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Abstract Background Allergic diseases are systemic chronic inflammatory diseases associated with multiorgan damage and complex pathogenesis. Several studies have revealed the association of gene expression abnormalities with the development of allergic diseases, but the biomedical field still lacks a public platform for comprehensive analysis and visualization of transcriptomic data of allergic diseases. Objective The aim of the study is to provide a comprehensive web tool for multiple analysis in allergic diseases. Methods We retrieved and downloaded human and mouse gene expression profile data associated with allergic diseases from the Gene Expression Omnibus (GEO) database and standardized the data uniformly. We used gene sets obtained from the MSigDB database for pathway enrichment analysis and multiple immune infiltration algorithms for the estimation of immune cell proportion. The basic construction of the web pages was based on the Shiny framework. Additionally, more convenient features were added to the server to improve the efficiency of the web pages, such as jQuery plugins and a comment box to collect user feedback. Results We developed CTPAD, an interactive R Shiny application that integrates public databases and multiple algorithms to explore allergic disease-related datasets and implement rich transcriptomic visualization capabilities, including gene expression analysis, pathway enrichment analysis, immune infiltration analysis, correlation analysis, and single-cell RNA sequencing analysis. All functional modules offer customization options and can be downloaded in PDF format with high-resolution images. Conclusions CTPAD largely facilitates the work of researchers without bioinformatics background to enable them to better explore the transcriptomic features associated with allergic diseases. CTPAD is available at https://smuonco.shinyapps.io/CTPAD/ .

Abstract Sea cucumbers are predominant deposit feeders in benthic ecosystems, providing protective benefits to coral reefs by reducing disease prevalence. However, how they receive sufficient nutrition from seabed sediments remains poorly understood. Here, we investigate Holothuria leucospilota, an ecologically significant tropical sea cucumber, to elucidate digestive mechanisms underlying marine deposit-feeding. Genomic analysis reveals intriguing evolutionary adaptation characterized by an expansion of digestive carbohydrase genes and a contraction of digestive protease genes, suggesting specialization in digesting microalgae. Developmentally, two pivotal dietary shifts, namely, from endogenous nutrition to planktonic feeding, and from planktonic feeding to deposit feeding, induce changes in digestive tract enzyme profiles, with adults mainly expressing carbohydrases and lipases. A nuanced symbiotic relationship exists between gut microbiota and the host, namely, specific resident bacteria supply crucial enzymes for food digestion, while other bacteria are digested and provide assimilable nutrients. Our study further identifies Holothuroidea lineage-specific lysozymes that are restrictedly expressed in the intestines to support bacterial digestion. Overall, this work advances our knowledge of the evolutionary innovations in the sea cucumber digestive system which enable them to efficiently utilize nutrients from seabed sediments and promote food recycling within marine ecosystems.

Abstract The cascade of metastasis in tumor cells, exhibiting organ-specific tendencies, may occur at numerous phases of the disease and progress under intense evolutionary pressures. Organ-specific metastasis relies on the formation of pre-metastatic niche (PMN), with diverse cell types and complex cell interactions contributing to this concept, adding a new dimension to the traditional metastasis cascade. Prior to metastatic dissemination, as orchestrators of PMN formation, primary tumor-derived extracellular vesicles prepare a fertile microenvironment for the settlement and colonization of circulating tumor cells at distant secondary sites, significantly impacting cancer progression and outcomes. Obviously, solely intervening in cancer metastatic sites passively after macrometastasis is often insufficient. Early prediction of metastasis and holistic, macro-level control represent the future directions in cancer therapy. This review emphasizes the dynamic and intricate systematic alterations that occur as cancer progresses, illustrates the immunological landscape of organ-specific PMN creation, and deepens understanding of treatment modalities pertinent to metastasis, thereby identifying some prognostic and predictive biomarkers favorable to early predict the occurrence of metastasis and design appropriate treatment combinations.

Abstract The continuous generation of multi-omics and phenotype data is propelling advancements in precision oncology. UCSCXenaShiny was developed as an interactive tool for exploring thousands of cancer datasets available on UCSC Xena. However, its capacity for comprehensive and personalized pan-cancer data analysis is being challenged by the growing demands. Here, we introduce UCSCXenaShiny v2, a milestone update through a variety of improvements. Firstly, by integrating multidimensional data and implementing adaptable sample settings, we create a suite of robust TPC (TCGA, PCAWG, CCLE) analysis pipelines. These pipelines empower users to conduct in-depth analyses of correlation, comparison, and survival in three modes: Individual, Pan-cancer and Batch screen. Additionally, the tool includes download interfaces that enable users to access diverse data and outcomes, several features also facilitate the joint analysis of drug sensitivity and multi-omics of cancer cell lines. UCSCXenaShiny v2 is an open-source R package and a web application, freely accessible at https://github.com/openbiox/UCSCXenaShiny .

The high extractives content in rice straw severely hinders surface adhesion, resulting in poor strength and dimensional stability of rice straw particleboard bonded with castor oil-based polyurethane (CPUR) resin. In this study, rice straw was pretreated with liquid hot water (LHW) at 150 °C for 20 min to reduce its extractives content for particleboard production with CPUR resin. The effects of LHW pretreatment on the chemical composition of the rice straws was evaluated. In addition, effects of CPUR resin dosage and density on mechanical properties and dimensional stability of the rice straw particleboards were investigated. The results indicated that LHW pretreatment significantly reduced the extractives and hemicellulose contents of the rice straw. The LHW pretreatment significantly improved the mechanical properties and dimensional stability of the rice straw particleboards. The overall performances of the rice straw particleboards were enhanced as the CPUR dosage increased. Increase of density led to upgraded mechanical properties but lowered dimensional stability of the rice straw particleboards.

Abstract Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathological mechanism remained unclear. Herein, we systematically investigated the large genome-wide association studies (6 cohorts with 1,357,645 samples), single-cell RNA (26 samples with 157,488 cells), and bulk-RNAseq (1595 samples) datasets to reveal the genetic causality and biological link between four air pollutants and nine psychiatric disorders. As a result, we identified ten positive genetic correlations between air pollution and psychiatric disorders. Besides, PM2.5 and NO2 presented significant causal effects on schizophrenia risk which was robust with adjustment of potential confounders. Besides, transcriptome-wide association studies identified the shared genes between PM2.5/NO2 and schizophrenia. We then discovered a schizophrenia-derived inhibitory neuron subtype with highly expressed shared genes and abnormal synaptic and metabolic pathways by scRNA analyses and confirmed their abnormal level and correlations with the shared genes in schizophrenia patients in a large RNA-seq cohort. Comprehensively, we discovered robust genetic causality between PM2.5, NO2, and schizophrenia and identified an abnormal inhibitory neuron subtype that links schizophrenia pathology and PM2.5/NO2 exposure. These discoveries highlight the schizophrenia risk under air pollutants exposure and provide novel mechanical insights into schizophrenia pathology, contributing to pollutant-related schizophrenia risk control and therapeutic strategies development. Graphical Abstract

Abstract Traditional video steganography methods primarily rely on modifying concealed spaces for embedding, thereby exhibiting a certain degree of security and embedding capacity. Nevertheless, these methods do not fully capitalize on the rich semantic information inherent in videos, limiting their overall effectiveness. In this paper, an AI‐generated video steganography scheme based on semantic segmentation is proposed. The mapping relationship between secret and semantic information is established by using a semantic segmentation model. The secret information can be converted into semantic labels by semantic histograms or pixels means, and semantic labels containing secret information are obtained and input into the video‐to‐video model to drive the generation of stego videos. After receiving the stego video, the receiver extracts the secret information using a pre‐defined specific embedding mode, including the methods of sub‐block partitioning and embedding capacity per frame. The experimental results show that the stego video has good visual quality, security, and robustness against various noise attacks.

Background: Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown. Methods: In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified. Results: We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy. Conclusion: This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.

Abstract Land use and cover change (LUCC) significantly impacts global carbon cycles and land surface properties, accounting for 25% of the historical atmospheric CO2 increase. We explore a previously overlooked role of LUCC in driving the land carbon cycle by using a three-level meta-analysis and Land Use Harmonization data to drive an ecosystem model. Our findings reveal that a loss of 39.2% of soil organic carbon (SOC) change in China due to LUCC, mitigated by afforestation, doubles gross primary productivity at 0.02 Pg C yr−1, countering central China’s urbanization decline. Indirect climate effects, especially soil bulk density, significantly impact SOC compared to direct climate effects. LUCC has significantly increased the Chinese terrestrial carbon sink, with net ecosystem productivity reaching 0.02 ± 0.12 Pg C yr−1. Our study underscores the importance of reforestation and afforestation in addressing climate change and enhancing carbon sinks in future carbon management.

Abstract A combined model was developed using contrast-enhanced CT-based radiomics features and clinical characteristics to predict liver fibrosis stages in patients with chronic liver disease (CLD). We retrospectively analyzed multiphase CT scans and biopsy-confirmed liver fibrosis. 160 CLD patients were randomly divided into 7:3 training/validation ratio. Clinical laboratory indicators associated with liver fibrosis were identified using Spearman's correlation and multivariate logistic regression correlation. Radiomic features were extracted after segmenting the entire liver from multiphase CT images. Feature dimensionality reduction was performed using RF-RFE, LASSO, and mRMR methods. Six radiomics-based models were developed in the training cohort of 112 patients. Internal validation was conducted on 48 randomly assigned patients. Receptor Operating Characteristic (ROC) curves and confusion matrices were constructed to evaluate model performance. The radiomics model exhibited robust performance, with AUC values of 0.810 to 1.000 for significant fibrosis, advanced fibrosis, and cirrhosis. The integrated clinical-radiomics model had superior diagnostic efficacy in the validation cohort, with AUC values of 0.836 to 0.997. Moreover, these models outperformed established biomarkers such as the aspartate aminotransferase to platelet ratio index (APRI) and the fibrosis 4 score (FIB-4), as well as the gamma glutamyl transpeptidase to platelet ratio (GPR), in predicting the fibrotic stages. The clinical-radiomics model holds considerable promise as a non-invasive diagnostic tool for the assessment and staging of liver fibrosis in the patients with CLD, potentially leading to better patient management and outcomes.

Abstract Recent observational studies suggest that gut microorganisms are involved in the onset and development of coronavirus disease 2019 (COVID-19), but the potential causal relationship behind them remains unclear. Exposure data were derived from the MiBioGen consortium, encompassing 211 gut microbiota (n = 18,340). The outcome data were sourced from the COVID-19 host genetics initiative (round 7), including COVID-19 severity (n = 1,086,211), hospitalization (n = 2,095,324), and susceptibility (n = 2,597,856). First, a two-sample Mendelian randomization (TSMR) was performed to investigate the causal effect between gut microbiota and COVID-19 outcomes. Second, a two-step MR was used to explore the potential mediators and underlying mechanisms. Third, several sensitivity analyses were performed to verify the robustness of the results. Five gut microbes were found to have a potential causality with COVID-19 severity, namely Betaproteobacteria (beta = 0.096, p = 0.034), Christensenellaceae (beta = -0.092, p = 0.023), Adlercreutzia (beta = 0.072, p = 0.048), Coprococcus 1 (beta = 0.089, p = 0.032), Eisenbergiella (beta = 0.064, p = 0.024). Seven gut microbes were found to have a potential causality with COVID-19 hospitalization, namely Victivallaceae (beta = 0.037, p = 0.028), Actinomyces (beta = 0.047, p = 0.046), Coprococcus 2 (beta = -0.061, p = 0.031), Dorea (beta = 0.067, p = 0.016), Peptococcus (beta = -0.035, p = 0.049), Rikenellaceae RC9 gut group (beta = 0.034, p = 0.018), and Proteobacteria (beta = -0.069, p = 0.035). Two gut microbes were found to have a potential causality with COVID-19 susceptibility, namely Holdemanella (beta = -0.024, p = 0.023) and Lachnospiraceae FCS020 group (beta = 0.026, p = 0.027). Multi-omics mediation analyses indicate that numerous plasma proteins, metabolites, and immune factors are critical mediators linking gut microbiota with COVID-19 outcomes. Sensitivity analysis suggested no significant heterogeneity or pleiotropy. These findings revealed the causal correlation and potential mechanism between gut microbiota and COVID-19 outcomes, which may improve our understanding of the gut-lung axis in the etiology and pathology of COVID-19 in the future.

Osteosarcoma is the most common malignant bone tumor without efficient management for improving 5-year event-free survival. Immunotherapy is also limited due to its highly immunosuppressive tumor microenvironment (TME). Pore-forming gasdermins (GSDMs)-mediated pyroptosis has gained increasing concern in reshaping TME, however, the expressions and relationships of GSDMs with osteosarcoma remain unclear. Herein, gasdermin E (GSDME) expression is found to be positively correlated with the prognosis and immune infiltration of osteosarcoma patients, and low GSDME expression was observed. A vector termed as LPAD contains abundant hydroxyl groups for hydrating layer formation was then prepared to deliver the GSDME gene to upregulate protein expression in osteosarcoma for efficient TME reshaping via enhanced pyroptosis induction. Atomistic molecular dynamics simulations analysis proved that the hydroxyl groups increased LPAD hydration abilities by enhancing coulombic interaction. The upregulated GSDME expression together with cleaved caspase-3 provided impressive pyroptosis induction. The pyroptosis further initiated proinflammatory cytokines release, increased immune cell infiltration, activated adaptive immune responses and create a favorable immunogenic hot TME. The study not only confirms the role of GSDME in the immune infiltration and prognosis of osteosarcoma, but also provides a promising strategy for the inhibition of osteosarcoma by pore-forming GSDME gene delivery induced enhanced pyroptosis to reshape the TME of osteosarcoma.

IntroductionThis study aimed to elucidate the differential immunological mechanisms and characteristics of hypertension induced by VEGF inhibitors (VEGFi) and VEGF receptor inhibitors (VEGFRi), with the goal of optimizing monitoring strategies and treatment protocols.MethodsWe investigated the risk of immune-related adverse events associated with VEGFi/VEGFRi-induced hypertension by analyzing the FDA Adverse Event Reporting System (FAERS) database. Findings were corroborated with blood pressure characteristics observed in clinical patients and preclinical models exposed to various VEGF/VEGFRi. Clinical and preclinical studies were conducted to compare immunological responses and hypertension profiles between inhibitor classes. An integrative analysis across cancer types and species was performed, focusing on key signaling pathways.ResultsAnalysis of FAERS data, in conjunction with clinical observations, revealed that both VEGFi and VEGFRi significantly elevated the risk of immune-mediated, blood pressure-related adverse events (ROR=7.75, 95% CI: 7.76-7.95). Subsequent clinical and preclinical studies demonstrated differential immunological responses and hypertension profiles between inhibitor classes. VEGFRi exhibited a more rapid onset, greater blood pressure elevation, and higher incidence of immune-mediated adverse events compared to VEGFi (Systolic BP: ROR=0 for VEGFi vs. ROR=12.25, 95% CI: 6.54-22.96 for VEGFRi; Diastolic BP: ROR=5.09, 95% CI: 0.60-43.61 for VEGFi vs. ROR=12.90, 95% CI: 3.73-44.55 for VEGFRi). Integrative analysis across cancer types and species, focusing on key signaling pathways, revealed that VEGF/VEGFRi-induced blood pressure elevation was associated with immunomodulation of the mitogen activated protein kinase (MAPK) pathway (R=-0.379, P=0.0435), alterations in triglyceride metabolism (R=-0.664, P=0.0001), modulation of myo-inositol 1,4,5-trisphosphate-sensitive calcium release channel activity (R=0.389, P=0.0378), and dysregulation of nitric oxide eNOS activation and metabolism (R=-0.439, P=0.0179).DiscussionThe temporal dynamics of these effects demonstrated greater significance than dose-dependent responses. Both VEGFi and VEGFRi significantly augmented the risk of immune-mediated, blood pressure-related adverse events, with VEGFRi inducing a more rapid and pronounced onset of blood pressure elevation and a higher incidence of immune-related, blood pressure-associated adverse events compared to VEGFi.

Arsenic (As) is a widespread metalloid and human carcinogen found in the natural environment, and multiple toxic effects have been shown to be associated with As exposure. As can be accumulated in the spleen, the largest peripheral lymphatic organ, and long-term exposure to As can lead to splenic injury. In this study, a Sprague-Dawley (SD) rat model of As-poisoned was established, aiming to explore the molecular mechanism of As-induced immune injury through the combined analysis of proteomics and metabolomics of rats’ spleen. After feeding the rats with As diet (50 mg/kg) for 90 days, the spleen tissue of the rats in the As-poisoned group was damaged, the level of As was significantly higher than that of the control group (P < 0.001), and the level of inflammatory cytokine interleukin-6 (IL-6) was decreased (P < 0.01). Proteomics and metabolomics results showed that a total of 134 differentially expressed proteins (DEPs) (P < 0.05 and fold change > 1.2) and 182 differentially expressed metabolites (DEMs) (VIP >1 and P < 0.05) were identified in the spleens of the As poisoned group compared to the control group (As/Ctrl). The proteomic results highlight the role of hypoxia-inducible factors (HIF), natural killer cell mediated cytotoxicity, and ribosomes. The major pathways of metabolic disruption included arachidonic acid (AA) metabolism, glycerophospholipid metabolism and folate single-carbon pool. The integrated analysis of these two omics suggested that Hmox1, Stat3, arachidonic acid, phosphatidylcholine and leukotriene B4 may play key roles in the mechanism of immune injury to the spleen by As exposure. The results indicate that As exposure can cause spleen damage in rats. Through proteomic and metabolomic analysis, the key proteins and metabolites and their associated mechanisms were obtained, which provided a basis for further understanding of the molecular mechanism of spleen immune damage caused by As exposure.

Abstract Homologous recombination deficiency (HRD) score is a reliable indicator of genomic instability. The significance of HRD in nasopharyngeal carcinoma (NPC), particularly its influence on prognosis and the immune microenvironment, has yet to be adequately explored. Understanding HRD status comprehensively can offer valuable insights for guiding precision treatment. We utilised three cohorts to investigate HRD status in NPC: the Zhujiang cohort from local collection and the Hong Kong (SRA288429) and Singapore (SRP035573) cohorts from public datasets. The GATK (genome analysis toolkit) best practice process was employed to investigate germline and somatic BRCA1/2 mutations and various bioinformatics tools and algorithms to examine the association between HRD status and clinical molecular characteristics. We found that individuals with a negative HRD status (no‐HRD) exhibited a higher risk of recurrence [hazard ratio (HR), 1.43; 95% confidence interval (CI), 2.03–333.76; p = 0.012] in the Zhujiang cohort, whereas, in the Singapore cohort, they experienced a higher risk of mortality (HR, 26.04; 95% CI, 1.43–34.21; p = 0.016) compared with those in the HRD group. In vitro experiments demonstrated that NPC cells with BRCA1 knockdown exhibit heightened sensitivity to chemoradiotherapy. Furthermore, the HRD group showed significantly higher tumour mutational burden and tumour neoantigen burden levels than the no‐HRD group. Immune infiltration analysis indicated that HRD tissues tend to have a non‐inflamed tumour microenvironment. In conclusion, patients with HRD exhibit a comparatively favourable prognosis in NPC, possibly associated with a non‐inflammatory immune microenvironment. These findings have positive implications for treatment stratification, enabling the selection of more precise and effective therapeutic approaches and aiding in the prediction of treatment response and prognosis to a certain extent.

Abstract Posttransplantation complications pose a major challenge to the long‐term survival and quality of life of organ transplant recipients. These complications encompass immune‐mediated complications, infectious complications, metabolic complications, and malignancies, with each type influenced by various risk factors and pathological mechanisms. The molecular mechanisms underlying posttransplantation complications involve a complex interplay of immunological, metabolic, and oncogenic processes, including innate and adaptive immune activation, immunosuppressant side effects, and viral reactivation. Here, we provide a comprehensive overview of the clinical features, risk factors, and molecular mechanisms of major posttransplantation complications. We systematically summarize the current understanding of the immunological basis of allograft rejection and graft‐versus‐host disease, the metabolic dysregulation associated with immunosuppressive agents, and the role of oncogenic viruses in posttransplantation malignancies. Furthermore, we discuss potential prevention and intervention strategies based on these mechanistic insights, highlighting the importance of optimizing immunosuppressive regimens, enhancing infection prophylaxis, and implementing targeted therapies. We also emphasize the need for future research to develop individualized complication control strategies under the guidance of precision medicine, ultimately improving the prognosis and quality of life of transplant recipients.

ObjectiveTo investigate the relationship between circulating receptor activator of nuclear factor-kappa B ligand (RANKL) levels and marrow adipose tissue in postmenopausal females.MethodsA total of 164 postmenopausal females were included in the study. Serum levels of osteoprotegerin (OPG) and RANKL were measured using ELISA kits. Body composition and bone mineral density (BMD) were assessed using dual-energy X-ray absorptiometry. Complex-based chemical shift imaging-based MRI was employed to evaluate the vertebral marrow proton density fat fraction (PDFF). A multivariate linear regression model was utilized to analyze the predictive effects of PDFF and BMD on circulating levels of OPG and RANKL.ResultsSimple regression analysis showed significant associations among the marrow PDFF, BMD at either site, serum RANKL, and the RANKL/OPG ratio. In multivariate linear regression models, marrow PDFF was found to have a positive correlation (β = 3.15, 95% CI 2.60 to 3.70) and BMD had negative correlations (β = −0.200, 95% CI −0.348 to −0.051 for vertebral BMD; β = −0.383, 95% CI −0.589 to −0.177 for total hip BMD; and β =−0.393, 95% CI −0.598 to −0.188 for femoral neck BMD, all p < 0.01) with circulating soluble RANKL levels after adjusting for age, body mass index, physical activity, total fat mass, android/gynoid ratio, and lean mass. Similar results were observed for the RANKL/OPG ratio. Additionally, multivariate linear regression analyses revealed that marrow PDFF was a significant independent contributor of circulating soluble RANKL (β = 1.34, 95% CI 1.10 to 1.58, p < 0.001) after further controlling for BMD. However, marrow PDFF or BMD had no associations with circulating levels of OPG after adjusting for all potential confounders mentioned above.ConclusionsVertebral marrow fat fraction is independently associated with circulating soluble RANKL levels in postmenopausal females.

The heterogeneity of immune cells and metabolic pathways in hepatocellular carcinoma (HCC) patients has not been fully elucidated, leading to diverse clinical outcomes. Accurately distinguishing different HCC subtypes and recommending appropriate treatments is are highly important. In this study, we conducted a comprehensive analysis of 28 immune cells and 85 metabolic pathways in the TCGA-LIHC and GSE14520 datasets. Metabolism-related first principal component (MRPC1) and cytotoxic T lymphocyte (CTL) infiltration were used to assess the metabolic and immune infiltration levels of HCC patients, respectively. These two quantifiable indicators were then used to construct an immune‒metabolic classifier, which categorized HCC patients into three distinct groups. The potential biological mechanisms were explored through multiomics analysis, revealing that group S1 exhibited high metabolic activity and a high level of immune infiltration, that group S2 presented a low level of immune infiltration, and that group S3 presented low metabolic activity. This new immune‒metabolic classifier was well validated in a pancancer cohort of 9296 patients. The efficacy of multiple treatment approaches was assessed in relation to different immune‒metabolic groups, indicating that group S1 patients may benefit from immunotherapy, that group S2 patients are suitable for transcatheter arterial chemoembolization (TACE), and that group S3 patients are appropriate candidates for tyrosine kinase inhibitors. In conclusion, this immune‒metabolic classifier is anticipated to address the differences in treatment efficacy among HCC patients due to the heterogeneity of the tumor microenvironment, and to help refine the individualized treatment choices for clinical patients.

In the steel industry, improving the hot charging and heat delivery ratio in the continuous casting of slabs and thick slabs can effectively reduce carbon emissions, lower energy consumption, and improve production efficiency. However, this puts very strict requirements on the surface quality of continuous-casting slab. Flame scarfing (or cleaning) technology is currently an important process in the metallurgy field, which can effectively remove impurities and crack defects on the surface and corners of continuous casting slab to ensure the quality and performance of the final product. Under the action of cleaning flame and iron oxide reaction heat, a cleaning molten pool is formed in the corner of the casting slab, and the shape and depth of the molten pool have an important influence on the cleaning effect. A three-dimensional mathematical model is developed to study the effect of slab cleaning speed and oxygen flow rate on the shape of the molten pool in the corners for the corner flame cleaning process. Simultaneously, a two-phase flow volume of fluid (VOF) model for gas-slag interactions is employed to examine the motion behavior of the molten slag. The results showed that when the operating conditions changed, the cleaning speed increased by 1 m/min, the length of the wide and narrow sides decreased by approximately 9.2 mm, and the depth of the corner pool decreased by approximately 5.3 mm. Moreover, a slag-blocking device was designed to mitigate the issue of slag splashing effectively based on the numerical simulation results compared with and without the slag-blocking device. These study works provide beneficial theoretical support and practical guidance for refining and optimizing flame-cleaning techniques.

Recycling is presently the most environmentally friendly approach that deals with wood waste. Each year a huge amount of particleboard completes its service life and needs to be disposed of or recycled. Scrap particleboard has the potential to be reused as raw material for particleboard production. A crucial step for producing particleboards using scrap particleboards is to break down urea-formaldehyde (UF) resins in the scrap particleboards to obtain separated particles. In this study, liquid hot water (LHW) pretreatment was employed to decompose the UF resins to detach and recover wood particles from the scrap particleboards. The recovered wood particles and fresh industrial wood particles in different proportions were used as the raw material for the particleboard. The physical and mechanical properties of the recycled particleboards were evaluated. The modulus of rupture (MOR), modulus of elasticity (MOE), and internal bond (IB) of the recycled particleboards were lower than those of the particleboards made with fresh wood particles. However, the 2 h thickness swelling (TS) of the recycled particleboards was better than that of the fresh particleboards, indicating that the recycled particleboards were more dimensionally stable. The mechanical properties of the particleboards containing up to 40% recycled wood particles met the minimum Chinese National Standard requirements for general-purpose particleboards. Conclusively, scrap wood particleboards could be utilized as raw material in particleboard production.

Abstract Background Epidemiological evidence links a close correlation between long-term exposure to air pollutants and autoimmune diseases, while the causality remained unknown. Methods Two-sample Mendelian randomization (TSMR) was used to investigate the role of PM10, PM2.5, NO2, and NOX (N = 423,796–456,380) in 15 autoimmune diseases (N = 14,890–314,995) using data from large European GWASs including UKB, FINNGEN, IMSGC, and IPSCSG. Multivariable Mendelian randomization (MVMR) was conducted to investigate the direct effect of each air pollutant and the mediating role of common factors, including body mass index (BMI), alcohol consumption, smoking status, and household income. Transcriptome-wide association studies (TWAS), two-step MR, and colocalization analyses were performed to explore underlying mechanisms between air pollution and autoimmune diseases. Results In TSMR, after correction of multiple testing, hypothyroidism was causally associated with higher exposure to NO2 [odds ratio (OR): 1.37, p = 9.08 × 10–4] and NOX [OR: 1.34, p = 2.86 × 10–3], ulcerative colitis (UC) was causally associated with higher exposure to NOX [OR: 2.24, p = 1.23 × 10–2] and PM2.5 [OR: 2.60, p = 5.96 × 10–3], rheumatoid arthritis was causally associated with higher exposure to NOX [OR: 1.72, p = 1.50 × 10–2], systemic lupus erythematosus was causally associated with higher exposure to NOX [OR: 4.92, p = 6.89 × 10–3], celiac disease was causally associated with lower exposure to NOX [OR: 0.14, p = 6.74 × 10–4] and PM2.5 [OR: 0.17, p = 3.18 × 10–3]. The risky effects of PM2.5 on UC remained significant in MVMR analyses after adjusting for other air pollutants. MVMR revealed several common mediators between air pollutants and autoimmune diseases. Transcriptional analysis identified specific gene transcripts and pathways interconnecting air pollutants and autoimmune diseases. Two-step MR revealed that POR, HSPA1B, and BRD2 might mediate from air pollutants to autoimmune diseases. POR pQTL (rs59882870, PPH4=1.00) strongly colocalized with autoimmune diseases. Conclusion This research underscores the necessity of rigorous air pollutant surveillance within public health studies to curb the prevalence of autoimmune diseases. Graphical abstract (Built by the Biorender)

Abstract Glutamate receptor (GluR)-mediated excitotoxicity is an important mechanism causing delayed neuronal injury after traumatic brain injury (TBI). Preso, as a core scaffolding protein of postsynaptic density (PSD), is considered an important regulator during excitotoxicity and TBI and combines with glutamate receptors to form functional units for excitatory glutamatergic neurotransmission, and elucidating the mechanisms of these functional units will provide new targets for the treatment of TBI. As a multidomain scaffolding protein, Preso directly interacts with metabotropic GluR (mGluR) and another scaffold protein, Homer. Because the mGluR-Homer complex plays a crucial role in TBI, modulation of this complex by Preso may be an important mechanism affecting the excitotoxic damage to neurons after TBI. Here, we demonstrate that Preso facilitates the interaction between metabotropic mGluR1 and Homer1 to activate mGluR1 signaling and cause excitotoxic neuronal injury and endoplasmic reticulum (ER) stress after TBI. The regulatory effect of Preso on the mGluR1-Homer1 complex is dependent on the direct association between Preso and this complex and also involves the phosphorylation of the interactive binding sites of mGluR1 and Homer1 by Preso. Further studies confirmed that Preso, as an adaptor of cyclin-dependent kinase 5 (CDK5), promotes the phosphorylation of the Homer1-binding site on mGluR1 by CDK5 and thereby enhances the interaction between mGluR1 and Homer1. Preso can also promote the formation of the mGluR1-Homer1 complex by inhibiting the phosphorylation of the Homer1 hinge region by Ca2+/calmodulin-dependent protein kinase IIα (CaMKIIα). Based on these molecular mechanisms, we designed several blocking peptides targeting the interaction between Preso and the mGluR1-Homer1 complex and found that directly disrupting the association between mGluR1 and scaffolding proteins significantly promotes the recovery of motor function after TBI.

Abstract Artificial Intelligence (AI) technologies have exposed more and more ethical issues while providing services to people. It is challenging for people to realize the occurrence of AI ethical issues in most cases. The lower the public awareness, the more difficult it is to address AI ethical issues. Many previous studies have explored public reactions and opinions on AI ethical issues through questionnaires and social media platforms like Twitter. However, these approaches primarily focus on categorizing popular topics and sentiments, overlooking the public’s potential lack of knowledge underlying these issues. Few studies revealed the holistic knowledge structure of AI ethical topics and the relations among the subtopics. As the world’s largest online encyclopedia, Wikipedia encourages people to jointly contribute and share their knowledge by adding new topics and following a well-accepted hierarchical structure. Through public viewing and editing, Wikipedia serves as a proxy for knowledge transmission. This study aims to analyze how the public comprehend the body of knowledge of AI ethics. We adopted the community detection approach to identify the hierarchical community of the AI ethical topics, and further extracted the AI ethics-related entities, which are proper nouns, organizations, and persons. The findings reveal that the primary topics at the top-level community, most pertinent to AI ethics, predominantly revolve around knowledge-based and ethical issues. Examples include transitions from Information Theory to Internet Copyright Infringement. In summary, this study contributes to three points, (1) to present the holistic knowledge structure of AI ethics, (2) to evaluate and improve the existing body of knowledge of AI ethics, (3) to enhance public perception of AI ethics to mitigate the risks associated with AI technologies.

Abstract Cytotoxic T lymphocytes (CTLs) play critical antitumor roles, encompassing diverse subsets including CD4+, NK, and γδ T cells beyond conventional CD8+ CTLs. However, definitive CTLs biomarkers remain elusive, as cytotoxicity-molecule expression does not necessarily confer cytotoxic capacity. CTLs differentiation involves transcriptional regulation by factors such as T-bet and Blimp-1, although epigenetic regulation of CTLs is less clear. CTLs promote tumor killing through cytotoxic granules and death receptor pathways, but may also stimulate tumorigenesis in some contexts. Given that CTLs cytotoxicity varies across tumors, enhancing this function is critical. This review summarizes current knowledge on CTLs subsets, biomarkers, differentiation mechanisms, cancer-related functions, and strategies for improving cytotoxicity. Key outstanding questions include refining the CTLs definition, characterizing subtype diversity, elucidating differentiation and senescence pathways, delineating CTL-microbe relationships, and enabling multi-omics profiling. A more comprehensive understanding of CTLs biology will facilitate optimization of their immunotherapy applications. Overall, this review synthesizes the heterogeneity, regulation, functional roles, and enhancement strategies of CTLs in antitumor immunity, highlighting gaps in our knowledge of subtype diversity, definitive biomarkers, epigenetic control, microbial interactions, and multi-omics characterization. Addressing these questions will refine our understanding of CTLs immunology to better leverage cytotoxic functions against cancer.

Abstract Regulatory T cells (Tregs) expressing the transcription factor FoxP3 are essential for maintaining immunological balance and are a significant component of the immunosuppressive tumor microenvironment (TME). Single-cell RNA sequencing (ScRNA-seq) technology has shown that Tregs exhibit significant plasticity and functional diversity in various tumors within the TME. This results in Tregs playing a dual role in the TME, which is not always centered around supporting tumor progression as typically believed. Abundant data confirms the anti-tumor activities of Tregs and their correlation with enhanced patient prognosis in specific types of malignancies. In this review, we summarize the potential anti-tumor actions of Tregs, including suppressing tumor-promoting inflammatory responses and boosting anti-tumor immunity. In addition, this study outlines the spatial and temporal variations in Tregs function to emphasize that their predictive significance in malignancies may change. It is essential to comprehend the functional diversity and potential anti-tumor effects of Tregs to improve tumor therapy strategies.

Abstract Background Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis. Methods Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC. Results A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1β, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group. Conclusions OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/ (Alternate URL: http://robinl-lab.com/OralExplorer ).

Abstract Host survival depends on the elimination of virus and mitigation of tissue damage. Herein, we report the modulation of D-mannose flux rewires the virus-triggered immunometabolic response cascade and reduces tissue damage. Safe and inexpensive D-mannose can compete with glucose for the same transporter and hexokinase. Such competitions suppress glycolysis, reduce mitochondrial reactive-oxygen-species and succinate-mediated hypoxia-inducible factor-1α, and thus reduce virus-induced proinflammatory cytokine production. The combinatorial treatment by D-mannose and antiviral monotherapy exhibits in vivo synergy despite delayed antiviral treatment in mouse model of virus infections. Phosphomannose isomerase (PMI) knockout cells are viable, whereas addition of D-mannose to the PMI knockout cells blocks cell proliferation, indicating that PMI activity determines the beneficial effect of D-mannose. PMI inhibition suppress a panel of virus replication via affecting host and viral surface protein glycosylation. However, D-mannose does not suppress PMI activity or virus fitness. Taken together, PMI-centered therapeutic strategy clears virus infection while D-mannose treatment reprograms glycolysis for control of collateral damage.

ABSTRACTRecent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

ABSTRACTMissing person crimes can seriously affect the well-being of Chinese families, and missing person destination prediction can help to solve this problem. Using nongovernmental organization (NGO) data to predict the locations of missing persons by random forest (RF) model has made progress. However, studies using these data have ignored the mass of oral information. Recent studies have demonstrated the effectiveness of oral information in detecting missing persons, but the impact on destination prediction remains unexplored. Therefore, this study proposes a missing person prediction (MP-Net) framework to incorporate oral information into missing person destination prediction and quantitatively describe the effect of different word properties on the prediction. The results show that compared to the baseline RF model, the proposed framework achieves a higher recall rate (87.18%) in the location prediction of missing persons. According to a quantitative word analysis, verbs and nouns in oral information significantly contributed to location prediction. After adjectives that might cause adverse effects were removed, the stability of the model was improved considerably. Overall, the findings of the proposed model and quantitative word analysis can help police or NGOs collect descriptive information in a targeted manner and make more accurate predictions about the whereabouts of missing persons.

Microsatellite DNA (SSR) is extensively used as molecular markers in diverse fields, including molecular ecology, conservation biology, and molecular breeding. Nevertheless, the efficient and easy acquisition of high-quality SSR molecular markers for economically cultured marine animals remains a challenge. Holothuria leucospilota is an economically and ecologically important tropical sea cucumber. Recently, artificial breeding and cultivation of this species have been reported. However, there is still a lack of highly polymorphic SSR molecular markers for geographical population differentiation and genetic breeding. This study aims to develop and utilize SSR markers from genomic data to assess the genetic variation and population structure of H. leucospilota in the South China Sea. A comprehensive pipeline was created, integrating microsatellite discovery, primer design, and assessment into a single platform. Through this, 15 highly polymorphic microsatellite loci were identified and validated. The results revealed high genetic diversity and low genetic differentiation among H. leucospilota populations. Larval dispersal and ocean currents in the South China Sea were identified as the primary factors influencing genetic connectivity between populations. In addition, the artificial culture and releasing of H. leucospilota have influenced the genetic structure of their wild populations in the South China Sea. This study provides a pipeline for identifying SSR markers within genome data, and applies it to assess the genetic diversity and structure of H. leucospilota. The outcomes of this research are expected to be beneficial and applicable to the future aquaculture of tropical sea cucumbers.

Abstract Renal cancer is a highlyheterogeneous malignancy characterized by rising global incidence and mortalityrates. The complex interplay and dysregulation of multiple signaling pathways,including von Hippel–Lindau (VHL)/hypoxia‐inducible factor (HIF), phosphoinositide 3‐kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR), Hippo–yes‐associated protein (YAP), Wnt/ß‐catenin, cyclic adenosine monophosphate (cAMP), and hepatocyte growth factor (HGF)/c‐Met, contribute to theinitiation and progression of renal cancer. Although surgical resection is thestandard treatment for localized renal cancer, recurrence and metastasiscontinue to pose significant challenges. Advanced renal cancer is associatedwith a poor prognosis, and current therapies, such as targeted agents andimmunotherapies, have limitations. This review presents a comprehensiveoverview of the molecular mechanisms underlying aberrant signaling pathways inrenal cancer, emphasizing their intricate crosstalk and synergisticinteractions. We discuss recent advancements in targeted therapies, includingtyrosine kinase inhibitors, and immunotherapies, such as checkpoint inhibitors.Moreover, we underscore the importance of multiomics approaches and networkanalysis in elucidating the complex regulatory networks governing renal cancerpathogenesis. By integrating cutting‐edge research and clinical insights, this review contributesto the development of innovative diagnostic and therapeutic strategies, whichhave the potential to improve risk stratification, precision medicine, andultimately, patient outcomes in renal cancer.

Aerosols are crucial constituents of the atmosphere, with significant impacts on air quality. Aerosol optical depth (AOD) is critical in assessing solar resources and modeling sky radiance. However, comprehensive aerosol studies at a continental scale are limited, and existing methodologies need to consider spatial characteristics. This study develops a spatio-temporal unmixing with heterogeneity (STUH) model to evaluate spatial patterns and temporal trends of atmospheric aerosols across the African continent. The spatio-temporal AOD data cube, comprising monthly averaged MODIS-derived AOD data from 2001 to 2015, was decomposed using spatially non-negative matrix variabilization to explore the spatial determinants and the impacts of their interactions to AOD using a geographically optimal zones-based heterogeneity (GOZH) model. Our findings reveal an increasing trend of aerosol levels across Africa in the past 15 years, combined with the spatio-temporal AOD pattern explained by five abundance variables. We find that in different regions across Africa, the impact of natural variables on AOD was 1.56 to 3.01 times the impact of human variables, with significant spatial variations. These results are essential for understanding the climatic implications of atmospheric aerosols in Africa.

Renal cell carcinoma (RCC) is recognized as one of the three primary malignant tumors affecting the urinary system, posing a significant risk to human health and life. Despite advancements in understanding RCC, challenges persist in its diagnosis and treatment, particularly in early detection and diagnosis due to issues of low specificity and sensitivity. Consequently, there is an urgent need for the development of effective strategies to enhance diagnostic accuracy and treatment outcomes for RCC. In recent years, with the extensive research on materials for applications in the biomedical field, some materials have been identified as promising for clinical applications, e.g., in the diagnosis and treatment of many tumors, including RCC. Herein, we summarize the latest materials that are being studied and have been applied in the early diagnosis and treatment of RCC. While focusing on their adjuvant effects, we also discuss their technical principles and safety, thus highlighting the value and potential of their application. In addition, we also discuss the limitations of the application of these materials and possible future directions, providing new insights for improving RCC diagnosis and treatment.

Abstract Background Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker‐drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter‐ or intra‐tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians. Review In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations. Conclusion In recent years, exploration of subtypes through cell lines, animal models, patient‐derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype‐specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR‐associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.

Abstract Background and main body The anti‐tumour and tumour‐promoting roles of B cells in the tumour microenvironment (TME) have gained considerable attention in recent years. As essential orchestrators of humoral immunity, B cells potentially play a crucial role in anti‐tumour therapies. Chemotherapy, a mainstay in cancer treatment, influences the proliferation and function of diverse B‐cell subsets and their crosstalk with the TME. Modulating B‐cell function by targeting B cells or their associated cells may enhance chemotherapy efficacy, presenting a promising avenue for future targeted therapy investigations. Conclusion This review explores the intricate interplay between chemotherapy and B cells, underscoring the pivotal role of B cells in chemotherapy treatment. We summarise promising B‐cell‐related therapeutic targets, illustrating the immense potential of B cells in anti‐tumour therapy. Our work lays a theoretical foundation for harnessing B cells in chemotherapy and combination strategies for cancer treatment. Key points Chemotherapy can inhibit B‐cell proliferation and alter subset distributions and functions, including factor secretion, receptor signalling, and costimulation. Chemotherapy can modulate complex B‐cell–T‐cell interactions with variable effects on anti‐tumour immunity. Targeting B‐cell surface markers or signalling improves chemotherapy responses, blocks immune evasion and inhibits tumour growth. Critical knowledge gaps remain regarding B‐cell interactions in TME, B‐cell chemoresistance mechanisms, TLS biology, heterogeneity, spatial distributions, chemotherapy drug selection and B‐cell targets that future studies should address.

Summary: Background: The FDA's alerts regarding the T-cell lymphoma risk post CAR-T therapy has garnered global attention, yet a comprehensive profile of second primary malignancies (SPMs) following CAR-T treatment is lacking. Methods: We extracted adverse event reports of hematological malignancies (HMs) patients with clearly definable SPMs from the FAERS and VigiBase databases (2017–2023). Disproportionality analysis using reporting odds ratio (ROR) and adjusted ROR was performed to assess associations between SPMs and CAR-T therapy. Time-to-onset analysis explored factors affecting SPM manifestation. Findings: SPMs post CAR T-cell therapy include HMs and solid tumors. T-cell lymphoma and myelodysplastic syndromes were consistently identified as positive signals across the overall and subgroup analyses. Hematological SPMs showed earlier onset with increasing annual incidence post CAR-T therapy, whereas solid tumors exhibit delayed manifestation. SPMs in CAR-T recipients had significantly earlier onset than non-recipients. Furthermore, age-specific characteristics reveal earlier SPM manifestations in pediatric, adolescent, and young adult populations compared to older populations post CAR-T therapy. Interpretation: The current SPM profile highlights the necessity of long-term safety monitoring for all CAR-T recipients given the observed yearly increase of SPMs. Customizing long-term SPM screening across different age groups may enhance early detection and intervention strategies, ultimately improving patient outcomes in the follow-up of CAR-T recipients. Funding: This work was supported by grants from the Natural Science Foundation of Guangdong Province (2018A030313846 and 2021A1515012593), the Science and Technology Planning Project of Guangdong Province (2019A030317020), the National Natural Science Foundation of China (81802257, 81871859, 81772457, 82172750, 82172811, and 82260546), the Guangdong Basic and Applied Basic Research Foundation (Guangdong–Guangzhou Joint Funds) (2022A1515111212), and the Science and Technology Program of Guangzhou (2023A04J1257).

This study evaluated the capabilities of the newly released ChatGPT-4V, a large language model with visual recognition abilities, in interpreting electrocardiogram waveforms and answering related multiple-choice questions for assisting with cardiovascular care.

BackgroundThe current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells.MethodsThis study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis.ResultsIn this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells.ConclusionThe impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

Abstract Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy‐to‐operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real‐time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy‐based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.

Internet of Electric Things (IoET) based on edge computing mode has advantages in functionality, communication efficiency and construction cost. However, the incompleteness of relevant security protocols restricts their application in smart grids. Therefore, we propose a lightweight three-layer device identity mutual authentication and key agreement protocol suitable for IoET with two-hops architecture. The simulation results of formal security analysis and automated security analysis toolset AVISPA on security attacks show that this protocol can meet the security requirements of identity authentication, device ID anti tracking, and session keys. Compared with recent lightweight third-party authentication protocols, our proposed scheme has advantages in resource consumption of servers and IoET Connector (IoetC) when multiple sensors require simultaneous authentication.

Abstract The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT’s writing capabilities, recent updates have integrated DALL-E 3’s image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT’s nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding “invisible watermarks” to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

Improving the application of nanomaterials has always been a research hotspot in the field of energetic materials (EMs) due to their obvious catalytic effect on the EMs, especially the uniformly dispersed nanomaterials. However, few studies have reported the dispersion of nanomaterials. In this study, the dispersity and mixing uniformity of nano-CuCr2O4 was evaluated based on the difference of solid UV light absorption between the nano-catalytic materials and EMs. The nano-CuCr2O4/ultrafine AP composites with different dispersity of nano-CuCr2O4 were prepared by manual grinding and mechanical grinding with different grinding strength and griding time. And then, the absorbance of different samples at 212 nm was obtained by solid UV testing due to the high repeatability of the absorbance at 210–214 nm for three parallel experiments, and the dispersity of different samples was calculated through the established difference equation. Furthermore, the samples were characterized by XRD, IR, SEM, EDS, DSC and TG-MS, which confirmed that different mixing methods did not change the structure of the samples (XRD and IR), and the mixing uniformity improved with the increase of grinding strength and grinding time (SEM and EDS). The scientificity and feasibility of the difference equation were further verified by DSC. The dispersity of nano-CuCr2O4 exhibits a positive intrinsic relationship with its catalytic performance, and the uniformly dispersed nano-CuCr2O4 significantly reduces the thermal decomposition temperature of ultrafine AP from 367.7 to 338.8 °C. The TG-MS patterns show that the dispersed nano-CuCr2O4 advanced the thermal decomposition process of ultrafine AP by about 700 s, especially in the high temperature decomposition stage, and the more concentrated energy release characteristic is beneficial to further enhance the energy performance of AP-based propellants. The above conclusions show that the evaluation method of dispersity based on solid UV curves could provide new ideas for the dispersity characterization of nano-catalytic materials in EMs, which is expected to be widely used in the field of EMs.

Abstract Type 2 diabetes (T2D)-related fragility fractures represent an increasingly tough medical challenge, and the current treatment options are limited. Mechanical loading is essential for maintaining bone integrity, although bone mechano-responsiveness in T2D remains poorly characterized. Herein, we report that exogenous cyclic loading-induced improvements in bone architecture and strength are compromised in both genetically spontaneous and experimentally-induced T2D mice. T2D-induced reduction in bone mechano-responsiveness is directly associated with the weakened Ca2+ oscillatory dynamics of osteocytes, although not those of osteoblasts, which is dependent on PPARα-mediated specific reduction in osteocytic SERCA2 pump expression. Treatment with the SERCA2 agonist istaroxime was demonstrated to improve T2D bone mechano-responsiveness by rescuing osteocyte Ca2+ dynamics and the associated regulation of osteoblasts and osteoclasts. Moreover, T2D-induced deterioration of bone mechano-responsiveness is blunted in mice with osteocytic SERCA2 overexpression. Collectively, our study provides mechanistic insights into T2D-mediated deterioration of bone mechano-responsiveness and identifies a promising countermeasure against T2D-associated fragility fractures.

Zinc (Zn)-based alloys are considered the next-generation biodegradable material for promising bone-implant devices due to their good degradability, formability, and functionality. Nevertheless, the as-cast Zn alloys showed a low strength-toughness, making it challenging to meet the mechanical properties requirements for high-load-bearing bone implants due to their hexagonal close-packed structure, coarse grain size, and less sliding system. Herein, we have developed biodegradable Zn–5Ce alloys by cerium (Ce) alloying followed by hot-rolling, hot-extrusion, and high-pressure torsion (HPT), respectively, for potential bone-implant application. Mechanical testing revealed that the HPT sample exhibited the best mechanical performance matching among the Zn–5Ce samples with a high ultimate tensile strength of ∼345 MPa, yield strength of ∼230 MPa, a moderate elongation of ∼11.4%, and macro-hardness of ∼96.6 HB. Electrochemical corrosion and static immersion testing revealed that the HPT sample showed the highest electrochemical corrosion rate of ∼416 μm/y and degradation rate of ∼43.8 μm/y in Hanks' solution among the Zn–5Ce samples, which is suitable for degradation rate requirements for bone-implant application. Bio-tribological testing revealed that the HPT sample showed high bio-tribological properties matching in Hanks’ solution with the lowest friction coefficients of ∼0.530 and moderate wear loss of ∼2.2 mg. Cytocompatibility and osteogenic differentiation assessment revealed that the diluted extract of the HPT sample showed the highest cytocompatibility towards 3T3 and MG-63 cells and osteogenic differentiation performance towards 3T3 cells among the most diluted extracts. Accordingly, the HPT Zn–5Ce sample is expected to be used for high-load-bearing degradable bone-implant devices, including bone fixation and guided bone regeneration systems.

Abstract Background Small‐cell lung cancer (SCLC) is characterized by its high malignancy and is associated with a poor prognosis. In the early stages of the disease, platinum‐based chemotherapy is the recommended first‐line treatment and has demonstrated efficacy. However, SCLC is prone to recurrence and is generally resistant to chemotherapy in its later stages. Methods Here, we collected samples from SCLC patients who received platinum‐based chemotherapy, performed genomic and transcriptomic analyses, and validated our results with publicly available data. Results SCLC patients with DNA polymerase binding pathway mutations had an improved prognosis after platinum chemotherapy compared with patients without such mutations. Patients in the mutant (MT) group had higher infiltration of T cells, B cells, and M1 macrophages compared with patients without DNA polymerase binding pathway mutations. Conclusions DNA polymerase binding pathway mutations can be used as prognostic markers for platinum‐based chemotherapy in SCLC.

Abstract Background Sea cucumbers exhibit a remarkable ability to regenerate damaged or lost tissues and organs, making them an outstanding model system for investigating processes and mechanisms of regeneration. They can also reproduce asexually by transverse fission, whereby the anterior and posterior bodies can regenerate independently. Despite the recent focus on intestinal regeneration, the molecular mechanisms underlying body wall regeneration in sea cucumbers still remain unclear. Results In this study, transverse fission was induced in the tropical sea cucumber, Holothuria leucospilota, through constrainment using rubber bands. Histological examination revealed the degradation and loosening of collagen fibers on day-3, followed by increased density but disorganization of the connective tissue on day-7 of regeneration. An Illumina transcriptome analysis was performed on the H. leucospilota at 0-, 3- and 7-days after artificially induced fission. The differential expression genes were classified and enriched by GO terms and KEGG database, respectively. An upregulation of genes associated with extracellular matrix remodeling was observed, while a downregulation of pluripotency factors Myc, Klf2 and Oct1 was detected, although Sox2 showed an upregulation in expression. In addition, this study also identified progressively declining expression of transcription factors in the Wnt, Hippo, TGF-β, and MAPK signaling pathways. Moreover, changes in genes related to development, stress response, apoptosis, and cytoskeleton formation were observed. The localization of the related genes was further confirmed through in situ hybridization. Conclusion The early regeneration of H. leucospilota body wall is associated with the degradation and subsequent reconstruction of the extracellular matrix. Pluripotency factors participate in the regenerative process. Multiple transcription factors involved in regulating cell proliferation were found to be gradually downregulated, indicating reduced cell proliferation. Moreover, genes related to development, stress response, apoptosis, and cell cytoskeleton formation were also involved in this process. Overall, this study provides new insights into the mechanisms of whole-body regeneration and uncover potential cross-species regenerative-related genes.