ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
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2024
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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
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
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
Joule
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2025
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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
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产品关联
DOI
高影响因子
Frontiers in Immunology
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2024
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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
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产品关联
DOI
PubMed
高影响因子
MICROCHEMICAL JOURNAL
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2025
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IF 5.1
Leijun Xu,Xiaoyan Geng,Jihong Deng,Zhenshuo Chen,Dengmin Li,Hui Xiao,Hui Jiang
The safety of wheat flour as a crucial food ingredient is paramount. Benzoyl peroxide (BPO) is banned in wheat flour in many regions due to health risks, yet it may be illegally added to enhance appearance. In the present study, BPO adulteration in wheat flour was quantitatively analyzed using terahertz time-domain spectroscopy (THz-TDS). First, the spectral characterization of wheat samples at 0.1–6 THz was obtained, and THz spectral data within specific frequency bands was processed using Savitzky-Golay (SG) filtering. Then, Competitive Adaptive Reweighted Sampling (CARS) and Variable Combination Population Analysis (VCPA) methods were used to select optimal frequency points. Finally, a chemometric model combining linear models (Lasso Regression, Ridge Regression, and Partial Least Squares Regression) with nonlinear models (Back-Propagation Neural Network, Random Forest, and Support Vector Machine) was employed for quantitative analysis. The experimental outcomes demonstrated that the detection models established through the standard analytical process met the detection requirements ( R P ≥ 0.95), and the best performance results were obtained from the chemometric models based on linear relationships. Among them, the Lasso Regression and Ridge Regression models obtained after optimization of the VCPA method had high prediction accuracy ( R P ≥ 0.97). The results showed that the THz spectroscopy technique combined with standard chemical analysis procedure could effectively realize the quantitative detection of BPO in wheat flour, providing an efficient and reliable tool for the safety detection of wheat flour.
研究领域:
食品安全
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光谱学
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分析化学
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化学计量学
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应用物理学
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产品关联
DOI
高影响因子
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