Abstract: The present disclosure includes cationic carrier units comprising (i) a water-soluble polymer, (ii) a positively charged carrier, (iii) a hydrophobic moiety, and (iv) a crosslinking moiety, wherein when the cationic carrier unit is mixed with an anionic payload (e.g., an antisense oligonucleotide) that electrostatically interacts with the cationic carrier unit, the resulting composition self-organizes into a micelle encapsulating the anionic payload in its core. The cationic carrier units can also comprise a tissue specific targeting moiety, which would be displayed on the surface of the micelle. The disclosure also includes micelles comprising the cationic carrier units of the disclosure, methods of manufacture of cationic carrier units and micelles, pharmaceutical compositions comprising the micelles, and also methods of treating diseases or conditions comprising administering the micelles to a subject in need thereof.

Abstract: The austenitic stainless steel that does not cause defects such as aging crack or delayed fracture even after the expansion and curling process of 5 steps or more is disclosed. In accordance with an aspect of the present disclosure, an austenitic stainless steel with excellent pipe expanding workability and aging crack resistance includes, in percent (%) by weight of the entire composition, C: 0.01 to 0.04%, Si: 0.1 to 1.0%, Mn: 0.1 to 2.0%, Cr: 16 to 20%, Ni: 6 to 10%, Cu: 0.1 to 2.0%, Mo: 0.2% or less, N: 0.035 to 0.07%, the remainder of iron (Fe) and other inevitable impurities, and the C+N satisfies 0.1% or less, the product of the Md30 (° C.) value and average grain size (?m) satisfies less than ?500.

Abstract: A deep learning-based stent prediction method including: setting as a region of interest, a region in which a procedure is to be performed among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image, which is a preprocedural IVUS image of the region of interest, obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals, extracting feature information about procedure information of the target patient, obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images, and predicting progress of a stent procedure containing a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information.

Abstract: A method of analyzing a plaque tissue component based on deep learning, the method including: extracting a plurality of first intravascular ultrasound (IVUS) cross-sectional images into which a first IVUS image that is a preprocedural IVUS image of a patient is divided at predetermined intervals; labeling each of the plurality of first IVUS cross-sectional images by using label indices corresponding to plaque tissue components to form labeled images, performing image conversion to obtain a polar coordinate image through which a distribution of tissue components for each angle is identifiable by performing a coordinate transformation based on the labeled images, extracting a label vector for each angle based on the polar coordinate image, and outputting output data obtained by quantifying the tissue components for each angle by using an artificial intelligence model that is trained by using, as training data, the label vector for each angle.

Abstract: A cathode for a lithium secondary battery includes a cathode current collector, and a cathode active material layer formed on the cathode current collector. The cathode active material layer includes cathode active material particles. The cathode active material particles include a lithium metal oxide particle containing nickel and having a mole fraction of cobalt of 0.02 or less among all elements except lithium and oxygen.

Abstract: The present disclosure includes cationic carrier units comprising (i) a water soluble polymer, (ii) a positively charged carrier, (iii) a hydrophobic moiety, and (iv) a crosslinking moeity, wherein when the cationic carrier unit is mixed with an anionic payload (e.g., an RNA and/or DNA) that electrostatically interacts with the cationic carrier unit, the resulting composition self-organizes into a micelle encapsulating the anionic payload in its core. The cationic carrier units can also comprise a tissue specific targeting moiety, which would be displayed on the surface of the micelle. The disclosure also includes micelles comprising the cationic carrier units of the disclosure, methods of manufacture of cationic carrier units and micelles, pharmaceutical compositions comprising the micelles, and also methods of treating diseases or conditions comprising administering the micelles to a subject in need thereof.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying the predicted type of one or more microorganisms. In one aspect, a system comprises a phase-contrast microscope and a microorganism classification system. The phase-contrast microscope is configured to generate a three-dimensional quantitative phase image of one or more microorganisms. The microorganism classification system is configured to process the three-dimensional quantitative phase image using a neural network to generate a neural network output characterizing the microorganisms, and thereafter identify the predicted type of the microorganisms using the neural network output.

Abstract: The disclosure purposes to provide an optical coherence tomography (OCT)-based system for diagnosing a high risk lesion such as a vulnerable atheromatous plaque by using an artificial intelligence model through deep learning. A deep learning-based diagnostic method of diagnosing a high risk lesion of a coronary artery includes: acquiring an OCT image of a coronary artery lesion of a patient; extracting a first feature of a thin cap from the OCT image; setting a region of interest included in the OCT image on a basis of the first feature; and determining whether the region of interest includes a high risk lesion.

Abstract: A non-label diagnosis apparatus for a hematologic malignancy may include a 3-D refractive index cell imaging unit configured to generate a 3-D refractive index slide image of a blood smear specimen by capturing a 3-D refractive index image in the form of the blood smear specimen in which blood (including a bone-marrow or other body fluids) of a patient has been smeared on a slide glass, an ROI detection unit configured to sample a suspected cell segment in the blood smear specimen based on the 3-D refractive index slide image and to determine, as ROI patches, cells determined as abnormal cells, and a diagnosis unit configured to determine a sub-classification of a cancer cell corresponding to each of the ROI patches using a cancer cell sub-classification determination model constructed based on a deep learning algorithm and to generate hematologic malignancy diagnosis results by gathering sub-classification results of the ROI patches.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying the predicted type of one or more microorganisms. In one aspect, a system comprises a phase-contrast microscope and a microorganism classification system. The phase-contrast microscope is configured to generate a three-dimensional quantitative phase image of one or more microorganisms. The microorganism classification system is configured to process the three-dimensional quantitative phase image using a neural network to generate a neural network output characterizing the microorganisms, and thereafter identify the predicted type of the microorganisms using the neural network output.

Abstract: A non-label diagnosis apparatus for a hematologic malignancy may include a 3-D refractive index cell imaging unit configured to generate a 3-D refractive index slide image of a blood smear specimen by capturing a 3-D refractive index image in the form of the blood smear specimen in which blood (including a bone-marrow or other body fluids) of a patent has been smeared on a slide glass, an ROI detection unit configured to sample a suspected cell segment in the blood smear specimen based on the 3-D refractive index slide image and to determine, as ROI patches, cells determined as abnormal cells, and a diagnosis unit configured to determine a sub-classification of a cancer cell corresponding to each of the ROI patches using a cancer cell sub-classification determination model constructed based on a deep learning algorithm and to generate hematologic malignancy diagnosis results by gathering sub-classification results of the ROI patches.

Abstract: A method for an electronic device to control an object includes recognizing a first object and a second object, identifying a first attribute of the first object and a second attribute of the second object, selecting an object to control based on the first attribute and the second attribute, generating an operation signal for the selected object, and transmitting the generated operation signal to the selected object.