11 results about "Meningitis" patented technology

Use of an NLRP3 inhibitor for treating encephalitis or meningitis. Specifically, provided is use of a compound represented by formula (I) or a pharmaceutically acceptable salt thereof in the preparation of a drug for treating encephalitis or meningitis, wherein each substituent is as defined in the description.

The application discloses a novel diterpenoid compound with anti-inflammatory activity separated from Origanum vulgare Linn. 20 H 18 O4, and a structural formula is as shown in formula I. A preparation method of the compound comprises the following steps: crushing dried medicinal materials of Origanum vulgare Linn., extracting by using ethanol, concentrating under reduced pressure to obtain total extract; after extraction by using petroleum ether, the target compound is obtained through silica gel column chromatography, Sephadex LH-20 gel column chromatography and preparation type high performance liquid phase chromatography separation and purification. In vitro anti-inflammatory experiments show that the compound can significantly inhibit NO generation in a LPS-induced RAW264.7 macrophage model, and the inhibition rate reaches 62.97%, and the compound shows good anti-inflammatory activity. The compound can be used for preparing medicines for preventing or treating inflammatory diseases such as enteritis, hepatitis and meningitis.

The invention provides quantum dot modified group B neisseria meningitidis and a preparation method of a vaccine of the quantum dot modified group B neisseria meningitidis. The preparation method comprises the following steps: taking corn straws and sunflower straws as a straw mixture according to a weight ratio of (1-1.5): 1, putting the straw mixture, an escherichia coli DH5alpha cell membrane and urea into water according to a weight ratio of 100: 100: 1, carrying out high-temperature heating reaction, and filtering and dialyzing reactants to prepare aminated quantum dots; extruding the aminated quantum dots and a group B neisseria meningitidis cell membrane through a liposome extruder according to a weight ratio of (8-10): 1, and centrifuging to obtain cell membrane hybrid aminated quantum dots; and co-incubating the cell membrane hybrid aminated quantum dots and the group B neisseria meningitidis to obtain the cell membrane hybrid aminated quantum dots. The group B neisseria meningitidis is modified by adopting the specific quantum dots, the toxicity of the modified group B neisseria meningitidis is obviously reduced, and the modified group B neisseria meningitidis is expected to be applied to related fields of vaccines and the like.

The invention discloses an accurate auxiliary meningitis typing method based on a machine learning model, electronic equipment and a storage medium, and the method comprises the steps: selecting medical detection items as features, and combining the features to construct a feature subset; constructing a meningitis subdivision discrimination framework, and disassembling meningitis diagnosis requirements into nine mutually independent binary discrimination tasks; taking the feature subset as input, executing each binary discrimination task to construct a training task, and training an independent machine learning model; obtaining a binary discrimination task selected by a doctor and a binary discrimination task selected by the doctor; and respectively calling independent machine learning models for execution, and outputting auxiliary typing discrimination. According to the accurate meningitis typing method based on multi-task discrimination of the machine learning model, clinicians are helped to judge meningitis categories by using a machine learning method, so that accurate and effective diagnosis and treatment are provided for patients in time.

The invention discloses a method for establishing a model for inflammation of mouse brain microvascular endothelial cells infected by haemophilus parasuis, which is used for simulating the process of porcine meningitis caused by haemophilus parasuis infection by using the mouse brain microvascular endothelial cells infected by haemophilus parasuis and researching the pathogenesis of the porcine meningitis caused by haemophilus parasuis. The optimal MOI of mouse brain microvascular endothelial cells infected by haemophilus parasuis is 1, the optimal infection time is 6 h, under the condition, the inflammatory factor level of the cells is remarkably increased, and the success rate of the cell inflammation model construction method is higher.

This invention discloses a serogroup B meningococcal vaccine and its application. The serogroup B meningococcal vaccine contains one or more recombinant proteins with amino acid sequences as shown in SEQ ID NO:1-7. The vaccine against serogroup B meningococcus of this invention induces a strong humoral immune response and broad-spectrum protective efficacy. Compared with existing technologies, the vaccine against serogroup B meningococcus of this invention has high antibody titers and broad protective efficacy. It has broad application value and positive social benefits in preventing diseases caused by serogroup B meningococcus infection and alleviating severe cases.

Provided is a composition for detecting multiple encephalitis / meningitis / respiratory pathogens. The composition includes primer sequences for the pathogens as shown in SEQ ID NOs: 1-7, 9-23 and 25-32, in which SEQ ID NOs: 1-7 and 9-16 carry fluorescent reporter groups. In addition, the present invention further provides a use of the foregoing composition in the preparation of a kit, and a related kit and a method for using the same.

The invention discloses a novel diterpenoid compound which is separated from origanum vulgare and has anti-inflammatory activity, the molecular formula of the diterpenoid compound is C20H18O4, and the structural formula of the diterpenoid compound is shown as a formula I. The preparation method of the compound comprises the following steps: crushing a dried medicinal material of origanum vulgare, extracting with ethanol, and concentrating under reduced pressure to obtain a total extract; and after extracting with petroleum ether, separating and purifying through silica gel column chromatography, Sephadex LH-20 gel column chromatography and preparative high performance liquid chromatography to obtain a target compound. In-vitro anti-inflammatory experiments show that the compound can significantly inhibit NO generation in an LPS-induced RAW264.7 macrophage model, the inhibition rate reaches 62.97%, and good anti-inflammatory activity is shown. The compound can be used for preparing medicines for preventing or treating inflammatory diseases such as enteritis, hepatitis and meningitis.

This application relates to the technical field of vaccine conjugates, specifically to a method for preparing a polysaccharide conjugate of group A meningococcal capsular polysaccharide, including the preparation of polysaccharide derivatives, mixing ratio, conjugation reaction, aging treatment, ultrafiltration and sterilization steps. This application eliminates the pH adjustment and conjugate purification steps in the activation process by optimizing the process parameters, but the quality of the harvested polysaccharide conjugate still meets the national standards, thereby simplifying the preparation process of polysaccharide conjugates and shortening the preparation cycle of polysaccharide conjugates.

An explainable artificial intelligence system for early prediction of missed severe diagnoses in intensive care unit patients is provided. The system comprises a data processing module configured to receive and process electronic health record data including patient demographics, vital signs, laboratory results, and diagnostic codes. A machine learning module with ensemble, deep learning, and large language models analyzes processed data to predict likelihood of severe diagnoses including acute myocardial infarction, acute pulmonary embolism, bacterial pneumonia, aortic dissection, meningitis, spinal abscess, bacterial endocarditis, and arterial thromboembolism. An explainability module generates feature importance rankings and provides interpretable explanations for predictions.