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Fibrinogen

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Application of Machine Learning to Assess Interindividual Variability in Rapid-Acting Insulin Responses After Subcutaneous Injection in People With Type 1 Diabetes.

Canadian journal of diabetes
OBJECTIVES: Circulating insulin concentrations mediate vascular-inflammatory and prothrombotic factors. However, it is unknown whether interindividual differences in circulating insulin levels are associated with different inflammatory and prothrombo...

Clinical observation of the efficacy of low-molecular-weight heparin calcium in prophylaxis of the deep venous thrombosis following the gynecological tumor surgery.

Pakistan journal of pharmaceutical sciences
Present study is conducted to investigate the efficacy and safety of application of low-molecular-weight heparin calcium in the prophylaxis of deep venous thrombosis (DVT) following the laparoscopic surgery for gynecological tumors, so as to provide ...

Quantitative design rules for protein-resistant surface coatings using machine learning.

Scientific reports
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the ...

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neura...

The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes Mellitus.

Metabolic syndrome and related disorders
Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase parameters that can also be predictors of cardiovascular disease. However, this inflammatory response can a...

Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise.

Frontiers in public health
BACKGROUND: Machine learning (ML) is a type of artificial intelligence (AI) and has been utilized in clinical research and practice to construct high-performing prediction models. Hidden blood loss (HBL) is prevalent during the perioperative period o...

Space-time-regulated imaging analyzer for smart coagulation diagnosis.

Cell reports. Medicine
The development of intelligent blood coagulation diagnoses is awaited to meet the current need for large clinical time-sensitive caseloads due to its efficient and automated diagnoses. Herein, a method is reported and validated to realize it through ...

Can machine learning provide preoperative predictions of biological hemostasis after extracorporeal circulation for cardiac surgery?

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: The goal of this study was to improve decision making regarding the transfusion of patients at the end of extracorporeal circulation for cardiac surgery through machine learning predictions of the evolution of platelets counts, prothrombi...

Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.

Medicine
Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine learning to identify VTE risk factors in MM, helping to pinpoint high-risk individuals for better clini...