Journal of Pathology Informatics最新文献

{"title":"Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma","authors":"Liam Burrows ,&nbsp;Declan Sculthorpe ,&nbsp;Hongrun Zhang ,&nbsp;Obaid Rehman ,&nbsp;Abhik Mukherjee ,&nbsp;Ke Chen","doi":"10.1016/j.jpi.2023.100351","DOIUrl":"https://doi.org/10.1016/j.jpi.2023.100351","url":null,"abstract":"<div><p>Whilst automated analysis of immunostains in pathology research has focused predominantly on the epithelial compartment, automated analysis of stains in the stromal compartment is challenging and therefore requires time-consuming pathological input and guidance to adjust to tissue morphometry as perceived by pathologists. This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. The methodology combines both mathematical models and deep learning techniques with the former requiring no training data and the latter as many inputs as possible. The novel mathematical model was used to produce a digital double marker overlay allowing for fast automated digital multiplex analysis of stromal stains. The results show that deep learning methodologies in combination with mathematical modelling allow for an accurate means of quantifying stromal stains whilst also opening up new possibilities of digital multiplex analyses.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001657/pdfft?md5=482b41c3d6029ab0f2c8de25168258df&pid=1-s2.0-S2153353923001657-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access","authors":"Arlen Brickman ,&nbsp;Yigit Baykara ,&nbsp;Miguel Carabaño ,&nbsp;Sean M. Hacking","doi":"10.1016/j.jpi.2023.100350","DOIUrl":"10.1016/j.jpi.2023.100350","url":null,"abstract":"<div><h3>Background</h3><p>Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data.</p></div><div><h3>Methods</h3><p>WSIs were created from non-human tissues and H&amp;E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN).</p></div><div><h3>Results</h3><p>A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection “Whole Slide Images as Non-fungible Tokens Project” on Open Sea: <span>https://opensea.io/collection/untitled-collection-126765644</span><svg><path></path></svg>. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API.</p></div><div><h3>Conclusion</h3><p>Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001645/pdfft?md5=4164e105de7f34e0cc4b4035d1797cb6&pid=1-s2.0-S2153353923001645-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135565552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive factors impacting patient understanding of laboratory test information","authors":"Edward C. Klatt","doi":"10.1016/j.jpi.2023.100349","DOIUrl":"10.1016/j.jpi.2023.100349","url":null,"abstract":"<div><p>Laboratory testing can provide information useful to promote patient health literacy and ultimately patient well-being. The human state of mind involves not only cognition but also emotion and motivation factors when receiving, processing, and acting upon information. The cognitive load for patients acquiring and processing new information is high. Modes of distribution can affect both attention to and receipt of information. Implicit unconscious biases can affect whom and what patients believe. Positive wording and framing of information with salience for patients can evoke positive emotions. Providing patients with the gist, or essential meaning, of information can positively influence decision-making. What laboratorians provide as information helps to combat mis- and disinformation. Laboratorians can actively participate in measures to improve the patient experience in health care by developing and contributing to high-quality information to enable timely, meaningful communication and interpretation of test results.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001633/pdfft?md5=00b861355861322a8aaa479615ff15fe&pid=1-s2.0-S2153353923001633-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review","authors":"Ricardo Gonzalez ,&nbsp;Peyman Nejat ,&nbsp;Ashirbani Saha ,&nbsp;Clinton J.V. Campbell ,&nbsp;Andrew P. Norgan ,&nbsp;Cynthia Lokker","doi":"10.1016/j.jpi.2023.100348","DOIUrl":"10.1016/j.jpi.2023.100348","url":null,"abstract":"<div><p>Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2–2.6) and 1.8 (95% CI, 1.3–2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11–3.29) for recurrence, and between 0.09 (95% CI, 0.01–0.70) and 0.65 (95% CI, 0.43–0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results. Increasing the availability of validation datasets and implementing standardized methods and reporting protocols may facilitate future analyses.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001621/pdfft?md5=065ed1f82e40c99cefb1eb56d5945b9c&pid=1-s2.0-S2153353923001621-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump","authors":"Dieter P. Hoyer ,&nbsp;Saskia Ting ,&nbsp;Nina Rogacka ,&nbsp;Sven Koitka ,&nbsp;René Hosch ,&nbsp;Nils Flaschel ,&nbsp;Johannes Haubold ,&nbsp;Eugen Malamutmann ,&nbsp;Björn-Ole Stüben ,&nbsp;Jürgen Treckmann ,&nbsp;Felix Nensa ,&nbsp;Giulia Baldini","doi":"10.1016/j.jpi.2023.100345","DOIUrl":"10.1016/j.jpi.2023.100345","url":null,"abstract":"<div><h3>Introduction</h3><p>Perihilar cholangiocarcinoma (PHCC) is a rare malignancy with limited survival prediction accuracy. Artificial intelligence (AI) and digital pathology advancements have shown promise in predicting outcomes in cancer. We aimed to improve prognosis prediction for PHCC by combining AI-based histopathological slide analysis with clinical factors.</p></div><div><h3>Methods</h3><p>We retrospectively analyzed 317 surgically treated PHCC patients (January 2009–December 2018) at the University Hospital of Essen. Clinical data, surgical details, pathology, and outcomes were collected. Convolutional neural networks (CNN) analyzed whole-slide images. Survival models incorporated clinical and histological features.</p></div><div><h3>Results</h3><p>Among 142 eligible patients, independent survival predictors were tumor grade (G), tumor size (T), and intraoperative transfusion requirement. The CNN-based model combining clinical and histopathological features demonstrates proof of concept in prognosis prediction, limited by histopathological complexity and feature extraction challenges. However, the CNN-based model generated heatmaps assisting pathologists in identifying areas of interest.</p></div><div><h3>Conclusion</h3><p>AI-based digital pathology showed potential in PHCC prognosis prediction, though refinement is necessary for clinical relevance. Future research should focus on enhancing AI models and exploring novel approaches to improve PHCC patient prognosis prediction.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001591/pdfft?md5=6306a1e73353f828e6b03fecfd67c6cf&pid=1-s2.0-S2153353923001591-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities","authors":"Ricardo Gonzalez ,&nbsp;Ashirbani Saha ,&nbsp;Clinton J.V. Campbell ,&nbsp;Peyman Nejat ,&nbsp;Cynthia Lokker ,&nbsp;Andrew P. Norgan","doi":"10.1016/j.jpi.2023.100347","DOIUrl":"10.1016/j.jpi.2023.100347","url":null,"abstract":"<div><p>This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support “Learning Health Systems” with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support \"Learning Health Systems\" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S215335392300161X/pdfft?md5=61eb80bf2823facaa480b73e1391ad92&pid=1-s2.0-S215335392300161X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges","authors":"Marie Brevet ,&nbsp;Zaibo Li ,&nbsp;Anil Parwani","doi":"10.1016/j.jpi.2023.100343","DOIUrl":"10.1016/j.jpi.2023.100343","url":null,"abstract":"<div><p>For the past 2 decades, pathologists have been accustomed to reporting the HER2 status of breast cancer as either positive or negative, based on HER2 IHC. Today, however, there is a clinical imperative to employ a 3-tier approach to interpreting HER2 IHC that can also identify tumours categorised as HER2-low. Meeting this need for a finer degree of discrimination may be challenging, and in this article, we consider the potential for the integration of computational approaches to support pathologists in achieving accurate and reproducible HER2 IHC scoring as well as outlining some of the practicalities involved.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001578/pdfft?md5=8fd6af9fd78e655c07d1b2afbe73f9a5&pid=1-s2.0-S2153353923001578-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology","authors":"Jen-Fan Hang ,&nbsp;Yen-Chuan Ou ,&nbsp;Wei-Lei Yang ,&nbsp;Tang-Yi Tsao ,&nbsp;Cheng-Hung Yeh ,&nbsp;Chi-Bin Li ,&nbsp;En-Yu Hsu ,&nbsp;Po-Yen Hung ,&nbsp;Yi-Ting Hwang ,&nbsp;Tien-Jen Liu ,&nbsp;Min-Che Tung","doi":"10.1016/j.jpi.2023.100346","DOIUrl":"10.1016/j.jpi.2023.100346","url":null,"abstract":"<div><h3>Background</h3><p>Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes.</p></div><div><h3>Methods</h3><p>Fourteen urine specimens were collected from patients with urothelial carcinoma. Each specimen was equally divided into 2 portions, prepared with Cytospin and ThinPrep methods and scanned for WSIs using Leica (Aperio AT2) and Hamamatsu (NanoZoomer S360) scanners, respectively. The scan settings included 3 focus modes (default, semi-auto, and manual) for single-layer scanning, along with a manual focus mode for 21 Z-layers scanning. Performance metrics were evaluated including scanning success rate, artificial intelligence (AI) algorithm-inferred atypical cell numbers and coverage rate (atypical cell numbers in single or multiple Z-layers divided by the total atypical cell numbers in 21 Z-layers), scanning time, and image file size.</p></div><div><h3>Results</h3><p>The default mode had scanning success rates of 85.7% or 92.9%, depending on the scanner used. The semi-auto mode increased success to 92.9% or 100%, and manual even further to 100%. However, these changes did not affect the standardized median atypical cell numbers and coverage rates. The selection of scanners, cytopreparations, and Z-stacking influenced standardized median atypical cell numbers and coverage rates, scanning times, and image file sizes.</p></div><div><h3>Discussion</h3><p>Both scanners showed satisfactory scanning. We recommend using semi-auto or manual focus modes to achieve a scanning success rate of up to 100%. Additionally, a minimum of 9-layer Z-stacking at 1 μm intervals is required to cover 80% of atypical cells. These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001608/pdfft?md5=2ee536dfedd3c029d54d522a3a6fa784&pid=1-s2.0-S2153353923001608-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to predict prostate cancer recurrence from tissue images","authors":"Mahtab Farrokh, Neeraj Kumar, Peter H. Gann, Russell Greiner","doi":"10.1016/j.jpi.2023.100344","DOIUrl":"https://doi.org/10.1016/j.jpi.2023.100344","url":null,"abstract":"Roughly 30% of men with prostate cancer who undergo radical prostatectomy will suffer biochemical cancer recurrence (BCR). Accurately predicting which patients will experience BCR could identify who would benefit from increased surveillance or adjuvant therapy. Unfortunately, no current method can effectively predict this. We develop and evaluate PathCLR, a novel semi-supervised method that learns a model that can use hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) to predict prostate cancer recurrence within 5 years after diagnosis. The learning process involves 2 sequential steps: PathCLR (a) first employs self-supervised learning to generate effective feature representations of the input images, then (b) feeds these learned features into a fully supervised neural network classifier to learn a model for predicting BCR. We conducted training and evaluation using 2 large prostate cancer datasets: (1) the Cooperative Prostate Cancer Tissue Resource (CPCTR) with 374 patients, including 189 who experienced BCR, and (2) the Johns Hopkins University (JHU) prostate cancer dataset of 646 patients, with 451 patients having BCR. PathCLR’s (10-fold cross-validation) F1 score was 0.61 for CPCTR and 0.85 for JHU. This was statistically superior (paired t-test with P<.05) to the best-learned model that relied solely on clinicopathological features, including PSA level, primary and secondary Gleason Grade, etc. We attribute the improvement of PathCLR over models using only clinicopathological features to its utilization of both learned latent representations of tissue core images and clinicopathological features. This finding suggests that there is essential predictive information in tissue images at the time of surgery that goes beyond the knowledge obtained from reported clinicopathological features, helping predict the patient’s 5-year outcome.","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455776","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}

{"title":"An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)","authors":"Anil V. Parwani ,&nbsp;Ankush Patel ,&nbsp;Ming Zhou ,&nbsp;John C. Cheville ,&nbsp;Hamid Tizhoosh ,&nbsp;Peter Humphrey ,&nbsp;Victor E. Reuter ,&nbsp;Lawrence D. True","doi":"10.1016/j.jpi.2022.100177","DOIUrl":"10.1016/j.jpi.2022.100177","url":null,"abstract":"<div><p>Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/72/90/main.PMC9841212.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9153396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}