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Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners and patients. Here, we describe BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency.
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The interconnection between brain regions in neurological disease encodes vital information for the advancement of biomarkers and diagnostics. Although graph convolutional networks are widely applied for discovering brain connection patterns that point to disease conditions, the potential of connection patterns that arise from multiple imaging modalities has yet to be fully realized. In this paper, we propose a multi-modal sparse interpretable GCN framework (SGCN) for the detection of Alzheimer's disease (AD) and its prodromal stage, known as mild cognitive impairment (MCI). In our experimentation, SGCN learned the sparse regional importance probability to find signature regions of interest (ROIs), and the connective importance probability to reveal disease-specific brain network connections. We evaluated SGCN on the Alzheimer's Disease Neuroimaging Initiative database with multi-modal brain images and demonstrated that the ROI features learned by SGCN were effective for enhancing AD status identification. The identified abnormalities were significantly correlated with AD-related clinical symptoms. We further interpreted the identified brain dysfunctions at the level of large-scale neural systems and sex-related connectivity abnormalities in AD/MCI. The salient ROIs and the prominent brain connectivity abnormalities interpreted by SGCN are considerably important for developing novel biomarkers. These findings contribute to a better understanding of the network-based disorder via multi-modal diagnosis and offer the potential for precision diagnostics. The source code is available at https://github.com/Houliang-Zhou/SGCN.
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CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, achieving reliable therapeutic effects with improved safety and efficacy requires informed target gene selection. This depends on a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) that regulate cell phenotype and function. Machine learning models have been previously used for GRN reconstruction using RNA-seq data, but current techniques are limited to single cell types and focus mainly on transcription factors. This restriction overlooks many potential CRISPR target genes, such as those encoding extracellular matrix components, growth factors, and signaling molecules, thus limiting the applicability of these models for CRISPR strategies. To address these limitations, we have developed CRISPR-GEM, a multi-layer perceptron (MLP)-based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts towards a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.
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Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering (DC), which can learn clustering-friendly representations using deep neural networks (DNNs), has been broadly applied in a wide range of clustering tasks. Existing surveys for DC mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this article, we provide a comprehensive survey for DC in views of data sources. With different data sources, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, DC methods are introduced according to four categories, i.e., traditional single-view DC, semi-supervised DC, deep multiview clustering (MVC), and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of DC.
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Antibiotics are a new type of environmental pollutants. Due to its wide application in many fields, antibiotic residues are ubiquitous in the wastewater environments. Given their potential threat on water ecosystem functioning and public health, the detection of antibiotic residues in wastewater environments has become very necessary. Based on the complexation of Al3+ with flumequine (FLU), ciprofloxacin (CIP) and doxycycline hydrochloride (DOX), their molecular conjugated area were increased and fluorescence intensity were enhanced, combined with synchronous fluorescence spectrometry (SFS) had good selectivity and high sensitivity, a novel method of Al3+ sensitized synchronous fluorescence spectrometry for the determination of FLU, CIP and DOX residues in wastewater was established. When the wavelength difference (Δλ) was selected 115.0 nm, synchronous fluorescence spectra of the three antibiotics could be well separated and the interference of wastewater matrix were eliminated primely. The new SFS made good use of spectral separation instead of conventional chemical separation, and the actual wastewater sample could be directly determined after simple filtration. The experiment results showed that the concentrations of FLU, CIP and DOX in the range of 0.5000-800.0 ng·mL-1, 0.5000-640.0 ng·mL-1 and 10.00-3500 ng·mL-1 had a good linear relationship with fluorescence intensity. The detection limits of three antibiotics were 0.02054 ng·mL-1, 0.03956 ng·mL-1 and 0.8524 ng·mL-1, respectively. Recovery rates of three antibiotics in wastewater samples were 90.72%-98.23%, 88.68%-95.08% and 85.94%-96.70%. The new SFS established in this experiment had the advantages of simple, rapid, sensitive, accurate and good selectivity. Simultaneous and rapid detection of FLU, CIP and DOX residues in wastewater was successfully realized. It had good application prospects in real-time water quality monitoring.
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Ciprofloxacina , Doxiciclina , Fluoroquinolonas , Espectrometria de Fluorescência , Águas Residuárias , Poluentes Químicos da Água , Águas Residuárias/química , Ciprofloxacina/análise , Espectrometria de Fluorescência/métodos , Poluentes Químicos da Água/análise , Doxiciclina/análise , Fluoroquinolonas/análise , Antibacterianos/análise , Alumínio/análiseRESUMO
In this paper, MoS2-CuGaO2 composites were successfully synthesized by hydrothermal method. The composites were characterized by X-ray diffraction (XRD), Scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), respectively. The specific surface area and pore size distribution were obtained by N2 adsorption-desorption isotherms. The gas sensing property measurements were conducted. The sensitivity of pure CuGaO2 based gas sensor to 100 ppm acetic acid and benzene vapor was 1.4 at 25oC. The sensitivity of MoS2-CuGaO2 composite (M - 5) was 6.0 towards 100 ppm ethanol vapor at 25oC. And the detection limit of MoS2-CuGaO2 (M - 5) based sensor was 0.1 ppm. The results demonstrated that MoS2 had effect on the selectivity and sensitivity of the MoS2-CuGaO2 composites. M - 5 composite is considered to be promising for ethanol sensing application in room temperature.
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Glutamine addiction represents a metabolic vulnerability of cancer cells; however, effective therapeutic targeting of the pathways involved remains to be realized. Here, we disclose the critical role of interferon-related developmental regulator 1 (IFRD1) in the adaptive survival of hepatocellular carcinoma (HCC) cells during glutamine starvation. IFRD1 is induced under glutamine starvation to inhibit autophagy by promoting the proteasomal degradation of the key autophagy regulator ATG14 in a TRIM21-dependent manner. Conversely, targeting IFRD1 in the glutamine-deprived state increases autophagy flux, triggering cancer cell exhaustive death. This effect largely results from the nucleophilic degradation of histone H1.0 and the ensuing unchecked increases in ribosome and protein biosynthesis associated with globally enhanced chromatin accessibility. Intriguingly, IFRD1 depletion in preclinical HCC models synergizes with the treatment of the glutaminase-1 selective inhibitor CB-839 to potentiate the effect of limiting glutamine. Together, our findings reveal how IFRD1 supports the adaptive survival of cancer cells under glutamine starvation, further highlighting the potential of IFRD1 as a therapeutic target in anti-cancer applications.
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Herein, SnO2QDs (<10 nm) with small size instead of conventional nanoparticles was employed to modify ZnFe2O4to synthesize porous and heterogeneous SnO2/ZnFe2O4(ZFSQ) composites for gas sensing. By an immersion process combined with calcination treatment, the resultant porous ZFSQ composites with different contents of SnO2QDs were obtained, and their sensing properties were investigated. Compared with bare ZnFe2O4and SnO2QDs, porous ZFSQ composites based-sensors showed much improved sensor response to acetone. For contrast, the sensor performance of ZFSQ composites was also compared with that of ZnFe2O4sphere modified by SnO2nanoparticles with different size. The porous ZFSQ composite with 5 wt% SnO2QDs (ZFSQ-5) showed a better acetone sensing response than that of other ZFSQ composites, and it exhibited a high response value of 110-100 ppm of acetone and a low detection limit of 0.3 ppm at 240 °C. In addition to the rich heterojunctions and porous structure, the size effect of SnO2QDs was other indispensable reasons for the improved sensor performance. Finally, the ZFSQ-5 composite sensor was attempted to be applied for acetone sensing in exhaled breath, suggesting its great potential in monitoring acetone.
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BACKGROUND: Mammalian enabled (MENA) protein is a member of the enabled/vasodilator stimulated phosphoprotein (Ena/VASP) protein family, which regulates cytoplasmic actin network assembly. It plays a significant role in breast cancer invasion, migration, and resistance against targeted therapy and chemotherapy. However, its role in the efficacy of endocrine therapy for the hormone receptor-positive (HR+) breast cancer patients is not known. This study investigated the role of MENA in the resistance against tamoxifen therapy in patients with HR+ breast cancer and the underlying mechanisms. METHODS: MENA expression levels in the clinical HR+ breast cancer samples (n = 119) were estimated using immunohistochemistry (IHC) to determine its association with the clinicopathological features, tamoxifen resistance, and survival outcomes. Western blotting (WB) and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) analysis was performed to estimate the MENA protein and mRNA levels in the tamoxifen-sensitive and -resistant HR+ breast cancer cell lines. Furthermore, CCK8, colony formation, and the transwell invasion and migration assays were used to analyze the effects of MENA knockdown on the biological behavior and tamoxifen sensitivity of the HR+ breast cancer cell lines. Xenograft tumor experiments were performed in the nude mice to determine the tumor growth rates and tamoxifen sensitivity of the control and MENA knockdown HR+ breast cancer cells in the presence and absence of tamoxifen treatment. Furthermore, we estimated the growth rates of organoids derived from the HR+ breast cancer patients (n = 10) with high and low MENA expression levels when treated with tamoxifen. RESULTS: HR+ breast cancer patients with low MENA expression demonstrated tamoxifen resistance and poorer prognosis compared to those with high MENA expression. Univariate and multivariate Cox regression analysis demonstrated that MENA expression was an independent predictor of tamoxifen resistance in patients with HR+ breast cancer. MENA knockdown HR+ breast cancer cells showed significantly reduced tamoxifen sensitivity in the in vitro experiments and the in vivo xenograft tumor mouse model compared with the corresponding controls. Furthermore, MENA knockdown increased the in vitro invasion and migration of the HR+ breast cancer cells. HR+ breast cancer organoids with low MENA expression demonstrated reduced tamoxifen sensitivity than those with higher MENA expression. Mechanistically, P-AKT levels were significantly upregulated in the MENA-knockdown HR + breast cancer cells treated with or without 4-OHT compared with the corresponding controls. CONCLUSIONS: This study demonstrated that downregulation of MENA promoted tamoxifen resistance in the HR+ breast cancer tissues and cells by enhancing the AKT signaling pathway. Therefore, MENA is a promising prediction biomarker for determining tamoxifen sensitivity in patients with HR+ breast cancer.
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Neoplasias da Mama , Animais , Feminino , Humanos , Camundongos , Antineoplásicos Hormonais/farmacologia , Antineoplásicos Hormonais/uso terapêutico , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/genética , Linhagem Celular Tumoral , Resistencia a Medicamentos Antineoplásicos/genética , Mamíferos/metabolismo , Camundongos Nus , Proteínas Proto-Oncogênicas c-akt , Transdução de Sinais , Tamoxifeno/farmacologia , Tamoxifeno/uso terapêuticoRESUMO
Objective: Invasive breast cancer (BRCA) poses a major challenge to women's health due to its high incidence and poor prognosis. Dysregulated Lysine oxidase-like (LOXL) family genes are implicated in tumor progression across malignancies. Understanding the role of Lysine oxidase-like (LOXL) family genes in BRCA is crucial for advancing treatment strategies. Methods: TIMER, Oncomine, TNMplot, TCGA, GTEx and GEPIA datasets were used to investigate LOXL1-4 expression in breast cancer. The UALCAN and HPA datasets were utilized to detect the protein levels of LOXL1-4 in BRCA. Kaplan-Meier Plotter was used to analyze the prognostic values of LOXL1-4 in BRCA patients. Gene ontology (GO) and KEGG pathway enrichment analyses, conducted through DAVID 6.8 and R, revealed potential biological functions. TIMER was used to explore the link between LOXL1-4 expression and tumor immune infiltration. The cBioPortal dataset was used to analyze LOXL1-4 alterations and CNV-survival links in breast cancer. GSCA was used to assess LOXL1-4's correlations with immune infiltration. LOXL1-4's links to the eight immune checkpoint genes were analyzed using R's pheatmap. Results: Our study revealed that aberrant expression of LOXL1/2/4 in BRCA significantly affects relapse-free survival (RFS), overall survival (OS), and distant metastasis-free survival (DMFS), particularly highlighting the prognostic importance of LOXL4. LOXL1-4 displayed substantial relationships with the tumor immune microenvironment and immune cell infiltration. Furthermore, copy number variations (CNVs) of LOXL1-4 are significantly associated with immune infiltration in BRCA, particularly LOXL2 CNV, which significantly impacts OS and progression-free survival (PFS). Conclusion: The correlation between LOXL1-4 expression, prognosis, and immune infiltration in BRCA underscores their potential as biomarkers for prognosis and targets for immunotherapy.
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Background: Newly graduated nurses with strong work readiness are more likely to smoothly transition from school to clinical settings. However, even before the pandemic, this transition from nursing graduate to clinical nurse was often challenging. Therefore, this study aims to investigate the impact of the initial wave of COVID-19 on the work readiness of nursing students. Methods: A convergent mixed-method design was employed. For the quantitative study, an online cross-sectional survey was conducted among 500 graduating nursing students from four Chinese public higher education institutions. The questionnaire comprises three parts: socio-demographic information, the Chinese version of the Work Readiness Scale for Graduate Nurse, and a questionnaire on the socio-behavioral impact of COVID-19 on the general population. In the qualitative study, a semi-structured interview was carried out with 8 students who took part in the survey. The results from both parts were integrated using the "Pillar Integration Process". Results: The study identified six key pillars: "Graduating nursing students possess fundamental knowledge, skills, and a preparedness in terms of attitude and psychology"; "Specialist knowledge and skills and soft skills for transition from nursing students to clinical nurses need to be strengthened"; "The most obvious impact of COVID-19 on nursing students are the adoption of preventive measures and the limitations in study and daily life due to household confinement"; "Growth in both personal and professional awareness"; "Negative effects of fear and anxiety"; and "Negative effects of household confinement". Conclusion: Graduating nursing students require additional preparation in specialized nursing knowledge, skills, and soft skills to make a successful transition from students to clinical nurses. It is important to acknowledge that the impact of COVID-19 on students' work readiness has both positive and negative aspects. Therefore, whether during the pandemic or post-graduation, these students will benefit from increased support from universities and hospitals.
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Checkpoint inhibitor therapy has become increasingly important and has been endorsed as a treatment regimen in breast cancer. But benefits were limited to a small proportion of patients. We aimed to develop an improved signature on the basis of immune genes for detection of potential benefit from immunotherapy. Gene expression data of patients with breast cancer initially extracted from The Cancer Genome Atlas were analyzed. Ten genes were selected from the interaction of differentially expressed genes as well as immune-related genes to develop a survival signature. We compared the high-risk and low-risk groups by gene set enrichment analysis, immune infiltration, checkpoint molecule expression and immunophenoscore. Ten genes were extracted from interactions of differentially expressed and immune-related genes. The immune risk score was determined on the basis of the Cox regression coefficient of hub genes and validated with the GSE96058 dataset. Immune cell infiltrates, including CD8â +â T cells, plasma cells, follicular helper T cells, CD4â +â memory T cells, M1 macrophages, regulatory T cells and resting NK cells, were more highly infiltrated in the high-risk group as compared to the low-risk group. Checkpoint molecules, including CTLA-4, PD-L1, TIM-3, VISTA, ICOS, PD-1, and PD-L2, were expressed at markedly lower levels in the high-risk group as compared to the low-risk group. Immunophenoscores, as a surrogate of response to immune checkpoint therapy, was observed significant lower in the high-risk group. The 10-gene prognostic signature could identify patients' survival and was correlated with the biomarkers of immune checkpoint inhibitor therapy, which may guide precise therapeutic decisions in clinical practice.
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Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/genética , Prognóstico , Fatores de Risco , Linfócitos T CD4-Positivos , Linfócitos T CD8-PositivosRESUMO
BACKGROUND: Arginine plays significant and contrasting roles in breast cancer growth and survival. However, the factors governing arginine balance remain poorly characterized. OBJECTIVE: We aimed to identify the molecule that governs arginine metabolism in breast cancer and to elucidate its significance. METHODS: We analyzed the correlation between the expression of solute carrier family 7 member 3 (SLC7A3), the major arginine transporter, and breast cancer survival in various databases, including GEPIA, UALCAN, Metascape, String, Oncomine, KM-plotter, CBioPortal and Prognosis. Additionally, we validated our findings through bioinformatic analyses and experimental investigations, including colony formation, wound healing, transwell, and mammosphere formation assays. RESULTS: Our analysis revealed a significant reduction in SLC7A3 expression in all breast cancer subtypes compared to adjacent breast tissues. Kaplan-Meier survival analyses demonstrated that high SLC7A3 expression was positively associated with decreased nodal metastasis (HR=0.70, 95% CI [0.55, 0.89]), ER positivity (HR=0.79, 95% CI [0.65, 0.95]), and HER2 negativity (HR=0.69, 95% CI [0.58, 0.82]), and increased recurrence-free survival. Moreover, low SLC7A3 expression predicted poor prognosis in breast cancer patients for overall survival. Additionally, the knockdown of SLC7A3 in MCF-7 and MDA-MB-231 cells resulted in increased cell proliferation and invasion in vitro. CONCLUSION: Our findings indicate a downregulation of SLC7A3 expression in breast cancer tissues compared to adjacent breast tissues. High SLC7A3 expression could serve as a prognostic indicator for favorable outcomes in breast cancer patients due to its inhibitory effects on breast cancer cell proliferation and invasion.
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The following sections are included:Introduction to the workshopWorkshop Presenters.
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An ideal adsorbent material that combines the multiple capabilities of sensitive detection, efficient adsorption, and repeatable removal of uranium (U) from the environment remains a serious challenge. Herin, a general method was developed for synthesizing a series of phosphate anions (such as: PO43-, P2O74-, P3O105- and P6O186-) modified magnetic mesoporous silica nanoparticles (Fe3O4 @mSiO2-Zn2+ NPs). The mesoporous surfaces and abundant phosphate groups provide potential, powerful uranium-binding sites for capturing U(VI) ions. Especially, the optimum adsorption capacity of Fe3O4 @mSiO2-Zn2+/P3O105- NPs was as high as 885.90 mg·g-1 (298 K), which was higher than that of unmodified or other phosphate anions-modified Fe3O4 @mSiO2-Zn2+ NPs. Meanwhile, P3O105--binding sites and mesoporous surfaces also strongly restrict U(VI) ions' fluorescence vibrational inactivation, the adsorption results in rapid green fluorescence enhancement (within 180 s), and an ultra-low detection limit (4.5 nmol·L-1), which is well below the standard in drinking water of the World Health Organization (WHO). Furthermore, even after 5 cycles, the adsorbent still maintained their original adsorption capacity of 80.21% and displayed excellent selectivity for detecting and removing U(VI) from seawater. Based on these results, the Fe3O4 @mSiO2-Zn2+/P3O105- NPs seem to be a suitable multifunctional adsorbent for the detection, adsorption, and removal of U(VI) from environment.
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Background: Neoadjuvant anthracycline-based chemotherapy (NAC) is a major regimen for the treatment of local advanced breast cancer (LABC), while resistance to NAC remains a paramount clinical obstacle. To investigate the role of heat shock protein 27 (Hsp27) and/or topoisomerase IIα (TopoIIα) in LABC patients treated with NAC, we performed this retrospective study. Methods: Associations of Hsp27 transcripts with clinic-pathological characteristics, survival and drug response were investigated in public databases. Hsp27-related genes were identified, followed by functional enrichment analyses. Besides, two protein-protein interaction networks were built. Then, tumors from 103 patients who were diagnosed with LABC and received NAC were collected, and Hsp27 and TopoIIα were examined by Immunohistochemistry (IHC). Chi-square or Fisher's exact tests were performed, as well as survival analyses. Results: Either at the transcriptional level in public databases or at the protein level tested by IHC, a high level of Hsp27 was associated with aggressive tumor characteristics such as lymph node invasion and chemotherapy resistance. Hsp27-related genes mostly involved in the metabolic pathway and the gamete generation biological process. An elevated Hsp27 indicated a poor prognosis in patients with breast cancer (log-rank test P = 0.002 and 0.004 for disease-free survival [DFS] and overall survival [OS], respectively), while it might not be an independent predictor. Of note, tumors with high TopoIIα expression (TopoIIα+) was less likely to express Hsp27 (Hsp27+), in contrast to those with TopoIIα negativity (31.1% vs. 86.2%, P<0.001), and survival analyses revealed that patients with Hsp27+ and TopoIIα- tumors had a significantly lower DFS and OS (log-rank test P < 0.001 and 0.001, respectively), in contrast to the other three groups. Conclusions: Hsp27 was associated with aggressive breast cancers and more predictable for the prognosis of LABC patients treated with NAC when concomitantly considering TopoIIα expression.
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Learning a comprehensive representation from multiview data is crucial in many real-world applications. Multiview representation learning (MRL) based on nonnegative matrix factorization (NMF) has been widely adopted by projecting high-dimensional space into a lower order dimensional space with great interpretability. However, most prior NMF-based MRL techniques are shallow models that ignore hierarchical information. Although deep matrix factorization (DMF)-based methods have been proposed recently, most of them only focus on the consistency of multiple views and have cumbersome clustering steps. To address the above issues, in this article, we propose a novel model termed deep autoencoder-like NMF for MRL (DANMF-MRL), which obtains the representation matrix through the deep encoding stage and decodes it back to the original data. In this way, through a DANMF-based framework, we can simultaneously consider the multiview consistency and complementarity, allowing for a more comprehensive representation. We further propose a one-step DANMF-MRL, which learns the latent representation and final clustering labels matrix in a unified framework. In this approach, the two steps can negotiate with each other to fully exploit the latent clustering structure, avoid previous tedious clustering steps, and achieve optimal clustering performance. Furthermore, two efficient iterative optimization algorithms are developed to solve the proposed models both with theoretical convergence analysis. Extensive experiments on five benchmark datasets demonstrate the superiority of our approaches against other state-of-the-art MRL methods.
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The emergence of immunotherapy has introduced a promising, novel approach to cancer treatment. While multiple chimeric antigen receptor (CAR) T-cell therapies have demonstrated remarkable clinical efficacy against leukemia, their effect on solid tumors has been limited. One potential option for treating solid tumors is the engineering of natural killer (NK) cells with CARs. Mesothelin (MSLN), a tumor differentiation antigen, is expressed on triple-negative breast cancer (TNBC) cells, making it a potential target for CAR-NK therapy in the treatment of TNBC. We first constructed induced pluripotent stem cells with stable anti-MSLN-CAR expression and subsequently differentiated these cells into mesothelin-targeted CAR-NK (MSLN-NK) cells. We then assessed the effects of MSLN-NK cells on TNBC cells both in vitro (using the MDA-MB-231 cell line), in vivo (in a CDX mouse model), and ex vivo (using patient-specific primary cells and patient-specific organoids), in which MSLN surface expression was confirmed. Our CDX study results indicated that MSLN-NK cells effectively killed MDA-MB-231 (MD231) cells in vitro, reduced tumor growth in the CDX mouse model of TNBC, and lysed patient-specific primary cells and patient-specific organoids derived from the tumor samples of TNBC patients. Our data demonstrated that MSLN-NK cells had high efficacy on killing TNBC cells in in vitro, in vivo, and ex vivo. Therefore, MSLN-NK could be a promising treatment option for TNBC patients.
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Células-Tronco Pluripotentes Induzidas , Neoplasias de Mama Triplo Negativas , Humanos , Animais , Camundongos , Mesotelina , Neoplasias de Mama Triplo Negativas/terapia , Células Matadoras Naturais , Imunoterapia Adotiva/métodos , Modelos Animais de Doenças , Linhagem Celular Tumoral , Antígenos de NeoplasiasRESUMO
In response to adverse environmental conditions, embryonic development may reversibly cease, a process termed diapause. Recent reports connect this phenomenon with the non-genetic responses of tumors to chemotherapy, but the mechanisms involved are poorly understood. Here, we establish a multifarious role for SMC4 in the switching of colorectal cancer cells to a diapause-like state. SMC4 attenuation promotes the expression of three investment phase glycolysis enzymes increasing lactate production while also suppressing PGAM1. Resultant high lactate levels increase ABC transporter expression via histone lactylation, rendering tumor cells insensitive to chemotherapy. SMC4 acts as co-activator of PGAM1 transcription, and the coordinate loss of SMC4 and PGAM1 affects F-actin assembly, inducing cytokinesis failure and polyploidy, thereby inhibiting cell proliferation. These insights into the mechanisms underlying non-genetic chemotherapy resistance may have significant implications for the field, advancing our understanding of aerobic glycolysis functions in tumor and potentially informing future therapeutic strategies.
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Neoplasias Colorretais , Diapausa , Humanos , Animais , Histonas/metabolismo , Glicólise , Proliferação de Células , Neoplasias Colorretais/metabolismo , Lactatos , Adenosina Trifosfatases/metabolismo , Proteínas Cromossômicas não Histona/metabolismoRESUMO
Acetaldehyde (CH3CHO) is known as a primary carcinogen, and the development of wearable gas sensors for its detection at room temperature has rarely been rarely reported. Herein, MoS2 quantum dots (MoS2 QDs) have been employed to dope with poly(3,4-ethylenedioxythiophene): polystyrenesulfonate (PEDOT: PSS) via a simple in situ polymerization technique, and the CH3CHO gas-sensing properties of the resultant flexible and transparent film were investigated. MoS2 QDs had been evenly dispersed into the polymer, and it was shown that PEDOT: PSS doped with the 20 wt % MoS2 QDs sensor exhibited the highest response value of 78.8% against 100 ppm CH3CHO and its detection limit reached 1 ppm. Moreover, the sensor response remained stable for more than 3 months. In particular, the different bending angles (from 60 to 240°) had little effect on the sensor response to CH3CHO. The possible reason for the enhanced sensing properties was attributed to the large number of reaction sites on the MoS2 QDs and the direct charge transfer between the MoS2 QDs and PEDOT: PSS. This work suggested a platform to inspire MoS2 QDs-doping PEDOT: PSS materials as wearable gas sensors for highly sensitive chemoresistive sensors to detect CH3CHO at room temperature.