Health Life

Deep learning helps explore the structural and strategic bases of autism?

Visualization of logics of classification learned by recurrent attention model (RAM). Credit: The Korea Advanced Institute of Science and Technology (KAIST)

Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person’s behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the ‘bible’ of mental health diagnosis.

However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult.

But what if (AI) could help? Deep learning, a type of AI, deploys based on the to recognize patterns in a way that is akin to, and in some cases can surpass, human ability. The technique, or rather suite of techniques, has enjoyed remarkable success in recent years in fields as diverse as voice recognition, translation, autonomous vehicles, and drug discovery.

A group of researchers from KAIST in collaboration with the YonseiUniversity College of Medicine has applied these to autism diagnosis. Their findings were published on August 14 in the journal IEEE Access.

Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal gray and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition.

These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy,

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Monoclonal Antibodies – National Cancer Institute

Monoclonal antibodies can cause side effects, which can differ from person to person. The ones you may have and how they make you feel will depend on many factors, such as how healthy you are before treatment, your type of cancer, how advanced it is, the type of monoclonal antibody you are receiving, and the dose.

Doctors and nurses cannot know for sure when or if side effects will occur or how serious they will be. So, it is important to know which signs to look for and what to do if you start to have problems.

Like most types of immunotherapy, monoclonal antibodies can cause skin reactions at the needle site and flu-like symptoms.

Needle site reactions include:

  • Pain
  • Swelling
  • Soreness
  • Redness
  • Itchiness
  • Rash

Learn more about skin changes caused by cancer treatment.

Flu-like symptoms include:

  • Chills
  • Fatigue
  • Fever
  • Muscle aches and pains
  • Nausea
  • Vomiting
  • Diarrhea

Learn more about flu-like symptoms caused by cancer treatment.

Monoclonal antibodies can also cause:

  • Mouth and skin sores that can lead to serious infections
  • High blood pressure
  • Congestive heart failure
  • Heart attacks
  • Inflammatory lung disease

Monoclonal antibodies can cause mild to severe allergic reactions while you are receiving the drug. In rare cases, the reaction is severe enough to cause death.

Some monoclonal antibodies can also cause capillary leak syndrome. This syndrome causes fluid and proteins to leak out of tiny blood vessels and flow into surrounding tissues, resulting in dangerously low blood pressure. Capillary leak syndrome may lead to multiple organ failure and shock.

Cytokine release syndrome can sometimes occur with monoclonal antibodies, but it is often mild. Cytokines are immune substances that have many different functions in the body, and a sudden increase in their levels can cause:

  • Fever
  • Nausea
  • Headache
  • Rash
  • Rapid heartbeat
  • Low blood pressure
  • Trouble breathing