Nature Biomedical Engineering最新文献

英文中文

Uncovering evolutionarily remote and highly potent antimicrobial peptides with protein language models. 用蛋白质语言模型揭示进化上遥远和高效的抗菌肽。

IF 28.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-03 DOI: 10.1038/s41551-026-01630-w

Qinze Yu,Hongbin Liu,Haimei Shi,Yerzhan Abdrakhmanov,Junbo Shen,Chunhe Zhang,Zhihang Dong,Licheng Zong,Longlong Si,Lei Dai,Yu Li

Identifying evolutionarily remote antimicrobial peptides (AMPs) is crucial for discovering underexplored clinical candidates to combat antibiotic resistance. Existing experimental and computational methods are limited by their reliance on sequence identity to known AMPs, missing distant homologues. Here we introduce HMD-AMP, a protein language model-based approach for AMP discovery. HMD-AMP outperforms previous methods in identifying evolutionarily distant AMPs and enables the discovery of unknown and highly potent AMPs from metagenomic data. Applied to host and gut microorganism genomes of nine mammals, HMD-AMP revealed over 37 million predicted AMPs. Of 91 high-confidence sequences experimentally validated, 74 showed strong antibacterial activity and 48 were evolutionarily remote from known AMPs. Four of these AMPs exhibited broad-spectrum antibacterial activity at low effective concentrations and showed low toxicity, with the most potent peptide demonstrating therapeutic efficacy in a mouse model of peritoneal Escherichia coli infection. This study introduces an effective strategy to uncover AMPs.

鉴定进化远程抗菌肽（amp）对于发现未开发的临床候选物以对抗抗生素耐药性至关重要。现有的实验和计算方法受限于它们依赖于已知amp的序列同一性，而缺少遥远的同源物。在这里，我们介绍HMD-AMP，一种基于蛋白质语言模型的AMP发现方法。HMD-AMP在识别进化上遥远的amp方面优于以前的方法，并能够从宏基因组数据中发现未知和高效的amp。将HMD-AMP应用于9种哺乳动物的宿主和肠道微生物基因组，发现超过3700万个预测amp。在实验验证的91个高置信度序列中，74个显示出较强的抗菌活性，48个在进化上与已知的AMPs距离较远。其中四种抗菌肽在低有效浓度下表现出广谱抗菌活性，并显示出低毒性，其中最有效的肽在小鼠腹膜大肠杆菌感染模型中显示出治疗效果。本研究介绍了一种发现amp的有效策略。

{"title":"Uncovering evolutionarily remote and highly potent antimicrobial peptides with protein language models.","authors":"Qinze Yu,Hongbin Liu,Haimei Shi,Yerzhan Abdrakhmanov,Junbo Shen,Chunhe Zhang,Zhihang Dong,Licheng Zong,Longlong Si,Lei Dai,Yu Li","doi":"10.1038/s41551-026-01630-w","DOIUrl":"https://doi.org/10.1038/s41551-026-01630-w","url":null,"abstract":"Identifying evolutionarily remote antimicrobial peptides (AMPs) is crucial for discovering underexplored clinical candidates to combat antibiotic resistance. Existing experimental and computational methods are limited by their reliance on sequence identity to known AMPs, missing distant homologues. Here we introduce HMD-AMP, a protein language model-based approach for AMP discovery. HMD-AMP outperforms previous methods in identifying evolutionarily distant AMPs and enables the discovery of unknown and highly potent AMPs from metagenomic data. Applied to host and gut microorganism genomes of nine mammals, HMD-AMP revealed over 37 million predicted AMPs. Of 91 high-confidence sequences experimentally validated, 74 showed strong antibacterial activity and 48 were evolutionarily remote from known AMPs. Four of these AMPs exhibited broad-spectrum antibacterial activity at low effective concentrations and showed low toxicity, with the most potent peptide demonstrating therapeutic efficacy in a mouse model of peritoneal Escherichia coli infection. This study introduces an effective strategy to uncover AMPs.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"268 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging tissue-resident memory T cells for non-invasive immune monitoring via microneedle skin patches. 利用组织驻留记忆T细胞通过微针皮肤贴片进行非侵入性免疫监测。

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-02 DOI: 10.1038/s41551-026-01617-7

Sasan Jalili, Ryan R Hosn, Wei-Che Ko, Khashayar Afshari, Ashok Kumar Dhinakaran, Namit Chaudhary, Laura Maiorino, Nazgol-Sadat Haddadi, Anusha Nathan, Matthew A Getz, Gaurav D Gaiha, Mehdi Rashighi, John E Harris, Paula T Hammond, Darrell J Irvine

Detecting antigen-specific lymphocytes is crucial for immune monitoring in vaccination, infection, cancer and autoimmunity. However, their low frequency and dispersed distribution make reliable detection challenging. We developed a strategy exploiting the functions of tissue-resident memory T cells (T_RMs) to locally concentrate target circulating immune cells in the skin, enabling their non-invasive sampling using a microneedle (MN) skin patch. T_RMs were first induced by antigen sensitization and subsequently restimulated by intradermal inoculation of the same antigen to trigger the 'alarm' and immune recruitment functions of these cells, leading to accumulation of antigen-specific T cells from the circulation over several days. In mouse vaccination models, MN patches with optimized hydrogel coatings effectively isolated thousands of live antigen-specific lymphocytes and innate immune cells. In a human participant with allergic contact dermatitis, allergen-induced T_RM stimulation followed by MN application recovered diverse lymphocyte populations absent from untreated skin sites. These results establish T_RM restimulation coupled with MN sampling as a non-invasive platform to monitor both local and systemic antigen-specific immune responses across disease or vaccination settings.

检测抗原特异性淋巴细胞对于疫苗接种、感染、癌症和自身免疫的免疫监测至关重要。然而，它们的低频率和分散分布给可靠的检测带来了挑战。我们开发了一种利用组织驻留记忆T细胞（TRMs）功能的策略，以局部集中皮肤中的目标循环免疫细胞，使其能够使用微针（MN）皮肤贴片进行非侵入性采样。trm首先通过抗原致敏诱导，随后通过皮内接种相同抗原重新刺激，触发这些细胞的“警报”和免疫募集功能，导致抗原特异性T细胞在数天内从循环中积累。在小鼠疫苗接种模型中，具有优化水凝胶涂层的MN贴片有效地分离了数千个抗原特异性淋巴细胞和先天免疫细胞。在一名患有过敏性接触性皮炎的人类参与者中，过敏原诱导的TRM刺激和MN应用恢复了未治疗皮肤部位缺失的多种淋巴细胞群。这些结果建立了TRM再刺激与MN采样相结合的非侵入性平台，以监测局部和全身抗原特异性免疫反应，跨越疾病或疫苗接种设置。

{"title":"Leveraging tissue-resident memory T cells for non-invasive immune monitoring via microneedle skin patches.","authors":"Sasan Jalili, Ryan R Hosn, Wei-Che Ko, Khashayar Afshari, Ashok Kumar Dhinakaran, Namit Chaudhary, Laura Maiorino, Nazgol-Sadat Haddadi, Anusha Nathan, Matthew A Getz, Gaurav D Gaiha, Mehdi Rashighi, John E Harris, Paula T Hammond, Darrell J Irvine","doi":"10.1038/s41551-026-01617-7","DOIUrl":"10.1038/s41551-026-01617-7","url":null,"abstract":"Detecting antigen-specific lymphocytes is crucial for immune monitoring in vaccination, infection, cancer and autoimmunity. However, their low frequency and dispersed distribution make reliable detection challenging. We developed a strategy exploiting the functions of tissue-resident memory T cells (TRMs) to locally concentrate target circulating immune cells in the skin, enabling their non-invasive sampling using a microneedle (MN) skin patch. TRMs were first induced by antigen sensitization and subsequently restimulated by intradermal inoculation of the same antigen to trigger the 'alarm' and immune recruitment functions of these cells, leading to accumulation of antigen-specific T cells from the circulation over several days. In mouse vaccination models, MN patches with optimized hydrogel coatings effectively isolated thousands of live antigen-specific lymphocytes and innate immune cells. In a human participant with allergic contact dermatitis, allergen-induced TRM stimulation followed by MN application recovered diverse lymphocyte populations absent from untreated skin sites. These results establish TRM restimulation coupled with MN sampling as a non-invasive platform to monitor both local and systemic antigen-specific immune responses across disease or vaccination settings.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":26.8,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147344771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Confounding factors and biases abound when predicting molecular biomarkers from histological images. 当从组织学图像预测分子生物标志物时，混杂因素和偏差比比皆是。

IF 28.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-02 DOI: 10.1038/s41551-026-01616-8

Muhammad Dawood,Kim Branson,Sabine Tejpar,Nasir Rajpoot,Fayyaz Ul Amir Afsar Minhas

Deep learning models that infer clinically relevant biomarker status from tissue images are being explored as rapid and low-cost alternatives to molecular testing. Here we show, through statistical analysis across multiple cancer types, datasets and modelling approaches, that the datasets used to train these models contain strong dependencies between biomarkers and clinicopathological features, which prevent models from isolating the effect of a single biomarker and lead them to learn confounded signals. Consequently, their prediction accuracy varies substantially with the status of codependent biomarkers and clinicopathological variables, and for several biomarkers, the gain over what a pathologist can already infer from routine histopathological features, such as grade, remains modest. These findings indicate that current approaches are not yet suitable as substitutes for molecular testing but can support triage or complementary decision-making with caution. Unconfounded biomarker prediction will require models that learn causal rather than correlational relationships between biomarkers and tissue morphology.

人们正在探索从组织图像中推断临床相关生物标志物状态的深度学习模型，作为分子检测的快速、低成本替代方案。在这里，我们通过对多种癌症类型、数据集和建模方法的统计分析表明，用于训练这些模型的数据集包含生物标志物和临床病理特征之间的强依赖性，这阻止了模型分离单个生物标志物的影响，并导致它们学习混淆信号。因此，他们的预测准确性随着相互依赖的生物标志物和临床病理变量的状态而变化很大，对于一些生物标志物，病理学家已经可以从常规组织病理学特征（如分级）中推断出的收益仍然有限。这些发现表明，目前的方法尚不适合作为分子检测的替代品，但可以谨慎地支持分诊或补充决策。无混淆的生物标记物预测将需要学习生物标记物和组织形态之间的因果关系而不是相关关系的模型。

{"title":"Confounding factors and biases abound when predicting molecular biomarkers from histological images.","authors":"Muhammad Dawood,Kim Branson,Sabine Tejpar,Nasir Rajpoot,Fayyaz Ul Amir Afsar Minhas","doi":"10.1038/s41551-026-01616-8","DOIUrl":"https://doi.org/10.1038/s41551-026-01616-8","url":null,"abstract":"Deep learning models that infer clinically relevant biomarker status from tissue images are being explored as rapid and low-cost alternatives to molecular testing. Here we show, through statistical analysis across multiple cancer types, datasets and modelling approaches, that the datasets used to train these models contain strong dependencies between biomarkers and clinicopathological features, which prevent models from isolating the effect of a single biomarker and lead them to learn confounded signals. Consequently, their prediction accuracy varies substantially with the status of codependent biomarkers and clinicopathological variables, and for several biomarkers, the gain over what a pathologist can already infer from routine histopathological features, such as grade, remains modest. These findings indicate that current approaches are not yet suitable as substitutes for molecular testing but can support triage or complementary decision-making with caution. Unconfounded biomarker prediction will require models that learn causal rather than correlational relationships between biomarkers and tissue morphology.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"51 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147329214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MyD88-mediated chimaeric antigen receptor macrophages suppress brain metastasis using target-specific phagocytosis. myd88介导的嵌合抗原受体巨噬细胞通过靶向特异性吞噬抑制脑转移。

IF 28.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-02 DOI: 10.1038/s41551-026-01613-x

Shih-Ying Wu,Abhishek Tyagi,Kerui Wu,Eleanor C Smith,Qianqian Song,Sambad Sharma,Lance D Miller,Wei Zhang,Bo-Syong Pan,Hui-Kuan Lin,Jung-Shun Lee,Ashok Pullikuth,Fei Xing,Ravindra Pramod Deshpande,Dan Zhao,Yin Liu,Jee Won Kim,Michael H Soike,Jimmy Ruiz,Michael Chan,Jeff Chou,Alexandra Parson,Kounosuke Watabe

Metastatic brain disease occurs in up to 30% of patients with lung, melanoma and breast cancers, and the median survival time remains less than a year. Treating these patients is a challenge because surgical approaches are limited and most chemotherapeutic drugs and immunotherapies are ineffective at crossing the blood-brain barrier (BBB). Given the unique abilities of macrophages to cross the BBB and exert their phagocytic function on tumour cells, we genetically engineer macrophages that express a chimaeric antigen receptor (CAR) targeting mesothelin (MSLN). To specifically target metastatic brain tumours, we fused the cells with the immune signalling molecule MyD88. This chimaeric antigen receptor macrophage (CARMA) penetrates the BBB and decreases brain metastasis growth in a humanized mouse model. MSLN-CARMA shows antigen-specific phagocytosis activity against tumour cells and exhibits a bystander effect by releasing TNF to act on surrounding tumour cells lacking the tumour antigen. These features of CARMA represent advantages over other immune therapies and CARMA may serve as a promising therapeutic tool for the treatment of brain metastasis.

高达30%的肺癌、黑色素瘤和乳腺癌患者发生转移性脑疾病，中位生存时间仍不到一年。治疗这些患者是一个挑战，因为手术方法有限，大多数化疗药物和免疫疗法在穿过血脑屏障（BBB）时无效。鉴于巨噬细胞跨越血脑屏障并对肿瘤细胞发挥吞噬功能的独特能力，我们对巨噬细胞进行基因工程改造，使其表达靶向间皮素（MSLN）的嵌合抗原受体（CAR）。为了特异性靶向转移性脑肿瘤，我们将细胞与免疫信号分子MyD88融合。在人源化小鼠模型中，嵌合抗原受体巨噬细胞（CARMA）穿透血脑屏障并降低脑转移生长。MSLN-CARMA对肿瘤细胞表现出抗原特异性吞噬活性，并通过释放TNF作用于周围缺乏肿瘤抗原的肿瘤细胞而表现出旁观者效应。CARMA的这些特点代表了其优于其他免疫疗法的优势，CARMA可能成为治疗脑转移的一种有前途的治疗工具。

{"title":"MyD88-mediated chimaeric antigen receptor macrophages suppress brain metastasis using target-specific phagocytosis.","authors":"Shih-Ying Wu,Abhishek Tyagi,Kerui Wu,Eleanor C Smith,Qianqian Song,Sambad Sharma,Lance D Miller,Wei Zhang,Bo-Syong Pan,Hui-Kuan Lin,Jung-Shun Lee,Ashok Pullikuth,Fei Xing,Ravindra Pramod Deshpande,Dan Zhao,Yin Liu,Jee Won Kim,Michael H Soike,Jimmy Ruiz,Michael Chan,Jeff Chou,Alexandra Parson,Kounosuke Watabe","doi":"10.1038/s41551-026-01613-x","DOIUrl":"https://doi.org/10.1038/s41551-026-01613-x","url":null,"abstract":"Metastatic brain disease occurs in up to 30% of patients with lung, melanoma and breast cancers, and the median survival time remains less than a year. Treating these patients is a challenge because surgical approaches are limited and most chemotherapeutic drugs and immunotherapies are ineffective at crossing the blood-brain barrier (BBB). Given the unique abilities of macrophages to cross the BBB and exert their phagocytic function on tumour cells, we genetically engineer macrophages that express a chimaeric antigen receptor (CAR) targeting mesothelin (MSLN). To specifically target metastatic brain tumours, we fused the cells with the immune signalling molecule MyD88. This chimaeric antigen receptor macrophage (CARMA) penetrates the BBB and decreases brain metastasis growth in a humanized mouse model. MSLN-CARMA shows antigen-specific phagocytosis activity against tumour cells and exhibits a bystander effect by releasing TNF to act on surrounding tumour cells lacking the tumour antigen. These features of CARMA represent advantages over other immune therapies and CARMA may serve as a promising therapeutic tool for the treatment of brain metastasis.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"40 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147329213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Author Correction: A thermogalvanic cell dressing for smart wound monitoring and accelerated healing. 作者更正：用于智能伤口监测和加速愈合的热电电池敷料。

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-01 DOI: 10.1038/s41551-026-01631-9

Jiwu Xin, Liheng Gao, Wenjie Zhang, Xinyu Song, Yuanmeng Yang, Wenrui Li, Xuhui Zhou, Haozhe Zhang, Zhe Wang, Zhixun Wang, Bing He, Yanting Liu, Tianzhu Zhou, Ting Xiong, Shuai Wang, Shixing Yuan, Wulong Li, Say Chye Joachim Loo, Lu Wang, Lei Wei

引用次数: 0

Beyond conceptual advances in cancer therapies. 除了癌症治疗的概念进步。

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-01 DOI: 10.1038/s41551-026-01649-z

引用次数: 0

Vaccination brings tumour cells out of hiding. 疫苗接种使肿瘤细胞不再隐藏。

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-01 DOI: 10.1038/s41551-026-01648-0

Jennifer Haskell

引用次数: 0

Author Correction: Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation. 作者更正：获取慢性适应性深部脑刺激皮层记录的挑战和机遇。

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-01 DOI: 10.1038/s41551-026-01641-7

Jeffrey Herron, Aura Kullmann, Timothy Denison, Wayne K Goodman, Aysegul Gunduz, Wolf-Julian Neumann, Nicole R Provenza, Maryam M Shanechi, Sameer A Sheth, Philip A Starr, Alik S Widge

引用次数: 0

mRNA delivery to the endothelium. mRNA向内皮细胞的传递。

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-03-01 DOI: 10.1038/s41551-026-01646-2

Rita Strack

引用次数: 0

Single-nucleus transcriptomics of an engineered pig model reveals microglia–T cell interactions driving Huntington’s disease neurodegeneration 工程猪模型的单核转录组学揭示了小胶质细胞- t细胞相互作用驱动亨廷顿病神经变性

IF 28.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering

Pub Date : 2026-02-24 DOI: 10.1038/s41551-026-01621-x

Jiawei Li, Yingqi Lin, Jiale Gao, Fan Yang, Caijuan Li, Yizhi Chen, Chunhui Huang, Xichen Song, Zebu Song, Jianhao Wu, Jiaxi Wu, Wei Wang, Junzhu Song, Chunxiang Shi, Guangchao Cao, Yan Xu, Yankuo Sun, Zhuchi Tu, Liangxue Lai, Shihua Li, Xiao-Jiang Li, Sen Yan

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Nature Biomedical Engineering

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀