MATERIALS CHEMISTRY AND PHYSICS
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2025
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IF 4.7
Xialin Guo,Zhen Yang,Yuwei Wang,Zhiyuan Qu,Yun Lu,Yingjun Lou,Meiling Chen,Xiaoliang Ren
Natural products and their derivatives are critical resources for drug discovery, with their pharmaceutical applications requiring efficient extraction and purification methodologies. In recent years, the emergence of sustainable development paradigms and advancements in naturally derived apt nanomaterials bring in graphene composites, featuring unique two-dimensional architectures and tunable surface chemical properties, as ideal adsorbents to amplify natural product processing efficiency. In this study, three graphene derivatives—graphene oxide (GO), carboxylated graphene oxide (GO-COOH), and tetraethylenepentamine-functionalized reduced graphene oxide (rGO-TEPA), were employed as adsorbents for the green extraction and purification of 28 types of structurally diverse natural products. By developing a LASSO regression model incorporating 1332 molecular descriptors, quantitative structure-adsorption relationships (QSARs) were established. Their critical descriptors (R7m, nRCOOH, and E3s) were identified to elucidate the significant influence of atomic polarizability, electronegativity, and hydrogen bonding on adsorption mechanisms. Structure-adsorption correlation analyses demonstrate that this machine learning framework facilitates high-throughput screening of natural product libraries using graphene-based adsorbents. This methodological advancement promises to enhance natural product utilization and streamline small-molecule drug discovery through rational adsorbent selection.
研究领域:
化学信息学
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材料科学
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药学
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机器学习
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环境科学
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纳米技术
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产品关联
DOI
高影响因子
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
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2024
·
IF 6.2
Jingyi Tang,Yao Chen,Peng Xue,Yang Chen,Huijun Kong,Cuilan Lin,Xiumin Wang,Shijian Liu
Synthetic steroid hormones are an emerging class of environmental pollutants, but their influence on pubertal timing remains unclear. This case-control study explored the association between synthetic steroid hormone exposure and precocious puberty. Using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), synthetic steroid hormones were detected in urine samples from 229 Chinese girls, aged 6–9 years. Puberty status was assessed using Tanner staging by professional pediatricians. We conducted the least absolute shrinkage and selection operator (LASSO) regression combined with logistic regression. Besides, we evaluated the joint effects of steroid hormone mixture and identified the main contributor using the Weighted quantile sum (WQS) model and Bayesian kernel machine regression (BKMR) model. The logistic regression model reflected an inverse individual association between precocious puberty and halcinonide [OR (95 %CI): 0.20 (0.07, 0.46)], and budesonide [OR (95 %CI): 0.77 (0.62, 0.95)]. In the joint effects utilizing the WQS model, precocious puberty showed a marginal association with steroid hormone mixture, but was not significant [OR (95 %CI): 0.88 (0.75, 1.04)]. Prednisolone (0.31), fluorometholone acetate (0.24), and dexamethasone acetate (0.12) had the highest weight. Consistently, mixture exposure was not associated with precocious puberty in the BKMR model. In conclusion, precocious puberty was associated with halcinonide and budesonide exposure, but not steroid hormone mixture among girls. It highlighted the management of the residual synthetic steroid hormones in the environment and provided a direction for the prevention of precocious puberty.
研究领域:
内分泌学
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环境健康科学
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DOI
PubMed
高影响因子
ACS Catalysis
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2026
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IF 13.1
Menghan Shi,Yuwei Duan,Shao-Yang Hou,Liangxu Xie,Weifeng Sun,Liubin Feng,Youming Zhang,Xiaoying Bian,Guannan Zhong
The domain of unknown function 6895 (DUF6895) constitutes a poorly characterized protein family. Its functional obscurity positions this family of proteins as high-value targets to unlock cryptic enzymatic activities and molecular mechanisms. Here, we identified a DUF6895-encoding gene within a lasso peptide biosynthetic gene cluster. The DUF6895 protein ShpE functions as a distinct epimerase that inverts the configuration of phenylalanine in the linear precursor peptide, a modification essential for the subsequent tryptophan dihydroxylation. Structural and mechanistic analyses demonstrated that ShpE provides a hydrophobic cavity to accommodate phenylalanine and employs acid–base chemistry to facilitate reversible epimerization via dual His/Glu catalytic dyads. These dyads are evolutionarily conserved across DUF6895 proteins, suggesting the majority of these family members function as epimerases through a common molecular mechanism. Our work on ShpE expands the enzymatic repertoire for epimerization in ribosomally synthesized and post-translationally modified peptides (RiPPs), providing crucial insights into the function and mechanism of the neglected DUF6895 proteins.
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DOI
高影响因子
Frontiers in Immunology
·
2023
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IF 7.3
Ling Yang,Xueyuan Yu,Meng Liu,Yang Cao
Introduction Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis. Methods A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx. Results A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein–protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results. Discussion In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.
研究领域:
医学
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生物信息学
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DOI
PubMed
高影响因子
COMPUTER JOURNAL
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2010
·Vol. 53
·期 9
·1401-1414
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IF 1.394
Anagnostopoulos Christoforos,Adams Niall M.,Hand David J.
Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis.
研究领域:
计算机科学
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统计学
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环境科学
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DOI
高影响因子
FOOD CHEMISTRY
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2025
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IF 9.8
Wenhui Qu,Luying Tang,Ruoshu Li,Weijia Liu,Xiaoming Jiang,Qing Kong,Yunqi Wen,Changhu Xue
Antarctic krill, a shrimp-like crustacean, whose quality strongly influences the quality of fermented shrimp sauce, yet the relationship and prediction methods remain unclear. This study evaluated 75 krill samples of varying freshness and their corresponding shrimp sauces, and successfully developed a high-performance predictive model linking raw material quality to product quality. Shrimp spoiled significantly after only 24 h at room temperature, exhibiting darkened color, increased spoilage-related compounds, with yield and conversion rate sharply decreasing from 80.4 % and 87.1 % to 58.9 % and 63.7 %, respectively. Machine learning with multiple feature selection methods and models showed that Lasso regression effectively reduced collinearity, and its integration with a linear model enhanced prediction accuracy, with nearly 80 % of features well-fitted and about 70 % showing mean absolute percentage error below 5 %. This study successfully established an accurate model linking krill freshness to shrimp sauce quality, which offered guidance for krill utilization and intelligent fermentation.
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产品关联
DOI
PubMed
高影响因子
Frontiers in Pharmacology
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2025
·
IF 4.8
Miao Li,Na Gao,Lei Xu,Peng Ge,Nan Jiang,Yuhong Shang
Background: The natural compound paeonol exhibits therapeutic promise against cervical carcinoma, though its precise molecular mechanisms remain undefined.Methods: First, we treated human cervical cancer (HeLa) cells with different concentrations of paeonol. Cellular proliferation and apoptotic responses were evaluated via cell-counting kit 8 (CCK8) assays and flow cytometric analysis. Subsequent transcriptomic profiling employed RNA sequencing coupled with alternative splicing assessment to detect differentially expressed genes (DEGs). Protein interaction networks were established for pivotal DEGs, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment investigations. Clinical data pertinent to cervical cancer were retrieved from The Cancer Genome Atlas (TCGA). Prognostic model development incorporated Kaplan–Meier survival estimation, Least Absolute Shrinkage and Selection Operator (LASSO) regression, alongside univariate and multivariate COX proportional hazards analyses, with model accuracy subsequently assessed. Finally, Quantitative reverse transcription polymerase chain reaction (qRT-PCR) validated DEG expression.Results: Paeonol treatment suppressed proliferation while inducing apoptosis in HeLa cells. Transcriptomic and splicing analyses revealed 12 critical DEGs: NLRP1, FN1, NQO2, NREP, B4GALNT1, ANK3, FAM219A, ODF3B, MAPK15, EPGN, MUC1, and MEG3. Enrichment analyses indicated these DEGs principally associate with inflammatory processes and the biological regulation of cellular proliferation and apoptotic death. Analysis of clinical outcomes in 197 TCGA patients demonstrated significantly enhanced five-year survival probability within the low-risk cohort. FN1, NQO2, and ODF3B were incorporated into a prognostic signature following LASSO regression. Univariate and multivariate COX analyses identified T stage, tumor grade, and differential expression of these three genes as significant outcome predictors; the resultant prognostic model exhibited robust accuracy. qRT-PCR results corroborated the RNA sequencing data concerning DEG expression patterns.Conclusion: Paeonol modulates HeLa cell proliferation and apoptosis through regulation of 12 key genes, including FN1. This activity involves governing inflammatory responses alongside cellular proliferation, migration, and differentiation processes. These findings offer a theoretical foundation supporting paeonol’s potential clinical utility in cervical cancer management.
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产品关联
DOI
PubMed
高影响因子
APPLIED CATALYSIS B-ENVIRONMENTAL
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2025
·
IF 21.1
Peng Wang,Zijing Liu,Yanwei Xing,Bin Fan,Yulong He,Ting Gao,Wenlei Zhang,Yunfei Gao,Shuang Wang,Jinping Li
Electrocatalytic nitrogen reduction reaction (ENRR) is considered as a potential pathway for green synthesis of ammonia. However, the kinetic barriers of ENRR remain a tough challenge, which is imperative to design high effective electrocatalysts for accelerated activation of N N. Herein, the FeS/ZnS/ZIF-8-L with rich heterogeneous interfaces was successfully fabricated by governing the ordered lasso arrangement of Fe and Zn nodes in Fe-2MIM/ZIF-8-L precursor, where Fe and Zn were separately fixed in the confined space of ZIF-8-L and Fe-2MIM. Such intertwined Fe-2MIM/ZIF-8-L structure was beneficial to control the location and concentration of FeS and ZnS after sulfidation pyrolysis. The high-efficiency FeS/ZnS/ZIF-8-L exposed more catalytic active sites and possessed optimal electronic structure resulting in the lowered energy barrier of ENRR and the enhanced adsorption and activation of N 2 , which confirmed by experimental and theoretical characterization results. As a result, FeS/ZnS/ZIF-8-L displayed an optimal NH 3 yield rate of 89.52 μg h −1 mg −1 and Faradaic efficiency of 16.78 %, which surpassed most reported heterostructured ENRR catalysts. Our findings shed a new insight to govern the interfaces density of heterostructured electrocatalysts by predesigning the location of two metals in precursor, which can motivate an in-depth exploration to advanced catalysts for various electrocatalytic reactions.
研究领域:
电催化
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材料科学
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纳米技术
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能源化学
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可持续化学
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产品关联
DOI
高影响因子
ACS Nano
·
2024
·
IF 15.8
Mengjie Wang,Xin Dai,Xu Yang,Baichuan Jin,Yueli Xie,Chenlu Xu,Qiqi liu,Lichao Wang,Lisha Ying,Weishan Lu,Qixun Chen,Ting Fu,Dan Su,Yuan Liu,Weihong Tan
Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.
研究领域:
医学诊断
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蛋白质组学
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机器学习
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癌症研究
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临床生物化学
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产品关联
DOI
PubMed
高影响因子
Joule
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2025
·
IF 35.4
Chenyang Zhang,Yuteng Jia,Bingqian Zhang,Qiangqiang Zhao,Ruida Xu,Shuping Pang,Han Wang,Stefaan De Wolf,Kai Wang
Perovskite solar cells’ performance is critically governed by interfacial chemistry. Here, we combine correlation analysis, hierarchical clustering, and least absolute shrinkage and selection operator (LASSO) regression to optimize feature engineering, followed by employing LASSO and elastic net (ENET) regression to develop a screening model. A multifunctional buried interface molecule, (2-(1,3-dioxo-1 H -benzo[ de ]isoquinolin-2(3H)-yl)ethyl)phosphonic acid (BIPA), was successfully screened and synthesized. As expected, BIPA binds strongly with SnO 2 through Sn···O–P coordination, effectively passivating Sn 2+ dangling bonds and oxygen vacancies. The Fermi level was shifted upward, facilitating electron extraction. Besides, BIPA modulated crystallization dynamics and alleviated compressive strain through C=O···Pb interaction, enabling the growth of dense perovskite films. Consequently, the BIPA-modified device achieved an efficiency of 26.3%. Varied band-gap performance improvements (23.2% for 1.67 eV and 18.5% for 1.85 eV) further underscored the universality of this approach. Unencapsulated devices retained 91% and 92% of their initial efficiency after 1,600 h of International Summit on Organic Photovoltaic Stability (ISOS-L-1) protocol and 2,000 h of ISOS-D-2 protocol, respectively.
研究领域:
材料科学
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光伏技术
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机器学习应用
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能源材料
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化学工程
✓ 已验证
产品关联
DOI
高影响因子
Discover Oncology
·
2024
·Vol. 15
·期 1
·1-18
·
IF 2.8
Feng Rui,Li Haolin,Meng Tong,Fei Mingtian,Yang Cheng
Purpose This study aimed to construct an m6A and cuproptosis-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using the information acquired from The Cancer Genome Atlas (TCGA) database. Methods First, the co-expression analysis was performed to identify lncRNAs linked with N6-methyladenosine (m6A) and cuproptosis in ccRCC. Then, a model encompassing four candidate lncRNAs was constructed via univariate, least absolute shrinkage together with selection operator (LASSO), and multivariate regression analyses. Furthermore, Kaplan–Meier, principal component, functional enrichment annotation, and nomogram analyses were performed to develop a risk model that could effectively assess medical outcomes for ccRCC cases. Moreover, the cellular function of NFE4 in Caki-1/OS-RC-2 cultures was elucidated through CCK-8/EdU assessments and Transwell experiments. Dataset outcomes indicated that NFE4 can have possible implications in m6A and cuproptosis, and may promote ccRCC progression. Results We constructed a panel of m6A and cuproptosis-related lncRNAs to construct a prognostic prediction model. The Kaplan–Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. Furthermore, the m6A and cuproptosis-related lncRNA model indicated higher diagnostic efficiency than other clinical features. Moreover, the NFE4 function analysis indicated a gene associated with m6A and cuproptosis-related lncRNAs in ccRCC. It was also revealed that the proliferation and migration of Caki-1 /OS-RC-2 cells were inhibited in the NFE4 knockdown group. Conclusion Overall, this study indicated that NFE4 and our constructed risk signature could predict outcomes and have potential clinical value.
研究领域:
生物信息学
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癌症生物学
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分子生物学
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基因组学
✓ 已验证
DOI
PubMed
高影响因子
Frontiers in Immunology
·
2024
·
IF 5.7
Han Xiaoxi,Leng Chuanyu,Zhao Shufen,Wang Shasha,Chen Shuming,Wang Shibo,Zhang Mengqi,Li Xiangxue,Lu Yangyang,Wang Bing,Qi Weiwei
Background: Gastric cancer (GC) poses a global health challenge due to its widespread prevalence and unfavorable prognosis. Although immunotherapy has shown promise in clinical settings, its efficacy remains limited to a minority of GC patients. Manganese, recognized for its role in the body’s anti-tumor immune response, has the potential to enhance the effectiveness of tumor treatment when combined with immune checkpoint inhibitors.Methods: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases was utilized to obtain transcriptome information and clinical data for GC. Unsupervised clustering was employed to stratify samples into distinct subtypes. Manganese metabolism- and immune-related genes (MIRGs) were identified in GC by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. We conducted gene set variation analysis, and assessed the immune landscape, drug sensitivity, immunotherapy efficacy, and somatic mutations. The underlying role of NPR3 in GC was further analyzed in the single-cell RNA sequencing data and cellular experiments.Results: GC patients were classified into four subtypes characterized by significantly different prognoses and tumor microenvironments. Thirteen genes were identified and established as MIRGs, demonstrating exceptional predictive effectiveness in GC patients. Distinct enrichment patterns of molecular functions and pathways were observed among various risk subgroups. Immune infiltration analysis revealed a significantly greater abundance of macrophages and monocytes in the high-risk group. Drug sensitivity analysis identified effective drugs for patients, while patients in the low-risk group could potentially benefit from immunotherapy. NPR3 expression was significantly downregulated in GC tissues. Single-cell RNA sequencing analysis indicated that the expression of NPR3 was distributed in endothelial cells. Cellular experiments demonstrated that NPR3 facilitated the proliferation of GC cells.Conclusion: This is the first study to utilize manganese metabolism- and immune-related genes to identify the prognostic MIRGs for GC. The MIRGs not only reliably predicted the clinical outcome of GC patients but also hold the potential to guide future immunotherapy interventions for these patients.
研究领域:
肿瘤学
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免疫学
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基因组学
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生物信息学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
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