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.

High-density stable glasses formed on soft substrates.

Enabled by surface-mediated equilibration, physical vapour deposition can create high-density stable glasses comparable with liquid-quenched glasses aged for millions of years. Deposition is often performed at various rates and temperatures on rigid substrates to control the glass properties. Here we demonstrate that on soft, rubbery substrates, surface-mediated equilibration is enhanced up to 170 nm away from the interface, forming stable glasses with densities up to 2.5% higher than liquid-quenched glasses within 2.5 h of deposition. Gaining similar properties on rigid substrates would require 10 million times slower deposition, taking ~3,000 years. Controlling the modulus of the rubbery substrate provides control over the glass structure and density at constant deposition conditions. These results underscore the significance of substrate elasticity in manipulating the properties of the mobile surface layer and thus the glass structure and properties, allowing access to deeper states of the energy landscape without prohibitively slow deposition rates.

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.

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma.

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer.

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well-documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

Pirfenidone inhibits TGF-ß1-induced metabolic reprogramming during epithelial-mesenchymal transition in non-small cell lung cancer.

Metastasis is an important contributor to increased mortality rates in non-small cell lung cancer (NSCLC). The TGF-ß signalling pathway plays a crucial role in facilitating tumour metastasis through epithelial-mesenchymal transition (EMT). Glycolysis, a key metabolic process, is strongly correlated with NSCLC metastasis. Pirfenidone (PFD) has been shown to safely and effectively inhibit TGF-ß1 in patients with lung diseases. Furthermore, TGF-ß1 and glycolysis demonstrate an interdependent relationship within the tumour microenvironment. Our previous study demonstrated that PFD effectively inhibited glycolysis in NSCLC cells, prompting further investigation into its potential antitumour effects in this context. Therefore, the present study aims to investigate the potential antitumour effect of PFD in NSCLC and explore the relationship among TGF-ß1, glycolysis and EMT through further experimentation. The antitumour effects of PFD were evaluated using five different NSCLC cell lines and a xenograft tumour model. Notably, PFD demonstrated a significant antitumour effect specifically in highly glycolytic H1299 cells. To elucidate the underlying mechanism, we compared the efficacy of PFD after pretreatment with either TGF-ß1 or a TGF-ß receptor inhibitor (LY2109761). The energy metabolomics analysis of tumour tissue demonstrated that PFD, a chemosensitizing agent, reduced lactate and ATP production, thereby inhibiting glycolysis and exerting synergistic antineoplastic effects. Additionally, PFD combined with cisplatin targeted TGF-ß1 to inhibit glycolysis during EMT and enhanced the chemosensitization of A549 and H1299 cells. The magnitude of the anticancer effect exhibited by PFD was intricately linked to its metabolic properties.

Regio- and Stereoselective Transfer Hydrogenation of Aryloxy Group-Substituted Unsymmetrical 1,2-Diketones: Synthetic Applications and Mechanistic Studies.

Developing a general method that leads to the formation of different classes of chiral bioactive compounds and their stereoisomers is an attractive but challenging research topic in organic synthesis. Furthermore, despite the great value of asymmetric transfer hydrogenation (ATH) in both organic synthesis and the pharmaceutical industry, the monohydrogenation of unsymmetrical 1,2-diketones remains underdeveloped. Here, we report the aryloxy group-assisted highly regio-, diastereo-, and enantioselective ATH of racemic 1,2-diketones. The work produces a myriad of enantioenriched dihydroxy ketones, and further transformations furnish all eight stereoisomers of diaryl triols, polyphenol, emblirol, and glycerol-type natural products. Mechanistic studies and calculations reveal two working modes of the aryloxy group in switching the regioselectivity from a more reactive carbonyl to a less reactive one, and the potential of ATH on 1,2-diketones in solving challenging synthetic issues has been clearly demonstrated.

CTLs heterogeneity and plasticity: implications for cancer immunotherapy.

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.

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.

Hydrophilic 1T-WS2 Nanosheet Arrays toward Conductive Textiles for High-Efficient and Continuous Hydroelectric Generation and Storage.

Flexible hydroelectric generators (HEGs) are promising self-powered devices that spontaneously derive electrical power from moisture. However, achieving the desired compatibility between a continuous operating voltage and superior current density remains a significant challenge. Herein, a textile-based van der Waals heterostructure is rationally designed between conductive 1T phase tungsten disulfide@carbonized silk (1T-WS2@CSilk) and carbon black@cotton (CB@Cotton) fabrics with an asymmetric distribution of oxygen-containing functional groups, which enhances the proton concentration gradients toward high-performance wearable HEGs. The vertically staggered 1T-WS2 nanosheet arrays on the CSilk fabric provide abundant hydrophilic nanochannels for rapid carrier transport. Furthermore, the moisture-induced primary battery formed between the active aluminum (Al) electrode and the conductive textiles introduces the desired electric field to facilitate charge separation and compensate for the decreased streaming potential. These devices exhibit a power density of 21.6 µW cm-2, an open-circuit voltage (Voc) of 0.65 V sustained for over 10 000 s, and a current density of 0.17 mA cm-2. This performance makes them capable of supplying power to commercial electronics and human respiratory monitoring. This study presents a promising strategy for the refined design of wearable electronics.

Large-scale genome-wide association studies reveal the genetic causal etiology between air pollutants and autoimmune diseases.

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.

OralExplorer: a web server for exploring the mechanisms of oral inflammatory diseases.

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 (IL1ß, 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 ).

Potential anti-tumor effects of regulatory T cells in the tumor microenvironment: a review.

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.

Large-scale genetic correlation studies explore the causal relationship and potential mechanism between gut microbiota and COVID-19-associated risks.

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.

Wavelength encoding spectral imaging based on the combination of deeply learned filters and an RGB camera.

Hyperspectral imaging is a critical tool for gathering spatial-spectral information in various scientific research fields. As a result of improvements in spectral reconstruction algorithms, significant progress has been made in reconstructing hyperspectral images from commonly acquired RGB images. However, due to the limited input, reconstructing spectral information from RGB images is ill-posed. Furthermore, conventional camera color filter arrays (CFA) are designed for human perception and are not optimal for spectral reconstruction. To increase the diversity of wavelength encoding, we propose to place broadband encoding filters in front of the RGB camera. In this condition, the spectral sensitivity of the imaging system is determined by the filters and the camera itself. To achieve an optimal encoding scheme, we use an end-to-end optimization framework to automatically design the filters' transmittance functions and optimize the weights of the spectral reconstruction network. Simulation experiments show that our proposed spectral reconstruction network has excellent spectral mapping capabilities. Additionally, our novel joint wavelength encoding imaging framework is superior to traditional RGB imaging systems. We develop the deeply learned filter and conduct actual shooting experiments. The spectral reconstruction results have an attractive spatial resolution and spectral accuracy.

Proteomic analysis of the effects of Dictyophora polysaccharide on arsenic-induced hepatotoxicity in rats.

Arsenic (As) is a highly toxic environmental toxicant and a known human carcinogen. Long-term exposure to As can cause liver injury. Dictyophora polysaccharide (DIP) is a biologically active natural compound found in the Dictyophora with excellent antioxidation, anti-inflammation, and immune protection properties. In this study, the Sprague-Dawley (SD) rat model of As toxicity was established using a feeding method, followed by DIP treatment in rats with As-induced liver injury. The molecular mechanisms of As toxicity to the rat liver and the protective effect of DIP were investigated by proteomic studies. The results showed that 172, 328 and 191 differentially expressed proteins (DEPs) were identified between the As-exposed rats versus control rats (As/Ctrl), DIP treated rats versus As-exposed rats (DIP+As/As), and DIP treated rats versus control rats (DIP+As /Ctrl), respectively. Among them, the expression of 90 DEPs in the As/Ctrl groups was reversed by DIP treatment. As exposure caused dysregulation of metabolic pathways, mitochondria, oxidative stress, and apoptosis-related proteins in the rat liver. However, DIP treatment changed or restored the levels of these proteins, which attenuated the damage to the livers of rats caused by As exposure. The results provide new insights into the mechanisms of liver injury induced by As exposure and the treatment of DIP in As poisoning.

Gaining Insight into Updated MR Imaging for Quantitative Assessment of Cartilage Injury in Knee Osteoarthritis.

PURPOSE OF THE REVIEW: Knee Osteoarthritis (KOA) entails progressive cartilage degradation, reviewed via MRI for morphology, biochemical composition, and microtissue alterations, discussing clinical advantages, limitations, and research applicability. RECENT FINDINGS: Compositional MRI, like T2/T2* mapping, T1rho mapping, gagCEST, dGEMRIC, sodium imaging, diffusion-weighted imaging, and diffusion-tensor imaging, provide insights into cartilage injury in KOA. These methods quantitatively measure collagen, glycosaminoglycans, and water content, revealing important information about biochemical compositional and microstructural alterations. Innovative techniques like hybrid multi-dimensional MRI and diffusion-relaxation correlation spectrum imaging show potential in depicting initial cartilage changes at a sub-voxel level. Integration of automated image analysis tools addressed limitations in manual cartilage segmentation, ensuring robust and reproducible assessments of KOA cartilage. Compositional MRI techniques reveal microstructural changes in cartilage. Multi-dimensional MR imaging assesses biochemical alterations in KOA-afflicted cartilage, aiding early degeneration identification. Integrating artificial intelligence enhances cartilage analysis, optimal diagnostic accuracy for early KOA detection and monitoring.

Effects of arsenic exposure on blood trace element levels in rats and sex differences.

Arsenic (As) is a widespread environmental metalloid and human carcinogen, and its exposure is associated with a wide range of toxic effects, leading to serious health hazards. As poisoning is a complex systemic multi-organ and multi-system damage disease. In this study, a rat model of As poisoning was established to investigate the levels of trace elements in the blood of rats and sex differences in the effect of As on every trace elements in rat blood. Twenty 6-week-old SD (Sprague Dawley) rats were randomly divided into the control group and the As-exposed group. After 3 months, the contents of 19 elements including As in the blood were detected in these two groups by inductively coupled plasma mass spectrometry (ICP-MS). As levels in the blood of As-exposed rats were significantly higher than those in the control group, with increased levels of Rb, Sr, Cs and Ce, and decreased levels of Pd. As showed a significant positive correlation with Rb. There were significant sex differences in blood Se, Pd, Eu, Dy, Ho, and Au levels in the As-exposed group. The results showed that As exposure can lead to an increase of As content in blood and an imbalance of some elements. There were sex differences in the concentration and the correlation between elements of some elements. Elemental imbalances may affect the toxic effects of As and play a synergistic or antagonistic role in As toxicity.

Design, synthesis and biological evaluation of new RNF126-based p300/CBP degraders.

Histone acetyltransferase CREB-binding protein (CBP) and its homologous protein p300 are key transcriptional activators that can activate oncogene transcription, which present promising targets for cancer therapy. Here, we designed and synthesized a series of p300/CBP targeted low molecular weight PROTACs by assembling the covalent ligand of RNF126 E3 ubiquitin ligase and the bromodomain ligand of the p300/CBP. The optimal molecule A8 could effectively degrade p300 and CBP through the ubiquitin-proteasome system in time- and concentration-dependent manners, with half-maximal degradation (DC50) concentrations of 208.35/454.35 nM and 82.24/79.45 nM for p300/CBP in MV4-11 and Molm13 cell lines after 72 h of treatment. And the degradation of p300/CBP by A8 is dependent on the ubiquitin-proteasome pathway and its simultaneous interactions with the target proteins and RNF126. A8 exhibits good antiproliferative activity in a series of p300/CBP-dependent cancer cells. It could transcriptionally inhibit the expression of c-Myc, induce cell cycle arrest in the G0/G1 phase and apoptosis in MV4-11 cells. This study thus provided us a new chemotype for the development of drug-like PROTACs targeting p300/CBP, which is expected to be applied in cancer therapy.