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Background/objectivesEngineered nanomaterials (ENMs) have been suggested as being capable of promoting inflammation, a key component in the pathways associated with carcinogenesis, cardiovascular disease, and other conditions. As a result, the risk assessment of biological markers as early-stage indicators has the potential to improve translation from experimental toxicologic findings to identifying evidence in human studies. The study aims to review the possible early biological changes in workers exposed to carbon black (CB), followed by an evidentiary quality evaluation to determine the predictive value of the biological markers.MethodsWe conducted a literature search to identify epidemiological studies that assessed biological markers that were involved in the inflammatory process at early stages among workers with exposure to CB. We reviewed the studies with specific reference to the study design, statistical analyses, findings, and limitations.ResultsWe identified five Chinese studies that investigated the potential impact of exposure to CB on inflammatory markers, bronchial wall thickening, genomic instability, and lung function impairment in CB production workers. Of the five Chinese studies, four were cross-sectional; another study reported results at two-time points over six years of follow-up. The authors of all five studies concluded positive relationships between exposure and the inflammatory cytokine profiles. The weak to very weak correlations between biomarkers and early-stage endpoints were reported.ConclusionMost inflammatory markers failed to satisfy the proposed evidentiary quality criteria. The significance of the results of the reviewed studies is limited by the cross-sectional study design, inconsistency in results, uncertain clinical relevance, and high occupational exposures. Based on this review, the risk assessment relying on inflammatory markers does not seem appropriate at this time. Nevertheless, the novel research warrants further exploration in assessing exposure to ENMs and corresponding potential health risks in occupational settings.

The influence of molasses (M) and Lactobacillus rhamnosus (LR) on fermentation products, microbial communities, and metabolites in big-bale alfalfa silage was investigated. Alfalfa (Medicago sativa L.) was harvested at the third growth stage during the flowering stage in the experimental field of Linhui Grass Company from Tongliao City, Inner Mongolia. An alfalfa sample without additives was used as a control (C). M (20 g/kg) and LR (106 cfu/g) were added either alone or in combination. Alfalfa was fermented for 7, 14, and 56 d. Lactic acid content in the M, LR, and MLR groups increased, whereas the pH value and butyric acid, 2,3-butanediol, and ethanol contents decreased compared to those of C group after 7, 14, and 56 d of fermentation. A two-way analysis of variance (ANOVA) was performed to estimate the results. The LR group exhibited increased Lactobacillus abundance, whereas the M and MLR groups showed increased Weissella abundance compared to the C group. The relative contents of amino acids (tyrosine, isoleucine, threonine, arginine, valine, and citrulline) in the M and MLR groups were higher than those in the C group. During fermentation, the M, LR, and MLR groups showed decreased phenylalanine, isoleucine, and ferulic acid contents. Amino acids such as isoleucine and L-aspartic acid were positively correlated with Lactobacillus but negatively correlated with Weissella. In conclusion, combining high-throughput sequencing and liquid chromatography–mass spectrometry during anaerobic alfalfa fermentation can reveal new microbial community compositions and metabolite profiles, supporting the application of M, LR, and MLR as feed fermentation agents.

Abstract The COVID-19 pandemic has sickened millions, cost lives and has devastated the global economy. Various animal models for experimental infection with SARS-CoV-2 have played a key role in many aspects of COVID-19 research. Here, we describe a humanized hACE2 (adenovirus expressing hACE2) NOD-SCID IL2Rγ−/− (NIKO) mouse model and compare infection with ancestral and mutant (SARS-CoV-2-∆382) strains of SARS-CoV-2. Immune cell infiltration, inflammation, lung damage and pro-inflammatory cytokines and chemokines was observed in humanized hACE2 NIKO mice. Humanized hACE2 NIKO mice infected with the ancestral and mutant SARS-CoV-2 strain had lung inflammation and production of pro-inflammatory cytokines and chemokines. This model can aid in examining the pathological basis of SARS-CoV-2 infection in a human immune environment and evaluation of therapeutic interventions.

Summary: Interleukin-37 (IL-37) has been shown to inhibit tumor growth in various cancer types. However, the immune regulatory function of IL-37 in the tumor microenvironment is unclear. Here, we established a human leukocyte antigen-I (HLA-I)-matched humanized patient-derived xenograft hepatocellular carcinoma (HCC) model and three murine orthotopic HCC models to study the function of IL-37 in the tumor microenvironment. We found that IL-37 inhibited HCC growth and promoted T cell activation. Further study revealed that IL-37 impaired the immunosuppressive capacity of myeloid-derived suppressor cells (MDSCs). Pretreatment of MDSCs with IL-37 before adoptive transfer attenuated their tumor-promoting function in HCC tumor-bearing mice. Moreover, IL-37 promoted both glycolysis and oxidative phosphorylation in MDSCs, resulting in the upregulation of ATP release, which impaired the immunosuppressive capacity of MDSCs. Collectively, we demonstrated that IL-37 inhibited tumor development through dampening MDSCs’ immunosuppressive capacity in the tumor microenvironment via metabolic reprogramming, making it a promising target for future cancer immunotherapy.

Abstract Chronic myeloid leukemia (CML) is a hematological tumor derived from hematopoietic stem cells. The aim of this study is to analyze the biological characteristics and identify the diagnostic markers of CML. We obtained the expression profiles from the Gene Expression Omnibus (GEO) database and identified 210 differentially expressed genes (DEGs) between CML and normal samples. These DEGs are mainly enriched in immune-related pathways such as Th1 and Th2 cell differentiation, primary immunodeficiency, T cell receptor signaling pathway, antigen processing and presentation pathways. Based on these DEGs, we identified two molecular subtypes using a consensus clustering algorithm. Cluster A was an immunosuppressive phenotype with reduced immune cell infiltration and significant activation of metabolism-related pathways such as reactive oxygen species, glycolysis and mTORC1; Cluster B was an immune activating phenotype with increased infiltration of CD4 + and CD8 + T cells and NK cells, and increased activation of signaling pathways such as interferon gamma (IFN-γ) response, IL6-JAK-STAT3 and inflammatory response. Drug prediction results showed that patients in Cluster B had a higher therapeutic response to anti-PD-1 and anti-CTLA4 and were more sensitive to imatinib, nilotinib and dasatinib. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage Selection Operator (LASSO) and Random Forest (RF) algorithms identified 4 CML diagnostic genes (HDC, SMPDL3A, IRF4 and AQP3), and the risk score model constructed by these genes improved the diagnostic accuracy. We further validated the diagnostic value of the 4 genes and the risk score model in a clinical cohort, and the risk score can be used in the differential diagnosis of CML and other hematological malignancies. The risk score can also be used to identify molecular subtypes and predict response to imatinib treatment. These results reveal the characteristics of immunosuppression and metabolic reprogramming in CML patients, and the identification of molecular subtypes and biomarkers provides new ideas and insights for the clinical diagnosis and treatment.

Sand and dust storms (SDSs) are particularly concerning natural disasters in East Asia. At present, there is still a lack of comprehensive knowledge of the characteristics of the cross-border SDSs between Mongolia and Northern China and the associated weather systems. This study identifies and documents the spring cross-border SDSs between Mongolia and Northern China based on the MODIS AOD map and Himawari-8/9 dust RGB images and analyzes the corresponding weather system types. A total of 76 spring cross-border SDSs were identified during 2000–2023, accounting for 55.1% of the total SDSs in both countries. The vast majority of the cross-border SDSs (86.8%) were related to Mongolian cyclones (MCs). Among them, 53.9% of the cross-border SDSs were mainly driven by Mongolian cyclones alone, and 32.9% were driven by the combination of MCs and cold highs (MC-CH type). Significant differences in the horizontal distribution of the SDSs were observed for different weather types. MCs alone trigger SDSs in the southern halves of the MCs, so the horizontal extent of the SDSs is consistent with that of MCs but larger than that associated with cold fronts. For the MC-CH type, strong winds in the southern flanks of MCs and their rear cold highs jointly drive a large-scale zonally extensive SDS belt. In recent years, particularly in 2021 and 2023, the strong cross-border SDSs have been dominated by the MC–CH type. This study provides a reference for the forecasting and early identification of cross-border SDS disasters.

Background: According to previous studies on professional English course teaching, the evaluation of course teaching was positive, but the vast majorities focus on medical English literature reading, professional English vocabulary, and professional English translation. As an alternative, the course design based on academic learning needs under the outcome-oriented education/model emphasizes the improvement of students’ comprehensive ability in oral expression, literature reading, writing, and academic communication.

Abstract Background Alternative splicing (AS) of RNA is a fundamental biological process that shapes protein diversity. Many non-characteristic AS events are involved in the onset and development of acute myeloid leukemia (AML). Abnormal alterations in splicing factors (SFs), which regulate the onset of AS events, affect the process of splicing regulation. Hence, it is important to explore the relationship between SFs and the clinical features and biological processes of patients with AML. Methods This study focused on SFs of the classical heterogeneous nuclear ribonucleoprotein (hnRNP) family and arginine and serine/arginine-rich (SR) splicing factor family. We explored the relationship between the regulation patterns associated with the expression of SFs and clinicopathological factors and biological behaviors of AML based on a multi-omics approach. The biological functions of SRSF10 in AML were further analyzed using clinical samples and in vitro experiments. Results Most SFs were upregulated in AML samples and were associated with poor prognosis. The four splicing regulation patterns were characterized by differences in immune function, tumor mutation, signaling pathway activity, prognosis, and predicted response to chemotherapy and immunotherapy. A risk score model was constructed and validated as an independent prognostic factor for AML. Overall survival was significantly shorter in the high-risk score group. In addition, we confirmed that SRSF10 expression was significantly up-regulated in clinical samples of AML, and knockdown of SRSF10 inhibited the proliferation of AML cells and promoted apoptosis and G1 phase arrest during the cell cycle. Conclusion The analysis of splicing regulation patterns can help us better understand the differences in the tumor microenvironment of patients with AML and guide clinical decision-making and prognosis prediction. SRSF10 can be a potential therapeutic target and biomarker for AML.

Abstract Background Acute myeloid leukemia (AML) is the most common malignancy of the hematological system, and there are currently a number of studies regarding abnormal alterations in energy metabolism, but fewer reports related to fatty acid metabolism (FAM) in AML. We therefore analyze the association of FAM and AML tumor development to explore targets for clinical prognosis prediction and identify those with potential therapeutic value. Methods The identification of AML patients with different fatty acid metabolism characteristics was based on a consensus clustering algorithm. The CIBERSORT algorithm was used to calculate the proportion of infiltrating immune cells. We used Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis to construct a signature for predicting the prognosis of AML patients. The Genomics of Drug Sensitivity in Cancer database was used to predict the sensitivity of patient samples in high- and low-risk score groups to different chemotherapy drugs. Results The consensus clustering approach identified three molecular subtypes of FAM that exhibited significant differences in genomic features such as immunity, metabolism, and inflammation, as well as patient prognosis. The risk-score model we constructed accurately predicted patient outcomes, with area under the receiver operating characteristic curve values of 0.870, 0.878, and 0.950 at 1, 3, and 5 years, respectively. The validation cohort also confirmed the prognostic evaluation performance of the risk score. In addition, higher risk scores were associated with stronger fatty acid metabolisms, significantly higher expression levels of immune checkpoints, and significantly increased infiltration of immunosuppressive cells. Immune functions, such as inflammation promotion, para-inflammation, and type I/II interferon responses, were also significantly activated. These results demonstrated that immunotherapy targeting immune checkpoints and immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) and M2 macrophages, are more suitable for patients with high-risk scores. Finally, the prediction results of chemotherapeutic drugs showed that samples in the high-risk score group had greater treatment sensitivity to four chemotherapy drugs in vitro. Conclusions The analysis of the molecular patterns of FAM effectively predicted patient prognosis and revealed various tumor microenvironment (TME) characteristics.