The rapid evolution of antibiotic resistance and the complicated bacterial infection microenvironments are serious obstacles to traditional antibiotic therapy. Developing novel antibacterial agents or strategy to prevent the occurrence of antibiotic resistance and enhance antibacterial efficiency is of the utmost importance. Cell membrane-coated nanoparticles (CM-NPs) combine the characteristics of the naturally occurring membranes with those of the synthetic core materials. CM-NPs have shown considerable promise in neutralizing toxins, evading clearance by the immune system, targeting specific bacteria, delivering antibiotics, achieving responsive antibiotic released to the microenvironments, and eradicating biofilms. Additionally, CM-NPs can be utilized in conjunction with photodynamic, sonodynamic, and photothermal therapies. In this review, the process for preparing CM-NPs is briefly described. We focus on the functions and the recent advances in applications of several types of CM-NPs in bacterial infection, including CM-NPs derived from red blood cells, white blood cells, platelet, bacteria. CM-NPs derived from other cells, such as dendritic cells, genetically engineered cells, gastric epithelial cells and plant-derived extracellular vesicles are introduced as well. Finally, we place a novel perspective on CM-NPs’ applications in bacterial infection, and list the challenges encountered in this field from the preparation and application standpoint. We believe that advances in this technology will reduce threats posed by bacteria resistance and save lives from infectious diseases in the future.

Background. Since 2020 COVID-19 pandemic became an emergent public sanitary incident. The epidemiology data and the impact on prognosis of secondary infection in severe and critical COVID-19 patients in China remained largely unclear.Methods. We retrospectively reviewed medical records of all adult patients with laboratory-confirmed COVID-19 who were admitted to ICUs from January 18th 2020 to April 26th 2020 at two hospitals in Wuhan, China and one hospital in Guangzhou, China. We measured the frequency of bacteria and fungi cultured from respiratory tract, blood and other body fluid specimens. The risk factors for and impact of secondary infection on clinical outcomes were also assessed. Results. Secondary infections were very common (86.6%) when patients were admitted to ICU for >72 hours. The majority of infections were respiratory, with the most common organisms being Klebsiella pneumoniae (24.5%), Acinetobacter baumannii (21.8%), Stenotrophomonas maltophilia (9.9%), Candida albicans (6.8%), and Pseudomonas spp. (4.8%). Furthermore, the proportions of multidrug resistant (MDR) bacteria and carbapenem resistant Enterobacteriaceae (CRE) were high. We also found that age ≥60 years and mechanical ventilation ≥13days independently increased the likelihood of secondary infection. Finally, patients with positive cultures had reduced ventilator free days in 28 days and patients with CRE and/or MDR bacteria positivity showed lower 28 day survival rate.Conclusions. In a retrospective cohort of severe and critical COVID-19 patients admitted to ICUs in China, the prevalence of secondary infection was high, especially with CRE and MDR bacteria, resulting in poor clinical outcomes.

Background: Since the clinical correlates, prognosis and determinants of AKI in patients with Covid-19 remain largely unclear, we perform a retrospective study to evaluate the incidence, risk factors and prognosis of AKI in severe and critically ill patients with Covid-19.Methods: We reviewed medical records of all adult patients (>18 years) with laboratory-confirmed Covid-19 who were admitted to the intensive care unit (ICU) between January 23rd 2020 and April 6th 2020 at Wuhan JinYinTan Hospital and The First Affiliated Hospital of Guangzhou Medical University. The clinical data, including patient demographics, clinical symptoms and signs, laboratory findings, treatment [including respiratory supports, use of medications and continuous renal replacement therapy (CRRT)] and clinical outcomes, were extracted from the electronic records, and we access the incidence of AKI and the use of CRRT, risk factors for AKI, the outcomes of renal diseases, and the impact of AKI on the clinical outcomes.Results: Among 210 subjects, 131 were males (62.4%). The median age was 64 years (IQR: 56-71). Of 92 (43.8%) patients who developed AKI during hospitalization, 13 (14.1%), 15 (16.3%) and 64 (69.6%) patients were classified as stage 1, 2 and 3, respectively. 54 cases (58.7%) received CRRT. Age, sepsis, Nephrotoxic drug, IMV and elevated baseline Scr were associated with AKI occurrence. The renal recover during hospitalization among 16 AKI patients (17.4%), who had a significantly shorter time from admission to AKI diagnosis, lower incidence of right heart failure and higher P/F ratio. Of 210 patients, 93 patients deceased within 28 days of ICU admission. AKI stage 3, critical disease, greater age and minimum P/F <150mmHg independently associated with it.Conclusions: Among patients with Covid-19, the incidence of AKI was high. age , sepsis, nephrotoxic drug, IMV and baseline Scr were strongly associated with the development of AKI. Time from admission to AKI diagnosis, right heart failure and P/F ratio were independently associated with the potential of renal recovery. Finally, AKI KIDGO stage 3 independently predicted the risk of death within 28 days of ICU admission.

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BackgroundAcinetobacter baumannii complex-caused bloodstream infection (ABCBSI) is a potentially fatal infection in intensive care units (ICUs). This study proposed an interpretable machine learning (ML) model to predict ABCBSI fulminant fatality.MethodsA retrospective study of ICU patients with ABCBSI was performed in China from 2009 to 2020. Patients were stratified into two groups: those that suffered from fulminant sepsis and died within 48 h, and those that survived for more than 48 h. The clinical score systems and ML models with Shapley additive explanation (SHAP) were used to develop the prediction models. The ML model was internally validated with five-fold cross-validation, and its performance was assessed using seven typical evaluation indices. The top 20 features ranked by the SHAP scores were also calculated.ResultsAmong 188 ICU patients with ABCBSI, 53 were assigned to the non-survival group and 135 to the survival group. The XGBoost model exhibited the greatest area under the receiver operating characteristic curve (AUC), which outperformed other models (logistic regression, AUC = 0.914; support vector machine, AUC = 0.895; random forest, AUC = 0.972; and naive Bayesian, AUC = 0.908) and clinical scores (Acute Physiology and Chronic Health Evaluation II (APACHE II), AUC = 0.855; Sequential Organ Failure Assessment (SOFA), AUC = 0.837). It also had a sensitivity of 0.868, a specificity of 0.970, an accuracy of 0.941, a positive predictive value of 0.920, a negative predictive value of 0.949, and an F1 score of 0.893. As well as identifying the top 12 different important predictors that contribute to early mortality, it also assessed their quantitative contribution and noteworthy thresholds.ConclusionBased on the XGBoost model, early mortality in ABCBSI is estimated to be more reliable than other models and clinical scores. The 12 most important features with corresponding thresholds were identified and more importantly, the SHAP method can be used to interpret this predictive model and support individual patient treatment strategies.