The effect of dietary oxidation balance scores and volatile organic compounds exposures on inflammation.

BACKGROUND: Inflammation is a significant factor in adverse health outcomes, but the combined effects of diets with varying oxidation levels and exposure to volatile organic compounds (VOCs) on inflammation are not well understood. This study aimed to elucidate the effects of the recognized Dietary Oxidative Balance Score (DOBS) and five VOCs on the systemic immune-inflammation index (SII) and C-reactive protein (CRP). METHODS: This cross-sectional study included data from participants in three cycles (2003-2004, 2005-2006, 2009-2010) of the National Health and Nutrition Examination Survey (NHANES). We used Spearman correlation, logistic regression, and trend tests to explore the associations between DOBS, VOCs, SII, and CRP. Additionally, we conducted restricted cubic spline (RCS) analysis to assess dose-response relationships between exposure and effect. G-computation and stratified analyses were performed to further elucidate these associations. RESULTS: We included 7033 eligible participants, with 48.8 % males and 51.2 % females. Spearman correlation revealed that DOBS was negatively correlated with SII and CRP, while the five VOCs were positively correlated with SII and CRP. Although fully adjusted logistic regression models did not yield statistically significant results, trend tests indicated a gradual decrease in SII and CRP with increasing DOBS, a finding validated by RCS analysis. G-computation results demonstrated that the combined effect of DOBS and VOCs on inflammation was positive, with DOBS exerting a negative effect and benzene, ethylbenzene, and 1,4-dichlorobenzene exerting positive effects. Stratified analysis showed that maintaining physical activity significantly influenced the effects of DOBS and VOCs on inflammation. CONCLUSION: This study suggests that adjusting dietary structure and reducing daily exposure to VOCs can effectively reduce inflammation in the body.

CTPAD: an interactive web application for comprehensive transcriptomic profiling in allergic diseases.

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/ .

A Multi-Omics Prognostic Model Capturing Tumor Stemness and the Immune Microenvironment in Clear Cell Renal Cell Carcinoma.

Background: Cancer stem-like cells (CSCs), a distinct subset recognized for their stem cell-like abilities, are intimately linked to the resistance to radiotherapy, metastatic behaviors, and self-renewal capacities in tumors. Despite their relevance, the definitive traits and importance of CSCs in the realm of oncology are still not fully comprehended, particularly in the context of clear cell renal cell carcinoma (ccRCC). A comprehensive understanding of these CSCs' properties in relation to stemness, and their impact on the efficacy of treatment and resistance to medication, is of paramount importance. Methods: In a meticulous research effort, we have identified new molecular categories designated as CRCS1 and CRCS2 through the application of an unsupervised clustering algorithm. The analysis of these subtypes included a comprehensive examination of the tumor immune environment, patterns of metabolic activity, progression of the disease, and its response to immunotherapy. In addition, we have delved into understanding these subtypes' distinctive clinical presentations, the landscape of their genomic alterations, and the likelihood of their response to various pharmacological interventions. Proceeding from these insights, prognostic models were developed that could potentially forecast the outcomes for patients with ccRCC, as well as inform strategies for the surveillance of recurrence after treatment and the handling of drug-resistant scenarios. Results: Compared with CRCS1, CRCS2 patients had a lower clinical stage/grading and a better prognosis. The CRCS2 subtype was in a hypoxic state and was characterized by suppression and exclusion of immune function, which was sensitive to gefitinib, erlotinib, and saracatinib. The constructed prognostic risk model performed well in both training and validation cohorts, helping to identify patients who may benefit from specific treatments or who are at risk of recurrence and drug resistance. A novel therapeutic target, SAA2, regulating neutrophil and fibroblast infiltration, and, thus promoting ccRCC progression, was identified. Conclusions: Our findings highlight the key role of CSCs in shaping the ccRCC tumor microenvironment, crucial for therapy research and clinical guidance. Recognizing tumor stemness helps to predict treatment efficacy, recurrence, and drug resistance, informing treatment strategies and enhancing ccRCC patient outcomes.

Development and validation of a novel immune‒metabolic-Based classifier for hepatocellular carcinoma.

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.

Loss-of-function mutation in anthocyanidin reductase activates the anthocyanin synthesis pathway in strawberry.

Fruit color substantially affects consumer preferences, with darker red strawberries being economically more valuable due to their higher anthocyanin content. However, the molecular basis for the dark red coloration remains unclear. Through screening of an ethyl methanesulfonate mutant library, we identified a rg418 mutant, that demonstrated anthocyanin accumulation during early fruit development stages. Furthermore, the ripening fruits of this mutant had higher anthocyanin content than wild-type (WT) fruits. An analysis of flavonoid content in WT and rg418 mutant fruits revealed substantial changes in metabolic fluxes, with the mutant exhibiting increased levels of anthocyanins and flavonols and decreased levels of proanthocyanidins. Bulked sergeant analysis sequencing indicated that the mutant gene was anthocyanidin reductase (ANR), a key gene in the proanthocyanidin synthesis pathway. Furthermore, transcriptome sequencing revealed the increased expression of MYB105 during the early development stage of mutant fruits, which promoted the expression of UFGT (UDP-glucose flavonoid 3-O-glucosyltransferase), a key gene involved in anthocyanin synthesis, thus substantially enhancing the anthocyanin content in the mutant fruits. Additionally, mutating ANR in a white-fruited strawberry variant (myb10 mutant) resulted in appealing pink-colored fruits, suggesting the diverse roles of ANR in fruit color regulation. Our study provides valuable theoretical insights for improving strawberry fruit color.

Corrigendum to 'Effective strategies to enhance the diagnosis and treatment of RCC: The application of biocompatible materials' [Mater. Today Bio 27 (2024) 101149].

[This corrects the article DOI: 10.1016/j.mtbio.2024.101149.].

Renal cancer: signaling pathways and advances in targeted therapies.

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.

Effective strategies to enhance the diagnosis and treatment of RCC: The application of biocompatible materials.

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.

B-cell performance in chemotherapy: Unravelling the mystery of B-cell therapeutic potential.

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.

Characterization of second primary malignancies post CAR T-cell therapy: real-world insights from the two global pharmacovigilance databases of FAERS and VigiBase.

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).

Immunomodulatory poly(L-lactic acid) nanofibrous membranes promote diabetic wound healing by inhibiting inflammation, oxidation and bacterial infection.

Background: Given the significant impact on human health, it is imperative to develop novel treatment approaches for diabetic wounds, which are prevalent and serious complications of diabetes. The diabetic wound microenvironment has a high level of reactive oxygen species (ROS) and an imbalance between proinflammatory and anti-inflammatory cells/factors, which hamper the healing of chronic wounds. This study aimed to develop poly(L-lactic acid) (PLLA) nanofibrous membranes incorporating curcumin and silver nanoparticles (AgNPs), defined as PLLA/C/Ag, for diabetic wound healing. Methods: PLLA/C/Ag were fabricated via an air-jet spinning approach. The membranes underwent preparation and characterization through various techniques including Fourier-transform infrared spectroscopy, measurement of water contact angle, X-ray photoelectron spectroscopy, X-ray diffraction, scanning electron microscopy, assessment of in vitro release of curcumin and Ag+, testing of mechanical strength, flexibility, water absorption and biodegradability. In addition, the antioxidant, antibacterial and anti-inflammatory properties of the membranes were evaluated in vitro, and the ability of the membranes to heal wounds was tested in vivo using diabetic mice. Results: Loose hydrophilic nanofibrous membranes with uniform fibre sizes were prepared through air-jet spinning. The membranes enabled the efficient and sustained release of curcumin. More importantly, antibacterial AgNPs were successfully reduced in situ from AgNO3. The incorporation of AgNPs endowed the membrane with superior antibacterial activity, and the bioactivities of curcumin and the AgNPs gave the membrane efficient ROS scavenging and immunomodulatory effects, which protected cells from oxidative damage and reduced inflammation. Further results from animal studies indicated that the PLLA/C/Ag membranes had the most efficient wound healing properties, which were achieved by stimulating angiogenesis and collagen deposition and inhibiting inflammation. Conclusions: In this research, we successfully fabricated PLLA/C/Ag membranes that possess properties of antioxidants, antibacterial agents and anti-inflammatory agents, which can aid in the process of wound healing. Modulating wound inflammation, these new PLLA/C/Ag membranes serve as a novel dressing to enhance the healing of diabetic wounds.

ChatGPT's ability to generate realistic experimental images poses a new challenge to academic integrity.

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.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Multimodal ChatGPT-4V for Electrocardiogram Interpretation: Promise and Limitations.

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.

Development and validation of a radiosensitivity model to evaluate radiotherapy benefits in pan-cancer.

Radiotherapy, one of the most fundamental cancer treatments, is confronted with the dilemma of treatment failure due to radioresistance. To predict the radiosensitivity and improve tumor treatment efficiency in pan-cancer, we developed a model called Radiation Intrinsic Sensitivity Evaluation (RISE). The RISE model was built using cell line-based mRNA sequencing data from five tumor types with varying radiation sensitivity. Through four cell-derived datasets, two public tissue-derived cohorts, and one local cohort of 42 nasopharyngeal carcinoma patients, we demonstrated that RISE could effectively predict the level of radiation sensitivity (area under the ROC curve [AUC] from 0.666 to 1 across different datasets). After the verification by the colony formation assay and flow cytometric analysis of apoptosis, our four well-established radioresistant cell models successfully proved higher RISE values in radioresistant cells by RT-qPCR experiments. We also explored the prognostic value of RISE in five independent TCGA cohorts consisting of 1137 patients who received radiation therapy and found that RISE was an independent adverse prognostic factor (pooled multivariate Cox regression hazard ratio [HR]: 1.84, 95% CI 1.39-2.42; p < 0.01). RISE showed a promising ability to evaluate the radiotherapy benefit while predicting the prognosis of cancer patients, enabling clinicians to make individualized radiotherapy strategies in the future and improve the success rate of radiotherapy.