Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach.

Objective: Triple-negative breast cancer (TNBC) poses significant diagnostic challenges due to its aggressive nature. This research develops an innovative deep learning (DL) model based on the latest multi-omics data to enhance the accuracy of TNBC subtype and prognosis prediction. The study focuses on addressing the constraints of prior studies by showcasing a model with substantial advancements in data integration, statistical performance, and algorithmic optimization. Methods: Breast cancer-related molecular characteristic data, including mRNA, miRNA, gene mutations, DNA methylation, and magnetic resonance imaging (MRI) images, were retrieved from the TCGA and TCIA databases. This study not only compared single-omics with multi-omics machine learning models but also applied Bayesian optimization to innovatively optimize the neural network structure of a DL model for multi-omics data. Results: The DL model for multi-omics data significantly outperformed single-omics models in subtype prediction, achieving a 98.0% accuracy in cross-validation, 97.0% in the validation set, and 91.0% in an external test set. Additionally, the MRI radiomics model showed promising performance, especially with the training set; however, a decrease in performance during transfer testing underscored the advantages of the DL model for multi-omics data in data consistency and digital processing. Conclusion: Our multi-omics DL model presents notable innovations in statistical performance and transfer learning capability, bearing significant clinical relevance for TNBC classification and prognosis prediction. While the MRI radiomics model proved effective, it requires further optimization for cross-dataset application to enhance accuracy and consistency. Our findings offer new insights into improving TNBC classification and prognosis through multi-omics data and DL algorithms.

Correction: Silver/silver halide supported on mesoporous ceria particles and photo-CWPO degradation under visible light for organic compounds in acrylonitrile wastewater.

[This corrects the article DOI: 10.1039/D1RA04465F.].

Correction: Catalytic wet peroxide degradation of acrylonitrile wastewater by ordered mesoporous Ag/CeO2: synthesis, performance and kinetics.

[This corrects the article DOI: 10.1039/D1RA01258D.].

TrEnD: A transformer-based encoder-decoder model with adaptive patch embedding for mass segmentation in mammograms.

BACKGROUND: Breast cancer is one of the most prevalent malignancies diagnosed in women. Mammogram inspection in the search and delineation of breast tumors is an essential prerequisite for a reliable diagnosis. However, analyzing mammograms by radiologists is time-consuming and prone to errors. Therefore, the development of computer-aided diagnostic (CAD) systems to automate the mass segmentation procedure is greatly expected. PURPOSE: Accurate breast mass segmentation in mammograms remains challenging in CAD systems due to the low contrast, various shapes, and fuzzy boundaries of masses. In this paper, we propose a fully automatic and effective mass segmentation model based on deep learning for improving segmentation performance. METHODS: We propose an effective transformer-based encoder-decoder model (TrEnD). Firstly, we introduce a lightweight method for adaptive patch embedding (APE) of the transformer, which utilizes superpixels to adaptively adjust the size and position of each patch. Secondly, we introduce a hierarchical transformer-encoder and attention-gated-decoder structure, which is beneficial for progressively suppressing interference feature activations in irrelevant background areas. Thirdly, a dual-branch design is employed to extract and fuse globally coarse and locally fine features in parallel, which could capture the global contextual information and ensure the relevance and integrity of local information. The model is evaluated on two public datasets CBIS-DDSM and INbreast. To further demonstrate the robustness of TrEnD, different cropping strategies are applied to these datasets, termed tight, loose, maximal, and mix-frame. Finally, ablation analysis is performed to assess the individual contribution of each module to the model performance. RESULTS: The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 92.20% and 85.81% on the mix-frame CBIS-DDSM, while 91.83% and 85.29% for the mix-frame INbreast, respectively. The segmentation performance outperforms the current state-of-the-art approaches. By adding the APE and attention-gated module, the Dice and IoU have improved by 6.54% and 10.07%. CONCLUSION: According to extensive qualitative and quantitative assessments, the proposed network is effective for automatic breast mass segmentation, and has adequate potential to offer technical assistance for subsequent clinical diagnoses.

The "Booster" of Corporate Eco-Innovation: Government Pressure Perceived by Chinese Private Firms.

This study explores the relationship between perceived government pressure for environmental regulation and corporate eco-innovation. Based on the questionnaire data of Chinese private firms in 2016, this study explores the role of government pressure perceived by private firms in corporate environmental innovation, and the moderating effects of foreign ownership and entrepreneurs' political status. The results show that there is a positive relationship between perceived government environmental regulatory pressure and corporate eco-innovation, and this relationship can be strengthened by foreign ownership and entrepreneurs' political status. These findings provide a new strategic motivation for firms to undertake eco-innovation, i.e., the environmental regulatory pressure released by the government can urge firms to undertake strategies as an external "booster."

Ivermectin contributes to attenuating the severity of acute lung injury in mice.

Ivermectin has been proposed as a potential anti-inflammatory drug in addition to its antiparasitic activity. Here we investigated the potential role of ivermectin in the pathogenesis of acute lung injury (ALI) using the lipopolysaccharide (LPS)- or bleomycin (BLM)-induced mice models. Male C57BL/6 mice were given ivermectin orally every day for the remainder of the experiment at doses of 1 or 2 mg/kg after 24 h of LPS or BLM treatment. Ivermectin reversed severe lung injury caused by LPS or BLM challenge, including mortality, changes in diffuse ground-glass and consolidation shadows on lung CT imaging, lung histopathological scores, lung wet/dry ratio, and protein content in the bronchoalveolar lavage fluid (BALF). Furthermore, ivermectin also reduced total lung BALF inflammatory cells, infiltrating neutrophils, myeloperoxidase activity, and plasma TNF-α and IL-6 levels in mice treated with LPS or BLM. Finally, the mechanism study showed that LPS or BLM administration increased JNK, Erk1/2, and p38 MAPK phosphorylation while decreasing IκBα expression, an inhibitor of NF-κB. However, ivermectin increased IκBα expression but blocked elevated phosphorylated JNK and p38 MAPK, not Erk1/2, in both ALI mice. These findings suggested that ivermectin may alleviate ALI caused by LPS or BLM in mice, partly via lowering the inflammatory response, which is mediated at least by the inhibition of MAPK and NF-κB signaling. Collectively, ivermectin might be used to treat acute lung injury/acute respiratory distress syndrome.

Overexpression of cyclin-dependent kinase 1 in esophageal squamous cell carcinoma and its clinical significance.

Cyclin-dependent kinase 1 (CDK1) plays a significant role in certain malignancies. However, it remains unclear whether CDK1 plays a role in esophageal squamous cell carcinoma (ESCC). The aim of this study was to analyze the expression and clinical value of CDK1 in ESCC. CDK1 protein in 151 ESCC tissues and 138 normal esophageal tissues was detected by immunohistochemistry. RNA-seq of eight pairs of ESCC and adjacent esophageal specimens was performed to evaluate the levels of CDK1 mRNA. Microarray and external RNA-seq data from 664 cases of ESCC and 1733 cases of control tissues were used to verify the difference in CDK1 expression between the two groups. A comprehensive analysis of all data was performed to evaluate the difference in CDK1 between ESCC tissues and control tissues. Further, functional enrichment analyses were performed based on differentially expressed genes (DEGs) of ESCC and co-expressed genes (CEGs) of CDK1. In addition, a lncRNA-miRNA-CDK1 network was constructed. The expression of CDK1 protein was obviously increased in ESCC tissues (3.540 ± 2.923 vs. 1.040 ± 1.632, P < 0.001). RNA-seq indicated that the mRNA level of CDK1 was also highly expressed in ESCC tissues (5.261 ± 0.703 vs. 2.229 ± 1.161, P < 0.0001). Comprehensive analysis revealed consistent up-regulation of CDK1 (SMD = 1.41; 95% CI 1.00-1.83). Further, functional enrichment analyses revealed that the functions of these genes were mainly concentrated in the cell cycle. A triple regulatory network of PVT1-hsa-miR-145-5p/hsa-miR-30c-5p-CDK1 was constructed using in silico analysis. In summary, overexpression of CDK1 is closely related to ESCC tumorigenesis.

The Indication of Poor Prognosis by High Expression of ENO1 in Squamous Cell Carcinoma of the Lung.

The purpose of this study is to investigate the significance of alpha-enolase (ENO1) expression in squamous cell carcinoma of the lung (LUSC), its prognostic value, and prospective molecular mechanism. Using multiplatforms data, including in-house immunohistochemistry, in-house real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), in-house microarray, and public high-throughput data, the expression significance and prognostic role of ENO1 in LUSC tissues were analyzed comprehensively. With the combination of all eligible cases, compared with 941 non-LUSC lung tissues, ENO1 was significantly overexpressed in 1163 cases of LUSC (standardized mean difference (SMD) = 1.23, 95% confidence interval (CI) = 0.76-1.70, P < 0.001). ENO1 also displayed a great ability to differentiate LUSC tissues from non-LUSC lung tissues (AUC = 0.8705) with the comprehensive sensitivity being 0.88 [0.83-0.92], and comprehensive specificity being 0.89 [0.84-0.94]). Moreover, in 1860 cases of LUSC with survival information, patients with higher expression of ENO1 had poorer prognosis (hazard ratio (HR) = 1.20, 95% CI = 1.01-1.43, P = 0.043). ENO1 and its related genes mainly participated in the pathways of cell division and proliferation. In conclusion, the upregulation of ENO1 could affect the carcinogenesis and unfavorable outcome of LUSC.

Silver/silver halide supported on mesoporous ceria particles and photo-CWPO degradation under visible light for organic compounds in acrylonitrile wastewater.

Silver/silver halide supported on ordered mesoporous ceria particles (Ag/AgCl/CeO2) were rapidly prepared by microwave-assisted soft template method, deposition precipitation method and photoreduction method, using cerium nitrate and silver nitrate as raw materials and block copolymer F127 as a template. The morphology, structure and chemical composition of the catalyst were characterized by XRD, SEM, EDS, TEM, N2 adsorption-desorption and UV-Vis Drs. Catalytic wet peroxide system assisted with visible light photocatalysis (photo-CWPO) was conducted to investigate the performance of organics degradation by Ag/AgCl/CeO2 as a catalyst in acrylonitrile wastewater. The results showed that the Ag/AgCl/CeO2 prepared has an ordered mesoporous structure, Ag and AgCl are formed on the surface of CeO2, with a specific surface area of 302.6-336.2 m2 g-1 and the average pore size is 8.04-8.90 nm. There is a strong absorption in the visible region and a band gap of 2.9 eV. The Ag/AgCl/CeO2 catalyst has higher catalytic performance in the photo-CWPO system than in the CWPO system alone. Ag loading, catalyst and H2O2 dosage, and pH value can affect the COD removal. When the concentration of COD in acrylonitrile wastewater was 500 mg L-1, the amount of catalyst was 200 mg, the amount of H2O2 (30%) was 8 mL, and the reaction time was 60 min, the COD removal reached 90%.

Catalytic wet peroxide degradation of acrylonitrile wastewater by ordered mesoporous Ag/CeO2: synthesis, performance and kinetics.

Ordered mesoporous Ag/CeO2 catalysts have been successfully synthesized by a microwave assisted soft template method. The morphology, structure and chemical composition of the catalyst were characterized by XRD, N2 adsorption-desorption, SEM, EDS, TEM and XPS. The study of catalytic performance and reaction kinetics of organic matter degradation in acrylonitrile wastewater was performed in a catalytic wet peroxide (CWPO) system. The degradation pathways of organic matter in acrylonitrile wastewater were elucidated by temporal evolution of intermediates and final products detected by GC/MS analysis along with a continuous flow experiment study. The results show that the Ag/CeO2 has an ordered mesoporous structure, the specific surface area is 91.4-118.2 m2 g-1 and the average pore size is 12.63-16.86 nm. 0.4-Ag/CeO2 showed the best catalytic performance, the COD removal rate reached 94.6%, which was 30% higher than that of CeO2. The degradation is in accordance with the second-order reaction kinetics of the Arrhenius empirical model and Langmuir-Hinshelwood kinetic model. However the latter fits better, and the linear correlation coefficient R 2 is more than 0.98, which describes the adsorption catalytic mechanism of Ag/CeO2. According to the analysis by GC/MS, the organic compounds in acrylonitrile wastewater oxidized into intermediate compounds and other small compounds, then are further oxidized into carbon dioxide and water. The catalytic activity of Ag/CeO2 was the result of the combination of Lewis acid-base position of CeO2 and redox cycle of Ce3+/Ce4+.

Clinical significance of transcription factor RUNX2 in lung adenocarcinoma and its latent transcriptional regulating mechanism.

RUNX family transcription factor 2 (RUNX2) overexpression has been found in various human malignancies. However, the expression levels of RUNX2 mRNA and protein in lung adenocarcinoma (LUAD) were not investigated. This study aims to thoroughly analysis the expression level and potential mechanisms of RUNX2 mRNA in LUAD. We applied in-house immunohistochemistry, high-throughput RNA-sequencing, and gene microarrays to comprehensively investigate the expression level of RUNX2 in LUAD. A pool standard mean difference (SMD) and summary receiver operating characteristic curves (SROC) were calculated to assess the integrated expression value of RUNX2 in LUAD. The hazard ratios (HRs) were integrated to evaluate the overall prognostic effect of RUNX2 on the LUAD patients. The differentially expressed genes (DEGs) of LUAD, the potential target genes of RUNX2, and its co-expressed genes were overlapped to obtain a set of specific genes for GO and KEGG enrichment analyses. RUNX2 overexpression in LUAD was validated using a large number of cases (2 418 LUAD and 1 574 non-tumor lung samples). The pooled SMD was 0.85 (95 % CI: 0.64-1.05) and the area under the curve (AUC) of the SROC was 0.86 (95 %CI: 0.83-0.89). The integrated HR was 1.20 [1.04-1.38], indicating that increased expression of RUNX2 was an independent risk factor for the poor survival of the LUAD patients. RUNX2 and its transcriptionally regulates potential target genes may promote cell proliferation and drug resistance of LUAD by modulating the cell cycle and MAPK signaling pathways. RUNX2 can provide new research directions for targeted drug therapy and drug resistance for LUAD treatment.

Integrated expression analysis revealed RUNX2 upregulation in lung squamous cell carcinoma tissues.

This study aimed to investigate the clinicopathological significance and prospective molecular mechanism of RUNX family transcription factor 2 (RUNX2) in lung squamous cell carcinoma (LUSC). The authors used immunohistochemistry (IHC), RNA-seq, and microarray data from multi-platforms to conduct a comprehensive analysis of the clinicopathological significance and molecular mechanism of RUNX2 in the occurrence and development of LUSC. RUNX2 expression was significantly higher in 16 LUSC tissues than in paired non-cancerous tissues detected by IHC (P < 0.05). RNA-seq data from the combination of TCGA and genotype-tissue expression (GTEx) revealed significantly higher expression of RUNX2 in 502 LUSC samples than in 476 non-cancer samples. The expression of RUNX2 protein was also significantly higher in pathologic T3-T4 than in T1-T2 samples (P = 0.031). The pooled standardised mean difference (SMD) for RUNX2 was 0.87 (95% CI, 0.58-1.16), including 29 microarrays from GEO and one from ArrayExpress. The co-expression network of RUNX2 revealed complicated connections between RUNX2 and 45 co-expressed genes, which were significantly clustered in pathways including ECM-receptor interaction, focal adhesion, protein digestion and absorption, human papillomavirus infection and PI3K-Akt signalling pathway. Overexpression of RUNX2 plays an essential role in the clinical progression of LUSC.

The Significance of Negative Lymph Nodes in Esophageal Cancer After Curative Resection: A Retrospective Cohort Study.

OBJECTIVE: The impact of negative lymph nodes (NLNs) count on prognosis in esophageal cancer (EC) was analyzed using two institutions surgical database. METHODS: We conducted a retrospective study of 768 EC patients treated by surgical resection between January 2010 and December 2012. The effects of the NLNs count on prognosis was analyzed. Cox regression model was conducted to determine the significant prognostic elements. RESULTS: The number of NLNs was studied as a categorical variable based on the quartiles (Q1: ≤15, Q2: 16-21, Q3: 22-30, Q4: ≥31). And a better overall survival (OS) was observed with increasing number of NLNs (HR= 0.762; 95% CI, 0.596-0.974 for Q2, HR= 0.666; 95% CI, 0.516-0.860 for Q3 and HR= 0.588; 95% CI, 0.450-0.768 for Q4) (all P<0.05). Multivariate regression analysis revealed that the NLNs count was an independent prognostic factor. Besides, for patients in T2 or T3 stage, a high number of NLNs was found to be significantly associated with a favorable OS (log rank P<0.001). CONCLUSION: A higher number of NLNs is independently related to the better OS in EC patients after surgical resection.

The Clinical Significance and Potential Molecular Mechanism of PTTG1 in Esophageal Squamous Cell Carcinoma.

Esophageal squamous cell carcinoma (ESCC) is the major histological type of esophageal cancers worldwide. Transcription factor PTTG1 was seen highly expressed in a variety of tumors and was related to the degree of tumor differentiation, invasion, and metastasis. However, the clinical significance of PTTG1 had yet to be verified, and the mechanism of abnormal PTTG1 expression in ESCC was not clear. In this study, the comprehensive analysis and evaluation of PTTG1 expression in ESCC were completed by synthesizing in-house immunohistochemistry (IHC), clinical sample tissue RNA-seq (in-house RNA-seq), public high-throughput data, and literature data. We also explored the possible signaling pathways and target genes of PTTG1 in ESCC by combining the target genes of PTTG1 (displayed by ChIP-seq), differentially expressed genes (DEGs) of ESCC, and PTTG1-related genes, revealing the potential molecular mechanism of PTTG1 in ESCC. In the present study, PTTG1 protein and mRNA expression levels in ESCC tissues were all significantly higher than in non-cancerous tissues. The pool standard mean difference (SMD) of the overall PTTG1 expression was 1.17 (95% CI: 0.72-1.62, P < 0.01), and the area under curve (AUC) of the summary receiver operating characteristic (SROC) was 0.86 (95% CI: 0.83-0.89). By combining the target genes displayed by ChIP-seq of PTTG1, DEGs of ESCC, and PTTG1-related genes, it was observed that PTTG1 may interact with these genes through chemokines and cytokine signaling pathways. By constructing a protein-protein interaction (PPI) network and combining ChIP-seq data, we obtained four PTTG1 potential target genes, SPTAN1, SLC25A17, IKBKB, and ERH. The gene expression of PTTG1 had a strong positive correlation with SLC25A17 and ERH, which suggested that PTTG1 might positively regulate the expression of these two genes. In summary, the high expression of PTTG1 may play an important role in the formation of ESCC. These roles may be completed by PTTG1 regulating the downstream target genes SLC25A17 and ERH.

IL-27 alleviates the bleomycin-induced pulmonary fibrosis by regulating the Th17 cell differentiation.

BACKGROUND: Interleukin-27 (IL-27) is a multifunctional cytokine with both pro-inflammatory and immunoregulatory functions. At present, the role of IL-27 in pulmonary fibrosis remains unknown. METHODS: In this study, we observed the expression of IL-27/IL-27R in a mouse model of bleomycin (BLM)-induced pulmonary fibrosis. We verified the role of IL-27 using hematoxylin and eosin as well as Masson's staining methods and measuring the content of hydroxyproline as well as collagen I and III. We assessed the differentiation of T lymphocytes in the spleen and measured the concentration of cytokines in bronchoalveolar lavage fluid (BALF) and the expression level of relevant proteins in the JAK/STAT and TGF-ß/Smad signaling pathways in lung tissue. RESULTS: Increased IL-27 expression in BLM-induced pulmonary fibrosis was noted. IL-27 treatment may alleviate pulmonary fibrosis and increase the survival of mice. IL-27 inhibited the development of CD4(+) IL-17(+), CD4(+) IL-4(+) T, and CD4(+) Foxp3(+) cells and the secretion of IL-17, IL-4, IL-6, and TGF-ß. IL-27 induced the production of CD4(+) IL-10(+) and CD4(+) INF-γ(+) T cells. IL-27 decreased the levels of phosphorylated STAT1, STAT3, STAT5, Smad1, and Smad3 but increased the level of SOCS3. CONCLUSIONS: This study demonstrates that IL-27 potentially attenuates BLM-induced pulmonary fibrosis by regulating Th17 differentiation and cytokine secretion.