Objective: The objective of this work is to introduce and demonstrate the effectiveness of a novel sensing modality for contact detection between an off-the-shelf aspiration catheter and a thrombus. Methods: A custom robotic actuator with a pressure sensor was used to generate an oscillatory vacuum excitation and sense the pressure inside the extracorporeal portion of the catheter. Vacuum pressure profiles and robotic motion data were used to train a support vector machine (SVM) classification model to detect contact between the aspiration catheter tip and a mock thrombus. Validation consisted of benchtop accuracy verification, as well as user study comparison to the current standard of angiographic presentation. Results: Benchtop accuracy of the sensing modality was shown to be 99.67%. The user study demonstrated statistically significant improvement in identifying catheter-thrombus contact compared to the current standard. The odds ratio of successful detection of clot contact was 2.86 (p=0.03) when using the proposed sensory method compared to without it. Conclusion: The results of this work indicate that the proposed sensing modality can offer intraoperative feedback to interventionalists that can improve their ability to detect contact between the distal tip of a catheter and a thrombus. Significance: By offering a relatively low-cost technology that affords off-the-shelf aspiration catheters as clot-detecting sensors, interventionalists can improve the first-pass effect of the mechanical thrombectomy procedure while reducing procedural times and mental burden.

An invention by scientists in the Netherlands aims to break up blood clots without surgery or drugs. Blood clots are a serious health problem that can cause strokes, heart attacks and even death. Some blood clots can be removed by doctors using a flexible tool that goes inside the affected vein or artery, but others are too hard to reach. What if there was a way to break up those clots without surgery or drugs? CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Scientists have created tiny robots that can swim through your blood vessels and drill into the clots.

Chain-of-Thought (CoT) guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs -- a non-sequential, creative paradigm involving strong associations and knowledge leaps. To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study. Then to investigate LLMs' LoT ability in the Oogiri game, we first build a multimodal and multilingual Oogiri-GO dataset which contains over 130,000 samples from the Oogiri game, and observe the insufficient LoT ability or failures of most existing LLMs on the Oogiri game. Accordingly, we introduce a creative Leap-of-Thought (CLoT) paradigm to improve LLM's LoT ability. CLoT first formulates the Oogiri-GO dataset into LoT-oriented instruction tuning data to train pretrained LLM for achieving certain LoT humor generation and discrimination abilities. Then CLoT designs an explorative self-refinement that encourages the LLM to generate more creative LoT data via exploring parallels between seemingly unrelated concepts and selects high-quality data to train itself for self-refinement. CLoT not only excels in humor generation in the Oogiri game but also boosts creative abilities in various tasks like cloud guessing game and divergent association task. These findings advance our understanding and offer a pathway to improve LLMs' creative capacities for innovative applications across domains. The dataset, code, and models will be released online. https://zhongshsh.github.io/CLoT/.

Online Class Incremental learning (CIL) is a challenging setting in Continual Learning (CL), wherein data of new tasks arrive in incoming streams and online learning models need to handle incoming data streams without revisiting previous ones. Existing works used a single centroid adapted with incoming data streams to characterize a class. This approach possibly exposes limitations when the incoming data stream of a class is naturally multimodal. To address this issue, in this work, we first propose an online mixture model learning approach based on nice properties of the mature optimal transport theory (OT-MM). Specifically, the centroids and covariance matrices of the mixture model are adapted incrementally according to incoming data streams. The advantages are two-fold: (i) we can characterize more accurately complex data streams and (ii) by using centroids for each class produced by OT-MM, we can estimate the similarity of an unseen example to each class more reasonably when doing inference. Moreover, to combat the catastrophic forgetting in the CIL scenario, we further propose Dynamic Preservation. Particularly, after performing the dynamic preservation technique across data streams, the latent representations of the classes in the old and new tasks become more condensed themselves and more separate from each other. Together with a contraction feature extractor, this technique facilitates the model in mitigating the catastrophic forgetting. The experimental results on real-world datasets show that our proposed method can significantly outperform the current state-of-the-art baselines.

Stroke is no longer just a danger for seniors as more young adults are suffering them. Lieutenant Governor John Fetterman is 52. Now, Main Line Health has a new technology to help stop strokes faster, if they're recognized quickly. Dominique Jones from Wynnefield just didn't feel right one morning in March. "When I was taking my shower, I had a headache," she recalled.

MIT engineers have developed a telerobotic system to help surgeons quickly and remotely treat patients experiencing a stroke or aneurysm. With a modified joystick, surgeons in one hospital may control a robotic arm at another location to safely operate on a patient during a critical window of time that could save the patient's life and preserve their brain function. The robotic system, whose movement is controlled through magnets, is designed to remotely assist in endovascular intervention -- a procedure performed in emergency situations to treat strokes caused by a blood clot. Such interventions normally require a surgeon to manually guide a thin wire to the clot, where it can physically clear the blockage or deliver drugs to break it up. One limitation of such procedures is accessibility: Neurovascular surgeons are often based at major medical institutions that are difficult to reach for patients in remote areas, particularly during the "golden hour" -- the critical period after a stroke's onset, during which treatment should be administered to minimize any damage to the brain.

Blood clots might be soon be cleared by using microscopic robots with screw-like propellers that swim through blood vessels bearing blockage-busting drugs. Developed by engineers from the Chinese University of Hong Kong, the microrobot design was inspired by the tails of bacteria like E. coli. The tiny robot and its propeller are driven by the application of an external magnetic field -- and it is capable of moving with and against the direction of the blood flow. Tests in a fake vein filled with pig's blood showed the robot makes its clot-dissolving'tissue plasminogen activator' cargo five times more effective than the drug alone. The robot's rotor, the team said, may help to circulate the drug around the blockage site -- better breaking up the clots and reducing the risk of large fragments.

Pulmonary embolisms are dangerous, lung-clogging blot clots. In a pilot study, scientists at the Icahn School of Medicine at Mount Sinai showed for the first time that artificial intelligence (AI) algorithms can detect signs of these clots in electrocardiograms (EKGs), a finding which may one day help doctors with screening. The results published in the European Heart Journal – Digital Health suggested that new machine learning algorithms, which are designed to exploit a combination of EKG and electronic health record (EHR) data, may be more effective than currently used screening tests at determining whether moderate- to high-risk patients actually have pulmonary embolisms. The study was led by Sulaiman S. Somani, MD, a former medical student in the lab of Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences and a member of the Hasso Plattner Institute for Digital Health at Mount Sinai. Pulmonary embolisms happen when deep vein blood clots, usually formed in the legs or arms, break away and clog lung arteries. These clots can be lethal or cause long-term lung damage.

Before 2019, Doctors at CHI Health had to wait up to three hours before diagnosing stroke victims. But thanks to artificial intelligence, they can now do that within just six minutes. On Aug. 28, Natalie Carr was getting ready for bed when she realized something wasn't right. "I remember sitting there and looking down at my hand. It felt like it was falling asleep," Carr said.

Children who spend hours playing video games could be at risk of developing a potentially deadly medical condition called deep vein thrombosis, experts warn. Deep vein thrombosis (DVT) is a blood clot that forms in the veins of one's legs - and the risks of getting DVT are higher if you sit still or lie down for extended periods of time without moving. While DVT is more common among the elderly, new research from the Medical Research Institute of New Zealand shows that it can also be triggered in young children who live sedentary lifestyles. This is why children who play video games - whether they're sitting or lying down - for up to three hours or more could potentially develop deep vein thrombosis. In one case, a boy as young as 12 suffered from DVT after he played video games for four hours straight in a kneeling position, The Telegraph reported.