11 results about "Clinical study" patented technology

The present invention provides radiolabeled 2,3,4,5-tetrahydro-1H-[1,4]diazepino[1,7-α]indol-9-yl]pyridin-2(1H)-one derivatives of general formula (I) or salts thereof, as well as precursors of the radioligands of general formulae (II) and (III), processes for their preparation, and intermediates of the processes. The radiolabeled compounds enable visualization and quantification of melanin-concentrating hormone receptor 1 (MCHR1) in various target tissues, including monitoring MCHR1 in such target tissues. The compounds exhibit high binding affinity for MCHR1 and are able to cross the blood-brain barrier, thus facilitating diagnosis of various disease states in human clinical studies.

The invention relates to a method for enriching a database of medical images intended for a clinical study, comprising the steps of: - retrieving (100) a medical image to be analyzed; - identifying (103) a type of organ in the medical image to be analyzed by implementing an artificial intelligence model generated by training on a database of reference medical images whose predefined parameter values ​​are fixed; - selecting predefined parameters of the image to be analyzed according to the type of organ identified; - identifying (106) the values ​​of the predefined parameters of the medical image to be analyzed by implementing the artificial intelligence model; - storing (108) the identified parameter values ​​in metadata associated with the image to be analyzed. Figure to be published with the abstract: Fig. 1

The invention relates to a method for enriching a database of medical images for a clinical study, the method comprising the steps of: - retrieving (100) a medical image to be analyzed; - identifying (103) an organ type in the medical image to be analyzed by implementing an artificial intelligence model generated by training on a database of reference medical images for which the values of the predefined parameters are fixed; - selecting the predefined parameters of the image to be analyzed according to the identified organ type; - identifying (106) the predefined parameter values of the medical image to be analyzed by implementing the artificial intelligence model; - storing (108) the identified parameter values in metadata associated with the image to be analyzed.

This invention belongs to the field of biomedical technology, specifically relating to an ointment and its application. The toad oil used in this invention differs significantly in composition from toad venom, and its ointment is effective for various acute and chronic pain conditions. Animal experiments have confirmed that the toad oil ointment significantly reduces the frequency and delays the onset of acetic acid-induced writhing responses, demonstrating a clear analgesic effect. Clinical studies have shown that this ointment is rapidly effective and has a definite therapeutic effect on acute gout attacks, postherpetic neuralgia, and arthritis-related pain and swelling. The formulation effectively improves drug retention and user comfort, and no skin irritation has been observed. This development achieves comprehensive utilization of toad resources, providing an example for the sustainable development of traditional Chinese medicine and the efficient utilization of biological resources.

This invention belongs to the field of gene editing technology and discloses an inosine-enriched adenine base editor off-target detection method (Ino-seq) and its applications. The method of this invention utilizes endonuclease V to specifically cleave inosine-containing DNA, combined with the stabilization of a thermostable single-stranded DNA-binding protein and the high affinity enrichment of streptavidin-biotin, to achieve direct capture of ABE enzymatic products. This method can simultaneously detect sgRNA-dependent and independent off-target effects, with systematic comparisons showing F1 scores of 0.892–0.931. Targeted deep sequencing validation showed an overall accuracy of 95.3%. Ino-seq can also detect endogenous genomic inosine, observing a variation range of >1,000-fold in 10 cell lines. This invention provides a crucial safety assessment tool for the clinical translation of ABE technology, supporting comprehensive off-target analysis in preclinical and clinical studies.

In one embodiment, a method includes receiving, via graphical user interface of a clinical study tool, a user input including one or more user selected sub-categories from one or more categories associated with a clinical study; selecting, for each clinical-study metric of a plurality of clinical-study metrics, a model from either a baseline model or one of a plurality of category-specific machine-learning models to predict the clinical-study metric using a model performance lookup table; predicting values for the plurality of clinical-study metrics using corresponding selected models; generating one or more predictions for assessing the clinical study based on the predicted values for the plurality of clinical-study metrics; and displaying, via the graphical user interface of the clinical study tool, the one or more predictions for assessing the clinical study.

This invention relates to the field of medical data processing technology and discloses a multi-center clinical data collaborative research system for complex endocrine cases. The system integrates heterogeneous case data from multiple centers into a unified time-series dataset through data preprocessing and semantic fusion modules. A collaborative control module uses a sliding window to calculate the gradient of data feature changes and intelligently triggers the collaborative collection phase accordingly. A task scheduling module generates differentiated collection tasks based on gradient information. A dynamic adjustment and transition module monitors task feedback, dynamically adjusts storage resource configuration, and automatically transitions the system from the collection phase to the analysis phase based on the decay of the changing gradient. This system achieves adaptive, closed-loop management of the multi-center data collaboration process, enabling precise resource scheduling based on dynamic data changes and node load, thus improving the efficiency and robustness of large-scale clinical research data collaboration.

This invention relates to the field of medical software development technology and discloses a system and storage medium for differentiating benign and malignant pulmonary nodules based on cfDNA methylation-radiomics-clinical studies. The system includes a multimodal data acquisition module, a feature extraction and alignment module, a multimodal fusion diagnosis module, and an interpretable report generation module. After collecting clinical information, low-dose CT images, and peripheral blood cfDNA methylation sequencing data from the subjects, features are extracted from each modality and mapped to a unified semantic space through contrastive learning. A collaborative attention mechanism is then used to dynamically weight and fuse the aligned multimodal features. The fused features are input into a deep neural network classifier to output a nodule malignancy probability score, and finally, an interpretable diagnostic report with risk stratification is generated. This invention solves the problems of insufficient multimodal heterogeneous data fusion, poor model robustness, and weak interpretability, achieving accurate differentiation between benign and malignant pulmonary nodules, and demonstrating good clinical applicability and promotional value.

The application provides a kind of clinical research test abstract key word intelligent extraction method based on large model, it is related to data analysis technical field.Adopt parameter inheritance system strategy training network, introduce load balancing loss optimization MoE model.Structured feature embedding is integrated on the basis vector to obtain the input vector, input large model to obtain token level context representation and test level backbone feature, combine task prompt vector input MoE model to obtain test level enhanced feature, respectively input multi-field classification head and multi-label output head to obtain field probability distribution and key word activation probability;Joint dynamic threshold, and then get structured field and key word set.Using the parallel design of pre-trained large model and MoE model and multi-field classification head and multi-label output head ensures the comprehensiveness and accuracy of information.Overcome the problems such as element loss, unstable generalization ability and poor robustness when processing clinical research abstracts in the prior art.

A system and method for autonomously optimizing clinical research processes through a distributed multi-agent artificial intelligence architecture. One or more processors comprising a neural processing hub and an AI inference accelerator execute a research process optimizing module across a distributed network of computing devices. An automated enrolment and retention module parses unstructured medical records using Natural Language Processing and generates high-dimensional semantic vector embeddings stored in a persistent vector database. A list generating module executes a Transformer-based Large Language Model to perform semantic vector matching against clinical study protocol criteria to generate a ranked list of qualified research participants. An integrated Generative AI chatbot facilitates participant engagement and informed consent. A Reinforcement Learning agent autonomously assigns principal investigators for screening. A primary source data capturing module utilizes Convolutional Neural Networks for automated Optical Character Recognition and deep learning anomaly detection. A financial module performs autonomous reconciliation and invoice generation with verified data recorded on a distributed ledger.

Systems and methods disclosed herein relate to using a machine-learning model to process an input of a subject's eye and to predict a current or future visual acuity of the subject. The subject may have been diagnosed with age-related macular degeneration. The predicted current or future visual acuity may be used to (for example) facilitate diagnosing the subject (e.g., with a particular type of age-related macular degeneration), facilitate identifying a treatment strategy for the subject, and / or facilitate designing a clinical study.