19 results about "Biochemical markers" patented technology

The invention discloses a single cell activity evaluation method based on cell nucleus geometric parameters and a Bayesian framework, which comprises the following steps: establishing a HeLa cell activity gradient model through adriamycin induction, carrying out cell nucleus segmentation by adopting Cell pose 3.0 and extracting 26 geometric features, screening a high-confidence sample in combination with a Bayesian deep learning framework, and evaluating the activity of a single cell according to the high-confidence sample. And constructing a multi-modal feature fusion model to integrate geometric features and deep semantic features, and finally realizing unmarked and high-precision single cell activity evaluation. The method overcomes the limitation that a traditional technology depends on biochemical markers, has the advantages of being easy and convenient to operate, high in flux and high in interpretability, and provides an innovative technical means for tumor liquid biopsy and precise medical treatment.

The invention discloses a multi-modal bone tumor classification method and system based on blood biochemical text guidance. The method comprises the following steps: acquiring multi-modal data; constructing a blood biochemical text guide factor based on the blood biochemical marker; judging whether a bone tumor image or a pathological section image in the multi-modal data is missing or not, extracting a bone tumor image structure feature or a pathological section image feature when the bone tumor image or the pathological section image is not missing, generating a compensation feature of missing modal data when the bone tumor image or the pathological section image is missing, and compensating an image feature corresponding to the missing modal data; performing cross guide fusion on the bone tumor image structure features and the pathological section image features based on the blood biochemical text guide factors to obtain multi-modal fusion features; and calculating the probability of each bone tumor category according to the multi-modal fusion features through a linear classification layer to obtain a bone tumor classification result. According to the method, effective guidance and supplement of non-image information on image features are realized, and the accuracy and interpretability of a bone tumor classification result are improved.

The invention discloses a high-efficiency separation method of breast milk oligosaccharide and application of the breast milk oligosaccharide in specific recognition of influenza A virus hemagglutinin protein.The method comprises the steps that a DE-52 weak anion exchange column, a graphite carbon column and Bio-GelP10 column chromatography are combined for use, breast milk oligosaccharide components with low abundance and high molecular weight are efficiently enriched, and the specific recognition of influenza A virus hemagglutinin protein is achieved; after derivatization labeling of a bifunctional reagent 2-amino-N-(2-aminoethyl) benzamide (AEAB), 56 breast milk oligosaccharide monomers with the purity higher than 90% are separated from the isomer level through one-dimensional amide normal phase chromatography and two-dimensional C18 reverse phase circulation chromatography, and a monomer substance library is constructed. AEAB is used as a connecting arm to construct a breast milk oligosaccharide chip, an active monomer DSLNT II which is specifically combined with four subtype influenza A virus hemagglutinin proteins is screened out in a high-throughput manner, and fine structure analysis is completed in combination with electrospray tandem mass spectrometry. According to the method, the large-scale preparation of the low-abundance breast milk oligosaccharide monomer is realized, and a key material basis and theoretical guidance are provided for researching an antiviral mechanism and developing related functional foods and medicines.

The invention provides an early warning method and system for pheochromocytoma and paraganglioma, and is applied to the technical field of data processing. The method comprises the following steps: processing clinical parameters, biochemical marker data and CT image feature data in a training sample set in combination with a Logistic regression analysis method to generate an initial neuroendocrine tumor early warning model; processing the initial neuroendocrine tumor early warning model based on K-fold cross validation and the validation sample set to generate a target neuroendocrine tumor early warning model; processing the biochemical marker data based on 24-hour urine catecholamine pairs in clinical parameters to generate standardized biochemical detection index information; processing the CT image feature data to generate imaging comprehensive score information; and processing the standardized biochemical detection index information and the imaging comprehensive score information based on the target neuroendocrine tumor early warning model to generate early warning information of pheochromocytoma and paraganglioma.

The invention provides a multi-parameter optical fiber health monitoring device. The device comprises a light source system, a light source conduction system, a sensing system, a sensing light conduction system and a spectrum processing system, the light source system can emit light signals of a single wavelength or a plurality of different wavelengths; the light source conduction system can transmit light signals, provide multi-channel light signal channels and realize random switching of the light signal channels; the sensing system comprises at least one optical fiber sensor, and one end of the optical fiber sensor acts under the action of a marker to cause the change of an optical signal; the sensing light conduction system can pass back a changed light signal and gate of a path; and the spectrum processing system can collect and process the returned optical signals to obtain spectrum data. According to the invention, multi-channel synchronous monitoring can be realized, a plurality of physiological and biochemical markers can be detected at the same time, and the detection efficiency is greatly improved; the method has high sensitivity and stability; the invention has a wide clinical application prospect.

This application provides a nanoprobe-based electrophysiological signal and biochemical biomarker detection system, comprising: a patch-clamp amplifier; a working electrode electrically connected to a signal acquisition module, the surface of which, except for the tip, is covered by an insulating protective layer, forming a GΩ-level impedance seal after implantation into the cell; a counter electrode connected to a BATH port, with its active end placed in the matrix environment of the target cell, forming a current loop; a reference electrode connected to a REF port, with its active end placed in the matrix environment of the target cell to provide a potential reference point; the patch-clamp amplifier is configured to operate in voltage-clamp mode, with the REF port connected to the reference electrode as a voltage sensing terminal, and the BATH port connected to the counter electrode as a current output terminal, simultaneously acquiring the electrophysiological signal of the target cell and the current signal corresponding to the concentration of the target biochemical biomarker. This application provides a powerful analytical tool for a deeper understanding of the electrochemical signal coupling mechanism of neurons.

The application discloses a kind of multi-modal bone tumor classification method and system based on blood biochemical text guide, the method includes the following steps: obtaining multi-modal data;Blood biochemical markers are used to construct blood biochemical text guide factor;Determine whether the bone tumor image or pathological section image in multi-modal data is missing, when not missing, the bone tumor image structure feature or pathological section image feature is extracted, when missing, the compensation feature of missing modal data is generated, and the image feature corresponding to the compensation of missing modal data is extracted;Blood biochemical text guide factor is used to cross guide fusion on bone tumor image structure feature and pathological section image feature, and multi-modal fusion feature is obtained;The probability of each bone tumor category is calculated by linear classification layer to multi-modal fusion feature, and bone tumor classification result is obtained.The application realizes the effective guidance and supplement of non-image information to image feature, and improves the accuracy and explainability of bone tumor classification result.

The application discloses a dyslipidemia risk prediction device and terminal equipment, method and storage medium, and relates to the technical field of medical detection equipment. The device comprises an acquisition module, a first processing module and a second processing module which are connected with each other. The first processing module is used for inputting SNP genotype data of a to-be-predicted object into a pre-constructed scoring model, obtaining a polygenic risk score, and inputting an actual age and a phenotype age into a pre-constructed age linear regression model after obtaining the phenotype age based on clinical biochemical marker data and the actual age of the to-be-predicted object, so as to obtain a phenotype age acceleration value. The second processing module is used for inputting the polygenic risk score, the phenotype age acceleration value and individual characteristic data into a pre-constructed integrated model, obtaining a prediction result set, and taking a mode of the prediction result set as a prediction result of the to-be-predicted object. The device can be used for more comprehensive and accurate prediction and can improve processing efficiency.

The invention relates to the technical field of biomarkers, in particular to a biomarker influencing the intramuscular fat content of chicken and application of the biomarker. The invention relates to a biomarker for influencing the intramuscular fat content of chicken. The biomarker is a blood coagulation factor X. Experiments find that the blood coagulation factor X has significant difference in high intramuscular fat chicken and low intramuscular fat chicken, and can be used for identifying the intramuscular fat content of Gushi chicken and breeding the Gushi chicken with high intramuscular fat. The invention further provides application and a screening method of the serum biochemical marker, and a new thought is provided for local chicken breeding.

The present invention relates to new methods for assessing the risk of a patient, in particular with chronic liver disease, to develop primary liver cancer over time, using functions combining blood biochemical markers.

Disclosed are methods and apparatuses for treating mental health disorders through electrical stimulation of the carotid sinus nerve (CSN). The systems may include implantable or wearable stimulating electrode arrays configured to preferentially activate baro-afferent fibers of the CSN while minimizing stimulation of chemo-afferent fibers. The stimulation may be delivered in open-loop, closed-loop, or semi-closed-loop modes, with feedback provided by sensors monitoring physiological parameters such as heart rate, blood pressure, respiratory rate, and biochemical markers. The apparatus may include a controller that maps electrode contacts to specific nerve fiber types and dynamically adjusts stimulation parameters to optimize therapeutic outcomes. These systems and methods are designed to address autonomic imbalances associated with neuropsychiatric conditions including depression, anxiety, post-traumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD), particularly in patients refractory to conventional therapies.

The invention discloses a baseline brain CT image processing method and platform and a storage medium. The platform comprises an image segmentation module used for obtaining a baseline brain CT image of a user and inputting the baseline brain CT image into a pre-trained segmentation model to obtain a brain parenchyma region bleeding image and a bleeding volume; the image vector construction module is used for inputting the brain parenchyma area bleeding image into a pre-trained radiomics model to obtain a radiomics score; constructing an image vector based on the radiomics score and the bleeding volume; the text vector construction module is used for extracting blood biochemical marker information and medical record information of the user and constructing a text vector; and the output module is used for inputting the image vector and the text vector into a pre-trained risk judgment model and outputting a risk score of the baseline brain CT image. According to the method, the baseline brain CT image can be automatically subjected to quantitative evaluation based on the image processing platform, and the cerebral hemorrhage expansion risk score is output for reference of the user.

The present invention belongs to the technical field of biochemical multi-item liquid quality control products containing glycocholic acid, and specifically relates to a biochemical multi-item liquid quality control product containing glycocholic acid and a preparation method thereof. The liquid quality control product uses human serum as a matrix and contains the following components: non-analytes, including a buffer solution, a mixed anti-interference agent of acetylcysteine ​​and cyclodextrin, a stabilizer, a surfactant, and a preservative, and the concentration of the mixed anti-interference agent in the quality control product is 0.1%-2% (w / v); analytes, including glycocholic acid, total bile acid, and other biochemical markers. In response to the shortcomings of the prior art, the present invention uses human serum as a matrix and solves the problems of glycocholic acid being easily oxidized and degraded, cross-reacting with total bile acid, and insufficient stability of the liquid quality control product by adding a mixed anti-interference agent of acetylcysteine ​​and cyclodextrin, thereby realizing a method for long-term stable and accurate detection of liquid quality control products containing CG and multiple biochemical markers.

This invention provides a deep learning-based leukocyte sorting and disease diagnostic instrument, comprising: a sample processing system for label-free high-throughput sorting of leukocytes in whole blood samples, a visual detection system for leukocyte identification and parameter detection, and a control and analysis system for overall control and data analysis. This invention employs label-free sorting and detection technology, achieving leukocyte sorting based on the principle of microfluidic inertial focusing, combined with visual recognition technology for detection. This avoids the damage to cell viability caused by biochemical markers. Sorted cells can be used for subsequent gene sequencing, drug screening, and other research, while reducing detection costs. Furthermore, it deeply integrates deep learning algorithms, microfluidic technology, and 3D printing technology to achieve an integrated design of sorting, detection, and analysis. The instrument has a compact structure, small size, and low cost, making it easy to promote and apply in primary healthcare settings. The detection process is highly automated, significantly improving diagnostic efficiency.

The application discloses a liver fibrosis detection grading model construction method applied to schistosomiasis, relates to the technical field of medical detection, collects multi-modal liver sample data for standardized processing to generate standard multi-modal data, adopts corresponding processing methods for feature processing on different standard multi-modal data, obtains liver image features, serum biochemical marker features and pathological section features representing the influence of schistosomiasis on liver fibrosis, constructs a multi-modal joint grading diagnosis model based on the features to detect liver fibrosis of patients with schistosomiasis, verifies the detection result of the liver fibrosis detection, dynamically optimizes the multi-modal joint grading diagnosis model based on the detection result, evaluates the model indication of the multi-modal joint grading diagnosis model after the dynamic optimization is completed, and determines whether to adjust the optimization parameter of the dynamic optimization of the multi-modal joint grading diagnosis model based on the model indication.

The present invention discloses a novel system for obtaining real-time images of bone and ligament movement by integrating an electrochemical biosensor, an accelerometer, and a piezoelectric transducer, utilized in conjunction with enzyme-based gold nanoparticles. The electrochemical biosensor detects specific biochemical markers related to musculoskeletal health, while the accelerometer measures dynamic motion and loads, and the piezoelectric transducer captures mechanical strain. The enzyme-based gold nanoparticles enhance the sensitivity and specificity of biochemical detection, providing valuable imaging contrast. This comprehensive system enables simultaneous monitoring of biochemical and biomechanical parameters, facilitating improved diagnostic capabilities in assessing musculoskeletal conditions. By delivering real-time insights into bone and ligament health, this invention offers significant advancements in personalized rehabilitation and treatment strategies, promoting optimal patient outcomes.

An intelligent diagnostic toilet system for automated stool analysis, consisting of: a diagnostic chamber integrated into the drainage path of a toilet system, wherein the chamber is equipped with a hydrophobic and oleophobic optical window made of quartz or coated acrylic material, configured to allow multispectral light transmission while preventing contamination; a multispectral optical sensor module positioned next to the diagnostic chamber and configured to emit and detect reflected or transmitted light in at least the visible, near-infrared (NIR) and ultraviolet A (UV-A) ranges to quantify stool components including water content, lipid content, hemoglobin and bile pigments; a fluidically coupled unit with the diagnostic chamber for the detection of volatile organic compounds (VOCs), comprising a variety of solid-state gas sensors selected from the group consisting of metal oxide semiconductor, conductive polymer and ion mobility sensors, and configured to measure gases containing ammonia, hydrogen sulfide, methane and volatile fatty acids; a chemical test system comprising an interchangeable cartridge assembly containing a plurality of reagent-impregnated filter paper strips, wherein the cartridge is connected to a swab actuator configured to dispense a micro-amount of stool onto one or more reagent strips to trigger colorimetric chemical reactions; an imaging unit that is optically aligned with the aforementioned chemical test system to acquire multispectral colorimetric data of the reacted reagent strips and thus determine the presence of specific biochemical markers; a self-cleaning subsystem consisting of an array of micro-nozzle spray nozzles directed at the optical window and chamber surfaces for applying cleaning fluid, and a UV-C sterilization module located inside or near the chamber to decontaminate the surfaces after each analysis cycle; and an embedded processing unit operationally connected to the multispectral optical sensor module, the VOC detection unit and the imaging unit, wherein the processing unit performs a data fusion technique configured to correlate spectral, gaseous and colorimetric data to determine stool properties, generate diagnostic indices and output user-specific health assessments via an encrypted communication interface.

Biochemical markers that can be measured to determine the level of mechanical activation of a population of platelets are provided, and their use to prepare molecular signatures thereof are provided. The markers include phosphatidylserine, thrombin, integrin GPIIb / IIIa activation, glycoprotein GP Ib, P-selectin; platelet size; microparticle generation, or lipidomic profile of the membrane. The disclosed markers, measurement thereof, and signatures composed therefrom can be used in a variety of methods of patient selection, treatment monitoring, treatment selection, including both devices and active agents, and methods of treating subjects in need thereof, particularly humans. Examples of such methods include, but are not limited to, reducing shear-activated platelets; selecting a blood-contacting medical device; selecting between two or more medical devices; identifying a subject at a risk of developing a thrombogenic event; monitoring the prophylactic treatment of a subject; selecting a mechanoceutical agent; and delivering an agent to a subject.