38 results about "Clinico pathological" patented technology

This novel therapeutic (reparative, etiotropic not merely symptomatolytic) electrostimulatory method hereinafter referred to as Coupled Neuraxial Mesoscopic Desynchronization Electrostimulation Therapy (cNMDET) is comprised of a varied combination of progressively sequenced electrode placements and its clinically targeted areas which are comprised of the CNS [regions of the brain and spinal cord including, but not limited to, the underlying clinicopathologic mechanisms of the ANS] and clinically involved components of the PNS variably coupled in such a manner as to apply this therapeutic electrostimulation (reparative modulation) along the coursing path (longitudinally) of the targeted neural pathway over a segment and / or in its entirety, as well as, the utilization of patterned applications for involved joints, muscles, fascia, ligaments, tendons and areas involved in inflammatory processes of clinical significance accomplished through the application of, preferably, but not limited to, Coupled Neuraxial Transcutaneous Electrical Nerve Stimulation (cNTENS) and Coupled Neuraxial Transcranial Direct Stimulation (ctDCSn).

The present invention relates to a clinic pathology data classification method based on combination of principal component analysis and an extreme learning machine. The classification method comprises the following steps of (1) normalizing clinic data, extracting features through principal component analysis, ordering characteristic values according to characteristic significance, removing data dimensions below a significance threshold, and achieving the purpose of data dimension reduction; (2) training a feedforward neural network classifier by using an extreme learning algorithm after data dimension reduction is carried out; and (3) using the trained feedforward neural network classifier to test test samples, and obtaining a classification result. According to the clinic pathology data classification method based on combination of the principal component analysis and the extreme learning machine, dimensions are effectively reduced, calculation is simplified, and the classification effect is good.

Predicting response to adjuvant therapy or predicting disease progression in breast cancer is realized by (1) first obtaining a breast cancer test sample from a subject; (2) second obtaining clinicopathological data from said breast cancer test sample; (3) analyzing the obtained breast cancer test sample for presence or amount of (a) one or more molecular markers of hormone receptor status, one or more growth factor receptor markers, (b) one or more tumor suppression / apoptosis molecular markers; and (c) one or more additional molecular markers both proteomic and non-proteomic that are indicative of breast cancer disease processes; and then (4) correlating (a) the presence or amount of said molecular markers and, with (b) clinicopathological data from said tissue sample other than the molecular markers of breast cancer disease processes. A kit of (1) a panel of antibodies; (2) one or more gene amplification assays; (3) first reagents to assist said antibodies with binding to tumor samples; (4) second reagents to assist in determining gene amplification; permits, when applied to a breast cancer patient's tumor tissue sample, (A) permits observation, and determination, of a numerical level of expression of each individual antibody, and gene amplification; whereupon (B) a computer algorithm, residing on a computer can calculate a prediction of treatment outcome for a specific treatment for breast cancer, or future risk of breast cancer progression.

The invention especially relates to molecular markers, internal reference genes and application thereof, a detection kit and a method for constructing a detection model, belonging to the field of biotechnology. With follow-up information used for comparison, the kit provided by the invention has accuracy of 70% in predication of the risk of recurrence or death of first-visit patients with ER-positive and PR-positive breast cancers in 3-10 years after operations, wherein the prediction accuracies of a low-risk group and a high-risk group are 81.1% and 54.4%, respectively while the prediction accuracies of corresponding FFPE pathological examination results are 71.9 to 56.8%, respectively. The risk prediction model matching with the kit only needs the Ct values of the molecular markers, theage of a patient, the stage of pT and the number of LN and does not depend on other clinical pathological information, so the risk prediction model has better performance in evaluation of prognosis ofbreast cancers than simple pathological prediction results, can reduce malpractice caused by errors in pathological prediction and further improves a technical method for evaluation of prognosis of breast cancers.

The invention belongs to the fields of medical evaluation, detection technology and experimental zoology, and specifically relates to a construction method of animal model of rat which is used for screening medicaments for treating chronic inflammation, and evaluating methods for treating systematic chronic inflammation. The method comprises the following step that healthy male SD rats are administered with lipopolysaccharide by tail intravenous injection, on an every first day of a week for 4-8 weeks persistently, at a dosage of 120-300 [mu]g per kg of body weight, so that pathologic changes, which meet pathological characteristics of chronic inflammation, mainly of an increased ratio of peripheral blood leucocyte count to neutrophile granulocyte, an increased serum hypersensitive C-creative protein level, infiltration of heart, liver, lung, kidney in inflammatory cells, and haemostasis and oedema of tissues are generated. The method has a good repeatability, and operation of the method is simple, easily controllable and mechanism-clear, besides indexes can be analyzed quantitatively and are convenient for statistics treatments. In addition, the constructed animal model is more ideal and more conform to clinic pathological and physiological changes of the type of disease.

The invention discloses a method for serologically detecting Matrix metalloproteinase 11 (MMP11) and the application thereof. The gene chip technology and the bioinformatics method are adopted to obtain stomach cancer gene expression spectra, and by taking the MMP11 gene for example, the expression characteristics of the gene in the tumor cell lines and tissues are verified at the levels of mRNA and protein. At the same time, a detection kit for the MMP11serum is established by utilizing the technique of the enzyme linked immuno sorbent assay (ELISA), and the expression level of the MMP11 protein in the serum of stomach cancer patients is detected. The level of the MMP11 protein in the serum of stomach cancer patients is obviously higher than that of a non-cancer comparison group (p is lower than 0.001), and the same tendency is also found in the breast cancer, the colon cancer / rectum cancer and the lung cancer. In the serum detection to the stomach cancer patients, the sensitivity of the MMP11 is higher than that of a tumor molecule marker such as CEA, CA199, CA72.4, CA242, etc., and MMP11 and CA199 have good correlation (p is equal to 0.017). Through the analysis of the clinic pathological data, the level of the serum MMP11 has the obvious correlation (p is equal to 0.009) with the cancerometastasis state of the stomach cancer patients. The MMP11 is possible to become a novel tumor serum marker for the diagnosis and the prognostic judgement of the tumors.

The invention discloses an analysis method of an immune prognosis diagnosis marker combination for the ovarian cancer. The marker combination comprises the following genes: CSPG5, CXCL10, CXCL11, DKK1, PI3, TNFRSF17, DUOX1, TNFRSF13B and PAEP. The analysis method comprises the following steps of: S1, collecting and preprocessing data; S2, performing TMB score calculation and prognosis analysis; S3, performing difference analysis; S4, constructing and evaluating a TMB-immune prognosis related risk scoring model; S5, determining the relationship between clinical pathological factors and the TMB-IRS model; S6, carrying out CIBERSORT database analysis; and S7, carrying out statistical analysis. According to the analysis method, the immune risk scoring model related to TMB is constructed through nine key tumor mutation load related immune prognosis genes of the ovarian cancer, patients are divided into a high-risk group and a low-risk group, and it is proved that the high-risk group is related to poor prognosis, and the immune cell infiltration proportion of the patients in the low-risk group is higher; and prognosis prompts are provided for clinical treatment of the ovarian cancer, and the marker combination can be used as a novel biomarker and a potential treatment target for predicting ovarian cancer prognosis.

The invention discloses a transplantation tumor model of colorectal tumor for medical scientific research, which is produced by improving the orthotopic transplantation tumor model of colorectal tumor in nude mice. The invention aims to overcome the defect that the orthotopic transplantation tumor model of colorectal tumor has high intestinal obstruction incidence rate and short model animal survival time and is not suitable for long-time experiments. In the invention, the prepared tumor is transplanted close to the blood vessel in mesocolon. The method comprises the following steps: (1) preparation of tumor: injecting single cell suspension of the tumor cell to subcutis of nude mice and preparing the source of the transplantation tumor; (2) transplanting the prepared tumor close to the blood vessel in mesenterium of nude mice but not the serosa of the intestinal tube; and (3) raising the nude mice in an aseptic laminar flow room, controlling the room temperature to be (25+ / -1) DEG C and relative humidity to be 40-50% and continuously observing the nude mice. The animal model built by the invention can reproduce the natural clinical pathology process of human colorectal tumor, can not suffer from obstruction of the lumen in the digestive tract in the short term and has transfer mode similar to that of the patients.

The invention provides a colorectal cancer image classification method and a colorectal cancer image classification system combining deep learning and imageomics, aiming at the problem of training a small sample by a deep learning model, the existing data is fully utilized to carry out data enhancement (rotation, translation, image transformation and the like) so as to solve the problem of time and labor consumption caused by manual marking by a doctor. An automatic segmentation network model of deep learning is introduced, and the region of interest is automatically marked from the image. In order to solve the problems that the explainability of the features extracted by the deep learning model is poor and the obtained feature information is not comprehensive, more and more comprehensive feature information is obtained by fusing the image omics features, the deep learning features and the clinical pathological information, and the classification accuracy and reliability of the image omics are further improved.

The invention belongs to the field of biological medicines and relates to an application of a GPS2 gene in preparation of drugs for prognosis, diagnosis or prevention and treatment of soft tissue tumor. The GPS2 gene is in low expression in soft-tissue sarcoma such as liposarcoma, and proliferation of the liposarcoma can be promoted by inhibiting the expression of the GPS2 gene. The expression of the GPS2 gene has obvious relevance with prognosis of the liposarcoma, which shows that the GPS2 gene can be used for clinical pathological diagnosis of patients with liposarcoma. The GPS2 gene can be used as a tumor-suppressing gene in the liposarcoma, and can be used as a possible prognostic marker and a therapeutic molecule of the liposarcoma.

The invention discloses an application of PLPP4 ((phospholipid phosphatase 4) as a diagnosis, treatment and prognosis target for NSCLC (non-small-cell lung carcinoma). High expression of PLPP4 in tumor tissue and cell lines of NSCLC is discovered, high expression of PLPP4 in lung malignant tumor tissue is related to low clinical pathological grades as well as short overall survival time and progression-free survival time of a patient, in-vitro NSCLC cell proliferation and in-vivo tumorigenesis can be affected by silencing PLPP4, and all the information indicates that PLPP4 can serve as the diagnosis, treatment and prognosis target for NSCLC. Primers performing fluorescent quantitative detection on PLPP4 are taken as reagents for diagnosis and prognosis of NSCLC, and LNA modified sisiRNA capable of specifically silencing PLPP4 is taken as a treatment medicine for NSCLC.

The invention discloses a method for constructing a congenital heart disease with diminutive pulmonary blood animal model, which is characterized in that: a surgical knife enters a fourth intercostal of right chest lateral incision, and a pericardium is opened and suspended; an interatrial septum is directly expanded by a puncture needle and a balloon dilator from the right chest lateral under ultrasound guidance to form an atrial septal defect with the diameter of 1.0cm; a main pulmonary artery is sleeved by an artificial blood vessel sheet to monitor peripheral arterial blood pressure and differential pressure of a girdle place, and then the differential pressure is decreased gradually; and when the differential pressure of the girdle place of mild-moderate pulmonary stenosis is 20 to 30mmHg, or the differential pressure of the girdle place of severe pulmonary stenosis is more than or equal to 30mmHg, and the arterial blood pressure is kept stable, a girdle belt is fixed so as to form the congenital heart disease with diminutive pulmonary blood animal model. The method has the advantages of exactness, feasibility, simple and reliable operation method, convenient operation and a few complications; and the constructed animal model is more ideal and better adapts to changes of clinical pathophysiology of the disease.

The invention relates to a method for establishing a prediction model for survival benefit of a gallbladder cancer patient after chemoradiotherapy. The method comprises the steps of acquiring clinical and pathological data of the gallbladder cancer patient; dividing the acquired data into a plurality of groups of data according to the TNM stages, and respectively judging whether the patient in each stage is benefited from adjuvant therapy or not; dividing the data corresponding to the TNM stages into a modeling group and an internal verification group; analyzing the prediction capability of clinical and pathological variables of the modeling group data on the total survival time OS through a single-factor COX risk scale model, and screening risk factors which significantly influence the total survival time OS of the patient in the modeling group; incorporating the screened risk factors into a multi-factor COX risk scale model for analysis, and screening out influence variables significantly influencing the total survival time OS of the patient; according to the obtained influence variables, drawing a column graph used for predicting the total survival time OS, and obtaining a prediction model; and verifying the model. Compared with the prior art, the method has the advantages of providing credible prediction and analysis results for the GBC patient, being simple and convenient to operate and the like.

The invention discloses a test strip and method for detecting urine A beta amyloid protein. The test strip for detecting the urine A beta amyloid protein comprises a PVC bottom plate (7), wherein a sample pad (1), a conjugated pad (2), a chromatography pad (3) and a water absorption pad (4) which are sequentially overlapped are paved on the PVC bottom plate (7); the conjugated pad (2) is coated with colloidal gold particles conjugated with a monoclonal antibody; a detection line (5) is arranged on one side, close to the conjugated pad (2), of the chromatography pad (3); a contrast control line(6) is arranged on one side, close to the water absorption pad (4), of the chromatography pad (3); the detection line (5) is coated with an A beta amyloid protein binding polymer; and the contrast control line (6) is coated with a goat anti-mouse IgG polyclonal antibody. The method is high in sensitivity, has a detection limit of 40 pg, is rapid and economical, is suitable for conventional clinical pathological examination, is suitable for general screening of a large number of people and self-screening of home terminal users, is especially used for clinically assisting early diagnosis and pre-judgment of mild cognitive impairment (MCI), and predicts and judges whether a patient has high risk of developing MCI and AD.

The invention relates to an application of ALDH18A1 in treatment and diagnosis of colorectal cancer. The ALDH18A1 is highly expressed in colorectal cancer tissue and related to clinical pathological parameters and prognosis of the colorectal cancer tissue; cell proliferation of the colorectal cancer can be inhibited through down-regulation of expression of the ALDH18A1; the ALDH18A1 is in positivecorrelation with expression of an MYC gene; MYC gene expression can be inhibited through down-regulation of the ALDH18A1; in patients with high expression of the ALDH18A1, c-MYC target genes are significantly enriched; the ALDH18A1 can be used as a prognosis factor for colorectal cancer patients, can inhibit proliferation of colorectal cancer cells by down-regulating expression of the MYC gene and has great significance in tretament and diagnosis of the colorectal cancer.

The invention relates to the field of medical detection, in particular to specific gene markers for TMT (thyroid malignant tumor) and an application of the specific gene markers. On the basis of a weighted gene coexpression network analysis method and by combination with clinical cases, the TMT is specifically correlated with abnormal expression of 17 gene markers and can be taken as a specific gene detection marker set. By means of verification of a large number of independent clinicopathological samples, specific mutation sites of 5 specific genes are further determined as molecular diagnosis markers. The related gene diagnosis marker set has important clinical application value for early molecular accurate diagnosis of the TMT, can be used for preparing a molecular diagnosis kit for detecting malignant thyroid nodules which are indeterminate according to clinical aspiration biopsy cytology, and particularly has important clinical application value for early molecular accurate diagnosis of patients with an uncertain FNAB (fine needle aspiration biopsy) cytology result of malignant thyroid nodules.

The invention belongs to the field of prognosis evaluation of patients with metastatic prostate cancer at first diagnosis, and particularly relates to a prognosis prediction model of patients with metastatic prostate cancer at first diagnosis and an establishment method and application thereof. By analyzing and screening follow-up visit data of a patient suffering from metastatic prostate cancer at first, six clinical and pathological factors related to progression to mCRPC or death of the patient are finally obtained, a prognosis prediction model of the patient suffering from metastatic prostate cancer at first is established, and the model is visually presented in a line graph mode. The prognosis prediction model provided by the invention has the characteristics of intuition, simplicity,convenience and easiness in clinical popularization, and can effectively assist a clinician in carrying out relatively accurate individualized evaluation on prognosis of a patient suffering from metastatic prostate cancer at first diagnosis, so that important reference is provided for clinical consultation of the patient and formulation of a treatment scheme of the patient by the clinician.

The invention discloses a method and system for constructing an oral cancer neck lymphatic metastasis diagnosis algorithm, and the method comprises the steps: screening out oral cancer patients meeting the standards according to the inclusion and exclusion standards, and corresponding MRI image data, clinical pathological data and follow-up visit data; grouping the oral cancer patients into a training set patient and a test set patient; performing data extraction on the MRI image data of the patient in the training set according to a preset data extraction rule, constructing an artificial intelligence diagnosis algorithm for oral cancer neck lymphatic metastasis based on the MRI image, and performing multi-dimensional verification; and performing secondary verification and optimization onthe artificial intelligence diagnosis algorithm according to the MRI image data of the patient in the test set to obtain a final oral cancer neck lymphatic metastasis diagnosis algorithm. The artificial intelligence diagnosis algorithm for oral cancer neck lymphatic metastasis based on MRI images constructed by the invention can be used for researching AI diagnosis and prediction of oral cancer neck lymphatic metastasis, and improves the accuracy and efficiency of AI diagnosis and prediction.

The invention relates to a method and equipment for predicting postoperative biochemical relapse of a prostate cancer patient. The method comprises the following steps: acquiring clinical pathological data of the prostate cancer patient, wherein the clinical pathology data at least comprise existence of IDC-P, age, NCCN standard prostate cancer risk classification and clinical T stage, and the clinical pathology data has certain correlation with the probability of biochemical recurrence of the prostate cancer patient. According to the clinical pathology data of the prostate cancer patient, based on a pre-trained prognosis model, a prediction probability of biochemical recurrence of the prostate cancer patient within a preset time is obtained. Prediction is performed through the prognosis model, compared with physicians, the prediction efficiency is higher, the prediction accuracy of the prognosis model can be continuously improved along with increase of sample data, and the prediction accuracy is higher compared with the physicians.

The invention discloses a method for applying a polyethylene foam blocking method to establish a myocardial infarction model. The polyethylene foam blocking method is used to block the left coronary artery of a Beagle dog through a cardiac catheter technology, and the animal model of the chronic myocardial infarction is arranged. After 30 pieces of 10-12 Kg Beagle dog is fully narcotized through a common carotid artery, firstly, after the ischemia is pre-adapted, the polyethylene foam is used to drop in the 1 / 3 position of the distant end at the left front part through a conduit tube embolus, and then the myocardial infarction model is caused. The model manufacture of all dogs succeeds, one dog is dead due to the damage of a coronary pulse and the cardiac tamponade during the operation process, and two dogs are dead in the operation room due to tremor. Two dogs are dead due to occurring III degree room conduction block during the operation process. The model manufacture to other dogs succeeds. The instant survival rate of the operation reaches 83.3percent, the survival rate after operating within two weeks is 66.6 percent. The effect that a polyethylene foam embolus blocking method that can not transmit X ray is used to manufacture the myocardial infarction model is definite, the invention is close to the clinical pathology physiology process, the repeatability is good, the invention is safer compared with a traditional thoracotome buckling coronary artery, the invention can be taken as an experimental platform used for researching the myocardial ischemia.

The invention provides a model based on clinical staging and functional status, immune checkpoints VISTA, PD-L1 and microvascular density that can be used for predicting the recurrence risk of G1 and G2 pancreatic neuroendocrine tumors. According to clinical stages and functional status, detection of PD-L1, VISTA, and CD34 by immunohistochemistry, determination of immunohistochemistry results by microscopy, a column diagram is made synthesizing the above five variables, risk of recurrence of pancreatic neuroendocrine tumor at 1, 3, and 5 years after surgery operation is predicted according to the column diagram. The model can accurately predict the recurrence risk of the G1 and G2 pancreatic neuroendocrine tumors, thus providing a reference for the development of individualized follow-up protocols. The model is jointly established based on clinical pathology, tumor immune microenvironment and microvessels, and has operability and comprehensiveness.

The present disclosure provides a device and a model training method for predicting the risk of kidney development against neutrophil cytoplasm antibody-associated small vasculitis (ANCA). The apparatus includes: an input unit configured to input, as input variables, values of three clinical pathological parameters of a target user having anti-neutrophil cytoplasm antibody-associated small vasculitis, the three clinical pathological parameters comprise a normal glomerular percentage as a first continuous variable, an estimated glomerular filtration rate as a second continuous variable and interstitial fibrosis / renal tubular atrophy as a third classification variable; a risk assessment unit configured to input the three input variables into a pre-trained risk assessment model based on a proportional risk regression model, the risk assessment model calculating, according to the three input variables, one or more percentage values indicating the likelihood of the target user progressing to end-stage nephropathy after a predetermined one or more time periods; and an output unit configured to output the one or more percentage values.

The invention relates to a method for establishing a prediction model for survival benefit of a gallbladder cancer patient after chemoradiotherapy, the method comprises the following steps: collecting data, calculating a tendency score PS by adopting a generalized enhancement model GBM, balancing the balance of each group of variables by adopting an inverse probability weighting method IPTW, drawing a Kaplan-Meier survival curve, comparing survival differences after different TNMs receive different adjuvant treatments by stages, so as to discover that patients with the gallbladder cancer in the III-IV stage can gain benefits from adjuvant therapy; dividing III-IV stage gallbladder cancer patient data into a modeling group and an internal verification group according to definite diagnosis time, using SPSS software, carrying out single-factor and multi-factor COX risk scale model analysis in sequence, obtaining a final variable, and incorporating the final variable into a final column graph model; drawing a column graph for predicting the OS for prediction; and extracting features by collecting conventional clinical pathological indexes of a patient. The result is objective and accurate, specific individuals are respectively predicted and analyzed, and credible prediction and analysis results are provided.

The invention belongs to the technical field of tumor diagnosis and treatment, and provides a method for predicting the PD-L1 level in breast cancer tumor tissue through peripheral blood PD-1 / PD-L1 and application of a corresponding reagent, and the method comprises a step of detecting the expression level of PD-1 and / or PD-L1 in circulating T lymphocytes, and a step of predicting the PD-L1 level in the tumor tissue on the basis of the expression level of PD-1 and / or PD-L1 in the circulating T lymphocytes. The research result shows that the clinical pathological state of a breast cancer patient may influence the PD-L1 positive T lymphocyte level in peripheral blood; the detection of PD-1 / PD-L1 positive lymphocytes in peripheral blood has the capability of evaluating the expression property of PD-L1 in breast cancer tissues, which indicates that the PD-1 / PD-L1 level on the surfaces of lymphocytes in a blood sample is expected to replace pathological tissues clinically to become an index of PD-L1 state evaluation.

The invention discloses a construction method and application of a megacolon animal model. Specifically, benzalkonium chloride is injected into mice at 5-8 days old, 12-15 days old and 19-21 days old through anus perfusion. According to the method, the BALB / C suckling mouse is used for modeling the megacolon model for the first time, clysis is used as a means, surgical operation is not needed, the operation difficulty is reduced, the modeling success rate is high, the BALB / C suckling mouse of 5-8 days old is selected as an experimental subject, benzalkonium chloride anus perfusion is carried out under the condition that the intestinal tract development is not mature, intestinal neurons are damaged, and the method is closer to the HSCR attack microenvironment; the method is more suitable for clinical HSCR diagnosis and treatment age, and is beneficial to HSCR pathological mechanism research and experimental optimization and evaluation of novel treatment means. And a good research model is provided for clinical HSCR pathological mechanisms and further diagnosis and treatment. And the diversity of the megacolon animal model is enriched.

The invention discloses a cervical adenocarcinoma prognosis method, and the method comprises the following steps: acquiring clinical pathological parameters, and establishing a corresponding array structure in a storage medium according to the clinical pathological parameters, wherein the clinical pathological parameters comprise histological grading, primary tumor staging, lymph node staging, distant metastasis staging, whether primary lesions are operated or not and the maximum diameter of primary tumors; storing the clinical pathological parameters in corresponding storage spaces in the array structure; arranging a space structure for storing a cervical adenocarcinoma prognosis model in the storage medium, and storing the established cervical adenocarcinoma prognosis model in the space structure; reading each clinical pathological parameter through a processor, inputting each clinical pathological parameter into the cervical adenocarcinoma prognosis model, and displaying data information in the form of a dynamic column diagram; and performing prediction according to the data information. According to the method, more intuitive and rational data are displayed through the dynamic column graph, so the decision-making and prognosis accuracy and efficiency of doctors are improved.