothed fevers—and have been unable to touch the biological code within cells that tells them to grow malignantly, pass along abnormal nerve signals, take in too much or too little energy, and swell with inflammation. The code is the DNA molecule in each cell that tells it what to do and when, and it triggers dreaded diseases when it goes wrong. The molecule, and its messengers, had remained tucked away, beyond the reach of almost all drugs, unfixable when broken. But as this special report explains, that is no longer the case.

Things began to change after the DNA sequence for the entire human genome was laid out early in this century, and within the past several years the ability to synthesize and custom-design shorter sequences has shown scientists that the best substance for reaching DNA is, well, DNA. Fabricating new genes to replace badly working versions, or to “silence” them, has produced 14 approved DNA-related drugs. And the latest research indicates that such therapies can be even more effective if scientists depart from the basic linear strands and instead make DNA spheres, which have enhanced abilities to enter cells. DNA analysis has also yielded new targets, showing that although newborn babies in the U.S. are typically screened for between 30 and 60 genetic conditions right now, it is possible to and nearly 1,000 genes linked to childhood diseases that could be new treatment points.

But that same science has also created troubling issues: some of the gene tests for infants can raise false alarms, for instance, and not every child with a disease-associated gene ends up getting that disease. Research has also revealed unfair bias in DNA targets. Most of the data about those sequences comes from studies of white people and has missed gene variants that cause disease in nonwhitesinequality in research that will produce inequality in health if it isn’t fixed. Geneticists are starting projects designed to improve this diversity level. DNA in medicine has great power, and that power should be used for the many, not the few.

Source: https://www.scientificamerican.com/