Journal of Pathology Informatics最新文献

英文中文

Technical considerations during validation of the Genius® Digital Diagnostic System Genius®数字诊断系统验证期间的技术考虑

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-19 DOI: 10.1016/j.jpi.2025.100532

Lakshmi Harinath , Sarah Harrington , Jonee Matsko , Amy Colaizzi , Esther Elishaev , Samer Khader , Rohit Bhargava , Chengquan Zhao , Liron Pantanowitz

Background

The aim of this study was to document technical errors encountered during validation of the Genius Digital Diagnostics System (GDDS).

Materials and methods

A total of 909 cases of archived ThinPrep Pap slides with follow-up biopsies were retrieved. Slides were cleaned, relabeled, and scanned with GDDS. Digital imager errors, including slide events and imager errors, were documented and evaluated.

Results

Of the 909 slides scanned, 21 (2.3 %) demonstrated slide events. For 5 cases, the slides had cell focus errors, 12 failed due to quality control (QC) errors, 2 had barcode issues, 1 showed an oversaturated frame, and 1 presented a problem because it was a duplicate. Some errors could be corrected, of which 8 cases with various diagnostic cytology interpretations were successfully rescanned. There were 13 (1.4%) cases that could not be scanned and thus were excluded from the study, predominantly because of focus QC errors due to scratched coverslips from long-term storage. There were 43 imager errors including failure of motor movement, cancellation of slide handling action, and failure to pick slides from the carrier station for which the scanning process had to be paused. Imager errors were solved by rebooting the system, correcting the positioning of the slide on the system, and technical help provided by the vendor.

Conclusion

Minor errors are to be expected when digitizing large volume of Pap slides. Total number of rescanned cases to address such technical problems were low in number and did not compromise the interpretation of Pap test slides using GDDS.

本研究的目的是记录天才数字诊断系统（GDDS）验证过程中遇到的技术错误。材料与方法共检索909例已存档的薄prep Pap切片并随访活检。切片清洗，重新贴上标签，并用GDDS扫描。数字成像仪错误，包括滑动事件和成像仪错误，被记录和评估。结果在扫描的909张幻灯片中，21张（2.3%）出现滑动事件。在5例中，载玻片有细胞聚焦错误，12例由于质量控制（QC）错误而失败，2例有条形码问题，1例显示过饱和帧，1例因为重复而出现问题。有一些错误是可以纠正的，其中有8例诊断细胞学解释不同的病例成功重新扫描。有13例（1.4%）病例无法扫描，因此被排除在研究之外，主要是因为长期储存造成的盖子划伤导致焦点QC错误。有43个成像仪错误，包括电机运动失败，取消载玻片处理动作，未能从载体站取玻片，扫描过程必须暂停。成像仪错误通过重新启动系统、纠正系统上载玻片的定位以及供应商提供的技术帮助来解决。结论对大量巴氏涂片进行数字化处理，误差较小。为解决此类技术问题而重新扫描病例的总数较少，并且不影响使用GDDS对巴氏涂片的解释。

{"title":"Technical considerations during validation of the Genius® Digital Diagnostic System","authors":"Lakshmi Harinath , Sarah Harrington , Jonee Matsko , Amy Colaizzi , Esther Elishaev , Samer Khader , Rohit Bhargava , Chengquan Zhao , Liron Pantanowitz","doi":"10.1016/j.jpi.2025.100532","DOIUrl":"10.1016/j.jpi.2025.100532","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this study was to document technical errors encountered during validation of the Genius Digital Diagnostics System (GDDS).</div></div><div><h3>Materials and methods</h3><div>A total of 909 cases of archived ThinPrep Pap slides with follow-up biopsies were retrieved. Slides were cleaned, relabeled, and scanned with GDDS. Digital imager errors, including slide events and imager errors, were documented and evaluated.</div></div><div><h3>Results</h3><div>Of the 909 slides scanned, 21 (2.3%) demonstrated slide events. For 5 cases, the slides had cell focus errors, 12 failed due to quality control (QC) errors, 2 had barcode issues, 1 showed an oversaturated frame, and 1 presented a problem because it was a duplicate. Some errors could be corrected, of which 8 cases with various diagnostic cytology interpretations were successfully rescanned. There were 13 (1.4%) cases that could not be scanned and thus were excluded from the study, predominantly because of focus QC errors due to scratched coverslips from long-term storage. There were 43 imager errors including failure of motor movement, cancellation of slide handling action, and failure to pick slides from the carrier station for which the scanning process had to be paused. Imager errors were solved by rebooting the system, correcting the positioning of the slide on the system, and technical help provided by the vendor.</div></div><div><h3>Conclusion</h3><div>Minor errors are to be expected when digitizing large volume of Pap slides. Total number of rescanned cases to address such technical problems were low in number and did not compromise the interpretation of Pap test slides using GDDS.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100532"},"PeriodicalIF":0.0,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital pathology imaging artificial intelligence in cancer research and clinical trials: An NCI workshop report 数字病理成像人工智能在癌症研究和临床试验：NCI研讨会报告

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-14 DOI: 10.1016/j.jpi.2025.100531

Hala R. Makhlouf , Miguel R. Ossandon , Keyvan Farahani , Irina Lubensky , Lyndsay N. Harris

Digital pathology imaging (DPI) is a rapidly advancing field with increasing relevance to cancer diagnosis, research, and clinical trials through large-scale image analysis and artificial intelligence (AI) integration. Despite these advances, regulatory adoption in digital pathology (DP) has lagged; to date, only three AI/ML Software as a Medical Device tool have received FDA clearance, highlighting a validation dataset gap rather than an absence of regulatory pathways. On March 6–7, 2024, the National Cancer Institute held a virtual workshop titled “Digital Pathology Imaging-Artificial Intelligence in Cancer Research and Clinical Trials,” bringing together experts in pathology, radiology, oncology, data science, and regulatory fields to assess current challenges, practical solutions, and future directions. This report summarizes expert opinions on key issues related to the use of DPI in cancer research and clinical trials, including data standardization, de-identification, and the application of Digital Imaging and Communication in Medicine (DICOM) standards. Key topics included data standardization, image quality assurance, validation strategies, AI applications, integration in clinical trials, biobanking, intellectual property, investigators' needs, and lessons from digital cytology and radiology domains. Solutions discussed included adoption of open standards such as DICOM, centralized imaging portals, and scalable cloud-based platforms. The expert consensus outlined in this report is intended to guide the development of DPI infrastructure, standardization, support AI validation, and align regulatory and data-sharing practices to advance precision oncology.

数字病理成像（DPI）是一个快速发展的领域，通过大规模图像分析和人工智能（AI）集成，与癌症诊断、研究和临床试验的相关性越来越大。尽管取得了这些进步，但数字病理学（DP）的监管采用却滞后；到目前为止，只有三个AI/ML软件作为医疗器械工具获得了FDA的许可，这突出了验证数据集的差距，而不是缺乏监管途径。2024年3月6日至7日，美国国家癌症研究所举办了一场名为“数字病理成像——癌症研究和临床试验中的人工智能”的虚拟研讨会，汇集了病理学、放射学、肿瘤学、数据科学和监管领域的专家，以评估当前的挑战、实用的解决方案和未来的方向。本报告总结了与DPI在癌症研究和临床试验中使用相关的关键问题的专家意见，包括数据标准化、去识别和医学数字成像和通信（DICOM）标准的应用。主要议题包括数据标准化、图像质量保证、验证策略、人工智能应用、临床试验集成、生物银行、知识产权、研究者需求以及数字细胞学和放射学领域的经验教训。讨论的解决方案包括采用开放标准，如DICOM、集中式成像门户和可扩展的基于云的平台。本报告中概述的专家共识旨在指导DPI基础设施的发展，标准化，支持人工智能验证，并协调监管和数据共享实践，以推进精准肿瘤学。

{"title":"Digital pathology imaging artificial intelligence in cancer research and clinical trials: An NCI workshop report","authors":"Hala R. Makhlouf , Miguel R. Ossandon , Keyvan Farahani , Irina Lubensky , Lyndsay N. Harris","doi":"10.1016/j.jpi.2025.100531","DOIUrl":"10.1016/j.jpi.2025.100531","url":null,"abstract":"<div><div>Digital pathology imaging (DPI) is a rapidly advancing field with increasing relevance to cancer diagnosis, research, and clinical trials through large-scale image analysis and artificial intelligence (AI) integration. Despite these advances, regulatory adoption in digital pathology (DP) has lagged; to date, only three AI/ML Software as a Medical Device tool have received FDA clearance, highlighting a validation dataset gap rather than an absence of regulatory pathways. On March 6–7, 2024, the National Cancer Institute held a virtual workshop titled “Digital Pathology Imaging-Artificial Intelligence in Cancer Research and Clinical Trials,” bringing together experts in pathology, radiology, oncology, data science, and regulatory fields to assess current challenges, practical solutions, and future directions. This report summarizes expert opinions on key issues related to the use of DPI in cancer research and clinical trials, including data standardization, de-identification, and the application of Digital Imaging and Communication in Medicine (DICOM) standards. Key topics included data standardization, image quality assurance, validation strategies, AI applications, integration in clinical trials, biobanking, intellectual property, investigators' needs, and lessons from digital cytology and radiology domains. Solutions discussed included adoption of open standards such as DICOM, centralized imaging portals, and scalable cloud-based platforms. The expert consensus outlined in this report is intended to guide the development of DPI infrastructure, standardization, support AI validation, and align regulatory and data-sharing practices to advance precision oncology.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"20 ","pages":"Article 100531"},"PeriodicalIF":0.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural language processing for deep phenotyping of patients receiving genomic testing enables effective gene prioritization in a clinical diagnostics pipeline 对接受基因组检测的患者进行深度表型分析的自然语言处理能够在临床诊断管道中实现有效的基因优先排序

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100503

Andy Drackley , Anthony Wong , Patrick McMullen , Alexander Ing , Pamela Rathbun , Kai Lee Yap

引用次数: 0

Evaluating a patient matching algorithm in a Hematopathology Database (HPDB2): preliminary data analysis and comparison to an epic-based approach 评估血液病数据库（HPDB2）中的患者匹配算法：初步数据分析和与基于史诗的方法的比较

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100477

Matthew Xi Luo , Willow Solem , Niklas Krumm

引用次数: 0

From detecting variants to matching trials: assessing ChatGPT-4o's utility in molecular pathology 从检测变异到匹配试验：评估chatgpt - 40在分子病理学中的效用

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100475

Sidra Zaheer , Mine Yilmaz , Lahari Koganti , Mahesh M. Mansukhani , Susan J. Hsiao

引用次数: 0

Using a Git service provider and the web browser as an application server for clinical pathology job aids 采用Git服务提供程序和web浏览器作为应用服务器，实现临床病理作业辅助

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100481

Christopher Williams

引用次数: 0

Weakly supervised deep learning-based detection of serous tubal intraepithelial carcinoma in fallopian tubes 基于弱监督深度学习的输卵管浆液性上皮内癌检测

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100522

Andrew L. Valesano, Stephanie L. Skala , Mustafa Yousif

Serous tubal intraepithelial carcinoma (STIC) is an uncommon, non-invasive carcinoma that occurs more frequently in individuals with germline BRCA mutations and is an established precursor to high-grade serous ovarian carcinoma. STIC can be challenging to detect during pathologist evaluation, as it can manifest as a small focus of atypia in an otherwise benign salpingectomy specimen. There is a clinical need for scalable, weakly supervised computational approaches to aid in the detection of STIC. We developed a deep learning model to identify STIC and serous tubal intraepithelial lesions (STIL) in whole-slide images. We obtained fallopian tube specimens diagnosed as STIC (n = 49), STIL (n = 48), and benign fallopian tube (n = 83) at a single academic medical center. We trained a weakly supervised, attention-based multiple instance learning model and evaluated performance on independent datasets, including an additional unbalanced dataset (n = 40 benign, n = 2 STIL, n = 1 STIC) and cases diagnosed descriptively as benign reactive atypia (n = 53). The model achieved high sensitivity and specificity on the balanced validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.96 (95% CI: 0.90–1.00), and demonstrated similarly strong performance on unbalanced validation cohorts (AUROC 0.98). Interpretability analyses indicated that model decisions were based on epithelial atypia. These results support the potential of integrating deep learning screening tools into clinical workflows to augment pathologist efficiency and diagnostic accuracy in fallopian tubes.

浆液性输卵管上皮内癌（STIC）是一种罕见的、非侵袭性的癌症，多发生于BRCA种系突变个体，是高级别浆液性卵巢癌的先兆。在病理评估中发现STIC是很有挑战性的，因为它可以在良性输卵管切除术标本中表现为一个小的异型灶。临床需要可扩展的、弱监督的计算方法来帮助检测STIC。我们开发了一个深度学习模型来识别全片图像中的STIC和浆液性输卵管上皮内病变（STIL）。我们在一个学术医疗中心获得诊断为STIC （n = 49）、STIL （n = 48）和良性输卵管（n = 83）的输卵管标本。我们训练了一个弱监督的、基于注意力的多实例学习模型，并在独立数据集上评估其性能，包括一个额外的不平衡数据集（n = 40个良性数据集，n = 2个STIL数据集，n = 1个STIC数据集）和被描述诊断为良性反应性非典型型的病例（n = 53）。该模型在平衡验证队列中具有很高的灵敏度和特异性，受试者工作特征曲线下面积（AUROC）为0.96 (95% CI: 0.90-1.00)，在不平衡验证队列中也表现出同样强的性能（AUROC为0.98）。可解释性分析表明，模型的决定是基于上皮异型性。这些结果支持将深度学习筛选工具整合到临床工作流程中，以提高输卵管病理学家的效率和诊断准确性。

{"title":"Weakly supervised deep learning-based detection of serous tubal intraepithelial carcinoma in fallopian tubes","authors":"Andrew L. Valesano, Stephanie L. Skala , Mustafa Yousif","doi":"10.1016/j.jpi.2025.100522","DOIUrl":"10.1016/j.jpi.2025.100522","url":null,"abstract":"<div><div>Serous tubal intraepithelial carcinoma (STIC) is an uncommon, non-invasive carcinoma that occurs more frequently in individuals with germline <em>BRCA</em> mutations and is an established precursor to high-grade serous ovarian carcinoma. STIC can be challenging to detect during pathologist evaluation, as it can manifest as a small focus of atypia in an otherwise benign salpingectomy specimen. There is a clinical need for scalable, weakly supervised computational approaches to aid in the detection of STIC. We developed a deep learning model to identify STIC and serous tubal intraepithelial lesions (STIL) in whole-slide images. We obtained fallopian tube specimens diagnosed as STIC (<em>n</em> = 49), STIL (<em>n</em> = 48), and benign fallopian tube (<em>n</em> = 83) at a single academic medical center. We trained a weakly supervised, attention-based multiple instance learning model and evaluated performance on independent datasets, including an additional unbalanced dataset (<em>n</em> = 40 benign, <em>n</em> = 2 STIL, <em>n</em> = 1 STIC) and cases diagnosed descriptively as benign reactive atypia (<em>n</em> = 53). The model achieved high sensitivity and specificity on the balanced validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.96 (95% CI: 0.90–1.00), and demonstrated similarly strong performance on unbalanced validation cohorts (AUROC 0.98). Interpretability analyses indicated that model decisions were based on epithelial atypia. These results support the potential of integrating deep learning screening tools into clinical workflows to augment pathologist efficiency and diagnostic accuracy in fallopian tubes.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100522"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The comparative pathology workbench: An update 比较病理学工作台：更新

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100523

Michael N. Wicks , Michael Glinka , Bill Hill , Derek Houghton , Bernard Haggarty , Jorge Del-Pozo , Ingrid Ferreira , Florian Jaeckle , David Adams , Shahida Din , Irene Papatheodorou , Kathryn Kirkwood , Albert Burger , Richard A. Baldock , Mark J. Arends

The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive “spreadsheet” style presentation of image data and associated analysis data. The software was developed to enable pathologists and other clinical and research users to compare histopathological images of diseased and/or normal tissues between different samples of the same or different patients/species. The CPW provides a grid layout of cells in rows and columns so that images that correspond to matching data can be organized in the form of an image-enabled “spreadsheet”. An individual workbench or bench can be shared with other users with read-only or full edit access as required. In addition, each bench cell or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data. Here, we present the updated system based on 2 years of active use in the field that generated constructive feedback. The updates deliver new capabilities, including automated importation of entire image collections, sorting image collections, long running tasks, public benches, uploading miscellaneous image types, refining search facilities, enabling use of tags, and improving efficiency, speed, and user-friendliness.

比较病理学工作台（CPW）是一个基于web浏览器的可视化分析平台，提供对交互式“电子表格”风格的图像数据和相关分析数据的共享访问。开发该软件是为了使病理学家和其他临床和研究用户能够比较相同或不同患者/物种的不同样本的病变和/或正常组织的组织病理学图像。CPW提供了行和列单元格的网格布局，以便与匹配数据相对应的图像可以以支持图像的“电子表格”的形式进行组织。可以根据需要与具有只读或完全编辑访问权限的其他用户共享单个工作台或工作台。此外，每个工作台单元或整个工作台本身都有一个相关的讨论线程，以允许对数据进行协作分析和共识解释。在这里，我们根据在该领域2年的积极使用，提出了更新的系统，产生了建设性的反馈。这些更新提供了新的功能，包括整个图像集合的自动导入、图像集合的排序、长时间运行的任务、公共工作台、上传各种图像类型、优化搜索工具、启用标签的使用，以及提高效率、速度和用户友好性。

{"title":"The comparative pathology workbench: An update","authors":"Michael N. Wicks , Michael Glinka , Bill Hill , Derek Houghton , Bernard Haggarty , Jorge Del-Pozo , Ingrid Ferreira , Florian Jaeckle , David Adams , Shahida Din , Irene Papatheodorou , Kathryn Kirkwood , Albert Burger , Richard A. Baldock , Mark J. Arends","doi":"10.1016/j.jpi.2025.100523","DOIUrl":"10.1016/j.jpi.2025.100523","url":null,"abstract":"<div><div>The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive “spreadsheet” style presentation of image data and associated analysis data. The software was developed to enable pathologists and other clinical and research users to compare histopathological images of diseased and/or normal tissues between different samples of the same or different patients/species. The CPW provides a grid layout of cells in rows and columns so that images that correspond to matching data can be organized in the form of an image-enabled “spreadsheet”. An individual workbench or bench can be shared with other users with read-only or full edit access as required. In addition, each bench cell or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data. Here, we present the updated system based on 2 years of active use in the field that generated constructive feedback. The updates deliver new capabilities, including automated importation of entire image collections, sorting image collections, long running tasks, public benches, uploading miscellaneous image types, refining search facilities, enabling use of tags, and improving efficiency, speed, and user-friendliness.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100523"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital pathology implementation in a multi-site hospital network: the devil is in the details 多站点医院网络中的数字病理学实施：细节决定成败

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100507

Blaise Clarke , Charlotte Carment-Baker , Amiee Langan , Christine Bruce , George M. Yousef

引用次数: 0

Enhancing multidisciplinary tumor board presentations: pathology trainees and faculty experiences with whole slide imaging integration 加强多学科肿瘤委员会报告：病理实习生和教师的经验与整个幻灯片成像整合

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100492

Yaqot Baban , Gopal Kumar , Devereaux Sellers , Agnes Loeffler , Sirisha Kundrapu

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

Journal of Pathology Informatics

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.

﹀