223 results about "Tumor marker" patented technology

The invention relates to the technical field of medical treatment, in particular to an intelligent general surgery preoperative risk assessment system which comprises a data acquisition module, a data fusion and processing module, a feature extraction module, a risk assessment module, a prediction and optimization module and a visual display module. The data fusion and processing module adopts a multi-modal data fusion algorithm to generate a patient comprehensive information model, the feature extraction module extracts cardiovascular health status, renal function and tumor marker key features, and the risk assessment module constructs a preoperative risk assessment model to generate a preoperative risk score; the prediction and optimization module predicts the risk of postoperative complications, and the visual display module displays an evaluation result and a prediction result in real time through a graphical interface; according to the method, the accuracy and scientificity of preoperative risk assessment and postoperative complication prediction are remarkably improved, and reliable support is provided for postoperative management of patients.

The invention belongs to the technical field of in-vitro diagnosis, and particularly relates to a detection kit and a detection method for a pretumor marker. The kit comprises an elisa plate coated with a captured antibody, an HRP labeled antibody, an antigen standard substance, a washing solution, a developing solution and a stop solution, the capture antibody and the HRP labeled antibody are both prepared from a monoclonal antibody of the anti-SLC31A1 protein. According to the detection method of the kit for detecting the pretumor marker, the SLC31A1 protein content in human serum is detected through the kit, the specificity is high, a large batch of samples can be rapidly detected in a high-throughput mode, the cancer risk of a to-be-detected crowd can be effectively judged, and therefore relevant patients can find and treat the pretumor marker in advance, treatment prognosis is improved, and the death rate is reduced.

The invention relates to the technical field of spectral analysis and biomedical detection, and discloses an SERS (Surface Enhanced Raman Scattering) spectrum quantitative detection method and system based on interpretable stacked ensemble learning. The method comprises the following steps: acquiring SERS spectral data of serum tumor marker standard substances with different concentration gradients; performing baseline correction and normalization preprocessing on the data, performing sparse feature selection by using an LASSO algorithm, and screening out key spectral features to construct a sample data set; constructing an interpretable stacking integration model, wherein the model adopts a base learner layer and a meta learner layer; training the model by using the training set, optimizing model hyper-parameters by using a cross validation strategy, and establishing a mapping relationship between spectral features and tumor marker concentrations; and collecting SERS spectral data of a to-be-detected serum sample, extracting key spectral features, inputting the key spectral features into the trained interpretable stacked integrated model, and outputting a concentration predicted value of the tumor marker in the to-be-detected serum sample. The method has the advantages of high precision, universality and molecular level interpretability.

The invention relates to the technical field of tumor markers, in particular to a pancreatic cancer early screening marker combination based on blood protein and application of the pancreatic cancer early screening marker combination. Through proteomics analysis and bioinformatics screening, 12 kinds of proteins such as NME3, HPGDS, CEACAM5, TNFSF12, SCG3, VTCN1, GFRA1, KRT19, TMPRSS12, NELL2, KAZALD and MMP12 are determined to serve as the potential early screening markers of the pancreatic cancer. Researches show that the combination of the proteins can effectively distinguish pancreatic cancer patients from non-pancreatic cancer patients, and has good sensitivity and specificity. A screening model constructed based on the protein combination can be applied to auxiliary diagnosis, risk assessment and population screening of pancreatic cancer, and has important clinical transformation value.

Methods for detecting a tumor using a sample in which tumor DNA fragments are present only in a very low concentration, beyond the statistical limit of detection, where methods include: obtaining sequence data for tumor nucleic acid from a tumor from a subject and analyzing the sequence data to identify a plurality of tumor-specific variants that are in the tumor nucleic acid and that are not in non-tumor nucleic acid of the subject; selecting a marker variant that appears duplicated in tumor nucleic acid (compared to non-tumor nucleic acid) a greater number of times than other ones of the tumor variants; performing an assay to detect the marker variant in a sample from the subject; and reporting the presence of the tumor in the subject when the assay is positive for the marker variant in the sample.

The invention belongs to the technical field of biomedical detection, and discloses a Raman spectrum analysis device for quantitative detection of hematologic tumor markers, which comprises a blood collection centrifugal unit with a self-adaptive load regulation mechanism for monitoring blood density and components in real time, centrifugal force and time parameters are automatically adjusted according to the blood density and components; the optical fiber Raman spectrometer is provided with an automatic wavelength calibration system, and an ultrafast laser pulse transmitter and a high-sensitivity photon counting detector are integrated on the optical fiber Raman spectrometer. Raman scattering signals are enhanced by adopting a quantum entanglement light source and a nonlinear optical crystal; according to the invention, the detection capability on low-concentration tumor markers can be obviously improved. Due to the improvement, the device can accurately detect the existence of the tumor marker at the early stage of cancer, and powerful support is provided for early treatment of a patient.

The invention discloses an immune quantitative detection device for tumor markers, relates to the technical field of medical detection, and aims to solve the problems of complicated operation, long detection period and poor portability in immune quantitative detection of tumor markers in the prior art. Comprising a shell, a disposable detection card, a multi-channel micro-fluidic chip, a high-sensitivity sensing system, a micro magnetic control incubation module, an AI auxiliary image recognition system and a wireless transmission module, the disposable detection card is used for accommodating the multi-channel micro-fluidic chip and a reaction reagent, and the multi-channel micro-fluidic chip is respectively aligned with the high-sensitivity sensing system and the micro magnetic control incubation module; the high-sensitivity sensing system is composed of a surface plasma resonance sensor and a quantum dot fluorescence labeling module, and the surface of a sensing chip of the surface plasma resonance sensor is modified with a tumor marker specific capture antibody. The method has the advantages of convenient detection and high efficiency and precision.

The invention discloses a big data-based digestive system department prediction analysis system. The system comprises a data acquisition module, a knowledge graph fusion module, a disease course evolution prediction module and an intelligent diagnosis and treatment decision module. The invention relates to the technical field of medical health information, in particular to a big-data-based predictive analysis system for the department of gastroenterology. According to the scheme, a physiological signal is reconstructed by using a multi-scale mask auto-encoder, and the signal and a text are aligned; mapping knowledge graph regularization is introduced, the joint code is mapped to a digestive system knowledge graph space, and medical semantic representation is obtained; constructing a time sequence attenuation dynamic graph through the dynamic memory graph, and adding graph convolution gating; an embedded differential equation module simulates inflammation and tumor marker evolution to realize accurate prediction; patient state vectors are constructed based on multi-modal coding, medicines are rapidly selected by using a scoring function, a treatment process is modeled as a Markov decision, and a strategy network, a value network and PPO optimization are combined to realize balance of curative effect and income.

This invention relates to aggregation-induced emission probes with specific responses to tumor markers and their applications, belonging to the field of biochemistry. This invention utilizes molecular structure modulation to prepare a series of molecules possessing large Stokes shifts, resistance to photobleaching, near-infrared II (NIR-II) emission, and aggregation-induced emission properties. Different self-eliminating groups are attached to the dimethylamino group of the molecule via quaternization or to the pyridine group via pyridinium salting. By controlling the structure of these self-eliminating groups, activatable luminescent probes responding to different tumor markers can be constructed. These probes integrate deep NIR-II imaging, aggregation-point luminescence, large Stokes shifts, and marker-specific responses, providing a new approach for precise imaging diagnosis of tumors.

The invention relates to the technical field of biology, in particular to an eccDNA marker and application thereof in prognosis prediction of colorectal cancer. According to the present invention, the chromosome outer circular DNA is adopted as the colorectal cancer prognosis prediction marker, and the colorectal cancer prognosis prediction marker is significantly superior to the common tumor marker when the colorectal cancer prognosis prediction marker is used; a colorectal cancer prognosis prediction model constructed by adopting the eccDNA marker provided by the invention is high in diagnosis accuracy, high in sensitivity and strong in specificity, and has excellent application potential in clinical risk stratification.

The invention discloses a brain tumor real-time identification system and method based on cortical electroencephalogram signals, and relates to the technical field of medical artificial intelligence, and the method comprises the following steps: obtaining multi-channel ECoG signals in real time; the multi-channel ECoG signals are updated in real time, the multi-channel ECoG signals updated in real time are preprocessed, and the preprocessed multi-channel ECoG signals are obtained; performing channel feature extraction on the ECoG signal of each channel to obtain various ECoG signal features under each channel as channel features; calculating segmentation thresholds of different channel characteristics under each channel based on an automatic threshold segmentation algorithm; carrying out confidence evaluation on the segmentation threshold values of different channel characteristics under each channel; and marking the channel of which the confidence coefficient is smaller than a set tumor marking threshold value as a tumor area. Accurate positioning of a tumor area can be achieved, functional state changes of the cerebral cortex can be reflected in real time, and doctors can conveniently master real-time changes of cortex functions in time.

The present invention provides a protein detection method and detection preparation, which realizes efficient enrichment of target protein through mesoporous silica-graphene oxide composite nanoporous material, completes specific immune capture in combination with magnetic nanoparticles modified with targeted antibodies, and realizes single molecule separation using a microfluidic chip of a three-dimensional hydrogel antibody array. Signal amplification adopts enzyme-labeled secondary antibody-gold nanorod conjugate, enhances the fluorescence signal through the plasma resonance effect, and corrects the background noise in combination with a convolutional neural network algorithm, which significantly improves the detection reliability; experiments show that the detection limit of this method is as low as 0.12pg / mL, the linear range covers 0.5-100,000pg / mL, the single cell detection error is ±7.8%, the batch repeatability (CV=4.2%), the nonspecific adsorption rate is only 1.1%, and the detection time is shortened to 1.2 hours. The detection method of the present invention is suitable for ultra-sensitive detection of tumor markers, especially in the in situ analysis of CTCs with clinical application potential.

This invention relates to the field of biomedical technology, providing the application of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents. The glycoprotein tumor biomarker is a CLU_N_291_HexNAc(4)Hex(5) site-specific glycosylation modification. This invention utilizes CLU_N_291_HexNAc(4)Hex(5) site-specific glycosylation modification as a pan-cancer biomarker, combining it with existing conventional tumor markers (CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA) for early cancer screening, assessment, diagnosis, postoperative monitoring, and / or prognostic analysis, particularly for the diagnosis of pancreatic cancer, cholangiocarcinoma, gastric cancer, lung cancer, breast cancer, or ovarian cancer.

The invention discloses a tumor specific signal analysis device and equipment for uncertain pulmonary nodules and a storage medium, and relates to the technical field of electrical digital data processing.The device constructs a LungTCR database containing lung cancer tissue enrichment type and blood enrichment type CDR3 sequences through TCR beta chain sequencing and IMGT database comparison, and the LungTCR database is used for analyzing the lung cancer tissue enrichment type and blood enrichment type CDR3 sequences. A basis is provided for tumor specific signal recognition; a Needleman-Wunsch algorithm and an editing distance threshold value are adopted for screening, so that high-precision sequence matching is realized; through quantification of lung cancer tissue scores, blood scores and mutation specificity scores, a TCR feature group with biological significance is formed; and finally, the TCRnodseek plus model trained by clinical and image features is combined, a reliable malignant probability prediction value can be output, the model AUC can reach 0.90 or above, and the model is obviously superior to traditional imaging diagnosis and tumor markers.

The invention belongs to the technical field of medical detection, and particularly relates to an electrochemical device for detecting tumor cells in blood and a preparation method of the electrochemical device. The electrochemical device comprises a biocompatible flexible substrate, a side gate electrode, a source electrode, a drain electrode, an insulating film, a hydrophilic semiconductor and a ligand for capturing a tumor marker, the hydrophilic semiconductor is arranged between the source electrode and the drain electrode, and the biological ligand for capturing the tumor cell marker is arranged on the side gate electrode; the side gate electrode is used for generating induced voltage when a tumor in a blood vessel is captured, so that induced current is generated between the source electrode and the drain electrode; the insulating film is deposited on the part outside the channel and the side gate electrode, so that crosstalk between adjacent electrodes is prevented. The invention also discloses a manufacturing method and a testing method of the electrochemical device. The electrochemical device can realize minimally invasive and real-time detection, and is simple in structure, low in power consumption, high in stability, small in detection wound and high in detection speed.

The invention discloses an SERS (Surface Enhanced Raman Scattering) diagnostic apparatus based on an enzyme-like catalysis Au-graphene nano composite material, and relates to the technical field of spectrum diagnostic equipment, the SERS diagnostic apparatus comprises a display, a shell, an imaging module, a sample loading module, a grating light source module, a host, a laser light source module and a light path module, the sample loading module comprises a disposable micro-fluidic chip box and a sample loading table, an enzyme-like catalysis Au-graphene nano composite material is fixed on the inner channel wall of the disposable micro-fluidic chip box and is used for performing dual-mode signal amplification on tumor markers in urine. Urine sampling-detection can be completed at a time, the whole process is non-invasive, the enzyme-like catalysis Au-graphene nano composite material has high SERS enhancement and peroxidase-like activity, does not agglomerate in high-ionic-strength urine, can be seamlessly connected into a conventional urine detection process through dual-mode signal amplification and high sensitivity in cooperation with a Raman imaging technology and an AI analysis technology, and can be applied to the field of urine detection. And early rapid screening and dynamic monitoring of the malignant tumor of the urinary system are realized.

The invention relates to the technical field of biomedical detection, in particular to a multi-dimensional tumor marker joint detection device and a using method thereof.The device comprises a sample processing module, a sequencing module, a marker extraction module, a feature fusion and detection module and a result output module, and 6 types of markers such as chromosome copy variation, microsatellite instability and the like are extracted respectively. The method comprises the following steps: collecting a body fluid sample to extract cfDNA, directly constructing a library without special treatment, obtaining effective data through whole genome sequencing, synchronously extracting six types of markers through each module of the device, optimizing an algorithm to adapt to low-depth data, inputting an integrated learning model to output a risk score, and finally outputting related information by a result output module. The method does not need special experimental treatment, realizes low-cost and high-accuracy joint detection, and meets the clinical large-scale screening requirements.

The present application relates to the field of tumor markers, in particular to a fascin acetylation modification which can be used as a tumor diagnosis and prognosis analysis marker, a detection reagent and application thereof. The present application is directed to the fact that acetylation modification at K41 site of fascin significantly inhibits the cancer-promoting activity of fascin protein, and in esophageal cancer tissue, the proportion of fascin with acetylation modification at K41 site is significantly lower than that of wild-type fascin in para-cancerous tissue. Based on the acetylation at K41 site of fascin, a specific antibody for acetylation at K41 site of fascin is developed, so as to conduct clinical application research on acetylation at K41 site of fascin, and establish an esophageal cancer prognosis analysis model and targeted treatment.

The application provides a DNA sensor based on gold nanoparticles (AuNPs) for visual detection of miRNA-10b. The sensor is obtained by coupling AuNPs-S1 through mercapto between gold nanoparticles (AuNPs) and probe S1; and coupling AuNPs-S2 through mercapto between gold nanoparticles (AuNPs) and probe S2. The combination of the probe and the gold nanoparticles is more firm due to mercapto modification, so that the stability and detection repeatability of the DNA sensor are improved. The aggregation of AuNPs is triggered by hybridization between the target miRNA-10b and the probe, the transition of AuNPs from dispersion to aggregation in the buffer solution is regulated, and the color change from red to blue is visible to the naked eye, so that the rapid and visual detection of the tumor marker miRNA-10b is realized. The linear range is 10 pM-200 nM, and the sensor has excellent stability in serum environments (1%, 10%) and enzymatic environments (5 U / mL DNase and 2 U / mL DNase), and high selectivity. An efficient tool is provided for cancer diagnosis.

Provided for herein in several embodiments are anti-BCMA binding moieties. These anti-BCMA binding moieties may be used in BCMA-directed chimeric antigen receptors (CARs). Also disclosed herein are immune cell-based compositions comprising the anti-BCMA binding moieties and BCMA-directed CARs. In several embodiments, the immune-cell based compositions also target an additional tumor marker and / or an additional epitope of BCMA. In several embodiments, the BCMA-directed CAR is expressed in a Natural Killer cell. In several embodiments, combinations of BCMA-CAR-expressing NK cells are administered in conjunction with, for example CAR-expressing NK cells and / or CAR-expressing T cells that are directed to an additional cancer marker and / or an additional epitope of BCMA. Also provided for herein are methods and uses of the chimeric antigen receptors in immunotherapy.

This application relates to the field of biotechnology, specifically disclosing the application of immune cell subsets in the preparation of detection products for assessing the efficacy of neoadjuvant therapy and the risk of recurrence and metastasis in breast cancer. The detection method described in this application is non-invasive and convenient, requiring only peripheral blood, and is easy to repeat sampling and dynamic monitoring. This application also provides a predictive model for the efficacy of neoadjuvant therapy in breast cancer, and a predictive model for the risk of recurrence and metastasis in breast cancer treated with neoadjuvant therapy. The constructed predictive model has a high AUC value (0.82-0.87), and good sensitivity and specificity. Furthermore, through regular postoperative monitoring, abnormal dynamic changes in immune cell subsets can be detected, potentially indicating signs of recurrence earlier than imaging and traditional tumor markers. This helps identify advantageous populations before treatment and high-risk populations after treatment, enabling optimization of treatment plans and individualized adjustment of follow-up density.

The invention discloses a magnetic nano-microsphere rapid separation and detection system for medical diagnosis of tumor markers, and relates to the technical field of medical diagnosis. The magnetic nano-microsphere rapid separation and detection system is prepared by the following steps: mixing serum of a patient with magnetic microspheres and carrying out constant-temperature pretreatment; applying an initial magnetic field to separate the microspheres, and synchronously collecting baseline data of a magnetoacoustic signal; constructing a dynamic function according to signals monitored in real time to guide a subsequent separation strategy; applying a self-adaptive alternating magnetic field, and selectively dissociating non-specifically adsorbed interferents; performing spatial enrichment on the target complex in a specific area by using a dynamic focusing magnetic field; and after fluorescence labeling and cleaning, correcting and calculating the final marker concentration in combination with whole-process signal data. By introducing the real-time feedback of the magnetoacoustic coupling signal and the dynamic magnetic field regulation and control strategy, the magnetic separation process can be actively sensed and optimized in a single detection period.

Provided is use of an endogenous micropeptide in cancer diagnosis or treatment. Particularly, the present invention relates to use of a micropeptide encoded on a long non-coding RNA as a tumor marker, a tumor treatment drug, and / or a tumor treatment target. Specifically, two micropeptides and variants thereof have been discovered for the first time. Moreover, provided are nucleotide sequences encoding the described micropeptides and recombinant vectors containing these nucleotide sequences, and also provided are use thereof in the preparation of a reagent or a drug for diagnosing, preventing, alleviating, or treating a tumor, and use of a drug or a reagent for an interfering micropeptide or a coding nucleic acid thereof in the preparation of a medicament or a pharmaceutical composition for preventing, alleviating, or treating a disease. The micropeptides not only provide biomarkers for the diagnosis of tumors, but also provide therapeutic drugs or therapeutic targets for the treatment of tumors.

The invention discloses application of METTL14 in inhibition of pituitary tumor cell proliferation and prognosis evaluation, and relates to the technical field of medicines. Experiments prove that when METTL14 is knocked out, E-cadherin can be lowered, and N-cadherin and waveform protein can be increased, so that the migration ability and proliferation and invasion ability of GH3 cells are promoted, and the tumor forming ability and in-vivo hormone level of pituitary growth hormone tumors are improved. Therefore, the METTL14 can be used for preparing a product for diagnosing or treating pituitary growth hormone adenoma, which is beneficial to theoretically researching pituitary adenoma and developing a novel targeted drug, and provides a new direction for finding a tumor marker or a new treatment strategy related to novel pituitary growth hormone adenoma diagnosis and controlling pituitary growth hormone adenoma.

The application relates to the technical field of biological medicine, and discloses a tumor marker micro-detection and analysis method based on liquid biopsy. The method comprises the following steps: extracting peripheral blood free DNA and performing double-end sequencing; jointly extracting multi-dimensional features such as ctDNA fragment length distribution, end sequence preference, nucleosome positioning signal and CpG methylation state from the sequencing data; constructing a feature matrix under unified genomic coordinates through space-time alignment; inputting a pre-trained cross-scale graph neural network model, taking a genomic region as a node and physical and functional proximity as an edge, learning a coupling mode of multi-modal features in a local chromatin structure, and outputting a malignant risk score; and combining a threshold to distinguish early lung cancer and benign and malignant nodules. The application comprises corresponding functional units and supports efficient parallel analysis. The system significantly improves detection sensitivity and specificity, reduces the false negative rate by 36%, and realizes non-invasive and accurate early lung cancer screening.

The present invention relates to the field of medical information processing technology, and specifically to a method for constructing a model for predicting the risk of ovarian cancer associated with hyperglycemia. The method comprises: performing an isolated analysis of a patient's blood sugar level at each physical examination and combining it with local trend characteristics of the blood sugar level to calculate the patient's blood sugar abnormality at each physical examination; analyzing the difference between the patient's blood sugar level and the overall trend information, and combining the patient's blood sugar abnormality to assess blood sugar abnormality; utilizing the correlation between blood sugar level and tumor marker concentration at different time periods, and combining the assessment results of the patient's blood sugar abnormality at each physical examination, calculating the prediction reliability of the patient's blood sugar level at each physical examination, thereby performing data cleaning on the blood sugar level data to further predict the patient's risk of ovarian cancer associated with hyperglycemia. The present invention effectively improves the accuracy of predicting the risk of ovarian cancer associated with hyperglycemia.

The application discloses a system, device or medium for diagnosing or predicting Alzheimer's disease based on a gene marker combination, and belongs to the technical field of tumor markers. The gene marker combination comprises TMEM74, CBLN4, SYDE2, SLFNL1, PITPNM3, MPPED1, SYNDIG1L, ADAMTS5, BMX, GLUL, KRT79, TULP2, PRSS16, IL25, KCNJ16, SLC22A25, CRYAA, DLX2, SHISA6, CRYGA, EFCAB3, GMFB, RIN1, DMRTA1, CER1, CYP26B1, REG3A, HTRA3, SLC2A4, CRYGB and SOX3. The expression level data of the gene marker combination are used to construct a machine learning model, which can be used to diagnose whether a subject has Alzheimer's disease or predict whether a subject has a risk of suffering from Alzheimer's disease, and has great clinical application value.

The embodiment of the application discloses a surgical field display method, device, equipment and medium, wherein the method comprises: acquiring a preset modality three-dimensional image and a three-dimensional ultrasound image of a preset region of interest, which displays preset tumor marker information; in response to an image fusion instruction, fusing the preset modality three-dimensional image and the three-dimensional ultrasound image to obtain an initial fusion effect image; in response to an image adjustment instruction of a user on the initial fusion effect image, adjusting the image display effect to obtain and display a target fusion effect image. The technical scheme of the embodiment of the application can display the surgical field intuitively and stereoscopically, so that the doctor can obtain more comprehensive and accurate structural morphological information and position information of the tumor, which is beneficial to assisting the doctor in positioning and diagnosing the lesion.

The invention discloses a method for detecting biomolecules, and relates to the technical field of micro-nano processing technology and LSPR biosensors. An existing LSPR sensor is low in sensitivity and large in spectral formant line width, and an existing MIM structure is complex in machining process, high in cost, tedious in modification step and insufficient in sensing performance stability. A metal-gap-metal composite structure is constructed, a pair of sensing chips is prepared, each sensing chip comprises a quartz substrate and an LSPR gold nanostructure array, a supporting structure is formed on the quartz substrate, buckling assembly is performed to accurately control the distance to form an MIM resonant cavity, and the active regulation and control of the electric field intensity are realized by combining biological modification and detection methods. The sensitivity of the sensor is remarkably improved, the spectral formant line width is reduced, the process is simplified, the cost is reduced, the stability is improved, and the sensor is used for detecting trace tumor marker antigens.

The present invention belongs to the field of clinical diagnostic technology and relates to a method for preparing an aptamer biosensor for rapid detection of dual tumor markers. The present invention prepares a composite nanofiber membrane of polyacrylonitrile and transition metal salt precursors based on a coaxial electrospinning process. The carbon nanofiber structure uniformly loaded with cobalt metal nanoparticles obtained by a thermal reduction method has a large specific surface area and a unique three-dimensional network electron transmission channel, which significantly improves the electron transfer capacity and the loading amount of aptamer molecules. The biosensor based on the core-shell nanofiber network structure exhibits excellent electrocatalytic ability and signal stability. The preparation process of the biosensor is simple and controllable, and it is easy to achieve large-scale and productized production. It can realize the rapid identification and detection of trace miRNA-155 and miRNA-21 nucleic acid molecules in real serum within 2 minutes, and has important application value in disease diagnosis and clinical biomedicine.