7 results about "Hepatobiliary disease" patented technology

The invention discloses postoperative nursing equipment for the hepatobiliary surgery department, and belongs to the field of postoperative nursing. Comprising a bed board, a penetrating winding groove is formed in the center of the upper surface of the bed board, and two moving grooves are integrally formed in the right side of the interior of the winding groove; a winding mechanism is arranged on the inner side of the winding groove. An unwinding mechanism is arranged above the left side of the winding mechanism; a shielding mechanism is arranged below the bed board; the winding mechanism comprises a circular ring, the inner side of the circular ring is rotationally connected with a gear ring through a rotating shaft, and the top of the circular ring is fixedly connected with a motor; the winding mechanism drives the unwinding mechanism to surround the wound of the patient to complete the binding operation, the patient does not need to turn over actively, the pain point that the wound is damaged easily due to turning over in traditional postoperative care is solved, the device is particularly suitable for old patients with immune system decline and high incidence of liver and gall diseases, the rehabilitation risk is reduced, and postoperative recovery of the patient is assisted.

The invention relates to the technical field of traditional Chinese medicine liver and gall disease detection, and provides a traditional Chinese medicine liver and gall disease detection method and system based on a video computing technology. Compensating the face video data through a nonlinear color compensation algorithm to obtain color compensation face video data; extracting a multi-modal pulse wave signal, and processing the multi-modal pulse wave signal through an enhancement and denoising fusion algorithm to obtain an enhanced and denoised pulse wave signal; motion artifact suppression is conducted on the enhanced and denoised pulse wave signals through a time-space domain motion artifact self-adaptive suppression algorithm, and artifact suppression pulse wave signals are obtained; calculating a heart rate variability parameter based on the artifact suppression pulse wave signal; the heart rate variability parameters are recognized through a traditional Chinese medicine liver and gall disease classification recognition algorithm, and a traditional Chinese medicine liver and gall disease detection result is obtained. The accuracy and efficiency of traditional Chinese medicine liver and gall disease detection are improved through heart rate variability parameter analysis guided by the traditional Chinese medicine syndrome theory and a disease classification recognition algorithm.

This invention discloses a veterinary drug composition containing Bupleurum chinense extract, its preparation method, and its application, belonging to the technical field of veterinary traditional Chinese medicine preparations. The composition consists of active ingredients (only saikosaponins a and d, in a mass ratio of 0.8–1.2:1, accounting for 5%–50% of the total content) and excipients (one or more of microcrystalline cellulose, magnesium stearate, and starch). The dosage form is tablets, granules, or powder, with each unit dose containing 5–20 mg of the total mass of both. In preparation, dried Bupleurum chinense roots are pulverized and enzymatically hydrolyzed with a 1:1 complex enzyme, ultrasonically extracted with an ethanol-sodium citrate mixture, purified with AB-8 macroporous resin, and freeze-dried to obtain saikosaponin powder. This powder is then mixed with the excipients and granulated, tableted, or packaged into the corresponding dosage form. This composition shows significant efficacy against fatty liver in chickens, hepatitis in pigs, and cholestasis in ducks, exhibiting stability, high purity, and high efficiency, providing a safe new option for the treatment of liver and gallbladder diseases in livestock and poultry.

This invention discloses a plasmon resonance detection method for hepatobiliary-specific methylation sites. The method first immobilizes a sequence-specific DNA capture probe on a gold membrane surface and covalently couples it with a methylation-binding domain protein, constructing a sensor interface with dual functions of sequence capture and methylation recognition. During detection, a pre-treated plasma circulating cell-free DNA single-stranded sample is introduced into the sensor. The target DNA first hybridizes with the capture probe, and then its methylation site is specifically bound by the adjacent methylation-binding domain protein, triggering two consecutive specific surface plasmon resonance signal responses. By analyzing the increment between the two stable signal values ​​and comparing it with a preset threshold, label-free, real-time, and highly specific determination of the methylation status of the target site can be achieved. This invention eliminates the need for bisulfate treatment, effectively preserving DNA integrity, and is particularly suitable for high-sensitivity detection of hepatobiliary disease-related epigenetic markers in trace circulating cell-free DNA.

The invention discloses a traditional Chinese medicine composition for preventing special aquatic liver and gall diseases. A preparation method comprises the following steps: crushing: crushing a traditional Chinese medicine formula, and grading to obtain traditional Chinese medicine powder; the traditional Chinese medicine formula comprises 150-160 parts of felwort, 65-75 parts of cape jasmine, 35-45 parts of scutellaria baicalensis, 130-140 parts of plantain herb, 50-60 parts of radix bupleuri, 30-40 parts of liquorice and 40-50 parts of angelica sinensis, and an aqueous two-phase system is prepared by adding 16-18% of PEG4000, 12-15% of K2HPO4 and 3-5% of citric acid at one time at the temperature of 20-25 DEG C to form two phases; extracting: adding the traditional Chinese medicine powder into the aqueous two-phase system according to a certain proportion, stirring for 20-30 minutes at the speed of 200-400r / min, standing for 30-40 minutes, and then carrying out split-phase recovery to obtain an aqueous two-phase concentrated solution; and mixing the aqueous two-phase concentrated solution with materials to prepare pellets. According to the invention, multi-functional synergy of taste masking, food calling and moisture prevention is realized, expensive liver and gall diseases are effectively prevented, the death rate is reduced, the food calling pellet can be directly mixed and fed, the labor intensity is low, and the compliance is good.

This invention discloses a deep learning-based method for predicting the disease evolution of hepatobiliary patients, belonging to the fields of medical imaging and deep learning technology. The invention first acquires multi-temporal abdominal CT images, time-series clinical laboratory indicators, and medical history follow-up data of hepatobiliary patients; it then uses a variant U-Net network with Lagrange pseudo-ordering structure constraints to accurately segment the CT images and extract radiomics features; based on the segmentation results, a three-dimensional model is constructed, and numerical simulations of hepatobiliary blood perfusion and bile fluid dynamics are performed using local entropy production theory to obtain dynamic biomechanical features; cross-dimensional feature alignment and fusion are achieved by fusing attention mechanisms and a multimodal network for complex community discovery, and then chaotic theory is introduced for phase space reconstruction to enhance dynamic features; this invention achieves an upgrade from statistical correlation to physiological causality in prediction, improving model generalization and clinical interpretability, and is suitable for prognostic assessment and intelligent early warning of disease progression in hepatobiliary diseases.

The invention provides a liver and gall disease prediction method and system based on an eye OCT image. The method comprises the steps that an original fundus OCT image is acquired; performing image preprocessing on the original fundus OCT image to obtain a standardized image; inputting the standardized image into a preset image recognition model to enable the image recognition model to sequentially perform feature extraction and global average pooling on the standardized image, outputting corresponding prediction labels through a plurality of binary classifiers in a full connection layer, and further generating a liver and gall disease prediction result; wherein the image recognition model is obtained based on a pre-trained neural network model and a plurality of historical fundus OCT images by adopting a cross entropy loss function with category weights and transfer learning training, and the category weights are set in an inverse proportion according to the proportion of each category in the plurality of historical fundus OCT images; and the accuracy of predicting liver and gall diseases by the artificial intelligence model and the use experience of the user are improved.