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arXiv 2023年

作者： Chen, Richard J. Ding, Tong Lu, Ming Y. Williamson, Drew F.K. Jaume, Guillaume Chen, Bowen Zhang, Andrew Shao, Daniel Song, Andrew H. Shaban, Muhammad Williams, Mane Vaidya, Anurag Sahai, Sharifa Oldenburg, Lukas Weishaupt, Luca L. Wang, Judy J. Williams, Walt Le, Long Phi Gerber, Georg Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Systems Biology Harvard University CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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Contributors to the Lab on a Chip cell and tissue engineering in microsystems special issue

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Lab on a Chip 2007年第6期7卷 667-671页

作者： Bhatia, Sangeeta N. Chen, Christoper Bashir, Rashid Beebe, David J. Desai, Tejal Gottwald, Eric Khademhosseini, Ali Langer, Robert S. Piel, Matthieu Takayama, Shuichi Thorsen, Todd Tien, Joe Voldman, Joel Vunjak-Novakovic, Gordana Wu, Mingming Yu, Hanry Massachusetts Institute of Technology Harvard-MIT Division of Health Sciences and Technology Department of Electrical Engineering and Computer Science United States Laboratory for Multiscale Regenerative Technologies United States Department of Innovation University of Pennsylvania Department of Bioengineering United States Cell Biology and Physiology Program Cell Growth and Cancer Program Tissue Microfabrication Laboratory Department of Electrical and Computer Engineering and Biomedical Engineering Purdue University United States Department of Electronics University of Glasgow United Kingdom Department of Physiology and Bioengineering University of California San Francisco CA United States California Institute for Quantitative Biomedical Research UCSF/UC Berkeley Graduate Group in Bioengineering United States Laboratory of Therapeutic Micro and Nanotechnology Institute for Biological Interfaces Germany 3D-Cell Culture Techniques Group Institute for Biological Interfaces Germany Department of Medicine and Health Sciences and Technology Massachusetts Institute of Technology (MIT) United States Institut Curie UMR 144 IC/CNRS France Biomedical Engineering Department University of Michigan United States Department of Biomedical Engineering Macromolecular Science and Engineering Program United States Sibley School of Mechanical and Aerospace Engineering School of Chemical and Molecular Engineering Cornell University United States National University of Singapore Singapore Institute of Bioengineering and Nanotechnology A STAR Singapore

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Smoking and Risk of Colorectal Cancer Sub-classified by Tumor-infiltrating T Cells (vol 111, pg 42, 2019)

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JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE 2019年第12期111卷 1365-1365页

作者： Hamada, Tsuyoshi Department of Oncologic Pathology Department of Medical Oncology Dana-Farber Cancer Institute and Harvard Medical School Boston MAProgram in Molecular Pathological Epidemiology Department of Pathology Department of Oncologic Pathology Clinical and Translational Epidemiology Unit and Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston MA Clinical and Translational Epidemiology Unit and Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston MADepartment of Nutrition Clinical and Translational Epidemiology Unit and Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston MADepartment of NutritionDivision of Public Health Sciences Department of Surgery Washington University School of Medicine St. Louis MO Department of Oncologic Pathology Department of Medical Oncology Dana-Farber Cancer Institute and Harvard Medical School Boston MA Department of Oncologic Pathology Department of Oncologic PathologyDepartment of NutritionDivision of Public Health Sciences Department of Surgery Washington University School of Medicine St. Louis MODepartment of Epidemiology and Biostatistics and the Ministry of Education Key Lab of Environment and Health School of Public Health Huazhong University of Science and Technology Hubei P.R. China Department of Oncologic PathologyDepartment of Medical Oncology Chinese PLA General Hospital Beijing P.R. China Department of Oncologic Pathology Department of Oncologic PathologyCollege of Pharmacy Zhejiang Chinese Medical University Zhejiang P.R. China Department of Oncologic Pathology Department of Gastroenterology Rheumatology and Clinical Immunology Sapporo Medical University School of Medicine Sapporo Japan Department of NutritionDepartment of Food Science and Biotechnology Dongguk University Goyang the Republic of Korea Department of Medical Oncology Dana-Farber Cancer Institute and Harvard Medical School Boston MADepartment of

BackgroundEvidence indicates not only carcinogenic effect of cigarette smoking but also its immunosuppressive effect. We hypothesized that the association of smoking with colorectal cancer risk might be stronger for tumors with lower anti-tumor adaptive immune *** follow-up of 134 981 participants (3 490 851 person-years) in the Nurses’ health Study and health Professionals Follow-up Study, we documented 729 rectal and colon cancer cases with available data on T-cell densities in tumor microenvironment. Using the duplication-method Cox regression model, we examined a differential association of smoking status with risk of colorectal carcinoma subclassified by densities of CD3⁺ cells, CD8⁺ cells, CD45RO (PTPRC)⁺ cells, or FOXP3⁺ cells. All statistical tests were *** association of smoking status with colorectal cancer risk differed by CD3⁺ cell density (P_{heterogeneity} = .007). Compared with never smokers, multivariable-adjusted hazard ratios for CD3⁺ cell-low colorectal cancer were 1.38 (95% confidence interval = 1.09 to 1.75) in former smokers and 1.59 (95% confidence interval = 1.14 to 2.23) in current smokers (P_trend = .002, across smoking status categories). In contrast, smoking status was not associated with CD3⁺ cell-high cancer risk (P_trend = .52). This differential association appeared consistent in strata of microsatellite instability, CpG island methylator phenotype, or BRAF mutation status. There was no statistically significant differential association according to densities of CD8⁺ cells, CD45RO⁺ cells, or FOXP3⁺ cells (P_{heterogeneity} > .04, with adjusted α of 0.01).ConclusionsColorectal cancer risk increased by smoking was stronger for tumors with lower T-lymphocyte response, suggesting an interplay of smoking and immunity in colorectal carcinogenesis.

关键词： Association (Psychology) cancer risk Smoking Colorectal Cancer Cancer of Colon Cigarette Smoking Immunosuppression (Physiology) Adaptive Immune Response Tumours T-Lymphocytes cigarette smoke

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Activity of caffeic acid phenethyl ester in Caenorhabditis elegans

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Future medicinal chemistry 2016年第17期8卷 2033-2046页

作者： Jeffrey J Coleman Tomomi Komura Julia Munro Michael P Wu Rakhee R Busanelli Angela N Koehler Méryl Thomas Florence F Wagner Edward B Holson Eleftherios Mylonakis Division of Infectious Diseases Warren Alpert Medical School of Brown University Rhode Island Hospital Providence RI 02903 USA. MIT Division of Health Sciences and Technology Harvard Medical School Boston MA 02115 USA. Department of Entomology & Plant Pathology Auburn University Auburn AL 36849 USA. Chemical Biology Program Broad Institute of MIT & Harvard Cambridge MA 02142 USA. Department of Biological Engineering Koch Institute for Integrative Cancer Research at MIT Cambridge MA 02139 USA. Atlas Venture Cambridge MA 02139 USA.

AIM:Caffeic acid (3,4-dihydroxycinnamic acid) phenethyl ester (CAPE), the major constituent of propolis, is able to increase the survival of the nematode Caenorhabditis elegans after infection with the fungal pathogen Candida albicans. RESULTS:CAPE increases the expression of several antimicrobial proteins involved in the immune response to C. albicans. Structural derivatives of CAPE were synthesized to identify structure-activity relationships and decrease metabolic liability, ultimately leading to a compound that has similar efficacy, but increased in vivo stability. The CED-10(Rac-1)/PAK1 pathway was essential for immunomodulation by CAPE and was a critical component involved in the immune response to fungal pathogens. CONCLUSION:Caenorhabditis elegans is an efficient heterologous host to evaluate immunomodulatory compounds and identify components of the pathway(s) involved in the mode of action of compounds.

关键词： Candida caffeic acid phenethyl ester innate immune response p21 kinase propolis

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In Brief

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Current Problems in Surgery 2007年第6期44卷 347-350页

作者： Dagi, T. Forcht, MD, MPH Berguer, Ramon, MD Moore, Stephen, MD Reines, H. David, MD Senior Lecturer The Harvard-MIT Program in Health Sciences and Technology Associated Clinical Professor of Surgery The Uniformed Services University of the Health Sciences Boston MA Clinical Professor of Surgery University of California at Davis Chief of Surgery Contra Costa County Regional Medical Center Martinez CA Senior Vice President Quality & Safety Inova Health System Falls Church VA Professor of Surgery Virginia Commonwealth University Vice Chair of Surgery Inova Fairfax Hospital Falls Church VA

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Detecting QT prolongation From a Single-lead ECG With Deep Learning

arXiv

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arXiv 2023年

作者： Alam, Ridwan Aguirre, Aaron D. Stultz, Collin M. Research Laboratory of Electronics MIT CambridgeMA United States Computer Science & Artificial Intelligence Laboratory MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Wellman Center for Photomedicine Massachusetts General Hospital BostonMA United States Division of Cardiology Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Background and Aims For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We aim to develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and we use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. Methods Using 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. Results QTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. Conclusions Drug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning. This research leads the path toward out-of-hospital care using wearable ECG devices for antiarrhythmic therapies. © 2023, CC BY-NC-ND.

关键词： Electrocardiograms

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Towards a Visual-Language Foundation Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Liang, Ivy Ding, Tong Jaume, Guillaume Odintsov, Igor Zhang, Andrew Le, Long Phi Gerber, Georg Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts1–13. However, model training is often difficult due to label scarcity in the medical domain and the model’s usage is limited by the specific task and disease for which it is trained1–3, 14, 15. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. © 2023, CC BY-NC-ND.

关键词： Pathology

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Biallelic null variants in C19orf44 cause a unique late-onset retinal dystrophy phenotype characterized by patchy perifoveal chorioretinal atrophy

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Genetics in Medicine 2025年第6期27卷 101401页

作者： Ehrenberg, Miriam Avraham, Maayan Asodu, Sandeep Sarma Moye, Abigail R. Sangermano, Riccardo Rizel, Leah Ali-Nasser, Tahleel Sher, Ifat Gurwitz, David Chao, Katherine R. Rivera, Antonio Webster, Andrew R. Rivolta, Carlo Newman, Hadas Pras, Eran Rotenstreich, Ygal Banin, Eyal Pierce, Eric A. Zur, Dinah Arno, Gavin Bujakowska, Kinga M. Lin, Siying Sharon, Dror Ben-Yosef, Tamar Department of Ophthalmology Schneider Children's Medical Center of Israel Petach Tikva Israel Faculty of Medical and Health Sciences Tel Aviv University Tel Aviv Israel The Ruth and Bruce Rappaport Faculty of Medicine Technion-Israel Institute of Technology Haifa Israel Division of Ophthalmology Hadassah Medical Center Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel Institute of Molecular and Clinical Ophthalmology Basel (IOB) Basel Switzerland Department of Ophthalmology University Hospital Basel Basel Switzerland Ocular Genomics Institute Massachusetts Eye and Ear Infirmary Department of Ophthalmology Harvard Medical School Boston MA United States Goldschleger Eye Institute Sheba Medical Center Tel Hashomer Israel Sagol School for Neuroscience Tel Aviv University Tel Aviv Israel Broad Center for Mendelian Genomics Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA United States National Institute of Health Research Biomedical Research Centre at Moorfields Eye Hospital and The Institute of Ophthalmology London United Kingdom Institute of Ophthalmology University College London London United Kingdom Department of Genetics and Genome Biology University of Leicester Leicester United Kingdom Division of Ophthalmology Tel Aviv Sourasky Medical Center Tel Aviv Israel Ophthalmology Department Shamir Medical Center Zerifin Israel Greenwood Genetic Center Greenwood SC United States

Purpose: To identify the genetic cause for disease in individuals affected with inherited retinal disease and to characterize their retinal phenotype and the properties of the underlying gene. Methods: Participants underwent a comprehensive ophthalmological evaluation, including best-corrected visual acuity, visual field testing, fundus autofluorescence, optical coherence tomography, and electroretinography. Genetic analyses included exome, genome, and Sanger sequencing. Gene expression pattern was analyzed by reverse transcription-polymerase chain reaction. Localization of the encoded protein in cells and in the human retina was examined by immunofluorescence staining. Results: Four different pathogenic variants in C19orf44 were identified in 15 biallelic individuals from 11 unrelated families. The most common variant was c.549_550del p.(Ser185ProfsTer2). Most individuals were affected with a unique clinical phenotype characterized by late-onset patchy perifoveal chorioretinal atrophy and electroretinographic features of rod-cone degeneration. C19orf44 is expressed in various human tissues, including the retina, where it was found in the outer nuclear layer and in the outer plexiform layer. In cultured cells (hTERT RPE-1 and HeLa) and in human primary fibroblasts, C19orf44 is found in the nucleus, and it is downregulated during mitosis. Conclusion: Based on our results, C19orf44 is crucial for normal human retinal function, and pathogenic variants in this gene are associated with autosomal recessive inherited retinal disease. © 2025 The Authors

关键词： C19orf44 Chorioretinal atrophy Inherited retinal disease Retina

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Left Atrial Appendage Occlusion: Current Stroke Prevention Strategies and a Shift Toward Data-Driven, Patient-Specific Approaches

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Journal of the Society for Cardiovascular Angiography and Interventions 2022年第5期1卷 100405-100405页

作者： Mendez, Keegan Kennedy, Darragh G. Wang, Dee Dee Roche, Ellen T. Harvard/MIT Health Sciences and Technology Program Massachusetts Institute of Technology Cambridge MA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Engineering Columbia University New York NY United States Henry Ford Health Detroit MI United States Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA United States

The left atrial appendage (LAA) is a complex structure with unknown physiologic function protruding from the main body of the left atrium. In patients with atrial fibrillation, the left atrium does not contract effectively. Insufficient atrial and LAA contractility predisposes the LAA morphology to hemostasis and thrombus formation, leading to an increased risk of cardioembolic events. Oral anticoagulation therapies are the mainstay of stroke prevention options for patients;however, not all patients are candidates for long-term oral anticoagulation. Percutaneous occlusion devices are an attractive alternative to long-term anticoagulation therapy, although they are not without limitations, such as peri-implant leakage and device-related thrombosis. Although efforts have been made to reduce these risks, significant interpatient heterogeneity inevitably yields some degree of device-anatomy mismatch that is difficult to resolve using current devices and can ultimately lead to insufficient occlusion and poor patient outcomes. In this state-of-the-art review, we evaluated the anatomy of the LAA as well as the current pathophysiologic understanding and stroke prevention strategies used in the management of the risk of stroke associated with atrial fibrillation. We highlighted recent advances in computed tomography imaging, preprocedural planning, computational modeling, and novel additive manufacturing techniques, which represent the tools needed for a paradigm shift toward patient-centric LAA occlusion. Together, we envisage that these techniques will facilitate a pipeline from the imaging of patient anatomy to patient-specific computational and bench-top models that enable customized, data-driven approaches for LAA occlusion that are engineered specifically to meet each patient's unique needs. © 2022 The Authors

关键词： computed tomography imaging left atrial appendage left atrial appendage occlusion stroke

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Family dementia caregivers with suicidal ideation improve with mentalizing imagery therapy: Results from a pilot study

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Journal of Affective Disorders Reports 2024年 16卷 100721-100721页

作者： Madarasmi, Saira Gutierrez-Ramirez, Paulina Barsoum, Nader Banerjee, Sreya Gomez, Liliana Ramirez Melero-Dominguez, Maria Gitlin, Laura N. Pederson, Aderonke Liu, Richard T. Jain, Felipe A. Department of Psychiatry Depression Clinical and Research Program Massachusetts General Hospital Boston MA United States Harvard Medical School Boston MA United States Department of Neurology Massachusetts General Hospital Boston MA United States College of Nursing and Health Professions Drexel University Philadelphia PA United States Harvard-Massachusetts Institute of Technology (MIT) Division of Health Sciences and Technology Boston MA United States

Background: Family caregivers of persons living with dementia often experience increased depression and suicidal ideation (SI). However, the feasibility and impact of therapies on caregiver SI has remained largely unexplored. Mentalizing imagery therapy (mit) helps reduce psychological symptoms through mindfulness and guided imagery. This pilot study examined the feasibility of participation by caregivers with SI in a randomized controlled trial (RCT) of mit versus a psychosocial support group (SG), and the respective impact of group on SI, depression, and secondary outcomes. Methods: A secondary analysis of data from an RCT of 4-week mit or SG for caregivers (n = 46) was performed, identifying SI (n = 23) and non-SI (n = 23) cohorts. Group attendance and home practice were compared between cohorts. In the SI cohort (total n = 23, mit n = 11, SG n = 12), group differences in SI, depression, and secondary outcomes were evaluated post-group and at 4-month follow-up. Results: Attendance in both groups and home practice in mit were similar between SI and non-SI cohorts. In the SI cohort, mit evinced greater improvements relative to SG in SI (p=.02) and depression (p=.02) post-group, and other secondary outcomes at follow-up. Limitations: Limitations include small sample size and single-item assessments of SI from validated depression rating scales. Conclusions: Participation in an RCT was feasible for caregivers with SI. mit resulted in important benefits for SI and depression, while SG showed no acute SI benefit. The role of mit in improving SI should be confirmed with adequately powered trials, as effective therapies to address caregiver SI are critical. © 2024 The Author(s)

关键词： Dementia Depression Family caregivers Guided imagery Mindfulness Suicidal ideation

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