[Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO2/FiO2), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K+ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS: The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.

[Induction of hairy roots of Panax ginseng and studies on suitable culture condition of ginseng hairy roots].

Ginseng is a valuable medicinal plant with ginsenosides as its mian effective components. Because ginseng is a perennial plant and has a very strict demand for soil conditions, the way of cultivating ginseng by cutting woods is still used in China at present and thus forest resources has been extremely destroyed. Increasing attention has been paid to the hairy roots induced by the infection of Agrobacterium rhizogenes in the production of plant secondary metabolic products for the hairy roots are characterized by rapid growth and stable hereditary and biochemical traits. That has opened a new way for the industrial production of ginseosides. However, there is little report for such studies from China. In this paper, hairy roots of ginseng were induced from the root explants of two-year-old ginseng by Agrobacterium rhizogenes A4 with directly inoculating. The transformed hairy roots could grow rapidly on MS medium and 1/2 MS medium without hormones. The cultured clones of the hairy roots were established on a solid 1/2 MS medium. After 4 - 5 subcultures the hairy roots still maintained a vigorous growth. A pair of primers were designed and synthesized according to the analytical results of RiA4TL-DNA sequence by Slightom et al . 0.8kb rolC was obtained by PCR using the genome DNA of hairy root of ginseng. Transformation was confirmed by PCR amplification of rolC genes from the hairy roots of P. ginseng. Growth rate of hairy roots on liquid medium increased by 2 times then that of the solid medium. The growth of the hairy roots can be divided into three stages: high speed in the first two weeks, middle speed in the 3 - 4 weeks and low speed hereafter. Changing the culture solution at 2 weeks regular intervals is conductive to maintaining the rapid growth of the hairy roots. By means of determination for specific growth rate and ginsenosides content, the high-yield hairy root clone R9923 was selected. The content of monomer gisenoside of Rg1, Re, Rf, Rbl, Rc, Rb2 and Rd in hairy root clone R9923 was determined by the HPLC. The total ginsenosides content in the hairy toot clone R9923 came up to 15.2 mg/g. The suitable culture conditions for ginseng hairy roots growing were 1/2 MS liquid medium (30 g/L glucose), in a shaker at 110 r/min, changing the culture solution at 2 weeks and subculture time 4 weeks. In the liquid fermented culture of 2L medium, the yield of the hairy roots could amount to 270.10 g in 4 weeks. The industrial production of ginsenosides has been preliminarily realized. Effect factors on biomass and ginsenosides content such as culture volume, inoculation, in steps cultural technology at the scale-up process of hairy roots culture were also explorated. Our results have laid a foundation for defining optimum culture manner for large-scale cultivation and large-scale production of ginsenosides.