This replica of the body's blood factory is made entirely with human cells. Breakthroughs, discoveries, and DIY tips sent every weekday. Without even thinking about it, the bone marrow in your body is churning out billions of cells every single day. Bone marrow is our body's strong and silent "blood factory," working hard in the background while heart pumps and brain controls. The spongy marrow really gets attention during a blood cancer diagnosis or when this crucial system stops working properly.

Things to Do in L.A. Tap to enable a layout that focuses on the article. 'I just wanted to help.' Father turns to 9-year-old son for lifesaving stem cell donation Stephen Mondek became what Cedars-Sinai Medical Center believes is its youngest known stem cell donor. His father was dying of acute myeloid leukemia, a cancer that affects blood-forming cells in the bone marrow, and needed a donation to rebuild his immune system. This is read by an automated voice. Please report any issues or inconsistencies here .

Representing and quantifying Minimal Residual Disease (MRD) in Acute Myeloid Leukemia (AML), a type of cancer that affects the blood and bone marrow, is essential in the prognosis and follow-up of AML patients. As traditional cytological analysis cannot detect leukemia cells below 5\%, the analysis of flow cytometry dataset is expected to provide more reliable results. In this paper, we explore statistical learning methods based on optimal transport (OT) to achieve a relevant low-dimensional representation of multi-patient flow cytometry measurements (FCM) datasets considered as high-dimensional probability distributions. Using the framework of OT, we justify the use of the K-means algorithm for dimensionality reduction of multiple large-scale point clouds through mean measure quantization by merging all the data into a single point cloud. After this quantization step, the visualization of the intra and inter-patients FCM variability is carried out by embedding low-dimensional quantized probability measures into a linear space using either Wasserstein Principal Component Analysis (PCA) through linearized OT or log-ratio PCA of compositional data. Using a publicly available FCM dataset and a FCM dataset from Bordeaux University Hospital, we demonstrate the benefits of our approach over the popular kernel mean embedding technique for statistical learning from multiple high-dimensional probability distributions. We also highlight the usefulness of our methodology for low-dimensional projection and clustering patient measurements according to their level of MRD in AML from FCM. In particular, our OT-based approach allows a relevant and informative two-dimensional representation of the results of the FlowSom algorithm, a state-of-the-art method for the detection of MRD in AML using multi-patient FCM.

ABSTRACT Typical state of the art flow cytometry data samples consists of measures of more than 100.000 cells in 10 or more features. AI systems are able to diagnose such data with almost the same accuracy as human experts. However, there is one central challenge in such systems: their decisions have far-reaching consequences for the health and life of people, and therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI method, called ALPODS, which is able to classify (diagnose) cases based on clusters, i.e., subpopulations, in the high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable for human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison to a selection of state of the art explainable AI systems shows that ALPODS operates efficiently on known benchmark data and also on everyday routine case data. KEYWORDS: Explainable AI, Expert System, Symbolic System, Biomedical Data 1. INTRODUCTION State of the art machine learning (ML) artificial intelligence (AI) algorithms are effectively and efficiently able to diagnose (classify) high-dimensional data sets in modern medicine, e.g., for multiparameter flow cytometry data [Hu et al., 2019; Zhao et al., 2020]. These are systems that, after a training (learning) phase using learning data, perform well on data that are not part of the training data, i.e., the test data. This is called supervised learning [Murphy, 2012].

An'electric nose' capable of sniffing out hard-to-detect cancers with 95 percent accuracy may soon change the way specialists diagnose the potentially fatal disease. Scientists at the University of Pennsylvania designed an artificial intelligent-powered system equipped with nanosensors to identify vapors from blood samples that are specific to benign, pancreatic and ovarian cancer cells. The tool also correctly identified all patients with early-stage cancers and did so in less than 20 minutes – traditional methods can take days or weeks to produce results. To test the electric nose, the team analyzed samples from 93 patients. Approximately 20 had ovarian cancer, 20 with benign ovarian tumors and 20 age-matched controls with no cancer, as well as 13 patients with pancreatic cancer, 10 patients with benign pancreatic disease, and 10 controls.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying and assessing lesion severity in cartilage, bone marrow, meniscus, and anterior cruciate ligament (ACL) in the knee, improving overall MRI interreader agreement. This retrospective study was conducted on 1435 knee MRIs (n 294 patients, mean age, 43 15 years, 153 women), collected within three previous studies (from 2011 to 2014).

The blood from human umbilical cords may be the key ingredient for a'fountain of youth' drug, a new study suggests. Researchers say a protein found in cord blood can reverse the effects of age-associated mental declines. The protein affects the hippocampus, the part of the brain that converts experiences into long-term memories and is essential for helping you remember information. If so, this would add to the growing number of benefits that have been discovered using cord blood. Researchers at Stanford University School of Medicine, in California, have identified a protein called tissue inhibitor of metalloproteases 2, or TIMP2, which is very common in human cord blood.

In this paper we explore representations of temporal knowledge based upon the formalism of Causal Probabilistic Networks (CPNs). Two different ?continuous-time? representations are proposed. In the first, the CPN includes variables representing ?event-occurrence times?, possibly on different time scales, and variables representing the ?state? of the system at these times. In the second, the CPN describes the influences between random variables with values in () representing dates, i.e. time-points associated with the occurrence of relevant events. However, structuring a system of inter-related dates as a network where all links commit to a single specific notion of cause and effect is in general far from trivial and leads to severe difficulties. We claim that we should recognize explicitly different kinds of relation between dates, such as ?cause?, ?inhibition?, ?competition?, etc., and propose a method whereby these relations are coherently embedded in a CPN using additional auxiliary nodes corresponding to "instrumental" variables. Also discussed, though not covered in detail, is the topic concerning how the quantitative specifications to be inserted in a temporal CPN can be learned from specific data.