6 results about "Procalcitonin" patented technology

This application discloses a chemiluminescence detection kit for acrylonitrile magnetic microparticles and its detection method. The kit includes a magnetic bead working solution and a luminescent label. The magnetic bead working solution includes acrylonitrile monoclonal antibody A and streptavidin. The luminescent label includes acrylonitrile monoclonal antibody B, modified nano-silica, anhydrous toluene, methoxy polyethylene glycol-succinimide ester, N,N-dimethylformamide, acrylonitrile succinimide ester, glutaraldehyde, and ethanolamine. This application uses modified nano-silica as a carrier and grafts a polyethylene glycol hydrophilic layer onto its surface, which amplifies the acrylonitrile ester molecule signal, reduces non-specific adsorption, and improves the sensitivity, specificity, and reliability of the detection.

Disclosed herein are methods for subtyping patients with a respiratory infection (e.g., community acquired pneuomonia or severe acute respiratory infection) to identify likely responders to corticosteroid therapy. The methods involve analyzing values of a plurality of biomarkers, examples of which include a first biomarker selected from procalcitonin, IL-6, and TREM-1, and a second biomarker selected from Pentraxin-3, MIP-3α, IL-1ra, IL-8, and NGAL. The methods enable personalized treatment decisions for patients with respiratory infections, potentially improving patient outcomes by targeting corticosteroid therapy to those most likely to benefit while avoiding harm to non-responders.

The invention relates to the field of hepatobiliary postoperative infection risk grading early warning, in particular to a hepatobiliary postoperative infection risk grading early warning method based on clinical parameters. The method comprises the steps that original clinical parameters are collected and preprocessed, and preprocessed standardized clinical parameters are obtained; a clinical basic weight is introduced, and based on the preprocessed standardized clinical parameters, an infection risk score is obtained through calculation by applying a multi-dimensional distribution mapping algorithm; and risk grades are divided based on infection risk scores, a dynamic weighting mechanism emphasizing effective updating is introduced, and clinical basic weights are dynamically updated. The problems that a traditional hepatobiliary postoperative infection risk assessment method neglects complex high-order interaction and non-linear correlation among indexes such as body temperature, leukocyte count, procalcitonin and C-reactive protein, cannot adjust parameter weights in real time according to dynamic changes of postoperative illness states of patients, lacks a clear risk grading rule and is poor in risk assessment accuracy are solved. And the engineering application value is limited.

This application belongs to the field of microbial technology, specifically disclosing the application of *Lactobacillus fermentum* BLF03 in anti-Candida activity. *Lactobacillus fermentum* BLF03 was deposited on November 6, 2025, at the China General Microbiological Culture Collection Center (CGMCC) with accession number CGMCC No. 36521. This application proposes a *Lactobacillus fermentum* BLF03 strain resistant to *Candida albicans* and *Candida krusei*, which can significantly inhibit the growth, reproduction, and hyphal transformation of *Candida albicans* and *Candida krusei* at the in vivo level, alleviate Candida-induced infection symptoms, reduce the expression level of the inflammatory marker procalcitonin (PCT) after Candida infection, improve the inflammatory stress response induced by infection, and has a significant protective effect against tissue damage caused by Candida invasion. It also has targeted preventive and adjunctive therapeutic effects on Candida infections in the urogenital tract and other sites, providing a new, safe, and highly effective strain for anti-Candida products.

The present application relates to the field of postoperative infection risk grading and early warning of hepatobiliary surgery, and in particular to a postoperative infection risk grading and early warning method based on clinical parameters. The content includes: collecting original clinical parameters and preprocessing to obtain preprocessed standardized clinical parameters; introducing a clinical basic weight, based on the preprocessed standardized clinical parameters, applying a multidimensional distribution mapping algorithm to calculate an infection risk score; dividing the risk level based on the infection risk score, and introducing a dynamic weighting mechanism with emphasis on effective updating to dynamically update the clinical basic weight. The method solves the problems of traditional postoperative infection risk assessment methods for hepatobiliary surgery, such as ignoring the complex high-order interaction and nonlinear correlation between body temperature, white blood cell count, procalcitonin, C-reactive protein and other indicators, being unable to adjust the parameter weight in real time according to the dynamic changes of the patient's postoperative condition, and lacking clear risk grading rules and limited engineering application value.

The invention discloses a model for early prediction of acute kidney injury risk after a sepsis patient is transferred into ICU based on clinical variables and immune inflammation indicators, and an immune-clinical model is established by adopting an XGBoost algorithm based on data of 1551 domestic sepsis patients in 24 hours after the sepsis patient is transferred into the ICU so as to predict the risk of SA-AKI of the patient. The model integrates five clinical variables including procalcitonin, APACHE II, SOFA score, gender and prothrombin time and four immune inflammation indexes including IgA, complement C3, Nave CD4 + T% and CD28 + CD4 + T%, the average ROC-AUC value is 0.914, an SHAP method is introduced to generate a feature importance map and an individual prediction interpretation map, the contribution degree of each index to risk prediction of a specific patient can be visually displayed, the cause of SA-AKI of the patient can be explained, and the risk of the specific patient can be predicted. Therefore, doctors can take targeted measures preventively.