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How artificial intelligence might disrupt diagnostics in hematology in the near future

#artificialintelligence

Artificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?


Artificial intelligence system can help prevent anemia in patients undergoing hemodialysis

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IMAGE: AISACS received a total of five inputs and churned out dosage direction probabilities for erythropoiesis-stimulating agents and iron supplements. It was noted that AISACS sometimes produces "clinically appropriate " directions that... view more Anemia, a condition characterized by the lack of healthy red blood cells in the body, is common in patients with chronic kidney disease who need to undergo routine hemodialysis (a process that helps to "clean" the blood when the kidneys don't function well). Thus, red blood cell-stimulating agents (called "erythropoiesis-stimulating agents" or ESAs) and iron supplements (ISs) are administered as part of this process. But, complications can arise if the patients have an altered iron metabolism or poor response to medications. Moreover, the medications tend to be expensive and impose a heavy financial burden on public health.


How New AI Cloud Technologies Could Help Save Lives in Healthcare Emergencies

#artificialintelligence

New technologies in healthcare are being implemented at an increasingly rapid pace, creating game changers in the way medical personnel approach, diagnose and treat life threatening emergencies. As a current example, a recent partnership between MedyMatch Technology and Samsung Neurologica could lead to invaluable information gained and time saved in the processes of providing aid to stroke victims. By doing so, EMTs and paramedics will be able to assess stroke victims faster in their pre-hospital environments. Ambulance-based mobile stroke units that are already equipped with Samsung Nuerologica's CereTom computed tomography scanner will receive integration with the artificial intelligence technology. This will enable first responders to more easily use CT scans to determine with more accuracy whether the patient is suffering from a blood clot or a brain hemorrhage.


AI helps assess pain levels in people with sickle cell disease

New Scientist

AI algorithms can assess the pain that someone with sickle cell disease is experiencing by using just their vital signs. Doing so could ensure people receive the most suitable pain management therapy for their condition. "There's always a trade-off between giving people sufficient medicine to reduce the pain and giving people too much medication so that they have bad side effects or a higher risk of addiction," says Daniel Abrams at Northwestern University in Illinois. But since pain is subjective, it is difficult to measure in a standardised way. Abrams and his colleagues set out to determine whether physiological data that is already routinely taken – including body temperature, heart rate and blood pressure – could be used to devise a system that assesses pain levels in a more objective manner.


Neuroscience: Artificial 'brain in a dish' is created that matures 'just like a human brain'

Daily Mail - Science & tech

A'brain in a dish' grown from stem cells in the lab can develop'just like a human brain' -- and may help shed light on conditions like Alzheimer's and schizophrenia. Researchers from the US conducted extensive genetic analyses of the so-called'organoids' which were allowed to grow in experimental dishes for up to 20 months. They found that the artificial brains appear to grow in phases accordance with an internal clock -- one that matches the development of real infant brains. The findings suggest that organoids are able to develop beyond a'foetal' stage, contrary to what had previously been assumed. Given this, brains organoids might well be able to be matured to such an extent that they can be used by scientists to investigate adult-onset diseases like dementia.


Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease

Science

Small-molecule screens aimed at identifying therapeutic candidates traditionally search for molecules that affect one to several outputs at most, limiting discovery of true disease-modifying drugs. Theodoris et al. developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell disease model of a common form of heart disease involving the aortic valve. Gene network correction by the most efficacious therapeutic candidate generalized to primary aortic valve cells derived from more than 20 patients with sporadic aortic valve disease and prevented aortic valve disease in vivo in a mouse model. Science , this issue p. [eabd0724][1] ### INTRODUCTION Determining the gene-regulatory networks that drive human disease allows the design of therapies that target the core disease mechanism rather than merely managing symptoms. However, small molecules used as therapeutic agents are traditionally screened for their effects on only one to several outputs at most, from which their predicted efficacy on the disease as a whole is extrapolated. In silico correlation of disease network dysregulation with pathways affected by molecules in surrogate cell types is limited by the relevance of the cell types used and by not directly testing compounds in patient cells. ### RATIONALE In principle, mapping the architecture of the dysregulated network in disease-relevant cells differentiated from patient-derived induced pluripotent stem cells (iPSCs) and subsequent screening for small molecules that broadly correct the abnormal gene network could overcome this obstacle. Specifically, targeting normalization of the core regulatory elements that drive the disease process, rather than correction of peripheral downstream effectors that may not be disease modifying, would have the greatest likelihood of therapeutic success. We previously demonstrated that haploinsufficiency of NOTCH1 can cause calcific aortic valve disease (CAVD), the third most common form of heart disease, and that the underlying mechanism involves derepression of osteoblast-like gene networks in cardiac valve cells. There is no medical therapy for CAVD, and in the United States alone, >100,000 surgical valve replacements are performed annually to relieve obstruction of blood flow from the heart. Many of these occur in the setting of a congenital aortic valve anomaly present in 1 to 2% of the population in which the aortic valve has two leaflets (bicuspid) rather than the normal three leaflets (tricuspid). Bicuspid valves in humans can also be caused by NOTCH1 mutations and predispose to early and more aggressive calcification in adulthood. Given that valve calcification progresses with age, a medical therapy that could slow or even arrest progression would have tremendous impact. ### RESULTS We developed a machine-learning approach to identify small molecules that sufficiently corrected gene network dysregulation in NOTCH1-haploinsufficient human iPSC-derived endothelial cells (ECs) such that they classified similar to NOTCH1 +/+ ECs derived from gene-corrected isogenic iPSCs. We screened 1595 small molecules for their effect on a signature of 119 genes representative of key regulatory nodes and peripheral genes from varied regions of the inferred NOTCH1-dependent network, assayed by targeted RNA sequencing (RNA-seq). Overall, eight molecules were validated to sufficiently correct the network signature such that NOTCH1 +/– ECs classified as NOTCH1 +/+ by the trained machine-learning algorithm. Of these, XCT790, an inverse agonist of estrogen-related receptor α (ERRα), had the strongest restorative effect on the key regulatory nodes SOX7 and TCF4 and on the network as a whole, as shown by full transcriptome RNA-seq. Gene network correction by XCT790 generalized to human primary aortic valve ECs derived from explanted valves from >20 patients with nonfamilial CAVD. XCT790 was effective in broadly restoring dysregulated genes toward the normal state in both calcified tricuspid and bicuspid valves, including the key regulatory nodes SOX7 and TCF4 . Furthermore, XCT790 was sufficient to prevent as well as treat already established aortic valve disease in vivo in a mouse model of Notch1 haploinsufficiency on a telomere-shortened background. XCT790 significantly reduced aortic valve thickness, the extent of calcification, and echocardiographic signs of valve stenosis in vivo. XCT790 also reduced the percentage of aortic valve cells expressing the osteoblast transcriptional regulator RUNX2, indicating a reduction in the osteogenic cell fate switch underlying CAVD. Whole-transcriptome RNA-seq in treated aortic valves showed that XCT790 broadly corrected the genes dysregulated in Notch1-haploinsufficient mice with shortened telomeres, and that treatment of diseased aortic valves promoted clustering of the transcriptome with that of healthy aortic valves. ### CONCLUSION Network-based screening that leverages iPSC and machine-learning technologies is an effective strategy to discover molecules with broadly restorative effects on gene networks dysregulated in human disease that can be validated in vivo. XCT790 represents an entry point for developing a much-needed medical therapy for calcification of the aortic valve, which may also affect the highly related and associated calcification of blood vessels. Given the efficacy of XCT790 in limiting valve thickening, the potential for XCT790 to alter the progression of childhood, and perhaps even fetal, valve stenosis also warrants further study. Application of this strategy to other human models of disease may increase the likelihood of identifying disease-modifying candidate therapies that are successful in vivo. ![Figure][2] Network-correcting therapeutic candidate for heart disease. A gene network–based screening approach leveraging human disease-specific iPSCs and machine learning identified a therapeutic candidate, XCT790, which corrected the network dysregulation in genetically defined iPSC-derived endothelial cells and primary aortic valve endothelial cells from >20 patients with sporadic aortic valve disease. XCT790 was also effective in preventing and treating a mouse model of aortic valve disease. ILLUSTRATION: CHRISTINA V. THEODORIS Mapping the gene-regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve (AV). Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary AV cells and was sufficient to prevent and treat AV disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery. [1]: /lookup/doi/10.1126/science.abd0724 [2]: pending:yes


Using AI-enhanced music-supported therapy to assist stroke patients

AIHub

Stroke currently ranks as the second most common cause of death and the second most common cause of disability worldwide. Motor deficits of the upper extremity (hemiparesis) are the most common and debilitating consequences of stroke, affecting around 80% of patients. These deficits limit the accomplishment of daily activities, affect social participation, are the origin of significant emotional distress, and cause profound detrimental effects on quality of life. Stroke rehabilitation aims to improve and maintain functional ability through restitution, substitution and compensation of functions. The restoration of motor deficits and improvements in motor function typically occurs during the first months following a stroke and therefore, major efforts are devoted to this acute stage.


2020 was a tough year, but there is a silver lining

Boston Herald

For obvious reasons, 2020 will not go down as a good year. At the same time, it has brought more scientific progress than any year in recent memory -- and these advances will last long after COVID-19 as a major threat is gone. Two of the most obvious and tangible signs of progress are the mRNA vaccines now being distributed across America and around the world. These vaccines appear to have very high levels of efficacy and safety, and they can be produced more quickly than more conventional vaccines. They are the main reason to have a relatively optimistic outlook for 2021.


Mouse embryonic stem cells self-organize into trunk-like structures with neural tube and somites

Science

Building mammalian embryos from self-organizing stem cells in culture would accelerate the investigation of morphogenetic and differentiation processes that shape the body plan. Veenvliet et al. report a method for generating embryonic trunk-like structures (TLSs) with a neural tube, somites, and gut by embedding mouse embryonic stem cell aggregates in an extracellular matrix surrogate. Live imaging and comparative single-cell transcriptomics indicate that TLS formation is analogous to mouse development. TLSs therefore provide a scalable, tractable, and accessible high-throughput platform for decoding mammalian embryogenesis at a high level of resolution. Science , this issue p. [eaba4937][1] ### INTRODUCTION Vertebrate development comprises multiple complex morphogenetic processes that shape the embryonic body plan through self-organization of pluripotent stem cells and their descendants. Because mammalian embryogenesis proceeds in utero, it is difficult to study the dynamics of these processes, including much-needed analysis at the cellular and molecular level. Various three-dimensional stem cell systems (“embryoids”) have been developed to circumvent this impediment. The most advanced models of post-implantation development achieved so far are gastruloids, mouse embryonic stem cell (mESC)–derived aggregates with organized gene expression domains but lacking proper morphogenesis. ### RATIONALE To advance the current models, we explored the usage of Matrigel, an extracellular matrix (ECM) surrogate. During embryonic development, the ECM provides essential chemical and mechanical cues. In vitro, lower percentages of Matrigel can drive complex tissue morphogenesis in organoids, which led us to use Matrigel embedding in various media conditions to achieve higher-order embryo-like architecture in mESC-derived aggregates. ### RESULTS We found that embedding of 96-hour gastruloids in 5% Matrigel is sufficient to induce the formation of highly organized “trunk-like structures” (TLSs), comprising the neural tube and bilateral somites with embryo-like polarity. This high level of self-organization was accompanied by accumulation of the matrix protein fibronectin at the Matrigel-TLS interface and the transcriptional up-regulation of fibronectin-binding integrins and other cell adhesion molecules. Chemical modulation of signaling pathways active in the developing mouse embryo [WNT and bone morphogenetic protein (BMP)] resulted in an excess of somites arranged like a “bunch of grapes.” Comparative time-resolved single-cell RNA sequencing of TLSs and embryos revealed that TLSs follow the same stepwise gene regulatory programs as the mouse embryo, comprising expression of critical developmental regulators at the right place and time. In particular, trunk precursors known as neuromesodermal progenitors displayed the highest differentiation potential and continuously contributed to neural and mesodermal tissue during TLS formation. In addition, live imaging demonstrated that the segmentation clock, required for rhythmic deposition of somites in vivo, ticks at an embryo-like pace in TLSs. Finally, a proof-of-principle experiment showed that Tbx6-knockout TLSs generate ectopic neural tubes at the expense of somite formation, mirroring the embryonic phenotype. ### CONCLUSION We showed that embedding of embryonic stem cell–derived aggregates in an ECM surrogate generates more advanced in vitro models that are formed in a process highly analogous to embryonic development. Trunk-like structures represent a powerful tool that is easily amenable to genetic, mechanical, chemical, or other modulations. As such, we expect them to facilitate in-depth analysis of molecular mechanisms and signaling networks that orchestrate embryonic development as well as studies of the ontogeny of mutant phenotypes in the culture dish. The scalable, tractable, and highly accessible nature of the TLS makes it a complementary in vitro platform for deciphering the dynamics of the molecular, cellular, and morphogenetic processes that shape the post-implantation embryo, at an unprecedented spatiotemporal resolution. ![Figure][2] Engineering the embryonic trunk in a dish. Embedding of mouse embryonic stem cell (mESC) aggregates in an extracellular matrix (ECM) enables generation of trunk-like structures (TLSs) with an in vivo–like architecture including gut, and neuromesodermal progenitor (NMP)–derived neural tube and somites. Comparative single-cell RNA sequencing revealed that TLS cell states and differentiation dynamics match those of the embryo. Chemical modulation and genetic manipulation highlight the utility of the TLS as a scalable, tractable, and accessible model for investigating mid-gestational embryogenesis. FN1, fibronectin. Post-implantation embryogenesis is a highly dynamic process comprising multiple lineage decisions and morphogenetic changes that are inaccessible to deep analysis in vivo. We found that pluripotent mouse embryonic stem cells (mESCs) form aggregates that upon embedding in an extracellular matrix compound induce the formation of highly organized “trunk-like structures” (TLSs) comprising the neural tube and somites. Comparative single-cell RNA sequencing analysis confirmed that this process is highly analogous to mouse development and follows the same stepwise gene-regulatory program. Tbx6 knockout TLSs developed additional neural tubes mirroring the embryonic mutant phenotype, and chemical modulation could induce excess somite formation. TLSs thus reveal an advanced level of self-organization and provide a powerful platform for investigating post-implantation embryogenesis in a dish. [1]: /lookup/doi/10.1126/science.aba4937 [2]: pending:yes


AI algorithm can detect, quantify brain infarcts

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Researchers discussed how they used a deep-learning algorithm to detect, quantify, and assess the severity of infarcts in the brain on diffusion-weighted MRI (DWI-MRI) exams in acute ischemic stroke patients in a Sunday presentation at the virtual RSNA 2020 meeting. A team of researchers led by presenter Seung Hyun Hwang of Yonsei University in Seoul, South Korea, developed a deep-learning model that can segment and quantify brain infarcts using DWI-MRI and then assess their severity by analyzing apparent diffusion coefficient (ADC) maps of the lesions. In testing, the model achieved high sensitivity and specificity. "The qualitative and quantitative results of our study show feasibility for detecting and quantifying infarcts," Hwang said. Due to its sensitivity for the detection of small and early infarcts, DWI-MRI is commonly used for evaluation of acute ischemic stroke, according to Hwang.