Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.
The 2019 novel coronavirus disease (COVID-19) is the disease that has been identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the prophylactic treatment of SARS-CoV-2 is still under investigation. The effective delivery of eukaryotic expression plasmids to the immune system’s inductive cells constitutes an essential requirement for generating effective DNA vaccines. Here, we have explored the use of
Salmonella typhimurium
as vehicles to deliver expression plasmids orally. The attenuated
Salmonella phoP
was constructed by the one-step gene inactivation method, and plasmid-encoded the spike protein of SARS-CoV-2 was transform into the
Salmonella phoP
by electroporation. Western blot experiment was used for the detection of SARS-CoV-2 expression on 293T cells. Wistar rats were immunized orally with
Salmonella
that carried a eukaryotic expression plasmid once a week for three consecutive weeks. The ELISA was performed to measure the SARS-CoV-2 specific IgG at rat’s serum samples. pSARS-CoV-2 can be successfully expression on 293T cells, and all immunized animals generated immunity against the SARS-CoV-2 spike protein, indicating that a
Salmonella-
based vaccine carrying the Spike gene can elicit SARS-CoV-2-specific secondary immune responses in rats. Oral delivery of SARS-CoV-2 DNA vaccines using attenuated
Salmonella typhimurium
may help develop a protective vaccine against SARS-CoV-2 infection.
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