20 results about "Patient group" patented technology

The present invention is in the field of ocular disease therapy. In particular, the invention relates to a composition comprising pimecrolimus for use in the topical treatment of moderate to severe blepharitis, in particular in a patient group comprising subjects with a clinical diagnosis of moderate to severe blepharitis, wherein the subjects to be treated have signs and symptoms of moderate to severe blepharitis characterised by swelling of the eyelid margin, ocular debris and ocular discomfort. In particular, the present invention relates to an ophthalmic composition that shows a high patient acceptance and a clinically meaningful improvement in the signs and symptoms of treated patients.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

This invention provides a method, apparatus, device, and readable storage medium for constructing a drug recommendation model, relating to the field of drug recommendation algorithm technology. The method includes acquiring diagnostic and surgical records to extract patient representations; calculating drug allocation weights based on the patient representations; constructing an initial model, training the initial model using the drug allocation weights, and updating the patient representations; recalculating the drug allocation weights based on the updated patient representations, and repeatedly training the initial model using the updated drug allocation weights to obtain a drug recommendation model. This invention addresses the technical problems of existing algorithms that cannot assign different weights based on the importance of neighboring nodes to the current node and ignore patient group characteristics with high representation similarity.

In the present invention, the accuracy of prediction of the therapeutic effect of the immune checkpoint inhibitor is sufficiently high.　Test data and training data for model training are prepared using the effect of administering an immune checkpoint inhibitor to a patient group and the results of RNA sequence analysis pertaining to the patient group (S11). A combination of a plurality of genes including the NLRC5 gene is selected (S12), and a model is trained using training data relating to the combination of genes (S13). The trained model is tested using the test data (S14). After training all combinations of genes (S15, Yes), a test result is output (S16). A biomarker comprising a combination of genes having high accuracy of prediction of therapeutic effect is determined (S17).

The application relates to the technical field of medical image processing, and particularly discloses a lung ventilation function evaluation system based on CT images, which adopts deep learning-based image processing technology to analyze lung CT images at the end of inspiration and expiration of a patient, so as to extract semantic feature representations of the lung CT images, and then, through fine-grained semantic interaction and semantic compensation of the lung CT image features at the end of inspiration and expiration, accurate characterization of a lung deformation field is obtained, and the lung capacity of the patient is intelligently estimated. In this way, non-invasive and accurate evaluation of lung function can be realized, and the method is particularly suitable for a patient group with poor physical conditions or unable to complete standard breathing tests as required.

The present invention relates to a novel microRNA for in vitro early diagnosis of mild cognitive impairment and Alzheimer's dementia, and a convergence diagnostic cartridge and diagnostic device using same. The novel miRNA of SEQ ID NO: 1 obtained by molecular analysis exhibits differences in expression levels in the plasma of a normal group, a mild cognitive impairment patient group, and an Alzheimer's dementia patient group, and has the effect of reducing the expression levels of mild cognitive impairment- and Alzheimer's dementia-related proteins. Therefore, by using a novel miRNA present in plasma as a molecular diagnostic index in a non-invasive manner, the present invention enables not only early diagnosis of Alzheimer's dementia but also screening for mild cognitive impairment and Alzheimer's dementia.

This disclosure provides a method for identifying patients requiring treatment for pressure ulcers and treating such patients with clinical interventions selected based on subcutaneous water content. This disclosure also provides a method for stratifying patient groups based on the risk of pressure ulcers and a method for reducing the incidence of pressure ulcers in care facilities. [Solution] A method is provided and includes for identifying and providing an appropriate level of pressure ulcer care to a patient based on multiple subcutaneous moisture (SEM) measurements. In one aspect, the patient receives progressively more effective intervention for pressure ulcers based on changes in SEM measurements. In another aspect, the patient receives low-intensity intervention for pressure ulcers based on changes in SEM measurements.

The invention relates to a method to indicate the clinical outcome of a cancer patient by labelling a cancer sample with labelled molecular probes, assaying the expression of a plurality of biomolecules at the resolution of a single cell and assigning a cellular identity (CI) to each single cell in the sample based on their expression pattern; then assigning a single cell pathology (SCP) patient group according to the proportion of each CI the sample contains.The invention in other aspects relates to methods of treatment of a patient with anticancer drugs according to the patient's assignment to particular SCPs. Alternatively, this aspect may be formulated as the provision of certain drugs for treatment of cancer in patients characterized by tumours assigned to certain SCPs.

The application relates to an L103G site modified anti-IFN-gamma autoantibody escape mutant and application thereof, and relates to the technical field of biological medicine and immunotherapy. The L103G site modified anti-IFN-gamma autoantibody escape mutant is a mutant of IFN-gamma, can effectively escape the attack of anti-IFN-gamma autoantibody compared with a wild type, and retains biological activity. For a part of patient groups which are not sensitive to other mutation sites, the mutant can effectively fill the treatment blind area, and can also be used as a probe to identify patients sensitive to the mutant in an AIGA patient group, so as to lay a foundation for guiding precise medication in the clinic.

This invention provides a disposable, simple endotracheal intubation fixator for use in the field of endotracheal intubation. It includes a fixation tube with an adjusting sleeve and a threaded bolt connected to its inner wall. A knob drives the threaded bolt to press and adjust the adjusting sleeve, thereby fixing and locking the intubation tube to prevent slippage, displacement, and dislodgement. It employs a biomimetic integrated dental arch design, consisting of upper and lower occlusal plates and an elastic arc strip, preventing occlusal force from directly acting on the intubation tube and dispersing the biting force. Buffer pads are provided on the occlusal plates for cushioning and protection. Suction cups are added to the cheekbone area on both sides of the patient, connected to the fixation tube via an arc-shaped fixation frame. The bony support of the cheekbone reduces stress on facial soft tissues. Non-woven fabric patches are placed on the skin-contacting surfaces of the suction cups to keep the skin dry and clean. Elastic bands are incorporated into the arc-shaped fixation frame for double fixation, making it compatible with the needs of different patient groups and clinical scenarios.

This application provides a knowledge graph-based rehabilitation diagnosis and treatment system and method for cerebrovascular diseases. It maps multimodal rehabilitation data onto a pre-defined knowledge graph, generating a feature subgraph. Starting with the core functional impairment node in the patient's feature subgraph, a multi-hop relationship expansion search is performed in the knowledge graph to obtain a set of candidate rehabilitation intervention nodes. Based on the empirical validity data of each candidate rehabilitation intervention node in a historical patient group with similar characteristics to the current patient, the topological association strength of the knowledge graph, and the feasibility constraints of the target patient's current medical scenario, the personalized fit of each candidate rehabilitation intervention node is determined. The results of ranking each candidate rehabilitation intervention node according to all personalized fits form a draft recommendation of a structured rehabilitation plan for the target patient. Using the scheme of this application, multimodal rehabilitation data and dynamic medical knowledge of patients can be integrated to conduct intelligent rehabilitation plan recommendations.

The application relates to an S84G site modified anti-IFN-gamma autoantibody escape mutant and application thereof, and relates to the technical field of biological medicine and immunotherapy. The mutant is an IFN-gamma mutant, the 84th amino acid of which is glycine; the mutant has a significant neutralization escape function, can effectively avoid the attack of autoantibodies, and retains biological activity. For a part of patient groups who are not sensitive to other mutation sites, the S84G site modified anti-IFN-gamma autoantibody escape mutant has unique and superior escape characteristics, and can effectively fill the treatment blind area.

An oral liquid composition according to the present invention comprises tegoprazan or a pharmaceutically acceptable salt thereof as an active ingredient, a solubilizing agent, and an extra solvent. The present invention has improved the problem of residual foreign body sensation of orally disintegrating tablets conventionally developed for patients with dysphagia. In addition, it is possible to provide a new treatment option for an increasing patient group with gastric acid-related diseases by securing excellent stability of the active ingredient in a solution state, which is a characteristic of an oral liquid dosage form, and presenting a dosage form that ensures uniformity of a preparation.

This invention relates to the field of biomedical technology and proposes RBP4 and Ferritin as biomarkers for evaluating the efficacy of hormone therapy for allergic rhinitis. The biomarkers include one or a combination of RBP4 and Ferritin. Studies have confirmed that both RBP4 and Ferritin are capable of distinguishing between hormone-sensitive and hormone-insensitive allergic rhinitis. RBP4 is expressed at higher levels in the hormone-sensitive allergic rhinitis group, while Ferritin is expressed at higher levels in the hormone-insensitive allergic rhinitis group. Compared to single biomarker detection, the combined use of RBP4 and Ferritin significantly improves the differential diagnostic efficacy, providing better differentiation between the two patient groups.

The application relates to the technical field of computers and discloses a tacrolimus dosage prediction method based on a clustering model combination, which comprises a data preprocessing module, a clustering and grouping module, a concentration prediction module, a dosage recommendation module and a display module. A patient grouping modeling end is arranged, seven clinical indexes are integrated by using a clustering algorithm, subgroups with large metabolic characteristic differences are divided, the limitation that patients are regarded as homogeneous groups in a traditional model is broken through, subgroup-specific modeling is realized, the group specificity of dosage prediction is improved, a subgrouping and prediction joint architecture is constructed, a model is independently deployed for each subgroup after subgrouping and dimension reduction, the sample quantity required by a single model is effectively reduced, the failure risk of machine learning under a small sample is reduced, reliable modeling can be realized in hundreds of clinical data, a dynamic prediction modeling end is arranged, memory functions are generated by fusing multi-time point historical data, and the prediction deviation rate caused by initial value disturbance is reduced.

The application discloses a kind of risk stratification model construction method and system for EGFR mutant non-small cell lung cancer meningeal metastasis patient, belong to tumor prognosis evaluation technical field.The multicenter retrospective clinical database is first constructed and data preprocessing is completed, then the predictor is screened by LASSO Cox regression, and the five core prognostic factors of ECOG PS score, brain metastasis, MRI meningeal enhancement, CSFC result and intracranial pressure are determined by multi-factor Cox proportional risk regression and linear predictor is constructed, then it is converted into integer type weighted risk score, finally the best cutoff value is determined based on survival function, and the patient is divided into low-risk group and high-risk group.The application can clearly distinguish the patient group with significant difference in prognosis, the required indicators are all clinical routine items, the operation is simple, the accessibility is high, can accurately guide individualized treatment decision and clinical trial stratification, and has important clinical application value.

This invention discloses an AI-driven IVF personalized treatment plan generation system, relating to the fields of medical and artificial intelligence technologies. Through the deep coupling of unsupervised clustering and long-term reinforcement learning planning, this invention achieves a leap from static classification to dynamic strategy generation, constructing a globally optimal personalized treatment path for complex patient groups. It utilizes unsupervised clustering algorithms to perform deep pattern mining on patient baseline data, automatically identifying fine subtypes with similar physiological characteristics. The reinforcement learning agent module models the patient's treatment process as a sequential decision-making process. The agent's state space integrates the patient's subtype attributes and dynamically changing cyclical treatment outcomes. By iteratively building a simulation environment with historical data, a higher success probability is achieved at the global level, transforming clinical decisions based on subjective experience into an automated decision-making process based on historical evidence and global optimization.

The application provides a postoperative patient care monitoring method, device, equipment and storage medium, and the method comprises the following steps: obtaining postoperative monitoring data of each patient in a target patient group at a current time and postoperative monitoring data of each patient at a past target time; generating an instantaneous value and a trend value corresponding to each patient according to the postoperative monitoring data of each patient at the current time and the postoperative monitoring data of each patient at the past target time, and forming a feature vector according to the instantaneous value and the trend value; clustering each feature vector to obtain a plurality of state clustering clusters; determining an abnormal state clustering cluster from the plurality of state clustering clusters, and executing a differentiated early warning information pushing strategy on the corresponding patient according to the feature vector corresponding to the abnormal state clustering cluster. Through the method in the application, intelligent monitoring of the postoperative patient group can be realized, the abnormal group with potential risks can be located, and hierarchical early warning can be provided, thereby effectively improving the safety and efficiency of postoperative care.