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Effectively neutralizing inflammatory cytokines is crucial for managing a variety of inflammatory disorders. Current techniques that target only a subset of cytokines often fall short due to the intricate nature of redundant and compensatory cytokine networks. A promising solution to this challenge is using cell membrane-coated nanoparticles (CNPs). These nanoparticles replicate the complex interactions between cells and cytokines observed in disease pathology, providing a potential avenue for multiplex cytokine scavenging. While the development of CNPs using experimental animal models has shown great promise, their effectiveness in scavenging multiple cytokines in human diseases has yet to be demonstrated. To bridge this gap, this study selected macrophage membrane-coated CNPs (MФ-CNPs) and assessed their ability to scavenge inflammatory cytokines in serum samples from patients with COVID-19, sepsis, acute pancreatitis, or type-1 diabetes, along with synovial fluid samples from patients with rheumatoid arthritis. The results show that MФ-CNPs effectively scavenge critical inflammatory cytokines, including interleukin (IL)-6, IL-8, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α, in a dose-dependent manner. Overall, this study demonstrates MФ-CNPs as a multiplex cytokine scavenging formulation with promising applications in clinical settings to treat a range of inflammatory disorders.

Abstract Effectively neutralizing inflammatory cytokines is crucial for managing a variety of inflammatory disorders. Current techniques that target only a subset of cytokines often fall short due to the intricate nature of redundant and compensatory cytokine networks. A promising solution to this challenge is using cell membrane‐coated nanoparticles (CNPs). These nanoparticles replicate the complex interactions between cells and cytokines observed in disease pathology, providing a potential avenue for multiplex cytokine scavenging. While the development of CNPs using experimental animal models has shown great promise, their effectiveness in scavenging multiple cytokines in human diseases has yet to be demonstrated. To bridge this gap, this study selected macrophage membrane‐coated CNPs (MФ‐CNPs) and assessed their ability to scavenge inflammatory cytokines in serum samples from patients with COVID‐19, sepsis, acute pancreatitis, or type‐1 diabetes, along with synovial fluid samples from patients with rheumatoid arthritis. The results show that MФ‐CNPs effectively scavenge critical inflammatory cytokines, including interleukin (IL)‐6, IL‐8, interferon (IFN)‐γ, and tumor necrosis factor (TNF)‐α, in a dose‐dependent manner. Overall, this study demonstrates MФ‐CNPs as a multiplex cytokine scavenging formulation with promising applications in clinical settings to treat a range of inflammatory disorders.

Iron (Fe) deficiency is one of the most common micronutrient imbalances limiting plant growth globally, especially in arid and saline alkali regions due to the decreased availability of Fe in alkaline soils. Malus halliana grows well in arid regions and is tolerant of Fe deficiency. Here, a physiological and metabolomic approach was used to analyze the short-term molecular response of M. halliana roots to Fe deficiency. On the one hand, physiological data show that the root activity first increased and then decreased with the prolongation of the stress time, but the change trend of root pH was just the opposite. The total Fe content decreased gradually, while the effective Fe decreased at 12 h and increased at 3 d. The activity of iron reductase (FCR) increased with the prolongation of stress. On the other hand, a total of 61, 73, and 45 metabolites were identified by GC–MS in three pairs: R12h (Fe deficiency 12 h) vs. R0h (Fe deficiency 0 h), R3d (Fe deficiency 3 d) vs. R0h, and R3d vs. R12h, respectively. Sucrose, as a source of energy, produces monosaccharides such as glucose by hydrolysis, while glucose accumulates significantly at the first (R12h vs. R0h) and third time points (R3d vs. R0h). Carbohydrates (digalacturonate, L-xylitol, ribitol, D-xylulose, glucose, and glycerol) are degraded into pyruvate through glycolysis and pentose phosphate, which participate in the TCA. Glutathione metabolism and the TCA cycle coordinate with each other, actively respond to Fe deficiency stress, and synthesize secondary metabolites at the same time. This study thoroughly examines the metabolite response to plant iron deficiency, highlighting the crucial roles of sugar metabolism, tricarboxylic acid cycle regulation, and glutathione metabolism in the short-term iron deficiency response of apples. It also lays the groundwork for future research on analyzing iron deficiency tolerance.

Ocean warming caused by global climate change influences the function, diversity, and community dynamics of commensal microorganisms, including the hemolymph and the gut microbiota in mussels. However, the microbiota in hard-shelled mussel (Mytilus coruscus) larvae and the effect of temperature on the microbial community structure have yet to be studied. Herein, we investigated the core microbiota of M. coruscus larvae and the impact of acute (4 h) and gradual (4 days) exposure to a rise in seawater temperature from 21 to 25 °C. Eleven core genera were identified in M. coruscus larvae by 16S rDNA gene sequencing: Alteromonas, Brevundimonas, Delftia, Microbacterium, Neptuniibacter, Neptunomonas, Pseudoalteromonas, Rhodococcus, Stenotrophomonas, Tenacibaculum, and Thalassotalea. The microbiota of larvae in the short exposure treatment was similar to the control. However, the abundance of Delftia, Neptunomonas, Pseudoalteromonadaceae, Rhodococcus, and Stenotrophomonas decreased significantly in the long-exposure larvae. In contrast, at the genus level, the abundance of Tenacibaculum increased significantly. Diversity and multivariate analyses confirmed that the microbiota patterns were linked to seawater warming over the long term. Microbiota diversity did not change significantly, regardless of whether the seawater temperature increased quickly or slowly; however, we observed a significant increase in the microbiota species abundance at higher temperatures. Among the altered bacterial genera, Delftia, Neptunomonas, and Rhodococcus function in the degradation of organic compounds; Pseudoalteromonas is closely associated with mussel attachment and metamorphosis, and Tenacibaculum is an opportunistic pathogen that can cause marine mollusk death. The results suggest that marine heat waves caused by climate change may reduce the ability of symbiotic bacteria to degrade environmental toxins, will affect mussel larvae metamorphosis, and increase the abundance of opportunistic pathogens, thereby increasing the risk of disease and death of mussel larvae.

Abstract Background Biomarkers, such as leukocyte count, C-reactive protein (CRP), and procalcitonin (PCT), have been commonly used to predict the occurrence of life-threatening bacteremia and provide prognostic information, given the need for prompt intervention. However, such diagnosis methods require much time and money. Therefore, we propose a method with a high prediction capability using machine learning (ML) models based on complete blood count (CBC) and differential leukocyte count (DC) and compare its performance with traditional CRP or PCT biomarker methods and those of models incorporating CRP or PCT biomarkers. Methods We collected 366,586 daily blood culture (BC) results, of which 350,775 (93.2%), 308,803 (82.1%), and 23,912 (6.4%) cases were issued CBC/DC (CBC/DC group), CRP with CBC/DC (CRP&CBC/DC group), and PCT with CBC/DC (PCT&CBC/DC group), respectively. For the ML methods, conventional logistic regression and random forest models were selected, trained, applied, and validated for each group. Fivefold validation and prediction capability were also evaluated and reported. Results Overall, the ML methods, such as the random forest model, demonstrated promising performances. When trained with CBC/DC data, it achieved an area under the ROC curve (AUC) of 0.802, which is superior to the prediction conventionally made with CRP/PCT levels (0.699/0.731). Upon evaluating the performance enhanced by incorporating CRP or PCT biomarkers, it reported no substantial AUC increase with the addition of either CRP or PCT to CBC/DC data, which suggests the predicting power and applicability of using only CBC/DC data. Moreover, it showed competitive prognostic capability compared to the PCT test with similar all-cause in-hospital mortality (45.10% vs. 47.40%) and overall median survival time (27 vs. 25 days). Conclusions The ML models using only CBC/DC data yielded more accurate bacteremia predictions compared to those by methods using CRP and PCT data and reached similar prognostic performance as by PCT data. Thus, such models are potentially complementary and competitive with traditional CRP and PCT biomarkers for conducting and guiding antibiotic usage.