Optimizing green supply chain circular economy in smart cities with integrated machine learning technology.

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively-figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

DIRMC: a database of immunotherapy-related molecular characteristics.

Cancer immunotherapy has brought about a revolutionary breakthrough in the field of cancer treatment. Immunotherapy has changed the treatment landscape for a variety of solid and hematologic malignancies. To assist researchers in efficiently uncovering valuable information related to cancer immunotherapy, we have presented a manually curated comprehensive database called DIRMC, which focuses on molecular features involved in cancer immunotherapy. All the content was collected manually from published literature, authoritative clinical trial data submitted by clinicians, some databases for drug target prediction such as DrugBank, and some experimentally confirmed high-throughput data sets for the characterization of immune-related molecular interactions in cancer, such as a curated database of T-cell receptor sequences with known antigen specificity (VDJdb), a pathology-associated TCR database (McPAS-TCR) et al. By constructing a fully connected functional network, ranging from cancer-related gene mutations to target genes to translated target proteins to protein regions or sites that may specifically affect protein function, we aim to comprehensively characterize molecular features related to cancer immunotherapy. We have developed the scoring criteria to assess the reliability of each MHC-peptide-T-cell receptor (TCR) interaction item to provide a reference for users. The database provides a user-friendly interface to browse and retrieve data by genes, target proteins, diseases and more. DIRMC also provides a download and submission page for researchers to access data of interest for further investigation or submit new interactions related to cancer immunotherapy targets. Furthermore, DIRMC provides a graphical interface to help users predict the binding affinity between their own peptide of interest and MHC or TCR. This database will provide researchers with a one-stop resource to understand cancer immunotherapy-related targets as well as data on MHC-peptide-TCR interactions. It aims to offer reliable molecular characteristics support for both the analysis of the current status of cancer immunotherapy and the development of new immunotherapy. DIRMC is available at http://www.dirmc.tech/. Database URL: http://www.dirmc.tech/.

A CXCR3-Activating Peptide Increases Tear Break Up Time and Corrects Corneal haze in a Rabbit Model of Environmental Dry Eye.

Purpose: Environmentally-triggered dry eye disease (DED) or keratoconjunctivitis sicca (KCS), which constitutes the majority of DED cases, currently is palliatively treated with aqueous replacement solutions that do not target the dysfunction of the mucin and lipid components of tears. We tested whether a peptide that increased goblet cell numbers in a model of scleral chemical injury would also improve tear quality in environmental DED. Methods: Environmental DED was established by exposing New Zealand white rabbits (8 per group, female) to 20% humidity with rapid air replacement and b.i.d. atropine sulfate eyedrops for 3 weeks prior to test article administration; this continued for the subsequent 3 weeks of testing. Animals were dosed by (A) saline, (B) b.i.d. eyedrop of peptide in saline, (C) b.i.d. eyedrop of peptide in coacervate, or (D) weekly subconjunctival injection of peptide. In vitro, human conjunctival epithelial cells (HCjE) were exposed to TNFα in the presence or absence of peptide to determine inflammatory responsiveness. Results: The environmental DED was established with both Schirmer and TBUT being reduced at the start of test article; these levels were maintained as low through the testing period. All three treatment regimens increased TBUT approximately 3x to levels greater than prior to desiccation (P < 0.01), with little effect on Schirmer. Corneal haze was present in all eyes after induction, and completely reversed in 36 of 48 eyes across the treatments (P < 0.05). Co-treatment of HCjE with peptide reduced the production of TNFα in response to an inflammatory stimulus. Conclusions: The treatment of environmental DED/KCS with a peptide that activates CXCR3 improved tear quality and reversed corneal pathology by promoting tear stability and likely dampening the corneal inflammation, while not affecting aqueous volume of the tears.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

Side Reactions/Changes in Lithium-Ion Batteries: Mechanisms and Strategies for Creating Safer and Better Batteries.

Lithium-ion batteries (LIBs), in which lithium ions function as charge carriers, are considered the most competitive energy storage devices due to their high energy and power density. However, battery materials, especially with high capacity undergo side reactions and changes that result in capacity decay and safety issues. A deep understanding of the reactions that cause changes in the battery's internal components and the mechanisms of those reactions is needed to build safer and better batteries. This review focuses on the processes of battery failures, with voltage and temperature as the underlying factors. Voltage-induced failures result from anode interfacial reactions, current collector corrosion, cathode interfacial reactions, overcharge, and overdischarge, while temperature-induced failure mechanisms include SEI decomposition, separator damage, and interfacial reactions between electrodes and electrolytes. The review also presents protective strategies for controlling these reactions. As a result, the reader is offered a comprehensive overview of the safety features and failure mechanisms of various LIB components. This article is protected by copyright. All rights reserved.

Transformation of metallo-elastomer grafts in a carotid artery interposition model over a year.

Current vascular grafts, primarily Gore-Tex® and Dacron®, don't integrate with the host and have low patency in small-diameter vessels (<6 mm). Biomaterials that possess appropriate viscoelasticity, compliance, and high biocompatibility are essential for their application in small blood vessels. We have developed metal ion crosslinked poly(propanediol-co-(hydroxyphenyl methylene)amino-propanediol sebacate) (M-PAS), a biodegradable elastomer with a wide range of mechanical properties. We call these materials metallo-elastomers. An initial test on Zn-, Fe-, and Cu-PAS grafts reveals that Cu-PAS is the most suitable because of its excellent elastic recoil and well-balanced polymer degradation/tissue regeneration rate. Here we report host remodeling of Cu-PAS vascular grafts in rats over one year. 76 % of the grafts remain patent and >90 % of the synthetic polymer is degraded by 12 months. Extensive cell infiltration leads to a positive host remodeling. The remodeled grafts feature a fully endothelialized lumen. Circumferentially organized smooth muscle cells, elastin fibers, and widespread mature collagen give the neoarteries mechanical properties similar to native arteries. Proteomic analysis further reveals the presence of important vascular proteins in the neoarteries. Evidence suggests that Cu-PAS is a promising material for engineering small blood vessels.

Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer.

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC.

ZBTB16 inhibits DNA replication and induces cell cycle arrest by targeting WDHD1 transcription in lung adenocarcinoma.

Lung adenocarcinoma is a malignant tumor with high morbidity and mortality. ZBTB16 plays a double role in various tumors; however, the potential mechanism of ZBTB16 in the pathophysiology of lung adenocarcinoma has yet to be elucidated. We herein observed a decreased expression of ZBTB16 mRNA and protein in lung adenocarcinoma and a significantly increased DNA methylation level of ZBTB16 in patients with lung adenocarcinoma. Analysis of public databases and patients' clinical data indicated a close association between ZBTB16 and patient survival. Ectopic expression of ZBTB16 in lung adenocarcinoma cells significantly inhibited cell proliferation, invasion, and migration. It also induced cell cycle arrest in the S phase. Meanwhile, mitotic catastrophe was induced, and DNA damage and apoptosis occurred. In line with these findings, the overexpression of ZBTB16 in xenograft mice resulted in the inhibition of tumor growth. Comprehensive analysis showed that WDHD1 was a potential target for ZBTB16. The overexpression of both isoforms of WDHD1 significantly reversed the ZBTB16-mediated inhibition of lung adenocarcinoma proliferation and cell cycle. These studies suggest that ZBTB16 impedes the progression of lung adenocarcinoma by interfering with WDHD1 transcription, making it a potential novel therapeutic target in the management of lung adenocarcinoma.

Catalytic Synthesis of (S)-CHBE by Directional Coupling and Immobilization of Carbonyl Reductase and Glucose Dehydrogenase.

Ethyl (S)-4-chloro-3-hydroxybutyrate ((S)-CHBE) is an important chiral intermediate in the synthesis of the cholesterol-lowering drug atorvastatin. Studying the use of SpyTag/SpyCatcher and SnoopTag/SnoopCatcher systems for the asymmetric reduction reaction and directed coupling coenzyme regeneration is practical for efficiently synthesizing (S)-CHBE. In this study, Spy and Snoop systems were used to construct a double-enzyme directed fixation system of carbonyl reductase (BsCR) and glucose dehydrogenase (BsGDH) for converting 4-chloroacetoacetate (COBE) to (S)-CHBE and achieving coenzyme regeneration. We discussed the enzymatic properties of the immobilized enzyme and the optimal catalytic conditions and reusability of the double-enzyme immobilization system. Compared to the free enzyme, the immobilized enzyme showed an improved optimal pH and temperature, maintaining higher relative activity across a wider range. The double-enzyme immobilization system was applied to catalyze the asymmetric reduction reaction of COBE, and the yield of (S)-CHBE reached 60.1% at 30 °C and pH 8.0. In addition, the double-enzyme immobilization system possessed better operational stability than the free enzyme, and maintained about 50% of the initial yield after six cycles. In summary, we show a simple and effective strategy for self-assembling SpyCatcher/SnoopCatcher and SpyTag/SnoopTag fusion proteins, which inspires building more cascade systems at the interface. It provides a new method for facilitating the rapid construction of in vitro immobilized multi-enzyme complexes from crude cell lysate.

Tumour-derived exosome SNHG17 induced by oestrogen contributes to ovarian cancer progression via the CCL13-CCR2-M2 macrophage axis.

Oestrogen is known to be strongly associated with ovarian cancer. There was much work to show the importance of lncRNA SNHG17 in ovarian cancer. However, no study has revealed the molecular regulatory mechanism and functional effects between oestrogen and SNHG17 in the development and metastasis of ovarian cancer. In this study, we found that SNHG17 expression was significantly increased in ovarian cancer and positively correlated with oestrogen treatment. Oestrogen could promote M2 macrophage polarization as well as ovarian cancer cells SKOV3 and ES2 cell exosomal SNHG17 expression. When exposure to oestrogen, exosomal SNHG17 promoted ovarian cancer cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) in vitro, and tumour growth and lung metastasis in vivo by accelerating M2-like phenotype of macrophages. Mechanically, exosomal SNHG17 could facilitate the release of CCL13 from M2 macrophage via the PI3K-Akt signalling pathway. Moreover, CCL13-CCR2 axis was identified to be involved in ovarian cancer tumour behaviours driven by oestrogen. There results demonstrate a novel mechanism that exosomal SNHG17 exerts an oncogenic effect on ovarian cancer via the CCL13-CCR2-M2 macrophage axis upon oestrogen treatment, of which SNHG17 may be a potential biomarker and therapeutic target for ovarian cancer responded to oestrogen.

N6-Methyladenosine-Modified KREMEN2 Promotes Tumorigenesis and Malignant Progression of High-Grade Serous Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) remains the most lethal female cancer by far. Herein, clinical HGSOC samples had higher N6-methyladenosine (m6A) modification than normal ovarian tissue, and its dysregulation had been reported to drive aberrant transcription and translation programs. However, Kringle-containing transmembrane protein 2 (KREMEN2) and its m6A modification have not been fully elucidated in HGSOC. In this study, the data from the high-throughput messenger RNA (mRNA) sequencing of clinical samples were processed using the weighted correlation network analysis and functional enrichment analysis. Results revealed that KREMEN2 was a driver gene in the tumorigenesis of HGSOC and a potential target of m6A demethylase fat-mass and obesity-associated protein (FTO). KREMEN2 and FTO levels were upregulated and downregulated, respectively, and correlation analysis showed a significant negative correlation in HGSOC samples. Importantly, upregulated KREMEN2 was remarkably associated with lymph node metastasis, distant metastasis, peritoneal metastasis, and high International Federation of Gynecology and Obstetrics stage (Ⅲ/Ⅳ), independent of the age of patients. KREMEN2 promoted the growth of HGSOC in vitro and in vivo, which was dependent on FTO. The methylated RNA immunoprecipitation qPCR and RNA immunoprecipitation assays were performed to verify the m6A level and sites of KREMEN2. FTO overexpression significantly decreased m6A modification in the 3' and 5' untranslated regions of KREMEN2 mRNA and downregulated its expression. In addition, we found that FTO-mediated m6A modification of KREMEN2 mRNA was recognized and stabilized by the m6A reader IGF2BP1 rather than by IGF2BP2 or IGF2BP3. This study highlights the m6A modification of KREMEN2 and extends the importance of RNA epigenetics in HGSOC.

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.

BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis. METHODS: To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features. RESULTS: The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs. CONCLUSIONS: The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau.

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.

Long-read sequencing unveils novel somatic variants and methylation patterns in the genetic information system of early lung cancer.

Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.

Preparation of phosphorus-doped chitosan derivative and its applications in polylactic acid: Crystallization, flame retardancy, anti-dripping and mechanical properties.

The topic of biobased flame-retardant PLA has always been of great interest. In our study, we successfully synthesized a phosphorus-containing chitosan derivative (PCS) and combined it with aluminum hypophosphate (AP) to create an effective flame-retardant PLA system. PCS acted as an enhancer, enhancing the thermal performance, crystallinity, and toughness of PLA/AP. Compared to PLA modified with 12 wt% AP achieving UL-94 V-2 level and 24.3 % of limited oxygen index, PLA containing 3 wt% PCS and 9 wt% AP achieved UL-94 V-0 level and limited oxygen index of 28 %. The system testing studies such as CCT, Raman, XPS, and TG-IR results indicated that PLA/AP/PCS exhibited a dual flame-retardant mechanism of condensed and gas phases.

Understanding the Formation and Growth of New Atmospheric Particles at the Molecular Level through Laboratory Molecular Beam Experiments.

Atmospheric new particle formation (NPF), which exerts comprehensive implications for climate, air quality and human health, has received extensive attention. From molecule to cluster is the initial and most important stage of the nucleation process of atmospheric new particles. However, due to the complexity of the nucleation process and limitations of experimental characterization techniques, there is still a great uncertainty in understanding the nucleation mechanism at the molecular level. Laboratory-based molecular beam methods can experimentally implement the generation and growth of typical atmospheric gas-phase nucleation precursors to nanoscale clusters, characterize the key physical and chemical properties of clusters such as structure and composition, and obtain a series of their physicochemical parameters, including association rate coefficients, electron binding energy, pickup cross section and pickup probability and so on. These parameters can quantitatively illustrate the physicochemical properties of the cluster, and evaluate the effect of different gas phase nucleation precursors on the formation and growth of atmospheric new particles. We review the present literatures on atmospheric cluster formation and reaction employing the experimental method of laboratory molecular beam. The experimental apparatuses were classified and summarized from three aspects of cluster generation, growth and detection processes. Focus of this review is on the properties of nucleation clusters involving different precursor molecules of water, sulfuric acid, nitric acid and NxOy, respectively. We hope this review will provide a deep insight for effects of cluster physicochemical properties on nucleation, and reveal the formation and growth mechanism of atmospheric new particle at the molecular level.

DVA: predicting the functional impact of single nucleotide missense variants.

BACKGROUND: In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants. RESULTS: We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods. CONCLUSIONS: DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

Single-cell profiling identifies IL1Bhi macrophages associated with inflammation in PD-1 inhibitor-induced inflammatory arthritis.

Inflammatory arthritis (IA) is a common rheumatic adverse event following immune checkpoint inhibitors treatment. The clinical disparities between IA and rheumatoid arthritis (RA) imply disease heterogeneity and distinct mechanisms, which remain elusive. Here, we profile CD45+ cells from the peripheral blood or synovial fluid (SF) of patients with PD-1-induced IA (PD-1-IA) or RA using single-cell RNA sequencing. We report the predominant expansion of IL1Bhi myeloid cells with enhanced NLRP3 inflammasome activity, in both the SF and peripheral blood of PD-1-IA, but not RA. IL1Bhi macrophages in the SF of PD-1-IA shared similar inflammatory signatures and might originate from peripheral IL1Bhi monocytes. Exhausted CD8+ T cells (Texs) significantly accumulated in the SF of patients with PD-1-IA. IL1Bhi myeloid cells communicated with CD8+ Texs possibly via the CCR1-CCL5/CCL3 and CXCL10-CXCR3 axes. Collectively, these results demonstrate different cellular and molecular pathways in PD-1-IA and RA and highlight IL1Bhi macrophages as a possible therapeutic target in PD-1-IA.

The p38 MAPK/snail signaling axis participates in cadmium-induced lung cancer cell migration and invasiveness.

To determine that p38 MAPK activation contributes to the migration and invasion of lung cancer cells caused by cadmium (Cd). A549 lung cancer cell migration and invasion were assessed using a transwell plate system, and the role of p38 was determined by knocking down p38 activity with two different inhibitors of p38. The activity of p38 was measured by western blot analysis using phospho-specific p38 antibodies and normalized to blots using antibodies directed to total p38 proteins. Snail transcripts were measured using qRT-PCR. The inhibition of p38 blocked Cd-induced migration and invasion, which correlated with an increased activation of p38 as a function of dose and time. Furthermore, Cd-induced activation of p38 MAPK controlled the increase of snail mRNA expression. The p38 MAPK/snail signaling axis was involved in Cd-induced lung cancer cell migration and invasion.

Risk factors for short-term prognosis of end-stage liver disease complicated by invasive pulmonary aspergillosis.

BACKGROUND AND AIM: Patients with end-stage liver disease (ESLD) are susceptible to invasive pulmonary aspergillosis (IPA). This study aimed to investigate the risk factors affecting the occurrence and short-term prognosis of ESLD complicated by IPA. METHODS: This retrospective case-control study included 110 patients with ESLD. Of them, 27 ESLD-IPA received antifungal therapy with amphotericin B (AmB); 27 AmB-free-treated ESLD-IPA patients were enrolled through 1:1 propensity score matching. Fifty-six ESLD patients with other comorbid pulmonary infections were enrolled as controls. The basic features of groups were compared, while the possible risk factors affecting the occurrence and short-term outcomes of IPA were analyzed. RESULTS: Data analysis revealed invasive procedures, glucocorticoid exposure, and broad-spectrum antibiotic use were independent risk factors for IPA. The 54 patients with ESLD-IPA exhibited an overall treatment effectiveness and 28-d mortality rate of 50.00% and 20.37%, respectively, in whom patients treated with AmB-containing showed higher treatment efficacy than patients treated with AmB-free antifungal regimens (66.7% vs. 33.3%, respectively, χ2 = 6.000, P = 0.014). Multivariate logistic regression analysis revealed that the treatment regimen was the only predictor affecting patient outcomes, with AmB-containing regimens were 4.893 times more effective than AmB-free regimens (95% CI, 1.367-17.515; P = 0.015). The only independent predictors affecting the 28-d mortality rate were neutrophil-to-lymphocyte ratio and IPA diagnosis (OR = 1.140 and 10.037, P = 0.046 and 0.025, respectively). CONCLUSIONS: Glucocorticoid exposure, invasive procedures, and broad-spectrum antibiotic exposure increased the risk of IPA in ESLD patients. AmB alone or combined with other antifungals may serve as an economical, safe, and effective treatment option for ESLD-IPA.