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Characterization of a new fluorescence lifetime imaging ophthalmoscope

ACTA OPHTHALMOLOGICA 2024年第S279期102卷

作者： Nycz, Julia Link, Dietmar Klemm, Matthias Klee, Sascha Haueisen, Jens Institute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems Austria

Aims/Purpose: Fluorescence lifetime imaging ophthalmoscopy (FLIO) allows in vivo measurement of autofluorescence intensity decays of endogenous fluorophores in the ocular fundus. So far, only devices from Heidelberg Engineering based on the Spectralis system have been used in FLIO research. Here, we present and characterize a new FLIO device based on the RETImap system from Roland Consult. Methods: The device is based on a confocal scanning laser ophthalmoscope (35° field, 512 × 512 px). A ps diode laser (BDL-SMN 473 nm, Becker & Hickl GmbH, Berlin, Germany) excites autofluorescence. The fluorescence photons are split into a short (498–560 nm, SSC) and a long (560–720 nm, LSC) spectral channel (one HPM-100-40 detector [Becker & Hickl GmbH] each) and are detected by time-correlated single photon counting (SPC-160, Becker & Hickl GmbH). We determined the maximum laser power (ILT2400, International Light Technologies, Inc. Peabody, MA, USA) and analysed the instrument's behaviour at three different laser power levels (150 μW, 200 μW and max.) in terms of laser spectrum (CAS140CT, Instrument Systems GmbH, Munich, Germany) and instrument response function (IRF). The IRF was determined using a 25 μM Eosin Y solution, mixed with a 5 M solution of potassium iodide, placed in a flat cuvette (110-OS, Hellma GmbH & Co. KG, Müllheim, Germany) in front of the objective lens of the FLIO device. Fluorescence measurements of approximately 1-min duration were performed three times for all three laser powers. The IRF and the full width at half maximum (FWHM) were calculated using FLIMX software ( *** ). Results: The max. laser power was 280 μW. The peak wavelengths of the laser spectra were 467.6 (150 μW), 467.9 (200 μW) and 468.0 nm (280 μW). IRF FWHM in SSC were 298.6 ± 1.1 ps (150 μW), 341.0 ± 2.5 ps (200 μW) and 347.5 ± 6.0 ps (280 μW). In LSC, the IRF FWHM were 290.4 ± 3.8 ps (150 μW), 344.0 ± 3.4 ps (200 μW) and 358.8 ± 1.3 ps (280 μW). Results are mean ± standard devi

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Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages 13

Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Cl...

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13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2023

作者： Bölücü, Necva Ameer, Iqra Al Bataineh, Ali Xu, Hua Data61 CSIRO Sydney Australia Division of Science and Engineering Penn State University AbingtonPA United States Norwich University United States Section of Biomedical Informatics and Data Science Yale School of Medicine United States

ISBN: (纸本)9781959429876

We present the results of the WASSA 2023 Shared-Task 2: Emotion Classification on codemixed text messages (Roman Urdu + English), which included two tracks for emotion classification: multi-label and multi-class. The participants were provided with a dataset of codemixed SMS messages in English and Roman Urdu labeled with 12 emotions for both tracks. A total of 5 teams (19 team members) participated in the shared task. We summarized the methods, resources, and tools used by the participating teams. We also made the data freely available for further improvements to the task. © 2023 Association for Computational Linguistics.

关键词： Classification (of information)

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High-Density Lipoproteins Associate with Age-Related Macular Degeneration in the All of Us Research Program

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Ophthalmology 2025年第6期132卷 684-691页

作者： Chen, Jimmy S. Esko, Jeffrey D. Walker, Evan Gordts, Philip L.S.M. Baxter, Sally L. Toomey, Christopher B. Division of Ophthalmology Informatics and Data Science Viterbi Family Department of Ophthalmology and Shiley Eye Institute University of California San Diego La Jolla California United States Glycobiology Research and Training Center University of California San Diego La Jolla California United States Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla California United States

Purpose: Extracellular lipoprotein aggregation is a critical event in age-related macular degeneration (AMD) pathogenesis. In this study, we sought to analyze associations between clinical and genetic-based factors related to lipoprotein metabolism and risk for AMD in the All of Us research program. Design: Cross-sectional retrospective data analysis. Participants: A total of 5028 healthy participants and 2328 patients with AMD from All of Us. Methods: Participants with and without AMD were age, race, and sex matched in a 1:2 ratio, respectively. Smoking status, history of hyperlipidemia, and statin use were extracted in a binary manner. Statin use was further subcategorized into hepatically versus nonhepatically metabolized statins. Laboratory values for low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TGs) were also extracted, and outliers were excluded from analysis. The PLINK toolkit was used to extract single nucleotide polymorphisms (SNPs) associated with LDL and HDL dysregulation, as published in prior work. Odds ratio curves were computed to assess the risk between LDL, TG, and HDL versus AMD. All clinical and genetic variables were input into a multivariable logistic regression model, and odds ratios and P values were generated. Main Outcome Measures: Statistical significance of risk factors for AMD, thresholded at P ≤ 0.05. Results: On multivariable regression analysis, statin use and low and high HDL were significantly associated with increased AMD risk (P < 0.001 for all variables). Additionally, the multivariable regression implicated HDL-associated SNP's increased risk for AMD. Last, LPA was identified (P = 0.007) as a novel SNP associated with increased AMD risk. Conclusions: There exists a U-shaped relationship between HDL and AMD risk, such that high and low HDL are significantly associated with increased AMD risk. Additionally, SNPs associated with HDL metabolism are associated with AMD risk. This analysis further e

关键词： Drusen HDL LDL Single nucleotide polymorphism TG

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ChatGPT versus Google Gemini: a comparison to evaluate patient education guide created on common neurological disorders

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Discover Artificial Intelligence 2024年第1期4卷 79页

作者： Phillips, Vidith Kiryakoza, Fadi Arefin, Shamsul Choudhary, Nishtha Garifullin, Renat Division of Biomedical Informatics & amp Data Science Institute School of Medicine John Hopkins University BaltimoreMD United States Ibn Sena Teaching Hospital Mosul Iraq NHS Trust Nottingham Univesity Hospitals Nottingham United Kingdom Jhalawar Medical College Rajasthan Jhalawar India University of New Mexico AlbuquerqueNM United States

Introduction: This study assesses the effectiveness of AI tools, ChatGPT and Google Gemini, in educating the public about neurological conditions such as Bell’s palsy, Tetanus, and Headaches, and assess chatbot-generated patient guides based on readability and ease of understanding. Methodology: In March 2024, the authors conducted a cross-sectional study to develop patient education brochures for Bell’s palsy, Tetanus vaccination, and Persistent Headache, leveraging AI models ChatGPT and Google Gemini. The brochures’ quality was assessed through readability, similarity, and a modified DISCERN score for reliability. Statistical analysis, performed in R software, compared responses from both AI models using unpaired T-tests. The correlation between ease score and reliability was explored using Pearson’s Coefficient of Correlation. Results: The study revealed no significant variances in word count, sentence count, or average words per sentence between the two AI tools. However, materials generated by ChatGPT exhibited significantly higher ease scores, highlighting its proficiency in creating more understandable content (p © The Author(s) 2024.

关键词： Reliability analysis

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Cardiovascular risk of gonadotropin-releasing hormone antagonist versus agonist in men with prostate cancer: an observational study in Taiwan (Jun, 10.1038/s41391-022-00555-0, 2022)

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PROSTATE CANCER AND PROSTATIC DISEASES 2023年第3期26卷 631-632页

作者： Shao, Yu-Hsuan Joni Hong, Jian-Hua Chen, Chun-Kai Huang, Chao-Yuan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Department of Urology National Taiwan University Hospital College of Medicine National Taiwan University Taipei Taiwan Institute of Biomedical Engineering National Taiwan University Taipei Taiwan Division of Cardiology Department of Internal Medicine National Taiwan University Hospital Hsinchu Branch Hsinchu Taiwan

Background The impact of gonadotropin-releasing hormone (GnRH) antagonist and agonist (GnRHa) treatment on cardiovascular disease (CVD) risk in prostate cancer (PCa) remains inconclusive due to conflicting findings. We compared the effects of GnRH antagonist and GnRHa treatments on CVD risk in patients with PCa and pre-existing CVD, in a Taiwan population-based *** We assessed the risk of major adverse CV events (MACE: ischemic heart disease [IHD], stroke, congestive heart failure [CHF] or all cause deaths) and composite CV events (IHD, stroke, CHF or CV deaths) occurring ≥90 days after androgen deprivation therapy (ADT) initiation in patients with PCa after 90 days of treatment with either GnRH antagonist (degarelix; n = 499) or GnRHa (goserelin, leuprolide, triptorelin; n = 15,127). Patients identified with pre-existing CVD had received cardiac therapy for IHD, reported a stroke or CHF within a year before ADT initiation. Adjusted hazard ratios (aHR) and 95% confidence interval (CI) were obtained for MACE and composite CV events risk after adjusting for age, baseline status of diabetes, hypertension and treatments *** All GnRH antagonist-treated patients showed lower risk of composite CV events than the GnRHa-treated patients. The lower composite CV events risk associated with GnRH antagonist was also observed in patients with metastasis at diagnosis (aHR 0.16; 95% CI, 0.04–0.38; p = 0.013) and those receiving ADT for more than six months (aHR 0.30; 95% CI, 0.16–0.54; p < 0.0001). In patients with pre-existing CVD, the MACE risk was 33% lower (aHR 0.67; 95% CI, 0.46–0.96; p = 0.0299) and composite CV events risk was 84% lower (aHR 0.16; 95% CI, 0.05–0.50; p = 0.0017) in GnRH antagonist-treated than the GnRHa-treated *** In patients with PCa and pre-existing CVD, GnRH antagonist use was associated with lower risks for composite CV events and MACE compared with GnRHa.

关键词： Cancer therapy Prostate cancer

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From Survey to Ontology, the challenges and opportunities in ontologizing questions and answers 13

From Survey to Ontology, the challenges and opportunities in...

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13th International Conference on biomedical Ontology, ICBO 2022

作者： Chan, Lauren Phuong, Jimmy Thessen, Anne Hong, Stephanie Essaid, Shahim Haendel, Melissa College of Public Health and Human Sciences Oregon State University CorvallisOR97331 United States Division of Biomedical Health and Informatics University of Washington Medicine SeattleWA98195 United States Department of Biomedical Informatics University of Colorado Anschutz Medical Campus AuroraCO80054 United States Section of Biomedical Informatics and Data Science Johns Hopkins University School of Medicine BaltimoreMD21218 United States

Surveys are an essential tool for gathering data from individuals in a wide variety of fields. Common data Elements (CDEs) are routinely used in surveys to streamline frequently asked questions and support question reuse. While CDEs are a widespread method for collecting survey data, they face a variety of problems including inconsistent file formats, lack of computational encoding, little to no community development standards, and frequent duplication of CDEs across various registries. Alternative approaches such as ontologies can serve as meaningful translation tools to not only support interoperability between similar/duplicate CDEs, but also to coordinate survey data with other ontology aligned data from heterogeneous methodologies including wet lab, clinical, or field research. To capitalize on the extensive data collected using CDEs, we propose a two-step approach of 1) aligning CDEs with ontology terminology to coordinate similar questions based on primary topic and 2) using standardized enumerations for CDE responses that can support data harmonization for meta analytics. Using this approach, greater data integration can support higher powered analytics and coordination of heterogeneous data types which are often difficult to study in tandem. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Assessing the Impact of Post-COVID Clinics on 6-Month Health Care Utilization for Patients With Long COVID: A Single-Center Experience

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Mayo Clinic Proceedings: Innovations, Quality and Outcomes 2025年第3期9卷 100603-100603页

作者： Gong, Kirby D. Afshar, Ali S. Brown, Fred Alavi, Reza Ganesh, Ravindra Kharrazi, Hadi ArtiMed Inc. Baltimore MD Division of General Internal Medicine Mayo Clinic Rochester MN Division of Biomedical Informatics and Data Science Johns Hopkins School of Medicine Baltimore MD

Objective: To assess the impact of post-COVID clinics by examining the association between their early usage and downstream health care utilization. Patients and Methods: In a case-control study spanning data from March 11, 2020 to June 1, 2023, patients with Long COVID were identified from a major health system using diagnosis codes. The Fast, Large-Scale Almost Matching Exactly algorithm was used to match patients who presented early to post-COVID clinics with patients with Long COVID who did not attend such clinics. Matching was performed on demographic characteristics, acute COVID severity, comorbidities, diagnosis date, and vaccination, to reduce confounders for the comparison of the health care utilization and mortality between cohorts. Results: When exactly matching on all 46 features, the algorithm yielded 2814 matched patients, of whom 692 (24.6%;66.6% females;mean [SD] age, 48.8 [14.5] years) were seen in post-COVID clinics within the first 6 months and 2122 (75.4%;64.1% females;mean [SD] age, 49.7 [15.2] years) who were not. The average treatment effect (95% CI) of early post-COVID clinic usage was −0.60 (−0.83 to −0.39) on inpatient visits, −0.19 (−0.26 to −0.11) on emergency department visits, 7.62 (6.96-8.56) on outpatient visits, −$3467 (−$6267 to −$754) on estimated costs, and −0.006 (−0.010 to −0.003) on mortality. Conclusion: Early usage of post-COVID clinics by patients with Long COVID is associated with not only fewer downstream inpatient stays, emergency department visits, estimated costs, and reduced mortality within the first 6 months but also greater outpatient utilization. Results suggest early post-COVID clinic involvement shifts care to outpatient settings, potentially reducing costs and mortality. © 2025 The Authors

关键词： COVID coronavirus disease COVID-19 COronaVIrus Disease 2019 ED emergency department EHR electronic health record ICD International Classification of Diseases SARS-CoV-2 severe acute respiratory syndrome coronavirus 2

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Using annotation for computerized support for fast skimming of cardiology electronic health record notes

Using annotation for computerized support for fast skimming ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dehkordi, Mahshad Koohi H. Einstein, Andrew J. Zhou, Shuxin Elhanan, Gai Perl, Yehoshua Keloth, Vipina K. Geller, James Liu, Hao Njit Department of Computer Science NewarkNJ United States Irving Medical Center Cardiology Division Dept. of Medicine New YorkNY United States Desert Research Institute Renown Institute for Health Innovation RenoNV United States Yale School of Medicine Biomedical Informatics and Data Science New HavenCT United States Njit Department of Data Science NewarkNJ United States Montclair State University Department of Computer Science MontclairNJ United States

ISBN: (纸本)9798350337488

Under the circumstances prevalent in current healthcare, medical professionals such as physicians and nurses need to read large numbers of Electronic Health Record (EHR) notes. This demand fosters a situation in which providers typically do not read a whole note but quickly skim it, to capture its essential content. Consequently, by fast skimming, one may miss a critical medical fact. Annotation highlights important content in EHR notes, and enables healthcare professionals to perform fast skimming, thereby minimizing the risk of missing critical information, which is detrimental to patient care. We designed the Cardiology Interface Terminology (CIT) for the purpose of annotation of cardiology EHRs. We emphasize that by annotation we refer to highlighting important information in the EHR notes for enabling fast skimming rather than just recognizing names of diseases, drugs, etc. from Reference Terminologies as is usually done by existing Named Entity Recognition (NER) systems. The CIT design starts with the cardiology components of SNOMED CT. It is enhanced by mining phrases from cardiology EHRs, as potential CIT concepts, which are of higher granularity than SNOMED concepts. Machine learning (ML), the state of the art technique for mining concepts from EHRs, requires training data. However, there is no training data for designing CIT. In the first stage, we introduce an innovative semi-automatic method for mining concepts from EHRs, to replace costly manual mining. The only manual portion is the review of the automatically mined phrases, before their insertion as CIT concepts. The effectiveness of annotation of cardiology EHRs with CIT was evaluated utilizing proper metrics, and compared to annotation with SNOMED CT. In a future second stage, ML mining techniques will be used for enhancing CIT with extra concepts from EHRs, utilizing the concepts added in the first stage as training data. This work focuses on a novel semi-automated method to design the Cardiology I

关键词： annotation cardiology EHR fast skimming

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CANCERLLM: A LARGE LANGUAGE MODEL IN CANCER DOMAIN

arXiv

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arXiv 2024年

作者： Li, Mingchen Blaes, Anne Johnson, Steven Liu, Hongfang Xu, Hua Zhang, Rui Division of Computational Health Sciences University of Minnesota Twin Cities United States Division of Hematology Oncology and Transplantation University of Minnesota Twin Cities United States Institute for Health Informatics University of Minnesota Twin Cities United States McWilliams School of Biomedical Informatics UTHealth Houston United States Department of Biomedical Informatics and Data Science Yale School of Medicine United States

Medical Large Language Models (LLMs) such as ClinicalCamel 70B, Llama3- OpenBioLLM 70B have demonstrated impressive performance on a wide variety of medical NLP task. However, there still lacks a large language model (LLM) specifically designed for cancer domain. Moreover, these LLMs typically have billions of parameters, making them computationally expensive for healthcare systems. Thus, in this study, we propose CancerLLM, a model with 7 billion parameters and a Mistral-style architecture, pre-trained on 2,676,642 clinical notes and 515,524 pathology reports covering 17 cancer types, followed by fine-tuning on three cancer-relevant tasks, including cancer phenotypes extraction, cancer diagnosis generation, and cancer treatment plan generation. Our evaluation demonstrated that CancerLLM achieves state-of-the-art results compared to other existing LLMs, with an average F1 score improvement of 8.1%. Additionally, CancerLLM outperforms other models on two proposed robustness testbeds. This illustrates that CancerLLM can be effectively applied to clinical AI systems, enhancing clinical research and healthcare delivery in the field of cancer. © 2024, CC BY.

关键词： Clinical research

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Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19:A Large-Scale Social Media Analysis with Natural Language Processing

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Health data science 2024年第1期4卷 152-161页

作者： Zhiyun Zhang Yining Hua Peilin Zhou Shixu Lin Minghui Li Yujie Zhang Li Zhou Yanhui Liao Jie Yang Department of Big Data in Health Science School of Public Health Zhejiang University School of MedicineHangzhouChina Department of Epidemiology Harvard T.H.Chan School of Public HealthBostonMAUSA Department of Biomedical Informatics Harvard Medical SchoolBostonMAUSA Division of General Internal Medicine and Primary Care Department of MedicineBrigham and Women’s HospitalBostonMAUSA Thrust of Data Science and Analytics The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina Department of Psychiatry Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina

Background:The COVID-19 pandemic has caused a disproportionate impact on the sexual and genderdiverse(SGD)*** with non-SGD populations,their social relations and health status are more vulnerable,whereas public health data regarding SGD are ***:To analyze the concerns and health status of SGD individuals,this cohort study leveraged 471,371,477 tweets from 251,455 SGD and 22,644,411 non-SGD users,spanning from 2020 February 1 to 2022 April *** outcome measures comprised the distribution and dynamics of COVID-related topics,attitudes toward vaccines,and the prevalence of ***:Topic analysis revealed that SGD users engaged more frequently in discussions related to“friends and family”(20.5%vs.13.1%,P<0.001)and“wear masks”(10.1%vs.8.3%,P<0.001)compared to non-SGD ***,SGD users exhibited a marked higher proportion of positive sentiment in tweets about vaccines,including Moderna,Pffzer,AstraZeneca,and Johnson&*** 102,464 users who self-reported COVID-19 diagnoses,SGD users disclosed signiffcantly higher frequencies of mentioning 61 out of 69 COVID-related symptoms than non-SGD users,encompassing both physical and mental health ***:The results provide insights into an understanding of the unique needs and experiences of the SGD community during the pandemic,emphasizing the value of social media data in epidemiological and public health research.

关键词： COV discussions users

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