Machine Learning-Based Prediction Model for Patients with Recurrent Staphylococcus aureus Bacteremia (preprint)

Objectives Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical treatment challenges. Particularly, since the outbreak of COVID-19, there has been a gradual increase in SAB patients, with a growing proportion of (Methicillin-resistant Staphylococcus aureus)MRSA infections. Therefore, we have constructed and validated a pediction model for recurrent Staphylococcus aureus bacteremia using machine learning. This model aids physicians in promptly assessing the condition and intervening proactively.Methods The patients data is sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database version 2.2. The patients were divided into training and testing datasets using a 7:3 random sampling ratio. The process of feature selection employed two methods: Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). Prediction models were built using Extreme Gradient Boosting (XGBoost),Random Forest (RF),Logistic Regression (LR),Support Vector Machine (SVM),and Artificial Neural Network (ANN). Model validation encompassed Receiver Operating Characteristic (ROC) analysis and Decision Curve Analysis (DCA). We utilized SHAP (SHapley Additive exPlanations) values to demonstrate the significance of each feature.Results After screening, MRSA, PTT, RBC, RDW, Neutrophils_abs, Sodium, Calcium, Vancomycin concentration, MCHC, MCV, and Prognostic Nutritional Index(PNI) were selected as features for constructing the model. Through combined evaluation using ROC and DCA analyses, XGBoost demonstrated the best predictive performance, achieving an AUC value of 0.76 (95% CI: 0.66–0.85). Building a website based on the Xgboost model.The SHAP plot depicted the importance of each feature within the model.Conclusions The adoption of XGBoost for model development holds widespread acceptance in the medical domain. The prediction model for recurrent Staphylococcus aureus bacteremia readmission, developed by our team, aids physicians in timely diagnosis and treatment of patients.

Plasma boosted the conversion of waste plastics into liquid fuel by a peroxymonosulfate-hydrothermal process

Plastic wastes are growing fast over the world and impose great burden to eco-system. However, the plastics nurture abundant carbon resource and can be used as ideal raw materials to produce fuels. Hydrothermal process is considered as a promising strategy to convert plastics into fuel under a mild condition, but the conversion efficiency is often limited by the chemical inertness of plastics. Here, we propose a facile plasma treatment method to modify the plastic surface, aimed to boost the conversion of waste plastics into liquid fuel through a peroxymonosulfate (PMS) coupled hydrothermal process. We find that the sample after plasma treatment for 40 s displays 9.2 wt% weight loss with 8 h, which is 6 times that of the pristine sample. The plasma treatment can not only activate the inert surface but also enhanced the PMS activation process by establishing a balance between the radical effect and etching effect.

Sensing of cytoplasmic chromatin by cGAS activates innate immune response in SARS-CoV-2 infection (preprint)

The global coronavirus disease 2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a positive-sense RNA virus. How the host immune system senses and responds to SARS-CoV-2 infection remain to be determined. Here, we report that SARS-CoV-2 infection activates the innate immune response through the cytosolic DNA sensing cGAS-STING pathway. SARS-CoV-2 infection induces the cellular level of 2'3'-cGAMP associated with STING activation. cGAS recognizes chromatin DNA shuttled from the nucleus as a result of cell-to-cell fusion upon SARS-CoV-2 infection. We further demonstrate that the expression of spike protein from SARS-CoV-2 and ACE2 from host cells is sufficient to trigger cytoplasmic chromatin upon cell fusion. Furthermore, cytoplasmic chromatin-cGAS-STING pathway, but not MAVS mediated viral RNA sensing pathway, contributes to interferon and pro-inflammatory gene expression upon cell fusion. Finally, we show that cGAS is required for host antiviral responses against SARS-CoV-2, and a STING-activating compound potently inhibits viral replication. Together, our study reported a previously unappreciated mechanism by which the host innate immune system responds to SARS-CoV-2 infection, mediated by cytoplasmic chromatin from the infected cells. Targeting the cytoplasmic chromatin-cGAS-STING pathway may offer novel therapeutic opportunities in treating COVID-19. In addition, these findings extend our knowledge in host defense against viral infection by showing that host cells’ self-nucleic acids can be employed as a “danger signal” to alarm the immune system.

Population Bottlenecks and Intra-host Evolution during Human-to-Human Transmission of SARS-CoV-2Population Bottlenecks and Intra-host Evolution during Human-to-Human Transmission of SARS-CoV-2 (preprint)

The emergence of the novel human coronavirus, SARS-CoV-2, causes a global COVID-19 (coronavirus disease 2019) pandemic. Here, we have characterized and compared viral populations of SARS-CoV-2 among COVID-19 patients within and across households. Our work showed an active viral replication activity in the human respiratory tract and the co-existence of genetically distinct viruses within the same host. The inter-host comparison among viral populations further revealed a narrow transmission bottleneck between patients from the same households, suggesting a dominated role of stochastic dynamics in both inter-host and intra-host evolutions. Author summaryIn this study, we compared SARS-CoV-2 populations of 13 Chinese COVID-19 patients. Those viral populations contained a considerable proportion of viral sub-genomic messenger RNAs (sgmRNA), reflecting an active viral replication activity in the respiratory tract tissues. The comparison of 66 identified intra-host variants further showed a low viral genetic distance between intra-household patients and a narrow transmission bottleneck size. Despite the co-existence of genetically distinct viruses within the same host, most intra-host minor variants were not shared between transmission pairs, suggesting a dominated role of stochastic dynamics in both inter-host and intra-host evolutions. Furthermore, the narrow bottleneck and active viral activity in the respiratory tract show that the passage of a small number of virions can cause infection. Our data have therefore delivered a key genomic resource for the SARS-CoV-2 transmission research and enhanced our understanding of the evolutionary dynamics of SARS-CoV-2.