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Summary The cellular mechanisms controlling infection-induced emergency granulopoiesis are poorly defined. Here we found that reactive oxygen species (ROS) concentrations in the bone marrow (BM) were elevated during acute infection in a phagocytic NADPH oxidase-dependent manner in myeloid cells. Gr1+ myeloid cells were uniformly distributed in the BM, and all c-Kit+ progenitor cells were adjacent to Gr1+ myeloid cells. Inflammation-induced ROS production in the BM played a critical role in myeloid progenitor expansion during emergency granulopoiesis. ROS elicited oxidation and deactivation of phosphatase and tensin homolog (PTEN), resulting in up-regulation of PtdIns(3,4,5)P3 signaling in BM myeloid progenitors. We further revealed that BM myeloid cell-produced ROS stimulated proliferation of myeloid progenitors via a paracrine mechanism. Taken together, our results establish that phagocytic NADPH oxidase-mediated ROS production by BM myeloid cells plays a critical role in mediating emergency granulopoiesis during acute infection.

Background: Most commercial TNF␣ inhibitors are biomacromolecules. Results: A lead compound named C87 was identified using computer-aided drug design and could attenuate murine acute hepatitis. Conclusion: C87 was one of the first effective small-molecule inhibitors of TNF␣ identified to date. Significance: The study highlights the effectiveness of combining virtual screening with functional assays for developing novel small-molecule TNF␣ inhibitors.

Luo et al. report that CXCR2 ligands are responsible for rapid neutrophil mobilization during early-stage acute inflammation and that G-CSF suppresses this mobilization by negatively regulating CXCR2-mediated intracellular signaling.

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Background: Since January 2020, coronavirus disease 2019 has spread rapidly and developing the pandemic model around the world. Data have been needed on the clinical characteristics of the affected patients in an imported cases as model in island outside Wuhan. Methods:We conducted a retrospective study included all 168 confirmed cases of Covidconfirmed by real-time RT-PCR and were analysed for demographic, clinical, radiological and laboratory data.Results: Of 168 patients, 160 have been discharged, 6 have died and 2 remain hospitalized. The median age was 51.0 years and 51.8% were females. 129 (76.8%) patients were imported cases, and 118 (70.2%), 51 (30.4%) and 52 (31%) of patients lived in Wuhan or traveled to Wuhan, had contact with Covid-19 patients, or had contact with Wuhan residents, respectively. The most common symptoms at onset of illness were fever (65.5%), dry cough (48.8%) and expectoration (32.1%). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (60.2%). The elderly people with diabetes, hypertension and CVD are more likely to develop severe cases. Follow-up of 160 discharged patients found that 20 patients (12.5%) had a positive RT-PCR test results of pharyngeal swabs or anal swabs or fecal.

BACKGROUND In emergency departments (ED), timely rescue is very important as patients’ conditions usually deteriorate rapidly. Early diagnosis can increase patients’ chances of survival. Early diagnosis can be improved by predictive models based on machine learning using Electronic Medical Record (EMR) data. However, ED data are usually imbalanced, having missing values and sparse features. These quality issues make it challenging to build early identification models for diseases in ED. OBJECTIVE The objective of this study is to propose a systematic approach to deal with missing, imbalanced and sparse feature problems of ED data. METHODS We used random forest and K-means algorithms to interpolate missing values and under-sample data. Regarding sparse features, we used principal component analysis to reduce dimensions. For continuous and discrete variables, the decision coefficient R2 and Kappa coefficient are used to evaluate the performance respectively. The area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC) are used to estimate the model performance. To further evaluate the proposed approach, we carried out a case study using an ED dataset extracted from Hainan Hospital of Chinese PLA General Hospital. A logistic regression model for patient condition worsening prediction was built out of the data processed by the proposed approach. RESULTS A total of 1085 patients with rescue record and 17959 patients without rescue record were collected, which were significantly imbalanced. 275, 402 and 891 variables are extracted from laboratory tests, medications and diagnosis, respectively. After data preprocessing, the median R2 of random forest interpolation for continuous variables is 0.623 (IQR: 0.647), and the median of Kappa coefficient for discrete variable interpolation is 0.444 (IQR: 0.285). The logistic regression model constructed using the initial diagnostic data has poor performance and variable separation, which is reflected in the abnormally high OR values of the two variables of cardiac arrest and respiratory arrest (27857.4 and 9341.6) and an abnormal confidence interval. Using the processed data, the recall of the model reaches 0.77, F1-SCORE is 0.74, and AUC is 0.64. CONCLUSIONS We proposed a machine learning method to deal with data quality issues such as missing data, data imbalance, and sparse features in emergency data, so as to improve data availability. A preliminary case study indicate the results produced by the proposed method can be used for building prediction model for emergency patients.

Ursolic acid (UA) has proved to have broad-spectrum anti-tumor effects, but its poor water solubility and incompetent targeting property largely limit its clinical application and efficiency. Here, we synthesized a nanoparticle-based drug carrier composed of chitosan, UA and folate (FA-CS-UA-NPs) and demonstrated that FA-CS-UA-NPs could effectively diminish off-target effects and increase local drug concentrations of UA. Using MCF-7 cells as in vitro model for anti-cancer mechanistic studies, we found that FA-CS-UA-NPs could be easily internalized by cancer cells through a folate receptor-mediated endocytic pathway. FA-CS-UA-NPs entered into lysosome, destructed the permeability of lysosomal membrane, and then got released from lysosomes. Subsequently, FA-CS-UA-NPs localized into mitochondria but not nuclei. The prolonged retention of FA-CS-UA-NPs in mitochondria induced overproduction of ROS and destruction of mitochondrial membrane potential, and resulted in the irreversible apoptosis in cancer cells. In vivo experiments showed that FA-CS-UA-NPs could significantly reduce breast cancer burden in MCF-7 xenograft mouse model. These results suggested that FA-CS-UA-NPs could further be explored as an anti-cancer drug candidate and that our approach might provide a platform to develop novel anti-cancer drug delivery system.