Military Medical Research最新文献

The cytokine storm, a life-threatening systemic inflammatory syndrome, is the primary driver of multiorgan failure in different clinical situations, including severe infections, autoimmune diseases, chimeric antigen receptor (CAR) T cell immunotherapy for cancer, and genetic syndromes. This review focuses primarily on cytokine storms triggered by severe infections such as viral pneumonia and bacterial sepsis, and explores the underlying mechanisms of cytokine storms and potential therapeutic interventions. Cytokine storms are characterized primarily by the excessive release of proinflammatory cytokines, which are triggered by pathogen-associated molecular patterns (PAMPs), damage-associated molecular patterns (DAMPs), and PANoptosis, all of which activate immune signaling cascades. Amplification mechanisms involve positive feedback loops and the failure of negative feedback mechanisms, leading to uncontrolled inflammation. Like a pyrrhic victory, the excessive activation of the immune system eliminated invading pathogens but caused catastrophic damage due to multiple organ dysfunction syndrome (MODS), turning the life-saving response into a life-threatening war. Therapeutic strategies, including cytokine antagonists, Janus kinase (JAK) inhibitors, caspase inhibitors, glucocorticoids, and blood purification therapies, aim to interrupt the self-amplifying cycle of inflammation that propagates organ injury, thereby reducing MODS and mortality. Challenges include optimizing the treatment timing and patient stratification. Future research should focus on combination therapies and personalized medicine based on the heterogeneity of infections and sepsis. Advances in multiomics and targeted therapies provide new hope for managing infections and sepsis.

Background: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.

Methods: Liquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.

Results: A global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.

Conclusions: As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.

Artificial intelligence (AI) offers transformative potential in pathology, where histopathological images remain the diagnostic gold standard due to their rich morphological and molecular information. While the rapid development of AI-driven computational pathology tools is revolutionizing disease interpretation, these technologies have not yet been systematically evaluated. Therefore, this review systematically evaluates AI applications across the diagnostic continuum, from image preprocessing and tumor classification to prognostic stratification and the discovery of predictive biomarkers. It presents a technical taxonomy of the algorithms and foundation models powering these applications, benchmarking their performance across diverse diagnostic tasks through rigorous comparative analyses. It also identifies critical challenges in clinical translation, including computational scaling, noisy annotations, interpretability gaps, and domain shifts. Finally, it proposes a roadmap for advancing AI applications in precision oncology and pathological research. By bridging technological innovation with clinical needs, this review aims to accelerate the integration of robust, unified, scalable AI solutions into diagnostic workflows.

Background: In 2022, China experienced an unprecedented heatwave event, raising concerns about the health impacts of heatwaves. This study aims to understand the devastating health risk of the exceptional heatwave in 2022 by comparing heatwave-related mortality burden in 2022 with that during 2000-2021.

Methods: We collected daily mortality and daily maximum temperature (DMT) during 2006-2017 in 364 locations (counties/districts) of China. Heatwave was defined as an event with 2 or more consecutive days of DMT exceeding the 92.5th percentile. We employed a distributed lag nonlinear model (DLNM) and a meta-analysis to examine the heatwave-mortality association based on the data from 364 counties/districts, and then this association was used to assess the mortality burden attributable to heatwaves during 2000-2022 in 368 cities in China. A percentage change (%) indicator, comparing the 2022 mortality burden to the average value from 2000 to 2021, was further calculated to highlight the severity of heatwaves in 2022.

Results: In the past 2 decades, the frequency and intensity of heatwaves in China significantly increased, with the cumulative excessive degree-day increasing to 31,626 in 2022 compared with the annual average value of 13,772 during 2000-2021 across China. In 2022, we observed 62,961 [95% confidence internal (CI) 54,945-70,413] heatwave-related deaths in China, which was much higher than the annual average [35,987 (95% CI 31,252-40,471)] attributable to heatwaves during 2000-2021. The vulnerability groups of heatwave-related mortality in 2022 primarily included patients with cardiovascular diseases [40,567 (95% CI 35,313-45,404)], females [35,876 (95% CI 31,035-41,005)], and people aged over 65 years [56,208 (95% CI 49,023-62,864)]; and greater heatwave-related mortality was found in eastern-central China. The attributable fraction (AF) of heatwave-related deaths increased from an annual average of 11.01‰ (95% CI 9.56-12.38) during 2000-2021 to 18.11‰ (95% CI 15.80-20.25) in 2022 with 64.43% increment (95% CI 38.10-93.78), and the increase rates were greater in Xizang Autonomous Region (159.77%, 95% CI 12.84-477.87) and Sichuan Province (133.64%, 95% CI 3.84-416.61).

Conclusions: This study indicated that the frequency and intensity of heatwaves significantly increased in the past 2 decades in China, and the 2022 heatwaves were linked to a substantial mortality burden in China, with significant population and regional heterogeneity. Our findings underscore the need for developing comprehensive heat adaptation plans in the context of rapid aging and ongoing global warming.

Background: Anemia is a major global health problem. There were 89% of all anemia-related disabilities in developing countries. We aim to analyze the burden of anemia and its underlying causes in China from 1990 to 2023.

Methods: Utilizing the data of the 2023 Global Burden of Disease (GBD 2023) study, this study analyzed the burden of anemia in China between 1990 and 2023. Then we analyzed the number and rate of anemia attributed to 16 underlying causes for all genders and ages. Drivers of change in prevalence and years lived with disability (YLD) numbers due to anemia were explored by decomposition analysis. And locally weighted regression was used to estimate the relationship between socio-demographic index (SDI) and age-standardized prevalence rate (ASPR) and age-standardized YLD rate due to anemia.

Results: From 1990 to 2023, the ASPR and age-standardized YLD rate showed a downward trend among all anemia types (P < 0.05), and the ASPR and age-standardized YLD rate of anemia in females were higher than those in males. The highest number and rate of prevalence were found in mild anemia, and the highest number and rate of YLD were found in moderate anemia. As age increased, the prevalence and YLD rate of anemia increased, with a significant increase in females aged 20-54, in particular of moderate anemia. In 2023, the highest ASPR and age-standardized YLD rate among all anemia types were in the Northwestern regions. Compared to 1990, 31 provinces, Hong Kong, and Macao exhibited declines in both the ASPR and the age-standardized YLD rate for anemia. In China, most of the prevalent cases and YLD were attributable to dietary iron deficiency in 2023. The total prevalence of anemia decreased by 46.14% [95% uncertainty interval (UI) 27.54-61.02], of which age-specific rate, population growth, and population aging accounted for -77.32%, 21.33%, and 9.84%, respectively. A negative association between SDI and the ASPR and age-standardized YLD rate of anemia was shown in China.

Conclusions: From 1990 to 2023, the burden of anemia in China has decreased but remained heavy among women of childbearing age, the elderly, and in the Northwestern region. Tailored prevention and control strategies should be strengthened to reduce the burden of anemia in high-risk areas.

Background: Physiological, pharmacological, and pathological alterations of consciousness provide critical windows into its neural substrates. Given the inherent complexity and multidimensionality of consciousness, defining quantitative, dynamic signatures of neural activity, and translating them into clinically applicable tools remains challenge. This study aimed to build an electroencephalography (EEG)-based methodological guideline for clinical consciousness assessment.

Methods: EEG signals were systematically categorized across periodic and aperiodic activity, connectivity and network topology, spatiotemporal dynamics, self-organized criticality, and transcranial magnetic stimulation (TMS)-evoked responses. These biomarkers were mapped onto a conceptual framework of consciousness, comprising wakefulness and internal/external awareness, based on their validation across clinical conditions. The discriminative efficacy of various biomarkers was then evaluated across 4 independent datasets.

Results: Integrated EEG features each captured distinct yet complementary dimensions of consciousness, supporting a unified neurophysiological architecture underlying diverse alterations of consciousness. Spectral power and peak frequency tracked the loss of consciousness during propofol anesthesia and sleep. Steeper aperiodic slopes, loss of frontoparietal connectivity, disrupted small-world organization, and reduced effective dimensionality were particularly effective in distinguishing minimally conscious state (MCS) from unresponsive wakefulness syndrome (UWS). Additionally, spatiotemporal patterns exhibited consciousness-specific alterations, with both pharmacological and pathological alterations influencing specific microstate dynamics.

Conclusions: Synthesizing integrated neural dynamics and multidimensional consciousness, this guideline establishes both methodological and theoretical foundations for translating neurophysiological biomarkers into clinical applications. While this work advances both conceptual clarity and practical methodology, large-scale validation across expanded clinical cohorts, experimental models, and multimodal platforms is essential to fully establish causal linkages and translational utility.

Background: Type 2 diabetes (T2D) is a global epidemic that reduces life expectancy. Evidence is limited on the benefits of achieving multiple guideline-recommended targets and cross-country differences. This study aimed to quantify the associations of risk factor control with mortality and life expectancy among individuals with T2D using nationwide cohorts from China, the USA, and the UK.

Methods: We included 46,351 adults with T2D at baseline from China Chronic Disease and Risk Factors Surveillance (CCDRFS; 2013, follow-up until 2021), USA National Health and Nutrition Examination Survey (USA NHANES; 1999-2018, follow-up until 2019), and UK Biobank (2006-2010, follow-up until 2022). Patients with T2D were matched to controls without T2D using propensity score matching based on key demographic factors. Cox regression estimated mortality associated with lifestyle and metabolic factors outside target ranges [physical inactivity, smoking, unhealthy diet, elevated hemoglobin A1c (HbA1c), dyslipidemia, high blood pressure].

Results: Only a small proportion of participants achieved ≥ 5 combined targets: 16.0% in CCDRFS, 9.9% in USA NHANES, and 6.8% in UK Biobank. During 470,369 person-years of follow-up, 7650 deaths (16.5%) occurred among individuals with T2D, and 9349 deaths (10.2%) occurred among controls over 965,249 person-years. At age 50, individuals with ≤ 1 risk factor outside the target lived 6-9 years longer than those with ≥ 5, and their life expectancy was comparable to that of controls without T2D. The association was independent of genetic predisposition to shorter lifespan in the UK Biobank. Additionally, individuals with T2D who failed to achieve optimal metabolic control but maintained a healthy lifestyle had a longer life expectancy compared with those who achieved optimal metabolic control but had an unhealthy lifestyle across all cohorts, with life expectancy gains ranging from 1.5 to 3.4 years depending on sex and cohort. Among individuals with T2D, healthy lifestyle behaviors (physical activity, non-smoking, a healthy diet) and HbA1c control contributed most to gains in life expectancy. Variations in multiple risk factor control and their associations with all-cause mortality were observed across different population subgroups.

Conclusions: Achievement of guideline targets for multiple risk factors was low among individuals with T2D in China, the USA, and the UK. Comprehensive management of multiple risk factors, particularly lifestyle factors, was associated with a substantial reduction in the life expectancy gap between those with and without T2D, underscoring the importance of guideline-based care and individualized management.

Disruptions in lipid metabolism cause numerous metabolic diseases, including obesity, diabetes, cardiovascular diseases, and liver disorders. Consequently, lipid metabolism serves as a potential therapeutic target, influencing the progression of various non-metabolic diseases such as kidney diseases, cancer, neurodegenerative disorders, aging, and bone-related diseases. The metabolic pathways involved in lipid metabolism are complex and highly interconnected. Although the abundance of metabolic targets presents opportunities for lipid metabolism regulation, the limited precision and safety of traditional therapeutic approaches remain significant challenges. These limitations have catalyzed the development of multifunctional nano-delivery platforms aimed at targeted intervention in lipid metabolic processes, further enhancing the flexibility of lipid metabolism regulation. This review outlines the latest advancements and representative applications of these multifunctional nano-delivery platforms. Notably, extensive research has been conducted on nanoparticles and liposomes, with these technologies being relatively mature. Furthermore, numerous novel biomaterials, including engineered adipocytes, exosome vesicles secreted by natural cells, smart-responsive nanomicelles, composite hydrogels, and engineered lipid droplets, are being increasingly explored. Finally, the review discusses the advantages of drug delivery strategies based on the targeted intervention of lipid metabolic processes, the limitations of current technologies, promising future research directions, and treatment challenges.