Identification and quantification of serum KIN17 protein based on ELISA assay and exploring its clinical diagnostic value in liver cancer.

It has been well-elaborated that KIN17 protein is closely related to the expression, development and prognosis of liver cancer; however, till date, there has been no study about detecting the KIN17 protein in serum, which is important to developing clinical applications. The objective of this work is to detect serum KIN17 protein by the ELISA method and to explore the diagnostic significance of the KIN17 protein in liver cancer. First, we verified the ELISA method for serum KIN17 measurement according to five aspects: accuracy, precision, specificity, stability and detection limit. Results illustrate that the recovery rate of the ELISA method can be controlled between 90% and 110%, the variation coefficient of intra-assay can be controlled within 16%, and the variation coefficient of inter-assay can be controlled within 10%. There is no non-specific reaction with common tumor markers, and the detection limit can reach 0.125 ng mL-1. The results show that the KIN17 protein can be detected by ELISA, and there is a significant rise in KIN17 concentration in a liver cancer group compared with a healthy group, whose average concentrations are 1.730 ng mL-1 and 0.3897 ng mL-1, respectively. On this basis, we hypothesize that the serum KIN17 protein can serve as a potential biomarker of liver cancer and be measurable with the verified ELISA system after specific ultrafiltration and centrifugation, which is of great significance for the diagnosis and treatment of liver cancer.

Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores.

OBJECT: To obtain Pulmonary Inflammation Index scores from imaging chest CT and combine it with clinical correlates of viral pneumonia to predict the risk and severity of viral pneumonia using a computer learning model. METHODS: All patients with suspected viral pneumonia on CT examination admitted to The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from December 2022 to March 2023 were retrospectively selected. The respiratory viruses were monitored by RT-PCR and categorized into patients with viral pneumonia and those with non-viral pneumonia. The extent of lung inflammation was quantified according to the Pulmonary Inflammation Index score (PII). Information on patient demographics, comorbidities, laboratory tests, pathogenetic testing, and radiological data were collected. Five machine learning models containing Random Forest(RF), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), K Nearest Neighbour Algorithm (KNN), and Kernel Ridge Regression (KRR) were used to predict the risk of onset and severity of viral pneumonia based on the clinically relevant factors or PII. RESULTS: Among the five models, the SVM model performed best in ACC (76.75 %), SN (73.99 %), and F1 (72.42 %) and achieved a better area under the receiver operating characteristic curve (ROC) (0.8409) when predicting the risk of developing viral pneumonia. RF had the best overall classification accuracy in predicting the severity of viral pneumonia, especially in predicting pneumonia with a PII classification of grade I, the RF model achieved an accuracy of 98.89%. CONCLUSION: Machine learning models are valuable in assessing the risk of viral pneumonia. Meanwhile, machine learning models confirm the importance in predicting the severity of viral pneumonia through PII. The establishment of machine learning models for predicting the risk and severity of viral pneumonia promotes the further development of machine learning in the medical field.