A capsule that could replace insulin shots. In Giovanni Traverso's lab, the focus is always on making life better for patients. Every Monday, more than a hundred members of Giovanni Traverso's Laboratory for Translational Engineering (L4TE) fill a large classroom at Brigham and Women's Hospital for their weekly lab meeting. With a social hour, food for everyone, and updates across disciplines from mechanical engineering to veterinary science, it's a place where a stem cell biologist might weigh in on a mechanical design, or an electrical engineer might spot a flaw in a drug delivery mechanism. And it's a place where everyone is united by the same goal: engineering new ways to deliver medicines and monitor the body to improve patient care. Traverso's weekly meetings bring together a mix of expertise that lab members say is unusual even in the most collaborative research spaces. But his lab--which includes its own veterinarian and a dedicated in vivo team--isn't built like most.

First, the integration of artificial intelligence-facilitated algorithms for the early detection of cardiovascular illness, which will move us closer toward early prevention. We also envision a focus on using genetically informed treatments to reduce the risk of atherosclerotic heart disease, valvular heart disease and heart failure. Together, these important advances will usher in an era of personalized health care in cardiovascular disease."

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

Researchers in the Mahmood lab at Brigham and Women's Hospital have developed a new deep learning algorithm that is capable of teaching itself to search large datasets of pathology images to identify similar cancer cases. The tool, called SISH for "Self-Supervised Image Search for Histology," has the ability to identify analogous features in pathology images and uses that information to both pinpoint the form of disease, while also helping doctors and other clinicians determine which therapies will be most effective for each patient. Details of the algorithm were published today in the journal Nature Biomedical Engineering. "We show that our system can assist with the diagnosis of rare diseases and find cases with similar morphologic patterns without the need for manual annotations, and large datasets for supervised training," said senior author Faisal Mahmood, PhD, in the Brigham's Department of Pathology. "This system has the potential to improve pathology training, disease subtyping, tumor identification, and rare morphology identification."

Multimodal AI models, trained on numerous types of data, could help doctors screen patients at risk of developing multiple different cancers more accurately.. Researchers from the Brigham and Women's Hospital part of Harvard University's medical school developed a deep learning model capable of identifying 14 types of cancer. Most AI algorithms are trained to spot signs of disease from a single source of data, like medical scans, but this one can take inputs from multiple sources. Predicting whether someone is at risk of developing cancer isn't always as straightforward, doctors often have to consult various types of information like a patient's healthcare history or perform other tests to detect genetic biomarkers. These results can help doctors figure out the best treatment for a patient as they monitor the progression of the disease, but their interpretation of the data can be subjective, Faisal Mahmood, an assistant professor working at the Division of Computational Pathology at the Brigham and Women's Hospital, explained. "Experts analyze many pieces of evidence to predict how well a patient may do. These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens. But that means that this multimodal prediction happens at the level of the expert. We're trying to address the problem computationally," he said in a statement.

We all have a craving for chocolate now and again, but not usually when we first wake up. However, a new study has claimed that eating the sugary snack for breakfast could actually have'unexpected benefits' by helping your body burn fat. Researchers in Boston, Massachusetts gave 100 grams of milk chocolate to 19 post-menopausal women within one hour after waking up and one hour before bedtime. Starting the day with chocolate could actually help your body burn fat, scientists at Brigham and Women's Hospital in Boston say That is about the equivalent of two standard-sized Mars bars (58g) – although the researchers used standard milk chocolate containing 18.1g of cocoa. Amazingly, the team discovered that neither morning or night time milk chocolate intake led to weight gain, likely because it acted as an appetite suppressant.

With the Covid-19 pandemic hitting hard and social distancing becoming a vital norm, this opens the door for using more robots to provide health care services to reduce in-person contact between the health care workers and the patients. Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and also the senior author of the study said, that they were actively working on robots that can help provide health care services to maximize the safety, of both the patients and the health care workforce. Traverso and his colleagues after the Covid-19 began last year, worked towards reducing interaction between the patients and the health care workers. In this process, they collaborated with Boston Dynamics in creating mobile robots that can interact with patients who waited in the emergency department. But the question here is, how patients are going to respond to the robots?

Google" and researching health issues online makes patients better at diagnosing illnesses and doesn't make them more anxious, a new study out of Harvard and Brigham and Women's Hospital shows. "Every doctor has their story about the patient who has pinky pain who thought they had cancer," said Dr. David Levine, corresponding author of the study and internist at Brigham and Women's. But Levine said that's certainly not the norm, and he loves when his patients Google their symptoms before arriving at his office, "I think it shows they're invested in what's going on." Levine and his colleagues found that study participants showed modest improvements in reaching an accurate diagnosis after looking up symptoms online and reported no increase in "cyberchondria," or anxiety about one's health associated with using the internet. Googling health symptoms has often been thought of as a no-no due to online misinformation or the potential to stoke fear in patients, but Levine said the research findings show that's not quite true.

In the era of social distancing, using robots for some health care interactions is a promising way to reduce in-person contact between health care workers and sick patients. However, a key question that needs to be answered is how patients will react to a robot entering the exam room. Researchers from MIT and Brigham and Women's Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women's, the team found that a large majority of patients reported that interacting with a health care provider via a video screen mounted on a robot was similar to an in-person interaction with a health care worker. "We're actively working on robots that can help provide care to maximize the safety of both the patient and the health care workforce. The results of this study give us some confidence that people are ready and willing to engage with us on those fronts," says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and the senior author of the study.

Researchers from MIT and Brigham and Women's Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women's, the team found that a large majority of patients reported that interacting with a health care provider via a video screen mounted on a robot was similar to an in-person interaction with a health care worker. "We're actively working on robots that can help provide care to maximize the safety of both the patient and the health care workforce. The results of this study give us some confidence that people are ready and willing to engage with us on those fronts," says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and the senior author of the study. In a larger online survey conducted nationwide, the researchers also found that a majority of respondents were open to having robots not only assist with patient triage but also perform minor procedures such as taking a nose swab.