Everyone Should Hear My COVID Vaccine Experience
On December 18th, 2020, I received my first dose of the Pfizer mRNA vaccine against SARS-CoV-2. On January 9th, 2021, I received my second. I am now a CDC-card-carrying, fully vaccinated person.
The build-up to the first dose was momentous. I was scheduled for the first dose of the morning. Our vaccine clinic was abuzz with excitement and hope, and some media folks were there to capture the moment. A couple of fellow emergency physicians were in the same cohort of recipients as I; we exchanged virtual high-fives and took a picture of socially distanced hugs. It was, after all, the closest thing we'd had to a celebration in months.
I walked in the vaccine administration room with anticipation – it was tough to believe this moment was truly, finally here. I got a little video of my getting the shot, took my obligate vaccine selfie, waited in the observation area for 15 minutes to ensure I didn't have a reaction, and then proudly joined 1000s of fellow healthcare workers across the country in posting #ThisIsMyShot on social media. "Here we go, America!"
The first shot, though, didn't actually do all that much for me. It hurt less than a flu shot (which, by the way, doesn't hurt much). I had virtually no side effects. I also knew that it did not yet protect me. The Pfizer (and Moderna) data show very clearly that although the immune response starts to grow 10-12 days after the first shot, one doesn't reach full protection against COVID-19 until much later.
So when, two days after my first shot, I headed back to work in the emergency department, I kept wondering "Will this be the day that I get sick? Wouldn't that be ironic!" Although I never go without an N95 during patient care, it just takes one slip – scratching one's eyes, eating lunch in a break room that an infected colleague had just been in – to get ill. Ten months into this pandemic, it is so easy to get fatigued, to make a small error just one time.
Indeed, I had a few colleagues fall ill in between their first and second shots; one was hospitalized. This was not surprising, but still sad, given how close they had come to escaping infection.
Scientifically speaking, one doesn't need to feel bad to develop an immune response. Emotionally, though, I welcomed the symptoms as proof positive that I would be protected.
This time period felt a little like we had our learner's permit for driving: we were on our way to being safe, but not quite there yet.
I also watched, with dismay, our failures as a nation at timely distribution of the vaccine. On December 18th, despite the logistical snafus that many of us had started to highlight, it was still somewhat believable that we would at least distribute (if not actually administer) 20 million doses by the New Year. But by December 31, my worst fears about the feds' lack of planning had been realized. Only 14 million doses had gone to states, and fewer than 3 million had been administered. Within the public health and medical community, we began to debate how to handle the shortages and slow vaccination rates: should we change prioritization schemes? Get rid of the second dose, in contradiction to what our FDA had approved?
Let me be clear: I really, really, really wanted my second dose. It is what is supported by the data. After living this long at risk, it felt frankly unfair that I might not get fully protected. I waited with trepidation, afraid that policies would shift before I got it in my arm.
At last, my date for my second shot arrived.
This shot was a little less momentous on the outside. The vaccine clinic was much more crowded, as we were now administering first doses to more people, as well as providing the second dose to many. There were no high fives, no media, and I took no selfies. I finished my observation period without trouble (as did everyone else vaccinated the same day, as is typical for these vaccines). I walked out the door planning to spend a nice afternoon outdoors with my kids.
Within 15 minutes, though, the very common side effects – reported by 80% of people my age after the second dose – began to appear. First I got a headache (like 52% of people my age), then body aches (37%), fatigue (59%), and chills (35%). I felt "foggy", like I was fighting something. Like 45% of trial participants who had received the actual vaccine, I took acetaminophen and ibuprofen to stave off the symptoms. There is some minimal evidence from other vaccines that pre-treatment with these anti-inflammatories may reduce antibodies, but given that half of trial participants took these medications, there's no reason to make yourself suffer if you develop side effects. Forty-eight hours later, just in time for my next shift, the side effects magically cleared. Scientifically speaking, one doesn't need to feel bad to develop an immune response. Emotionally, though, I welcomed the symptoms as proof positive that I would be protected.
My reaction was truly typical. Although the media hype focuses on major negative reactions, they are – statistically speaking – tremendously rare: fewer than 11/million people who received the Pfizer vaccine, and 3/million who received the Moderna vaccine, developed anaphylaxis; of these, all were treated, and all are fine. Compare this with the fact that approximately 1200/million Americans have died of this virus. I'll choose the minor, temporary, utterly treatable side effects any day.
Now, more than 14 days after my second dose, the data says that my chance of getting really sick is, truly, infinitesimally low. I don't have to worry that each shift will put me into the hospital. I feel emotionally lighter, and a little bit like I have a secret super-power.
But I also know that we are not yet home free.
I may have my personal equivalent of Harry Potter's invisibility cloak – but we don't yet know whether it protects those around me, at all. As Dr. Fauci himself has written, while community spread is high, there is still a chance that I could be a carrier of infection to others. So I still wear my N95 at work, I still mask in public, and I still shower as soon as I get home from a shift and put my scrubs right in the washing machine to protect my husband and children. I also won't see my parents indoors until they, too, have been vaccinated.
At the end of the day, these vaccines are both amazing and life-changing, and not. My colleagues are getting sick less often, now that many of us are a week or more out from our second dose. I can do things (albeit still masked) that would simply not have been safe a month ago. These are small miracles, for which I am thankful. But like so many things in life, they would be better if shared with others. Only when my community is mostly vaccinated, will I breathe easy again.
My deepest hope is that we all have – and take - the chance to get our shots, soon. Because although the symbolism and effect of the vaccine is high, the experience itself was … not that big a deal.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.