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Sepsis is a syndrome of dysregulated host response caused by infection, which leads to life-threatening organ dysfunction. It is a familiar reason of death in critically ill patients. Liver injury frequently occurs in septic patients, yet the development of targeted and effective treatment strategies remains a pressing challenge. Macrophages are essential parts of immunity system. M1 macrophages drive inflammation, whereas M2 macrophages possess anti-inflammatory properties and contribute to tissue repair processes. Mesenchymal stem cells (MSCs), known for their remarkable attributes including homing capabilities, immunomodulation, anti-inflammatory effects, and tissue regeneration potential, hold promise in enhancing the prognosis of sepsis-induced liver injury by harmonizing the delicate balance of M1/M2 macrophage polarization. This review discusses the mechanisms by which MSCs regulate macrophage polarization, alongside the signaling pathways involved, providing an idea for innovative directions in the treatment of sepsis-induced liver injury.

This study introduces a hybrid data assimilation method that significantly improves the predictive accuracy of the time-dependent Susceptible-Exposed-Asymptomatic-Infected-Quarantined-Removed (SEAIQR) model for epidemic forecasting. The approach integrates real-time Ensemble Kalman Filtering (EnKF) with the K-Nearest Neighbors (KNN) algorithm, combining dynamic real-time adjustments with pattern recognition techniques tailored to the specific dynamics of epidemics. This hybrid methodology overcomes the limitations of single-model predictions in the face of increasingly complex transmission pathways in modern society. Numerical experiments conducted using COVID-19 case data from Xi’an, Shaanxi Province, China (December 9, 2021, to January 8, 2022) demonstrate a marked improvement in forecasting accuracy relative to traditional models and other data assimilation approaches. These findings underscore the potential of the proposed method to enhance the accuracy and reliability of predictive models, providing valuable insights for future epidemic forecasting and disease control strategies.

Psychiatric disorders are the leading cause of disability worldwide while the pathogenesis remains unclear. Genome-wide association studies (GWASs) have made great achievements in detecting disease-related genetic variants. However, functional information on the underlying biological processes is often lacking. Current reports propose the use of metabolic traits as functional intermediate phenotypes (the so-called genetically determined metabotypes or GDMs) to reveal the biological mechanisms of genetics in human diseases. Here we conducted a two-sample Mendelian randomization analysis that uses GDMs to assess the causal effects of 486 human serum metabolites on 5 major psychiatric disorders, which respectively were schizophrenia (SCZ), major depression (MDD), bipolar disorder (BIP), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Using genetic variants as proxies, our study has identified 137 metabolites linked to the risk of psychiatric disorders, including 2-methoxyacetaminophen sulfate, which affects SCZ (P = 1.7 × 10–5) and 1-docosahexaenoylglycerophosphocholine, which affects ADHD (P = 5.6 × 10–5). Fourteen significant metabolic pathways involved in the 5 psychiatric disorders assessed were also detected, such as glycine, serine, and threonine metabolism for SCZ (P = .0238), Aminoacyl-tRNA biosynthesis for both MDD (P = .0144) and ADHD (P = .0029). Our study provided novel insights into integrating metabolomics with genomics in order to understand the mechanisms underlying the pathogenesis of human diseases.

Evidence shows that intestinal inflammation, oxidative stress, and injury of mucosal barrier are closely related to the pathogenesis of ulcerative colitis (UC). Huang-lian-Jie-du Decoction (HLJDD) is a well-known prescription of traditional Chinese medicine with anti-inflammatory and antioxidative activities, which may be used to treat UC. However, its therapeutic effect and mechanism are still unclear. In this study, the UC model of BABL/c mice were established by DSS [3.5% (w/v)], and HLJDD was given orally for treatment at the same time. During the experiment, the clinical symptoms of mice were scored by disease activity index (DAI). Besides, the effects of HLJDD on immune function, oxidative stress, colon NF-κB and Nrf2 signaling pathway, and intestinal mucosal barrier function in UC mice were also investigated. The results showed that HLJDD could alleviate body weight loss and DAI score of UC mice, inhibit colonic shortening and relieve colonic pathological damage, and reduce plasma and colon MPO levels. In addition, HLJDD treatment significantly up-regulated plasma IL-10, down-regulated TNF-α and IL-1β levels, and inhibited the expression of NF-κB p65, p-IκKα/β, and p-IκBα proteins in the colon. Moreover, NO and MDA levels in colon tissues were significantly reduced after HLJDD treatment, while GSH, SOD levels and Nrf2, Keap1 protein expression levels were remarkably elevated. Additionally, HLJDD also protected intestinal mucosa by increasing the secretion of mucin and the expression of ZO-1 and occludin in colonic mucosa. These results indicate that HLJDD could effectively alleviate DSS-induced mice UC by suppressing NF-κB signaling pathway, activating Nrf2 signaling pathway, and enhancing intestinal barrier function.

In recent years, there has been an increase in the incidence and mortality rates of prostate cancer (PCa). However, the specific molecular mechanisms underlying its occurrence and development remain unclear, necessitating the identification of new therapeutic targets. Through bioinformatics analysis, we discovered a previously unstudied differential gene called HIST3H2A in prostate cancer. Our study revealed that HIST3H2A is highly expressed in PCa tissues, as confirmed by analysis of both the GEO and UALCAN databases. Further analysis using the KEGG database demonstrated that HIST3H2A regulates the pathway of programmed necroptosis in cells. Additionally, we observed significant up-regulation of HIST3H2A in PCa tissues and cell lines. HIST3H2A was found to regulate cell proliferation, migration, invasion, and the epithelial-mesenchymal transition (EMT) process in tumors. Notably, HIST3H2A’s role in regulating programmed necroptosis in prostate cancer cells differs from its role in apoptosis. In vitro and in vivo experiments collectively support the key role of HIST3H2A in promoting the development of prostate cancer, highlighting its potential as a therapeutic target for patients with PCa.

The escalating demand for long-term high-temperature microwave-absorbing materials (HTMAMs) in high-speed aerospace stealth is hindered by limitations such as magnetic loss degradation or oxidation risks. Herein, we introduce rare earth zirconate ceramics that exhibit air stability up to 1600 °C. Abundant oxygen vacancies significantly enhance permittivity and thus microwave-absorbing performance through activated thermionic migration at elevated temperatures. Moreover, the thermionic-facilitated permittivity can be meticulously modulated by electron delocalization, with the extent governed by lattice disorder. We demonstrate this concept through a dual-layer Er 2 Zr 2 O 7 /Gd 2 Zr 2 O 7 structure to further optimize impedance matching, achieving an ultra-wide bandwidth (8.27 GHz) and strong absorption (−64.61 dB) at ultrathin thicknesses under 1.2 mm at 600 °C mainly by macroscopic interfacial resonance, alongside an ultralow thermal conductivity (1.61 W•m -1 •K -1 ). This work presents an innovative approach to design high-performance and anti-oxidative HTMAMs through thermionic migration tuned by electron delocalization, advancing structural-functional integrated materials for extreme environments. High-temperature, ultrathin, and high-performance microwave absorption is realized in the dual-layer structure with rare earth zirconate ceramics via electron delocalization-mediated thermionic migration and macroscopic interfacial resonance.

In this study, a novel methodology for the fabrication of gradient porous structures is introduced, predicated upon the phase evolution characteristics of immiscible polymer blends. Initially, a comprehensive flow-phase field dynamics model is developed. This model couples the principles of phase field theory and the dynamics of fluid flow to the two-phase evolution process, facilitating a numerical simulation of the phase evolution. Subsequently, the phase field parameters of model are determined and combined with the temperature field, thereby enabling a targeted and controlled fabrication of gradient porous structures. Finally, the efficacy and practical applicability of the proposed methodology are substantiated through the construction of illustrative examples. This approach, as delineated herein, provides a robust framework for the efficient design and realization of intricate, interconnected gradient porous structures with potential applications in various scientific and engineering domains.

As artificial intelligence transitions from industry-exclusive tool to public-facing technology, society faces critical decisions about its integration into socioecological systems. This paper proposes a reimagining of AI as a synthetic participant in the circular bioeconomy (CBE)—a regenerative model emphasizing cyclical flows of resources, information, and energy. Drawing on Bruno Latour’s Actor-Network Theory and Donna Haraway’s posthumanism, we reconceptualize AI as a non-living organism capable of functioning within multispecies systems, analogous to viruses that shape ecosystems without conventional life. Conventional, in that it meets the standard biological criteria for like: metabolism, reproduction, and homeostasis. AI, like viruses, does not meet this biological criteria. Current AI applications in CBE—from biowaste recycling to precision agriculture—demonstrate both transformative potential and ethical concerns. While AI enables unprecedented efficiency through advanced algorithms and embodied robotics, it risks perpetuating extractive logics that treat information as a resource to be mined rather than circulated. Critical ethical challenges emerge including algorithmic bias amplifying inequalities, epistemic opacity eroding stakeholder trust, blurred accountability for AI-driven harm, displacement of human labor, and marginalization of indigenous and local ecological knowledge. Through examples in medicine and remote sensing, we argue that AI becomes a “friend” to the Circular Bioeconomy (CBE) only when designed as circular and relational rather than linear and extractive. This requires synthetic datasets preserving privacy, multimodal architectures enabling dimensional understanding, and human-machine-ecosystem feedback loops replacing terminal outputs with ongoing accountability. Ultimately, AI’s role depends on intentional design grounded in justice and multispecies dignity—transforming it from extractive tool into participant in shared regenerative futures.

Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F 1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI. Diagnosing rare diseases, such as choroid neoplasias, remains a critical challenge. Here, the authors develop a multimodal concept-based interpretable model (MMCBM) to distinguish uveal melanoma from hemangioma and metastatic carcinoma, obtaining performance comparable to senior ophthalmologists in a large cohort of Asian patients with choroid neoplasms.

Flexible magnetic-field-based tactile sensors (FMFTS) have numerous advantages including low cost, ease of manufacture, simple wiring, high sensitivity, and so on. Flexible magnetic-field-based tactile sensors need to be calibrated before use to build accurate mapping between contact force and magnetic field intensity measured by magnetic sensors; however, when considering remanence inconsistency of magnetic source, each FMFTS needs to be calibrated independently to enhance accuracy, and the complex preparation prevents FMFTS from being used conveniently. A robust estimation method of contact force and location that can tolerate remanence inconsistency of magnetic source in FMFTS is proposed. Firstly, the position and orientation of magnetic source were tracked using the Levenberg–Marquart algorithm, and the tracking results were insensitive to the remanence of magnetic source with appropriate cost function. Secondly, the mapping between magnitude and location of contact force and position and orientation of magnetic source was built with calibration of one sensor; the mapping only depends on the structural response of flexible substrate, and thus can be extended to estimate external force and location for other sensors with the same structure. The proposed method was evaluated in both simulations and experiments, and the results confirm that the estimation of magnitude and location of external force for FMFTS with the same structure and different remanence could reach acceptable accuracy, depending on single calibration. The proposed method can be used to simplify the calibration procedure and remove the barrier for large-scale application of FMFTS and replacement of damaged FMFTS.