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: Polytrauma is a complex condition associated with poor outcomes and high mortality rates resulting from severe damage and complicated complications. This study sought to ascertain the incidence of chronic complications in polytrauma patients, as well as the early immune changes and risk factors. Methods: A multicenter, prospective and observational cohort study was conducted at the emergency surgery or traumatic intensive care unit (TICU) of the Advanced Trauma Center from August 2020 to July 2023. A total of 2033 consecutive trauma patients were included in the study. In the first 1, 7, and 14 days after admission, flow cytometry and immunoassay kits were used to examine cytokine release and lymphocyte count. Results: Trauma patients were reported 33.8% (687/2033) chronic complication rates, with monotrauma patients reported 8.1% (55/683) and polytrauma patients reported 59.4% (802/1350). And the four most frequent chronic complications in polytrauma patients were chronic musculoskeletal pain (30.4%), post-traumatic osteoarthritis (27.2%), chronic wound (21.6%), and chronic lung injury (14.1.%) .There were significant differences in lymphocyte ratios and cytokine levels, at 1, 7, and 14 day of admission between chronic complication groups (CCP) and not chronic complication groups (N-CCP) in polytrauma. Polytrauma patients with characteristics of higher ratio of Ts_7d ratio (95% CI: 2.01–6.21), Treg_14d (95% CI: 1.12–5.43) and level of IL-6_7d (95% CI: 1.22–4.43), TNF-α_7d (95% CI: 1.05–3.83), IL-10_14d (95% CI: 2.01–6.84) were found to have a higher likelihood of experiencing a chronic complication. Conversely, a higher ratio of Tc_1d (95% CI: 0.53–0.86), Th_1d (95% CI: 0.64–0.95) and Th/Ts_14d (95% CI: 0.21–0.64) were identified as independent protective factors against a chronic complication event. Conclusion: Polytrauma patients exhibit a notable prevalence of chronic complications. Some immune and inflammatory indicators can be observed early in combination after injury to predict the risk of chronic complications after polytrauma.

Global disability from osteoarthritis (OA) remains a staggering burden, yet disease-modifying pharmacological therapies remain limited. Current evidence supports OA as a whole-joint disease in which subchondral bone remodeling acts as an early and active driver of structural progression and pain. Osteoclasts are key regulators of this process, but clinical trials targeting osteoclast-dependent remodeling have yielded inconsistent results. This translational gap suggests that the traditional view of osteoclasts as a homogeneous, terminally differentiated resorptive population is no longer sufficient. Latest breakthroughs in single-cell and spatial multi-omics have begun to redefine osteoclast biology by revealing heterogeneity across precursor origin, lineage state, functional output, and niche-specific adaptation. In OA, these studies have clarified the osteoclastogenic microenvironment more clearly than the terminal taxonomy of mature osteoclast subsets, thereby shifting the field toward a state-spectrum framework. In this review, we synthesize recent high-resolution evidence to examine how osteoclast-lineage heterogeneity is organized across disease stages and osteochondral microenvironments, and how distinct osteoclast-lineage programs contribute to subchondral remodeling, angiogenic coupling, interface instability, and pain-related pathology. We further discuss how this framework may inform patient stratification, mechanism-matched intervention, and the development of program-specific therapies in OA.

Sepsis is a dysregulated host response in which hyperinflammation, immunoparalysis, lymphoid depletion, myeloid dysfunction, tissue injury and defective pathogen control frequently coexist and evolve over time. Immune checkpoint pathways have therefore attracted interest as candidates for immune restoration, but receptor upregulation alone is an unreliable therapeutic signal. This review develops a cell type- and phase-dependent framework for immune checkpoint targeting in sepsis, using TIM-3 as a model checkpoint node. We first discuss the conceptual pitfalls in translating checkpoint blockade from cancer and chronic infection to acute heterogeneous sepsis. We then examine why the immune phase should be defined by dynamic host response states rather than by the clock time and why checkpoint signals must be interpreted according to their cellular source, including effector lymphocytes, NKT cells, CD4+ T cells, monocytes/macrophages, dendritic cells and suppressive myeloid populations. TIM-3 illustrates this complexity. In sepsis-relevant studies, TIM-3 has been linked to NKT cell and hepatic CD8+ T-cell apoptosis, CD4/HMGB1/TIM-3-associated hyporesponsiveness, Galectin-9-related ligand effects and potential myeloid or tissue-resident functions involving phosphatidylserine-positive debris, extracellular vesicles and tissue homeostasis. These observations argue against treating TIM-3 as a single-purpose inhibitory receptor or assuming that antibody blockade, ligand administration, receptor mutation and Galectin–glycan interference perturb the same biology. We propose that future checkpoint-directed studies should enroll immune states rather than syndromic sepsis alone; integrate the inflammatory burden, innate immune competence, lymphoid function and ligand context; and prioritize mechanistic endpoints such as lymphocyte apoptosis, IFN-γ recovery, monocyte HLA-DR restoration, LPS-induced TNF responses, bacterial clearance and secondary infection. Future precision immunomodulation studies in sepsis should evaluate defined checkpoint-associated circuits rather than infer therapeutic relevance from receptor expression alone.