Severe cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) strongly hampered the broad clinical applicability of chimeric antigen receptor T cell (CAR-T) therapy. Vascular endothelial activation has been suggested to contribute to the development of CRS and ICANS after CAR-T therapy. However, therapeutic strategies targeting endothelial dysfunction during CAR-T therapy have not been well studied yet. Here, we found that tumor necrosis factor α (TNFα) produced by CAR-T cells upon tumor recognition and interleukin 1β (IL1β) secreted by activated myeloid cells were the main cytokines in inducing endothelial activation. Therefore, we investigated the potential effectiveness of TNFα and IL1β signaling blockade on endothelial activation in CAR-T therapy. The blockade of TNFα and IL1β with adalimumab and anti-IL1β antibody respectively, as well as the application of focal adhesion kinase (FAK) inhibitor, effectively ameliorated endothelial activation induced by CAR-T, tumor cells, and myeloid cells. Moreover, adalimumab and anti-IL1β antibody exerted synergistic effect on the prevention of endothelial activation induced by CAR-T, tumor cells, and myeloid cells. Our results indicate that TNFα and IL1β blockade might have therapeutic potential for the treatment of CAR-T therapy-associated CRS and neurotoxicity.

To provide the epidemiological dissemination and the genetic characteristics of bla(NDM-1) in a teaching hospital in Shanghai, China. Here, the carbapenemase genes of 114 CRE isolates were evaluated by polymerase chain reaction (PCR). Clonal relatedness was assessed by pulsed-field gel electrophoresis (PFGE). Conjugation experiments and Southern blot hybridization were performed to determine the transferability of plasmids. Then plasmids were completely sequenced by the shotgun method. Two Klebsiella pneumoniae strains (RJA1227 and RJF866) and one Raoultella planticola strain (RJA274) were identified as NDM-1 positive. The two K. pneumoniae isolates belonged to ST11 and exhibited highly similar PFGE patterns. Shotgun sequencing showed that plasmid pRJF866 (ca. 110 kb) contained genes associated with the IncFII-FIB group and was highly similar to plasmid pKOX_NDM1. RJA274 (ca. 50 kb) harbored bla(NDM-1) on an IncX3 plasmid, which was nearly identical to plasmid pNDM-HN380 except that part of the ISAba125 element is missing. This is the first report of NDM-1-positive Enterobacteriaceae from Shanghai, China. IncX3 plasmids, reported in various species in the United Arab Emirates and China, may contribute to the dissemination of bla(NDM-1.). More attention should be devoted to monitoring the dissemination of the NDM-1 gene due to its potential horizontal transfer via mobile genetic elements.

Sepsis involves endothelial cell (EC) dysfunction, which contributes to multiple organ failure. To improve therapeutic prospects, elucidating molecular mechanisms of vascular dysfunction is of the essence. ATP-citrate lyase (ACLY) directs glucose metabolic fluxes to de novo lipogenesis by generating acetyl-Co-enzyme A (acetyl-CoA), which facilitates transcriptional priming via protein acetylation. It is well illustrated that ACLY participates in promoting cancer metastasis and fatty liver diseases. Its biological functions in ECs during sepsis remain unclear. We found that plasma levels of ACLY were increased in septic patients and were positively correlated with interleukin (IL)-6, soluble E-selectin (sE-selectin), soluble vascular cell adhesion molecule 1 (sVCAM-1), and lactate levels. ACLY inhibition significantly ameliorated lipopolysaccharide challenge-induced EC proinflammatory response in vitro and organ injury in vivo. The metabolomic analysis revealed that ACLY blockade fostered ECs a quiescent status by reducing the levels of glycolytic and lipogenic metabolites. Mechanistically, ACLY promoted forkhead box O1 (FoxO1) and histone H3 acetylation, thereby increasing the transcription of c-Myc (MYC) to facilitate the expression of proinflammatory and gluco-lipogenic genes. Our findings revealed that ACLY promoted EC gluco-lipogenic metabolism and proinflammatory response through acetylation-mediated MYC transcription, suggesting ACLY as the potential therapeutic target for treating sepsis-associated EC dysfunction and organ injury.

To determine whether infection-prevention and control (IPC) interventions can reduce the colonisation and infection of intensive care unit (ICU)-acquired carbapenem-resistant Klebsiella pneumoniae (CRKP) in a general ICU ward in China. We used a quasi-experimental before-and-after study design. The study was conducted in 4 stages: baseline period, January 2013–June 2013; IPC interventions period including de-escalation and targeted bundle interventions, July 2013–June 2014; modified IPC interventions period, July 2014–June 2015; and follow-up period, July 2015–June 2016. We used modified de-escalation interventions according to patient-risk assessments to prevent the transmission of CRKP. A total of 629 patients were enrolled in study. The incidence of ICU-acquired CRKP colonisation/infection was 10.08 (4.43–16.43) per 1000 ICU patient-days during the baseline period, and significantly decreased early during the IPC interventions, but the colonisation/infections reappeared in April 2014. During the modified IPC intervention and follow-up periods, the incidence of ICU-acquired CRKP colonisations/infections reduced to 5.62 (0.69–6.34) and 2.84 (2.80–2.89), respectively, with ongoing admission of cases with previously acquired CRKP. The incidence of ICU-acquired CRKP catheter-related bloodstream infections decreased from 2.54 during the baseline period to 0.41 during the follow-up period. The incidence of ventilator-associated pneumonia and skin and soft tissue infections showed a downward trend from 2.84 to 0.41 and from 3.4 to 0.47, respectively, with slight fluctuations. Comprehensive IPC interventions including de-escalation and targeted bundle interventions showed a significant reduction in ICU-acquired CRKP colonisations/infections, despite ongoing admission of patients colonised/infected with CRKP.

We aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for interpretative analysis of the predictions. In particular, we focus on the deployment of the system in a target medical center with small historical samples. Light Gradient Boosting Machine (LightGBM) and multilayer perceptron (MLP) were trained on Medical Information Mart for Intensive Care (MIMIC-III) dataset and then finetuned on the private Historical Database of local Ruijin Hospital (HDRJH) using transfer learning technique. The Shapley Additive Explanations (SHAP) analysis was employed to characterize the feature importance in the prediction inference. Ultimately, the performance of the sepsis prediction system was further evaluated in the real-world study in the ICU of the target Ruijin Hospital. The datasets comprised 6891 patients from MIMIC-III, 453 from HDRJH, and 67 from Ruijin real-world data. The area under the receiver operating characteristic curves (AUCs) for LightGBM and MLP models derived from MIMIC-III were 0.98 − 0.98 and 0.95 − 0.96 respectively on MIMIC-III dataset, and, in comparison, 0.82 − 0.86 and 0.84 − 0.87 respectively on HDRJH, from 1 to 5 h preceding. After transfer learning and ensemble learning, the AUCs of the final ensemble model were enhanced to 0.94 − 0.94 on HDRJH and to 0.86 − 0.9 in the real-world study in the ICU of the target Ruijin Hospital. In addition, the SHAP analysis illustrated the importance of age, antibiotics, net balance, and ventilation for sepsis prediction, making the model interpretable. Our machine learning model allows accurate real-time prediction of sepsis within 5-h preceding. Transfer learning can effectively improve the feasibility to deploy the prediction model in the target cohort, and ameliorate the model performance for external validation. SHAP analysis indicates that the role of antibiotic usage and fluid management needs further investigation. We argue that our system and methodology have the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention. Trial registration: NCT05088850 (retrospectively registered).

Sepsis has heterogeneous clinical trajectories, but conventional severity scores offer only static risk estimates. Timely, dynamic prediction could enable personalized intervention. In this multicenter retrospective study of 47,936 ICU patients meeting Sepsis-3 criteria from one institutional and two public datasets (MIMIC-III, eICU; sensitivity in MIMIC-IV), group-based trajectory modeling identified latent recovery patterns. An ensemble machine-learning model incorporating dynamic physiological variability was trained, temporally validated, and externally tested; clinical impact was assessed following implementation. Three trajectories emerged: rapid recovery (41.5%), slow recovery (36.4%), and clinical deterioration (22.1%). In the final binary classification task, AUROC was 0.92 (development), 0.89 (internal), 0.84 (MIMIC-III) and 0.77 (eICU); median warning time before deterioration was 17.6 h (Overall pooled across all cohorts). Reduced heart rate variability (SD < 10 bpm) predicted mortality (adjusted HR 2.17). Implementation reduced ICU stay by 1.8 days, machanical ventilation by 2.3 days, and 28-day mortality by 5.7%. This externally validated trajectory-based model offers accurate, early risk stratification for sepsis, supporting proactive, individualized critical care.