5 results about "Urothelial Cell" patented technology

Among hospital–acquired infections, Pseudomonas aeruginosa-associated urinary tract infections (UTIs) are mainly caused by indwelling urethral catheters (catheter-associated UTIs or CAUTIs) and are difficult to treat, resulting in high rates of morbidity among hospitalized patients. While antibiotics can successfully treat bacteria in the bladder lumen, they are inefficient at crossing stratified urothelium plasma membranes to kill persistent intracellular bacterial communities (IBCs). Herein, UTI IBCs are targeted by locally delivering the antibiotic gentamicin and / or the steroid hormone estradiol via polymeric nanogels conjugated with a cell-penetrating peptide Cys-Gly-Lys-Arg-Lys (CGKRK - SEQ ID NO:1). This approach delivered more intracellular gentamicin compared to drug delivered in solution in vitro and more estradiol compared to conventional methods. In an acute UTI murine model, the nanogel cell-penetrating peptide drug delivery system facilitated the transport of gentamicin into the urothelium and resulted in > 90% clearance of a uropathogenic P. aeruginosa clinical strain in vivo.

The invention belongs to the technical field of molecular biomedicine, and particularly relates to a urinary tract epithelial cell marker and application thereof in diagnosis of urinary tract epithelial cell carcinoma. The molecular marker is cytoplasm thyroid hormone binding protein CRYM, and an immunochemical staining experiment proves that the cytoplasm thyroid hormone binding protein CRYM has the potential to serve as a single immunomarker to be used for distinguishing normal urinary tract epithelial cells / reactive hyperplastic urinary tract epithelial cells and urinary tract epithelial cancer cells. Therefore, the kit plays a key identification role in histopathologic diagnosis or urine noninvasive screening of urothelial carcinoma, and provides important technical support for clinical noninvasive diagnosis.

The present application relates to the field of cancer cell identification, and particularly relates to a method and system for identifying different types of cancer cells of a renal pelvis cancer patient based on urine, the method comprising: collecting a urine sample, obtaining a detection sample by using the urine sample; sequencing the detection sample to obtain a cell expression profile of the detection sample; obtaining cell expression profile data of a mixed urothelial cell subpopulation by using the cell expression profile, the mixed urothelial cell subpopulation comprising normal urothelial cells and cancerous urothelial cells; extracting different proto-oncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation; analyzing the different proto-oncogene expression data to obtain a cancer cell ratio and identify different types of cancer cells. The present method quickly and accurately obtains different types of cancerous urothelial cells through non-invasive examination and proto-oncogene expression profile data, and provides a reference and basis for preoperative neoadjuvant therapy of renal pelvis cancer.

The invention discloses a urinary tract epithelial cell joint classification method, device and equipment and a storage medium, and the method comprises the steps: obtaining a liquid-based slice image and a fluorescence in-situ hybridization slice image of urinary tract epithelial cells, dividing the liquid-based slice image into a plurality of first sub-images, and dividing the fluorescence in-situ hybridization slice image into a plurality of second sub-images; obtaining a first depth feature of each first sub-image, determining a first classification result corresponding to the first sub-image based on the first depth feature of each first sub-image, and determining a first statistical result of the liquid-based slice image according to each first classification result; acquiring a second depth feature of each second sub-image, determining a second classification result corresponding to the second sub-image based on the second depth feature of each second sub-image, and determining a second statistical result of the fluorescence in situ hybridization slice image according to each second classification result; and determining a classification result of the urinary tract epithelial cells according to the first depth feature, the first statistical result, the second depth feature and the second statistical result.

Conventional, manual identification and scoring of a whole slide image (WSI) to detect Cytokeratin 17 (K17) expression has limitations in terms of lower accuracy in the medium grade cases, slower turn-around time, and high inter and intra-observer variability. Each WSI can take a pathologist six to ten minutes to examine. This is because the brown color staining indicating the presence of K17 may be one of thousands or tens of thousands or other expressions present on the slide. Apparatus and methods are provided for an artificial intelligence (AI) solution that identifies and quantifies positively stained urothelial cells in WSI of urine cytology to aid the diagnosis of urinary bladder carcinoma. An illustrative AI solution may include one or computer models that are programmed to run one more machine learning (ML) models. The ML models may be trained to recognize subtle and complex patterns and features associated with specific diseases.