19 results about "Drug Sample" patented technology

The invention discloses online detection equipment for a medicine material, relates to the technical field of medicine material detection, and solves the problem of detection distortion caused by slight inclination of a medicine sample. The equipment comprises an equipment shell, a console and a detection bin, wherein a state detection assembly and an infrared detection assembly are arranged in the detection bin. An integrated circuit board in the console is integrated with a sensing acquisition module, a data processing module and a control module, the data processing module comprises a tiny inclination identification unit, a spectrum offset correction unit and a component data correction unit, and the real component content is calculated after the inclination angle is identified through pressure data and spectrum distortion is corrected. And the control module judges the qualified state of the sample in combination with a preset standard, controls the equipment to operate and sends a regulation instruction to an external production system. According to the equipment, the detection accuracy and the operation convenience are improved, the linkage of detection and production is realized, the online quality control requirement is met, and reliable support is provided for medicine production quality control.

The application discloses a drug qualitative detection method based on multi-concentration mixed SERS data, comprising the following steps: constructing a data set of different concentrations of drugs based on SERS spectra of the same drug at high and low concentrations; mixing SERS spectrum data of low and high concentrations in the data set to construct a mixed data set; optimizing hyperparameters of a random forest model based on an improved NSGA-II algorithm to construct an optimized classification model; and classifying drugs based on the mixed data set and the optimized classification model. The application effectively improves the classification accuracy of machine learning models for low-concentration drug samples by mixing SERS data sets, and solves the problem of reliability decline of the prior art on low-concentration samples. In the hyperparameter optimization of the machine learning model, the improved NSGA-II algorithm is used for model hyperparameter optimization, which can simultaneously process multiple conflicting objectives to achieve a balance between model accuracy and efficiency.

The invention relates to the technical field of analytical chemistry detection, in particular to a method for determining trace nitrite in pharmaceutic adjuvants.The method comprises the steps that an acetonitrile solution, a potassium hydroxide solution, a phosphate buffer solution, a sulfuric acid solution, a 2, 3-diaminonaphthalene solution and a reference substance working solution are provided; the method comprises the following steps: preparing a standard curve solution and a test sample solution, and carrying out ultra-high performance liquid chromatography-tandem mass spectrometry analysis detection: respectively carrying out sample injection detection on the test sample solution and the prepared standard curve solutions with different concentrations under preset chromatographic conditions and mass spectrometry conditions, recording detection results of the standard curve solutions with different concentrations to make a standard curve, and calculating the content of nitrite in the test solution by adopting a standard addition method according to the standard curve and the detection result of the test solution. The method disclosed by the invention can be used for rapidly determining trace nitrite in a drug sample, and can be used for determining the trace nitrite in pharmaceutical adjuvants and detecting the nitrite in drugs.

The embodiment of the invention provides a medicine component identification method and related equipment, and belongs to the technical field of medicine identification. The method comprises: acquiring preset drug sample data including positive drug sample data and negative drug sample data, the positive drug sample data including a first thin-layer chromatography, and the negative drug sample data including a second thin-layer chromatography; constructing a model training map set through the first thin-layer chromatography map to train a preset twin neural network model to obtain a first twin neural network model; constructing a model test atlas set through the first thin-layer chromatography atlas and the second thin-layer chromatography atlas, and performing parameter optimization on the model in combination with the model training atlas set to obtain a target neural network model; and inputting the to-be-identified thin-layer chromatogram into the target neural network model for component identification analysis to obtain a component identification result. According to the embodiment of the invention, reference substances and reference medicinal materials are not used as much as possible in medicine identification, and the purposes of economy, greenness and environmental protection are achieved.

The utility model discloses a multi-functional sampler for medicine quality detection, which comprises a cylinder body, the outer wall of one side of the cylinder body is fixedly connected with a handle, the top of the cylinder body is in threaded connection with a top cover, the top of the top cover is fixedly connected with a feeding channel, the inner wall of the cylinder body is provided with a material collecting box, and the inner wall of the material collecting box is provided with a crushing treatment mechanism. And a ventilation treatment mechanism is arranged on the inner wall of the material collecting box. Solid medicine entering from the feeding channel can be loaded by arranging the material collecting box, the medicine can be crushed by arranging the crushing treatment mechanism in the material collecting box, the medicine can be blown away by arranging the ventilation treatment mechanism in the material collecting box, the medicine crushing uniformity is guaranteed, and the medicine crushing efficiency is improved. The medicine can be filtered by arranging the filter plate, and further grinding and refining treatment can be performed on the medicine by arranging the grinding and refining mechanism in the material guide channel, so that the refining degree of the medicine sample finally collected in the bottom cover is ensured.

The utility model discloses sampling storage equipment for pharmaceutical analysis, which comprises a low-temperature storage box main body and an upper cover, the top of the low-temperature storage box main body is provided with the upper cover, the center of the top of the upper cover is provided with a control seat, and the top of the control seat is provided with a display screen for displaying the internal temperature of the low-temperature storage box main body. One side of the control seat is provided with a power supply socket used for being connected with an external power supply; and four groups of opposite mounting seats are fixedly mounted at two ends of the center of the bottom of the upper cover. According to the low-temperature preservation box, the low-temperature preservation box body, the upper cover, the control base, the display screen, the power socket, the mounting base, the ultraviolet disinfection lamp, the lens and other parts are arranged and used in cooperation, so that the surfaces of glass test tubes for storing medicine in the low-temperature preservation box body are conveniently disinfected and sterilized, bacterium breeding is reduced, and pollution to medicine samples is reduced; and meanwhile, through cooperation of a lens and an ultraviolet disinfection lamp, the interior of the low-temperature preservation box body can be disinfected and sterilized comprehensively.

The embodiment of the application provides a drug component identification method and related equipment, and belongs to the technical field of drug identification. The method comprises the following steps: obtaining preset drug sample data, including positive drug sample data and negative drug sample data, wherein the positive drug sample data comprises a first thin layer chromatogram, and the negative drug sample data comprises a second thin layer chromatogram; constructing a model training atlas set through the first thin layer chromatogram, so as to train a preset twin neural network model, and obtain a first twin neural network model; constructing a model test atlas set through the first thin layer chromatogram and the second thin layer chromatogram, so as to optimize parameters of the model in combination with the model training atlas set, and obtain a target neural network model; and inputting a to-be-identified thin layer chromatogram into the target neural network model for component identification analysis, and obtaining a component identification result. The embodiment of the application can try not to use control samples and control medicinal materials in drug identification, so that the economic, green and environmentally-friendly purposes are achieved.

The utility model relates to the technical field of sampling devices, in particular to a powder sampling device for medicine production, which comprises a handle and a sampling assembly, and the sampling assembly comprises a sleeve, a moving block, a connecting rod and a plurality of shielding parts; the sleeve is provided with a plurality of sampling ports; when medicine powder is sampled, a worker holds the sleeve by hand, inserts the sleeve into a medicine sample, then pulls the handle to move upwards, the handle drives the connecting rod and the multiple shielding components to move upwards, the multiple shielding components and the multiple sampling openings are staggered to be exposed out of the sampling openings, then the sleeve is rotated, and the medicine powder is sampled. During sampling, medicine powder can enter the multiple sampling openings with different height positions, then the handle is pushed to move downwards, the multiple shielding components return to the side edges of the sampling openings again, the sampling openings are closed, and sampling is completed by moving the sleeve out of a medicine sample. By means of the mode, medicine powder at different depth positions of a medicine sample can be sampled at the same time, and the medicine powder sampling efficiency is improved.

The utility model relates to the technical field of dispensing barrel sampling devices, and particularly discloses an automatic dispensing barrel sampling device which comprises a circulating assembly, a sampling assembly is arranged on one side of the circulating assembly, a moving assembly is arranged on the other side of the circulating assembly, and a supporting table is arranged below the sampling assembly. The sampling assembly comprises a dispensing barrel located on the supporting table, a stirring motor is arranged on the dispensing barrel, a stirring shaft connected with the stirring motor is arranged in the dispensing barrel, and stirring blades are arranged on the stirring shaft; the automatic sampling device for the dispensing barrel is simple in overall structure and reasonable in design, and a worker can sample a medicine sample conveniently; due to the design of the circulating assembly, the automatic operation of the sampling process of the medicine in the medicine dispensing barrel can be realized, so that a relatively uniform sampling sample can be obtained; due to the design of the moving assembly, manpower time and manpower cost can be saved, and manual operation errors are reduced, so that the automation degree of operation is improved, and the working efficiency of sampling in the dispensing barrel is improved.

The invention provides a method for determining related substances in a vitamin B6 drug, which comprises the following steps: dissolving a vitamin B6 drug sample in a prefabricated solution in a dark environment, filtering, and diluting to a target concentration interval to obtain a sample solution; placing the blank solvent, the contrast solution and the sample solution in a chromatographic column, introducing the mobile phase A and the mobile phase B, and sequentially carrying out gradient elution to obtain a chromatogram; and identifying and separating impurity chromatographic peaks in the chromatogram, performing quantitative or semi-quantitative analysis on each impurity chromatographic peak, and calculating the content proportion of each impurity chromatographic peak relative to the main chromatographic peak of the vitamin B6. Through collaborative design of the sample pretreatment state, the gradient elution path and the peak interpretation mode, known impurities and unknown impurities in the vitamin B6 medicine are in a separable state before entering a chromatographic system, and are fully expanded in the chromatographic separation process, so that accurate identification and effective separation of various impurities are realized.

The invention provides an AD drug affinity prediction method based on data enhancement and a graph neural network, and the method comprises the steps: obtaining a drug sample database by looking up a target protein of an AD target, hybridizing the drug sample database with target protein sequence information to form input data, and then arranging the input data into an input data table; the method comprises the following steps: preprocessing data, performing data enhancement on a drug sample, extracting expression of a drug and a target sequence, constructing a drug molecular map, calculating a protein contact matrix, constructing a protein residue sub-map, and splicing and enhancing the protein residue sub-map; constructing a joint graph, enhancing the connectivity of the joint graph, and constructing full connectivity and root node self-loop by using node index offset; carrying out characterization coding and node initialization; and inputting the joint graph feature vector and the node feature into a model for reasoning and regression prediction, and finally outputting affinity and writing the affinity into a collection table. According to the method, the prediction process is optimized, and the robustness and accuracy of the AD target drug affinity prediction process are improved.

The application discloses a kind of bicolour detection probe for zearalenone naked eye visual detection, comprising the following steps: step one: configuration solution 1;Step two: configuration solution 2;Step three: configuration solution 3;Step four: 3 μL of solution 2 is added to 37 μL of solution 1, then 10 μL of solution 3 is added to carry out reaction, and the absorption value of solution at 418 nm and 480 nm within 1-30 min is recorded using enzyme label instrument, the color change of solution is recorded by mobile phone photographing every minute, and the picture is digitized using RGB image conversion technology to improve the discernibility of naked eye.The application belongs to the technical field of ZEN detection, and is a kind of bicolour detection probe for zearalenone naked eye visual detection, which has high specificity, high sensitivity, good stability, simple operation, easy to carry, low cost, does not need professional technical personnel, fast detection time (10 minutes can be naked eye to see color change), and can be used as a useful tool for on-site analysis of ZEN in food and drug samples.

The present application relates to the technical field of drug identification, and particularly relates to a drug identification method, system, storage medium and electronic device, the method comprising: obtaining a Raman spectrum of each preset drug sample of a preset kind, preprocessing to obtain a plurality of first Raman spectra, and then calculating a threshold value; using an L1-KernelPCA method to perform non-multiplication kernel operation on all the first Raman spectra to obtain a matrix and a characteristic vector sorted in descending order of eigenvalue; selecting the first m characteristic vectors as principal component vectors, calculating the cumulative variance of each first Raman spectrum and the corresponding first reconstruction vector, and judging the quality of each preset drug sample according to the size relationship between the cumulative variance and the threshold value. The accuracy can be ensured, and the method is efficient and fast.

Smart Cartridge for Drug Sample Loading Without Cross-Contamination

This invention discloses a drug stability analysis system, belonging to the field of drug analysis technology. The system includes: a sample data acquisition module, a data fusion and normalization module, an intelligent analysis and state determination module, and a result output and trend prediction module. This invention achieves multi-dimensional and all-round scanning of drug samples during stability testing through the comprehensive application of optical imaging, fluorescence imaging, and infrared imaging technologies. This significantly improves the comprehensiveness and accuracy of data acquisition. Optical imaging technology captures macroscopic surface morphology information of the drug, while fluorescence imaging technology visualizes and tracks specific components in the drug to obtain component distribution information. Infrared imaging technology analyzes the chemical bond vibrations between drug molecules to obtain chemical composition information, providing material for subsequent data analysis and processing.

The invention provides an intelligent detection system for drug production quality, and relates to the technical field of drug quality inspection, and the intelligent detection system comprises a drug sample obtaining module used for obtaining a to-be-detected chiral drug sample and determining a stereoisomer list of the to-be-detected chiral drug sample; the parameter optimization module optimizes chromatographic control parameters according to the stereoisomer list and generates a stereoisomer retention time list; the chromatographic column effluent acquisition and chromatogram generation module is used for separating a sample and generating a chromatogram; partitioning the atlas, processing the atlas by a ratio parameter calculation module, and calculating enantiomer parameters; and the medicine identification module judges the medicine quality according to the parameter threshold value and performs corresponding identification. According to the method, the problem that the accuracy and reliability of chiral drug chromatographic analysis are influenced due to insufficient parameter stability caused by setting chromatographic control parameters depending on experience in the prior art can be solved, the accuracy of spectrum analysis and parameter calculation is improved, drug quality control and identification are enhanced, and the detection precision and sensitivity are improved.

This utility model relates to the technical field of solid drug sample testing devices; and discloses a near-infrared sample testing device for solid drugs, including a base plate, a rotating structure at the top inner end of the base plate, and a clamping structure at the bottom inner end of the base plate; the rotating structure includes a rotating groove, which is located at the top inner end of the base plate. This utility model allows the operating rod to rotate inside the rotating groove. When the pulling force is greater than the spring force, the inner wall of the positioning groove abuts against the inclined surface of the locking block and forces the locking block to move into the groove until it returns to a suitable position. This cancels the positioning effect of the locking block on the set of placement frames. After rotating a suitable distance, another set of positioning grooves corresponds to the locking block. Under the elastic action of the spring, the locking block can be ejected and locked into the interior of the positioning groove.

The application discloses a kind of microbial separation device for drug microbiological test, belong to drug microbiological test technical field.It includes shell, detachable seal installation is equipped with end cap on the upper end of shell, detachable installation is equipped with storage tank and controller outside shell, the whole device is reasonably arranged, easy to operate, by the way of centrifugal separation and filter membrane layer interception combination, and based on the pore size design of filter membrane layer is different, different kinds of microorganism can be quickly and effectively separated from drug sample, greatly shorten the separation time, improve the inspection efficiency, the setting of multilayer filtration adsorption component, not only can effectively intercept microorganism, but also remove impurities and harmful substances in sample, can be convenient to provide culture medium liquid to filter membrane layer, so that microorganism grows in the relatively sealed and constant-temperature environment in shell, and located at filter membrane layer, guarantee the excellent of microorganism growth environment, avoid impurities influence.

The application provides a drug data enhancement method and device, electronic equipment and storage medium. First, the molecular structure data of the target drug sample is obtained, and the molecular structure data is represented by a graph structure. Then, at least one of operation mode one, operation mode two and operation mode three is used to process the graph structure of the target drug sample to obtain an enhanced sample corresponding to the target drug sample. The method can perturb the graph structure of the target drug sample by deleting and / or adding operations, and the obtained enhanced sample can be used to increase the number and diversity of drug samples, expand the small sample drug data set, effectively alleviate the overfitting problem of the machine learning model on the small sample drug data set, and improve the generalization ability and robustness of the machine learning model. The accuracy of the machine learning model trained by the expanded drug data set is greatly improved, which is helpful for the wide application of the machine learning model.