Abstract: Disclosed is a use of trimanganese tetraoxide particles in preparation of a vaccine adjuvant. The adjuvant is a particle adjuvant, the particle adjuvant is trimanganese tetraoxide particles externally wrapped with or without an excipient, and the particle size of the particle adjuvant is 5 nm to 3000 nm. The trimanganese tetraoxide particle adjuvant provided in the present invention can be effectively combined with a single-stranded nucleotide adjuvant and can effectively carry an immune antigen, and a more excellent immunotherapy effects can be achieved when a fewer antigen dose and a relatively low injection amount are used; immune cells are efficiently activated, and body fluid balance and cellular immunity are achieved.

Abstract: A holder for intervertebral foramen endoscope fusion includes an inner rod, an anti-torsion sleeve and an outer sleeve which are sleeved from inside to outside and are movable relatively along an axial direction. The inner rod is configured for pushing the expanding part inside a fusion cage. A head end of the anti-torsion sleeve is provided with an external thread section, the external thread section is configured for matching and connecting with the screw plug mounting hole of a holding body of the fusion cage, and the tail end of the anti-torsion sleeve is provided with a matching structure for matching with the bone grafting funnel. A head end of the outer sleeve is matched with the holding body of the fusion cage to restrict the rotation of the fusion cage along an axis of the fusion cage. A surgical instrument assembly is also provided.

Abstract: Provided is an antibody or an antigen-binding fragment thereof that binds to human TIGIT (T cell immunoreceptor with immunoglobulin and ITIM domains). Also provided are a nucleic acid molecule encoding the antibody or the antigen-binding fragment thereof, a vector and host cell containing the nucleic acid molecule, an immunoconjugate and composition comprising the antibody or the antigen-binding fragment thereof, and the use of the antibody or the antigen-binding fragment thereof in the preparation of a product for positively adjusting the activity of immune cells and/or improving the immune response, a product for reducing or eliminating the immunosuppressive effect of cells expressing TIGIT (such as Treg cells), and a kit for detecting the presence or absence of TIGIT and the level or activity of TIGIT.

Abstract: Provided are a fusion protein of an anti-TIGIT antibody and IL2 or a variant thereof, and an application thereof. Specifically, a fusion protein is provided, which comprises: (a) a first polypeptide comprising an anti-TIGIT antibody or an antigen-binding fragment thereof; and (b) a second polypeptide comprising interleukin-2 (IL-2) or a variant thereof having a lymphocyte growth promoting activity, the second polypeptide being fused to the first polypeptide. Also provided are an application of such a fusion protein in positively regulating immune cell activity and/or improving immune responses; and/or, an application thereof in the treatment of cancer, immunodeficiency or inflammatory diseases, or infectious diseases.

Abstract: Provided are an anti-TNFR2 monoclonal antibody and an application thereof, Specifically, provided is an TNFR2-targeted monoclonal antibody or an antigen binding fragment thereof, which specifically binds to a cysteine-rich domain (CRD) of human TNFR2. Furthermore, provided are a murine antibody, human-mouse chimeric antibody, and humanized antibody of the TNFR2 antibody, a pharmaceutical composition containing the antibody, and a use thereof in a related disease diagnostic agent and therapeutic drug. The anti-TNFR2 monoclonal antibody has high specificity and high affinity against human TNFR2, can further achieve an immunomodulatory effect, and is used in applications such as anti-tumor, anti-infection and/or anti-autoimmune diseases.

Abstract: A support structure member and a display device. The support structure member includes a first support assembly, including a plurality of support members arranged in a first direction, in which the plurality of support members can be slidably arranged relative to one another in the first direction, and a gap is formed between two adjacent support members, and the first support assembly is bent by each position of the gap; a second support assembly, movably arranged relative to the first support assembly at least one side of the support members in a thickness direction, in which the second support assembly includes a connecting unit and a hinge unit connected to the connecting unit, the second support assembly moves to the gap of the first support assembly, and in the gap, the hinge unit and the support members together support a to-be-supported member.

Abstract: The present invention relates to a method for multidimensional evaluation of the similarity of a sample to breast milk, wherein the method is performed in the following steps: (1) establishing a local breast milk phospholipid database; (2) collecting and pretreating a sample to be evaluated; (3) determining the contents of characteristic phospholipid subclasses, the contents of characteristic phospholipid molecules in the sample to be evaluated, and obtaining a microstructural image of milk fat globules in the sample by confocal laser scanning microscopy; (4) calculating the score G for the sample to be evaluated.

Abstract: The present invention provides a marker set for evaluating the development of an infant's communication ability. The marker set is composed of the following fatty acids: C18:3n6, C18:2n6t, C20:3n3, C18:3n3, C18:2n6c, C20:4n6, C20:3n6, C22:6n3, and C20:5n3. The present invention further provides a method for evaluating the development of an infant's communication ability using the marker set. The present invention achieves the evaluation on the development of an infant's communication ability by extracting and detecting the contents of particular fatty acids in a breast milk or dairy product sample fed to an infant, and the development level of the infant among the peers can be inferred based on the evaluation result, so as to give suggestions relevant to diet and nutrition for the mother timely. The method is low in cost, simple and effective, and suitable for large-scale evaluation.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, and more specifically, to computer network architectures in the context of program rules, using combinations of defined patient clinical episode metrics and other clinical metrics, thus enabling superior performance of computer hardware. Aspects of embodiments herein are specific to patient clinical episode definitions, and are applied to the specific outcomes of highest concern to each episode type. Furthermore, aspects of embodiments herein produce more accurate and reliable predictions of possible patient outcomes and metrics.

Abstract: Computer network architectures for machine learning, and more specifically, computer network architectures for the automated completion of healthcare claims. Embodiments of the present invention provide computer network architectures for the automated completion of estimated final cost data for claims for healthcare clinical episodes using incomplete data for healthcare insurance claims and costs, known to date. Embodiments may use an automatic claims completion web application, with other computer network architecture components. Embodiments may include a combination of third-party databases to generate estimated final claims for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, social-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and active updates of outcomes. Embodiments of computer network architecture automatically update forecasts of outcomes of patient episodes and annual costs for each patient of interest after hospital discharge. Embodiments may generate such updated forecasts either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Computer network architectures for machine learning, and more specifically, computer network architectures for the automated completion of healthcare claims. Embodiments of the present invention provide computer network architectures for the automated completion of estimated final cost data for claims for healthcare clinical episodes using incomplete data for healthcare insurance claims and costs, known to date. Embodiments may use an automatic claims completion web application, with other computer network architecture components. Embodiments may include a combination of third-party databases to generate estimated final claims for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, social-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and active updates of outcomes. Embodiments of computer network architecture automatically update forecasts of outcomes of patient episodes and annual costs for each patient of interest after hospital discharge. Embodiments may generate such updated forecasts either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments relate generally to computer network architectures for machine learning, and more specifically, to computer network architectures in the context of program rules, using combinations of defined patient clinical episode metrics and other clinical metrics, thus enabling superior performance of computer hardware. Aspects of embodiments herein are specific to patient clinical episode definitions, and are applied to the specific outcomes of highest concern to each episode type. Furthermore, aspects of embodiments herein produce more accurate and reliable predictions of possible patient outcomes and metrics.

Abstract: A pixel arrangement structure of display panel and a display apparatus, which are used to solve the technical problem of low pixel aperture ratio of an organic light emitting diode display panel. Herein, a pixel arrangement structure of display panel includes: a plurality of sub-pixels including a first sub-pixel, a second sub-pixel, and a third sub-pixel; where the first sub-pixel, the second sub-pixel, and the third sub-pixel have a polygonal structure with different numbers of sides, and distances between any adjacent two of the plurality of sub-pixels are equal.

Abstract: A holder for intervertebral foramen endoscope fusion includes an inner rod, an anti-torsion sleeve and an outer sleeve which are sleeved from inside to outside and are movable relatively along an axial direction. The inner rod is configured for pushing the expanding part inside a fusion cage. A head end of the anti-torsion sleeve is provided with an external thread section, the external thread section is configured for matching and connecting with the screw plug mounting hole of a holding body of the fusion cage, and the tail end of the anti-torsion sleeve is provided with a matching structure for matching with the bone grafting funnel. A head end of the outer sleeve is matched with the holding body of the fusion cage to restrict the rotation of the fusion cage along an axis of the fusion cage. A surgical instrument assembly is also provided.

Abstract: A support structure member and a display device. The support structure member includes a first support assembly, including a plurality of support members arranged in a first direction, in which the plurality of support members can be slidably arranged relative to one another in the first direction, and a gap is formed between two adjacent support members, and the first support assembly is bent by each position of the gap; a second support assembly, movably arranged relative to the first support assembly at least one side of the support members in a thickness direction, in which the second support assembly includes a connecting unit and a hinge unit connected to the connecting unit, the second support assembly moves to the gap of the first support assembly, and in the gap, the hinge unit and the support members together support a to-be-supported member.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated improvement and regularization of forecasting models, providing rapid improvement of the models. Embodiments may generate such rapid improvement of the models either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may indicate, after the improvement of the models, that various web applications using the models may be rerun to seek improved results for the web applications. Embodiments may include a combination of third-party databases to drive the forecasting models, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and dynamic patient guidance. Embodiments automatically update patient guidance in the patient care plan, based on the effectiveness of the guidance to date, attributes of the patient, other updated information, ongoing experience of the network, and updated predictions of possible patient outcomes and metrics.

Abstract: A computer network architecture for a pipeline of models with machine learning and artificial intelligence for healthcare outcomes is presented. A machine learning prediction module and an artificial intelligence learning model are in electronic communication with a web application, which is also in electronic communication with a user device. An expanding updating database supports automatically recalibrating, re-evaluating, and reselecting the evolving and improving algorithms.