Accurate classification of pulmonary nodules is critical for early diagnosis of lung cancer. However, non-invasive and accurate diagnosis of benign and malignant pulmonary nodules faces great challenges. In this study...Accurate classification of pulmonary nodules is critical for early diagnosis of lung cancer. However, non-invasive and accurate diagnosis of benign and malignant pulmonary nodules faces great challenges. In this study, we develop a nano zero-valent iron(nZVI)-assisted laser desorption/ionization mass spectrometry(LDI MS) platform, which enables ultra-high-throughput acquisition of abundant metabolic fingerprint information of serum in negative ion mode. We further recruit a large-scale multicenter prospective cohort and collect 1099 serum samples from participants with benign and malignant nodules. The accurate machine learning models are built and validated based on n ZVI-assisted LDI MS metabolomics to achieve efficient classification of benign and malignant nodules. Using our established stacking ensemble learning model, the AUC of the ROC curve for benign and malignant lung nodule classification can be as high as 0.9, and the sensitivity can reach 85.5%, which is significantly better than existing clinical models. This work provides an integrated workflow from detection technology to diagnostic models for biomarkerbased pulmonary nodule diagnosis, which would be widely used in rapid and large-scale screening of pulmonary nodules.展开更多
In the twenty-first century,water contamination with pharmaceutical residues is becoming a global phenomenon and a threat.Antibiotic residues and antibiotic resistance genes(ARGs)are recognized as new emerging water p...In the twenty-first century,water contamination with pharmaceutical residues is becoming a global phenomenon and a threat.Antibiotic residues and antibiotic resistance genes(ARGs)are recognized as new emerging water pollutants because they can negatively affect aquatic ecosystems and human health,thereby posing a complex environmental problem.These nano-adsorbents of the next generation can remove these pollutants at low concentrations.This study focuses on the chemical synthesis of copper oxide nanoparticles(CuONPs)and nano-zero-valent iron(nZVI)used as nano-adsorbents for levofloxacin removal from water samples and antibiotic-resistant genes.The CuONPs and nZVI are initially characterized by transmission electron microscopy,scanning electron microscopy,and X-ray diffraction.The levofloxacin adsorption isotherm on the CuONPS and nZVI shows the best fit with the Langmuir isotherm model,exhibiting correlation coefficients(R^(2))of 0.993 and 0.999,respectively.The adsorption activities of CuONPS and nZVI were fitted to a pseudo-second-order kinetic model with correlation coefficients(R^(2))of 0.983 and 0.994,respectively.The maximum levofloxacin removal capacity was observed at(89%),(84%),(89%),(88%)and(71.6)at pH 7 and adsorbent dose(0.06 mg/L),initial LEV concentration(1 mg/L),temperature 25℃,and contact time 120 min for CuONPs.Removal efficiency was(91%),(90.6%),(91%),(89%),and(80%),at pH 7,adsorbent dose(0.06),initial LEV concentration(1 mg/L),temperature 35℃,and contact time 120 min.The levofloxacin adsorption is an exothermic process for nZVI and CuONPs,according to thermodynamic analysis.A thermodynamic analysis indicated that each adsorption process is spontaneous.Several genera,including clinically pathogenic bacteria(e.g.,Acinetobacter_baumannii,Helicobacter_pylori,Escherichia_coli,Pseudomonas_aeruginosa,Clostridium_beijerinckii,Escherichia/Shigella_coli,Helicobacter_cetorum,Lactobacillus_gasseri,Bacillus_cereus,Deinococcus_radiodurans,Rhodobacter_sphaeroides,Propionibacterium_acnes,and Bacteroides_vulgatus)were relatively abundant in hospital wastewater.Furthermore,37 antibiotic resistance genes(ARGs)were quantified in hospital wastewater.The results demonstrated that 95.01%of nZVI and 91.4%of CuONPs are effective adsorbents for removing antibiotic-resistant bacteria from hospital effluent.The synthesized nZVI and CuONPs have excellent reusability and can be considered cost effective and eco-friendly adsorbents.展开更多
【背景】硫化纳米零价铁(sulfidated nano-scale zero valent iron,S-nZVI)作为一种新型铁基还原性反应材料被广泛用于氯代烃污染地下水的修复研究。硫化改性能够提高常规纳米零价铁(nano-scale zero valent iron,nZVI)对氯代烃的还原...【背景】硫化纳米零价铁(sulfidated nano-scale zero valent iron,S-nZVI)作为一种新型铁基还原性反应材料被广泛用于氯代烃污染地下水的修复研究。硫化改性能够提高常规纳米零价铁(nano-scale zero valent iron,nZVI)对氯代烃的还原脱氯活性,同时抑制nZVI与水的副反应,但常用的化学硫化方法存在成本高、工艺复杂等问题,限制了其应用。近年来,基于硫酸盐还原菌(sulfate-reducing bacteria,SRB)的生物硫化途径受到关注,该方法利用SRB代谢产物S^(2-)实现nZVI的原位硫化,具有绿色、可持续的优势。然而,目前针对SRB体系中nZVI投加量对生成的生物硫化nZVI(S-nZVI^(bio))颗粒界面结构和还原脱氯性能的影响机制仍缺乏系统认识。【目的】通过构建不同nZVI投加量的SRB培养体系,阐明不同nZVI投加量对S-nZVI^(bio)界面结构及其对三氯乙烯(trichloroethene,TCE)还原脱氯性能的影响机制。【方法】在SRB介导的nZVI生物硫化体系中,通过设置0.1、1.0、5.0 g/L 3种nZVI投加条件,获得不同S-nZVI^(bio)颗粒,利用透射电子显微镜(transmission electron microscopy,TEM)和X射线光电子能谱仪(X-ray photoelectron spectroscopy,XPS)表征其界面结构,并通过批式实验评估TCE的降解动力学、产物分布及电子效率。【结果】低投加量(0.1 g/L)时Fe^(2+)供给不足,颗粒表面形成不完整FeSx包覆层,并且附着较多的微生物产物,TCE降解速率和电子效率均偏低;高投加量(5 g/L)时因S^(2-)相对不足导致硫化不完全,虽表现出最高的TCE降解速率,但电子效率下降;中等投加量(1 g/L)下Fe^(2+)与S^(2-)供给良好,颗粒表面生成均一致密的FeSx层,同步实现了较高的TCE降解速率与电子效率。【结论】SRB介导的生物硫化能够显著提升nZVI的脱氯性能,但其效果受不同nZVI投加量条件下Fe^(2+)与S^(2-)的供给关系影响。适量投加有利于获得界面结构完整、电子效率与降解性能兼优的S-nZVI^(bio)。该发现可为“定制”合理高效的nZVI“生物硫化”策略提供理论支持。展开更多
基金financially supported by the Fundamental Research Funds for the Central Universities (No. WHU 2042024kf0009)National Key Research and Development Program of China (No. 2021YFC2700700)the National Natural Science Foundation of China (Nos. 22074111, 22004093)。
文摘Accurate classification of pulmonary nodules is critical for early diagnosis of lung cancer. However, non-invasive and accurate diagnosis of benign and malignant pulmonary nodules faces great challenges. In this study, we develop a nano zero-valent iron(nZVI)-assisted laser desorption/ionization mass spectrometry(LDI MS) platform, which enables ultra-high-throughput acquisition of abundant metabolic fingerprint information of serum in negative ion mode. We further recruit a large-scale multicenter prospective cohort and collect 1099 serum samples from participants with benign and malignant nodules. The accurate machine learning models are built and validated based on n ZVI-assisted LDI MS metabolomics to achieve efficient classification of benign and malignant nodules. Using our established stacking ensemble learning model, the AUC of the ROC curve for benign and malignant lung nodule classification can be as high as 0.9, and the sensitivity can reach 85.5%, which is significantly better than existing clinical models. This work provides an integrated workflow from detection technology to diagnostic models for biomarkerbased pulmonary nodule diagnosis, which would be widely used in rapid and large-scale screening of pulmonary nodules.
基金funding provided by The Science,Technology&Innovation Funding Authority(STDF)in cooperation with The Egyptian Knowledge Bank(EKB).
文摘In the twenty-first century,water contamination with pharmaceutical residues is becoming a global phenomenon and a threat.Antibiotic residues and antibiotic resistance genes(ARGs)are recognized as new emerging water pollutants because they can negatively affect aquatic ecosystems and human health,thereby posing a complex environmental problem.These nano-adsorbents of the next generation can remove these pollutants at low concentrations.This study focuses on the chemical synthesis of copper oxide nanoparticles(CuONPs)and nano-zero-valent iron(nZVI)used as nano-adsorbents for levofloxacin removal from water samples and antibiotic-resistant genes.The CuONPs and nZVI are initially characterized by transmission electron microscopy,scanning electron microscopy,and X-ray diffraction.The levofloxacin adsorption isotherm on the CuONPS and nZVI shows the best fit with the Langmuir isotherm model,exhibiting correlation coefficients(R^(2))of 0.993 and 0.999,respectively.The adsorption activities of CuONPS and nZVI were fitted to a pseudo-second-order kinetic model with correlation coefficients(R^(2))of 0.983 and 0.994,respectively.The maximum levofloxacin removal capacity was observed at(89%),(84%),(89%),(88%)and(71.6)at pH 7 and adsorbent dose(0.06 mg/L),initial LEV concentration(1 mg/L),temperature 25℃,and contact time 120 min for CuONPs.Removal efficiency was(91%),(90.6%),(91%),(89%),and(80%),at pH 7,adsorbent dose(0.06),initial LEV concentration(1 mg/L),temperature 35℃,and contact time 120 min.The levofloxacin adsorption is an exothermic process for nZVI and CuONPs,according to thermodynamic analysis.A thermodynamic analysis indicated that each adsorption process is spontaneous.Several genera,including clinically pathogenic bacteria(e.g.,Acinetobacter_baumannii,Helicobacter_pylori,Escherichia_coli,Pseudomonas_aeruginosa,Clostridium_beijerinckii,Escherichia/Shigella_coli,Helicobacter_cetorum,Lactobacillus_gasseri,Bacillus_cereus,Deinococcus_radiodurans,Rhodobacter_sphaeroides,Propionibacterium_acnes,and Bacteroides_vulgatus)were relatively abundant in hospital wastewater.Furthermore,37 antibiotic resistance genes(ARGs)were quantified in hospital wastewater.The results demonstrated that 95.01%of nZVI and 91.4%of CuONPs are effective adsorbents for removing antibiotic-resistant bacteria from hospital effluent.The synthesized nZVI and CuONPs have excellent reusability and can be considered cost effective and eco-friendly adsorbents.
