Jialiu Luo

Background: Severe trauma rapidly reprograms peripheral myeloid cells. We hypothesised that trauma-derived transcriptional programmes may exhibit distinct patterns across Alzheimer’s disease (AD) brain pathology states; this comparison does not test whether trauma exposure alters AD mechanisms or pathology. We aimed to define a reproducible NAMPT-linked trauma-response architecture and examine its associations with AD pathology axes. Methods: Trauma-responsive programmes were defined only in GSE162806 using donor-level pseudobulk analysis, assessed for replication in GSE36809 and directional preservation across NAMPT perturbation datasets, and then projected without redefinition into the SEA-AD middle temporal gyrus atlas using donor-level programme scores. Results: NAMPT increased most strongly 4 h after trauma (log2 fold change 2.53; FDR 1.45 × 10⁻⁶). A time-adjusted 350-gene NAMPT-associated network showed partial replication in GSE36809, with 82.6% directional concordance and 227/304 mapped genes meeting the prespecified replication criterion. Cross-context analysis identified SP1 and SP2 transcriptional clusters, with directional preservation supported for SP2 but not SP1. In SEA-AD (1,178,694 nuclei; 84 donors), neither the primary ADNC analysis nor the continuous MTG-local CPS analysis survived multiplicity correction. Secondary CERAD analyses showed lower NAMPT-architecture, SP1, and SP2 scores in excitatory neurons (FDR = 0.042), and the prespecified GuHCl Aβ42-plus-pTau model showed opposing conditional associations: higher Aβ42 was associated with lower programme scores, whereas higher pTau was associated with greater programme engagement. Aβ42 × pTau interactions and Micro-PVM state analyses and cognition models were not supported after correction. Conclusions: A NAMPT-associated gene network showed partial replication across trauma cohorts, while its projection into SEA-AD yielded selected, mainly excitatory-neuron pathology associations. These findings are hypothesis-generating and do not establish conserved regulatory mechanisms across peripheral myeloid and brain cells, causal effects of trauma on AD pathology, or functional recovery capacity.

Sepsis-associated lung injury remains a major cause of respiratory failure and death, yet treatment is still largely supportive once inflammatory tissue damage is established. Macrophage inflammasomes, especially the NLRP3–ASC–caspase-1 axis, occupy a central but paradoxical position in this process. They contribute to pathogen sensing and antimicrobial defense, but excessive or poorly timed activation promotes IL-1β and IL-18 release, gasdermin D-mediated pyroptosis, extracellular ASC speck propagation, neutrophil amplification, barrier disruption, alveolar edema and acute respiratory distress syndrome. Therefore, a clinically meaningful strategy may not be to abolish inflammasome activity, but to tame it. Bats provide a natural model of this principle. Despite hosting many viruses and maintaining high metabolic activity, bats limit inflammatory execution through dampened NLRP3 responsiveness, altered DNA inflammasome sensing, restrained caspase-1/IL-1β processing and ASC2-mediated inhibition of ASC-dependent inflammasomes, while preserving antiviral readiness. These findings suggest a host-defense-preserving framework for septic lung injury. This review proposes bat-inspired design rules: raising inflammasome activation thresholds, braking ASC-dependent propagation, limiting lytic effector output, targeting inflammasome-active macrophage states instead of macrophages as a bulk population, and applying biomarker-guided timing to avoid intervention during immune paralysis. We also discuss the need to define an “inflammasome-high septic lung injury” phenotype using lung injury progression, inflammasome output markers, macrophage-state signatures and immune competence indicators. Bats do not provide a ready-made therapy, but they offer a useful biological lesson: damaging inflammatory amplification can be restrained without erasing host defense.

Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host–pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies—particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics—has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice.

Traditionally considered as homogeneous innate immune cells, neutrophils are now found to exhibit phenotypic and functional heterogeneity. How to determine whether the functional changes of neutrophils are caused by activation or the result of gene reprogramming? Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics, have comprehensively explained the mechanism of neutrophil heterogeneity. At the same time, artificial intelligence, especially machine learning, has promoted the in-depth analysis of multi-omics. Here, we introduce the latest progress in the discovery of neutrophil subsets by omics research. We will further discuss the application of machine learning in analyzing the heterogeneity of neutrophils through omics methods. Our goal is to provide a comprehensive overview of how machine learning and multi-omics are reshaping our understanding of neutrophil biology and pathophysiology.

Objective Previous reports have indicated the occurrence of acute gastrointestinal injury (AGI) in critically ill individuals. Yet, there is limited information regarding the frequency and potential causes of AGI in individuals with polytrauma. The complicated diagnostic tools often mistaken and mislead the evaluation of AGI. The objective of this research is to create a nomogram that can predict the likelihood of AGI in individuals with polytrauma. Results Among 836 polytrauma patients, AGI occurred in 61.2%, significantly higher than the 9.5% in monotrauma patients (P < 0.001).The predictors included Injury Severity Score (ISS) > 16, Glasgow Coma Scale (GCS) < 8, Acute Physiology and Chronic Health Evaluation II (APACHE II) > 16, Sequential Organ Failure Assessment (SOFA) > 5, presence of shock, lactate level > 3.2, and Activated Partial Thromboplastin Time (APTT) > 40 in the individualized prediction nomogram. The nomogram showed good discrimination (C-index = 0.719) and satisfactory calibration.

Venous thrombus embolism (VTE) is common after polytrauma, both of which are considered significant contributors to poor outcomes and mortality. Traumatic brain injury (TBI) is recognized as an independent risk factor for VTE and one of the most common components of polytraumatic injuries. Few studies have assessed the impact of TBI on the development of VTE in polytrauma patients. This study sought to determine whether TBI further increases the risk for VTE in polytrauma patients. A retrospective, multi-center trial was performed from May 2020 to December 2021. The occurrence of venous thrombosis and pulmonary embolism from injury to 28 days after injury was observed. Of 847 enrolled patients, 220 (26%) developed DVT. The incidence of DVT was 31.9% (122/383) in patients with polytrauma with TBI (PT + TBI group), 22.0% (54/246) in patients with polytrauma without TBI (PT group), and 20.2% (44/218) in patients with isolated TBI (TBI group). Despite similar Glasgow Coma Scale scores, the incidence of DVT in the PT + TBI group was significantly higher than in the TBI group (31.9% vs. 20.2%, p < 0.01). Similarly, despite no difference in Injury Severity Scores between the PT + TBI and PT groups, the DVT rate was significantly higher in the PT + TBI group than in the PT group (31.9% vs. 22.0%, p < 0.01). Delayed anticoagulant therapy, delayed mechanical prophylaxis, older age, and higher D-dimer levels were independent predictive risk factors for DVT occurrence in the PT + TBI group. The incidence of PE within the whole population was 6.9% (59/847). Most patients with PE were in the PT + TBI group (64.4%, 38/59), and the PE rate was significantly higher in the PT + TBI group compared to the PT (p < 0.01) or TBI (p < 0.05) group. In conclusion, this study characterizes polytrauma patients at high risk for VTE occurrence and emphasizes that TBI markedly increases the incidence of DVT and PE in polytrauma patients. Delayed anticoagulant therapy and delayed mechanical prophylaxis were identified as the major risk factors for a higher incidence of VTE in polytrauma patients with TBI.

Background: Shock after traumatic injury is likely to be hypovolemic, but different types of shock (distributive shock, obstructive shock, or cardiogenic shock) can occur in combination, known as multifactorial shock. Multifactorial shock is a neglected area of study, and is only reported sporadically. Little is known about the incidence, characteristics, and outcomes of multifactorial shock after polytrauma. Methods: A retrospective, observational, multicenter study was conducted in four Level I trauma centers involving 1051 polytrauma patients from June 2020 to April 2022. Results: The mean Injury Severity Score (ISS) was 31.1, indicating a severely injured population. The most common type of shock in the early phase after polytrauma (≤48 h) is hypovolemic shock (83.2%), followed by distributive shock (14.4%), obstructive shock (8.7%), and cardiogenic shock (3.8%). In the middle phase after polytrauma (>48 h or ≤14 days), the most common type of shock is distributive shock (70.7%), followed by hypovolemic shock (27.2%), obstructive shock (9.9%), and cardiogenic shock (7.2%). Multifactorial shock accounted for 9.7% of the entire shock population in the early phase and 15.2% in the middle phase. In total, seven combinations of multifactorial shock were described. Patients with multifactorial shock have a significantly higher complication rate and mortality than those with single-factor shock. Conclusions: This study characterizes the incidence of various types of shock in different phases after polytrauma and emphasizes that different types of shock can occur simultaneously or sequentially in polytrauma patients. Multifactorial shock has a relatively high incidence and mortality in polytrauma patients, and trauma specialists should be alert to the possibility of their occurrence.