Yes, You Should Still Get The Flu Shot Even Though It Was Super-Crappy Last Year

The flu shot was kind of a crapshoot last year after a ton of people went under the needle but got the flu anyway because of a “vaccine mismatch,” according to a commentary in The New England Journal of Medicine.

But yeah, that still doesn’t mean you should be thinking about skipping this year’s injection. According to Amesh Adalja, M.D., a senior scholar at the Johns Hopkins Center for Health Security, the flu shot usually provides about a 65 percent protection rate against contracting the flu—and that number is nothing to sneeze at. (FYI: The effectiveness rate of last year’s flu shot? Just 36 percent, per the CDC.)

“Just because the vaccine isn’t 100 percent [effective] doesn’t mean it’s worthless,” says Adalja. “And even if you do get the flu, [if you’re vaccinated] you are much less likely to have a severe case requiring hospitalization, less likely to have major destruction to your life, and less likely to spread it.”

Plus, there’s some good news about the 2018-2019 flu shot: Researchers think it will be more successful than last year’s vaccine. TG, right?

FYI: The flu shot can’t actually give you the flu.

Also: Rumors of the shot’s many side effects are greatly exaggerated. It can’t actually give you the flu, and while there are some possible side effects, Adalja says most are rare.

Read through this list and then roll up your sleeve anyway, because flu season is coming and the vaccine is still your best defense.

1. Shoulder soreness

If you receive the flu shot as an intramuscular injection (a.k.a. in your arm, typically), you have a 10 to 64 percent chance of experiencing some muscle soreness in your upper arm, according to the CDC.

That’s because the needle is injected directly into the muscle, causing microscopic damage to the cells, and is designed to cause an inflammatory immune system response. You can take OTC pain relievers while you wait for the soreness to fade, but if the pain is very noticeable or decreasing your mobility, Adalja recommends checking with your doctor.

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2. Redness or swelling at the injection site

Anytime you pierce the skin and put something into the body it can cause a topical reaction, says Adalja. This is just a sign that your immune system is activating.

But this redness and swelling where you get your shot is a common side effect that only typically lasts a few days. It’ll go away on its own, but if it’s really bugging you, you can take ibuprofen or acetominophen.

3. Body aches

Any vaccine can cause body aches because of the immune system activation, says Adalja.

If you’re feeling sore in places other than your arm, it’s usually nothing to worry about, though Adalja notes that the flu shot does take two weeks to become fully effective—so your body aches could be a sign of the actual flu, since viral strains are probably circulating around the time you get the vaccine.

4. Itching at the injection site or a full-body rash

This would signal an allergic reaction, but “it’s very rare to have an allergic reaction to the flu shot,” Adalja says. “There are lots of myths about egg allergies and the vaccine, but if you can eat scrambled eggs, you’re not going to have a problem with the flu shot.” And even if you have a confirmed egg allergy, you can likely still get the flu shot, per the CDC.

That said, if you experience severe itching at injection site, a rash all over your body, or signs of anaphylactic shock, seek immediate medical attention.

One thing to note: If you’ve had an allergic reaction to the flu shot in the past, you are in one of the few groups of people who the CDC recommends not get the flu shot.

5. Fever

You probably won’t get a fever because of the vaccine, but if you do, it should be low-grade (i.e. less than 101 degrees). If it’s higher than that, don’t blame your flu shot—you probably have a totally unrelated illness. “Remember that you’re getting the vaccine at the height of respiratory virus season,” says Adalja. “So you may have been incubating another virus [without knowing it].”

And once again for the people in the back: The flu shot cannot give you the flu. While some flu vaccines contain virus strains, they’re not live strains, so they can’t get you sick. Meanwhile some flu shots don’t contain the virus at all (they only contain a specific protein from the influenza virus), per the CDC.

6. Dizziness or fainting

This is less a side effect of the vaccine itself and more a side effect of a needle phobia, says Adalja. If you think you might have a stress reaction or faint, give your healthcare provider a heads-up so they can make sure you stay seated after the shot to prevent injury.

7. Guillain-Barre Syndrome

Guillain-Barre Syndrome (GBS) is an auto-immune disorder that’s triggered by a wide variety of things, from vaccines to viral infections.

GBS causes damage to the nervous system, resulting in symptoms like muscle weakness, numbness, difficulty walking or an odd gait, and even paralysis, says Adalja. While 70 percent of people fully recover from the disorder, the recovery period can range from weeks to even years, according to the National Institute of Neurological Disorders and Stroke.

But he also says the connection between GBS and the flu vaccine has been overhyped: “People should remember that influenza itself is much more likely to cause GBS than the vaccine.” And since no more than one or two cases per million people vaccinated will have this side effect, it’s better to take your (super small) chances with GBS than with one of the many common, severe complications which often come with the flu itself.

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Machine-learning system determines the fewest, smallest doses that could still shrink brain tumors

MIT researchers are employing novel machine-learning techniques to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for glioblastoma, the most aggressive form of brain cancer.

Glioblastoma is a malignant tumor that appears in the brain or spinal cord, and prognosis for adults is no more than five years. Patients must endure a combination of radiation therapy and multiple drugs taken every month. Medical professionals generally administer maximum safe drug doses to shrink the tumor as much as possible. But these strong pharmaceuticals still cause debilitating side effects in patients.

In a paper being presented next week at the 2018 Machine Learning for Healthcare conference at Stanford University, MIT Media Lab researchers detail a model that could make dosing regimens less toxic but still effective. Powered by a “self-learning” machine-learning technique, the model looks at treatment regimens currently in use, and iteratively adjusts the doses. Eventually, it finds an optimal treatment plan, with the lowest possible potency and frequency of doses that should still reduce tumor sizes to a degree comparable to that of traditional regimens.

In simulated trials of 50 patients, the machine-learning model designed treatment cycles that reduced the potency to a quarter or half of nearly all the doses while maintaining the same tumor-shrinking potential. Many times, it skipped doses altogether, scheduling administrations only twice a year instead of monthly.

“We kept the goal, where we have to help patients by reducing tumor sizes but, at the same time, we want to make sure the quality of life—the dosing toxicity—doesn’t lead to overwhelming sickness and harmful side effects,” says Pratik Shah, a principal investigator at the Media Lab who supervised this research.

The paper’s first author is Media Lab researcher Gregory Yauney.

Rewarding good choices

The researchers’ model uses a technique called reinforced learning (RL), a method inspired by behavioral psychology, in which a model learns to favor certain behavior that leads to a desired outcome.

The technique comprises artificially intelligent “agents” that complete “actions” in an unpredictable, complex environment to reach a desired “outcome.” Whenever it completes an action, the agent receives a “reward” or “penalty,” depending on whether the action works toward the outcome. Then, the agent adjusts its actions accordingly to achieve that outcome.

Rewards and penalties are basically positive and negative numbers, say +1 or -1. Their values vary by the action taken, calculated by probability of succeeding or failing at the outcome, among other factors. The agent is essentially trying to numerically optimize all actions, based on reward and penalty values, to get to a maximum outcome score for a given task.

The approach was used to train the computer program DeepMind that in 2016 made headlines for beating one of the world’s best human players in the game “Go.” It’s also used to train driverless cars in maneuvers, such as merging into traffic or parking, where the vehicle will practice over and over, adjusting its course, until it gets it right.

The researchers adapted an RL model for glioblastoma treatments that use a combination of the drugs temozolomide (TMZ) and procarbazine, lomustine, and vincristine (PVC), administered over weeks or months.

The model’s agent combs through traditionally administered regimens. These regimens are based on protocols that have been used clinically for decades and are based on animal testing and various clinical trials. Oncologists use these established protocols to predict how much doses to give patients based on weight.

As the model explores the regimen, at each planned dosing interval—say, once a month—it decides on one of several actions. It can, first, either initiate or withhold a dose. If it does administer, it then decides if the entire dose, or only a portion, is necessary. At each action, it pings another clinical model—often used to predict a tumor’s change in size in response to treatments—to see if the action shrinks the mean tumor diameter. If it does, the model receives a reward.

However, the researchers also had to make sure the model doesn’t just dish out a maximum number and potency of doses. Whenever the model chooses to administer all full doses, therefore, it gets penalized, so instead chooses fewer, smaller doses. “If all we want to do is reduce the mean tumor diameter, and let it take whatever actions it wants, it will administer drugs irresponsibly,” Shah says. “Instead, we said, ‘We need to reduce the harmful actions it takes to get to that outcome.'”

This represents an “unorthodox RL model, described in the paper for the first time,” Shah says, that weighs potential negative consequences of actions (doses) against an outcome (tumor reduction). Traditional RL models work toward a single outcome, such as winning a game, and take any and all actions that maximize that outcome. On the other hand, the researchers’ model, at each action, has flexibility to find a dose that doesn’t necessarily solely maximize tumor reduction, but that strikes a perfect balance between maximum tumor reduction and low toxicity. This technique, he adds, has various medical and clinical trial applications, where actions for treating patients must be regulated to prevent harmful side effects.

Optimal regimens

The researchers trained the model on 50 simulated patients, randomly selected from a large database of glioblastoma patients who had previously undergone traditional treatments. For each patient, the model conducted about 20,000 trial-and-error test runs. Once training was complete, the model learned parameters for optimal regimens. When given new patients, the model used those parameters to formulate new regimens based on various constraints the researchers provided.

The researchers then tested the model on 50 new simulated patients and compared the results to those of a conventional regimen using both TMZ and PVC. When given no dosage penalty, the model designed nearly identical regimens to human experts. Given small and large dosing penalties, however, it substantially cut the doses’ frequency and potency, while reducing tumor sizes.

The researchers also designed the model to treat each patient individually, as well as in a single cohort, and achieved similar results (medical data for each patient was available to the researchers). Traditionally, a same dosing regimen is applied to groups of patients, but differences in tumor size, medical histories, genetic profiles, and biomarkers can all change how a patient is treated. These variables are not considered during traditional clinical trial designs and other treatments, often leading to poor responses to therapy in large populations, Shah says.

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Study finds greater public awareness still needed about dementia

Common beliefs and misconceptions in the community about dementia are still proving obstacles to treatment despite a rise in public awareness campaigns, an Australian study has found.

Researchers from Flinders University in South Australia pooled the results of 32 dementia surveys from around the world published between 2012 and 2017 and found that public awareness of the causes of dementia has not changed.

Almost half of the total 36,519 respondents had the common misconception that dementia was a normal part of ageing and was not preventable. The importance of formal educational attainment and management of cardiovascular were acknowledged by less than half of respondents even though regular exercise has been proven as the single most powerful influencer of brain health.

The public also tended to endorse poorly supported risk reduction strategies such as taking vitamin supplements, ahead of more effective but time consuming and energetic strategies, such as exercise regimes.

“We were surprised to find that dementia literacy is still so poor, given how much effort has been put into improving understanding,” said lead researcher Monica Cations.

“The view that dementia is a normal part of ageing with few treatment options is a demonstrated barrier to both preventive health behaviours and to help-seeking and diagnosis in the event that symptoms emerge.”

The 32 surveys were sourced from Europe (12), the United States (11), Asia (7) and Australia (2).

The findings and associated problems are outlined in the paper, “What does the general public understand about prevention and treatment of dementia? A systematic review of population-based surveys,” which has been published by PLoS ONE

There are about 47 million people living with dementia worldwide.

While research has not yet discovered a cure, there is accumulating evidence about the potential to prevent approximately one third of cases of dementia with management of risk factors such as poor educational attainment, hypertension, and depression.

The recently adopted World Health Organization (WHO) Global Action Plan on Dementia urges all countries to implement campaigns to raise awareness about dementia. The plan includes a global target that all member countries will have at least one public awareness campaign on dementia by 2025.

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