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The impact of the duration of the integrated disease management program on COPD-related outcomes

European Journal of Medical Research 2023年第1期28卷 178-178页

作者： Lin, Ching-Hsiung Li, Yi-Rong Wang, Bing-Yen Lin, Sheng-Hao Huang, Kuo-Yang Chen, Cheng-Hsiung Kor, Chew-Teng Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital 135 Nanhsiao Street Changhua 50006 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan National Chung Hsing University Taichung Taiwan Department of Recreation and Holistic Wellness MingDao University Changhua Taiwan Thoracic Medicine Research Center Changhua Christian Hospital Changhua 500 Taiwan Department of Surgery Division of Thoracic Surgery Changhua Christian Hospital Changhua 500 Taiwan Big Data Center Changhua Christian Hospital Changhua 500 Taiwan Graduate Institute of Statistics and Information Science National Changhua University of Education Changhua 500 Taiwan

Background: The aim of this study is to assess the impact of the duration of the integrated disease management (IDM) program on COPD-related outcomes in real-world setting. Methods: A retrospective cohort study among 3771 patients with COPD who had regularly completed 4 visits of IDM program within 1 year between April 1, 2017 and December 31, 2018. CAT score as the primary outcome used to investigate the association between IDM intervention duration and improvement in CAT score. Change in CAT score from baseline to each follow-up visit determined by using least-squares means (LSMeans) approach. The cut-off value of IDM duration for improving the CAT score was determined by the Youden index. Logistic regression was used to analyze the relationship between IDM intervention duration and MCID (the minimal clinically important difference) improvement in CAT score and the factor associated CAT improvement. Risks of COPD exacerbation events (COPD-related ED visit and COPD-related hospitalization) were estimated by using the cumulative incidence curve and Cox proportional hazards models. Result: Among 3771 enrolled COPD patients, the majority of the study cohort were males (91.51%) and 42.7% of patients had CAT score of ≥ 10 at baseline. The mean of age was 71.47 years and the mean CAT at baseline were 10.49. The mean change from baseline in CAT score was − 0.87, − 1.19, − 1.23 and − 1.40 at 3-, 6-, 9- and 12 month follow-up (p < 0.0001 for all visits), respectively. Statistically significantly lower likelihood of achieving MCID improvement in CAT were observed at 3- and 6 month compared to 9 month (at 3 month: OR: 0.720, 95% CI 0.655–0.791;at 6 month: OR: 0.905, 95% CI 0.825–0.922). And only a modest increase likelihood of achieving MCID improvement in CAT at 12 month (OR: 1.097, 95% CI 1.001–1.201) compared with 9-month follow-up. In logistic regression on the entire cohort, CAT MCID improvement was most associated with baseline CAT scores ≥ 10, followed by frequent exac

关键词： COPD Exacerbation Integrated care model Intervention duration MCID improvement for CAT

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OncoEnrichR: cancer-dedicated gene set interpretation

arXiv

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arXiv 2021年

作者： Nakken, Sigve Gundersen, Sveinung Bernal, Fabian L.M. Polychronopoulos, Dimitris Hovig, Eivind Wesche, Jørgen Department of Tumor Biology Institute for Cancer Research Oslo University Hospital Norway Centre for Cancer Cell Reprogramming Institute of Clinical Medicine Faculty of Medicine University of Oslo Norway Centre for Bioinformatics Department of Informatics University of Oslo Norway University Center for Information Technology University of Oslo Norway Early Data Science and Computational Oncology Research and Early Development Oncology R&D AstraZeneca Cambridge United Kingdom

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations, and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based workflow hosted by the Galaxy platform (https://***), and can also be accessed as a stand-alone R package (https://***/sigven/oncoEnrichR). © 2021, CC BY-NC-SA.

关键词： Tumors

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database Management System Design with Time Series Modification for Child Growth and Malnutrition Monitoring in The Regency of Serdang Bedagai

Database Management System Design with Time Series Modificat...

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Ika Nurlaila Anzaludin Samsinga Perbangsa Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia BINUS Online Learning Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

A poor data management has led zero stunting strategy in Indonesia moved nowhere. Some justifications on the demography and numbers of affected children remain a matter of plain assumptions without a clear supporting data. We hence develop a database system which enables time series monitoring on the expected increment of children's physical growth. At this initial step, our database system will be implemented at Public Health Office (Dinas Kesehatan) of Serdang Bedagai, South Celebes. This is a modification from Connoly and Begg's principles. Our database was built through 3-steps design started with conceptual followed by logical and at final step was normalizations-enriched physical design. By implementing this proposed system, the assigned healthcare workers will be able record every bit of superficial growth increase. Information obtained in the study is of essence for the efficient and effective intervention formula to achieve national zero stunting goal.

关键词： Pediatrics Monitoring database systems Dictionaries Drugs Public healthcare

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Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical Records 15th

Exploring the Pan-Relationship Between Disease and Symptom R...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Yang, Ronggen Wang, Yichen Wang, Bo Gong, Lejun Faculty of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210046 China School of Geography and Bioinformatics Nanjing University of Posts and Telecommunication Nanjing China

ISBN: (纸本)9783030267650

Electronic Medical Records (EMRs) arises from the clinical treatment process, which reflects a patient’s physical condition, the disease, the treatment process, etc. It has an important significance for the recovery of the patient. As the amount of electronic medical records continues to increase, people are actively looking for ways to get the valid data contained in electronic medical records. More effective methods are urgently needed for processing EMRs. In this paper, 108 real EMRs of coronary heart disease were analyzed, through named entity recognition and entity relation extraction. Pan-relationship between entities was defined in the process. Experimental results showed the analysis of pan-relation between coronary heart disease and symptom is helpful for treatment of coronary heart disease. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Correction to: Optimal Decision Criteria for the Study Design and Sample Size of a Biomarker‑Driven Phase III Trial

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Therapeutic innovation & regulatory science 2020年第5期54卷 1035-1036页

作者： Akira Takazawa Satoshi Morita Data Science Department ONO Pharmaceutical Co. Ltd 8-2 Kyutaromachi 1-Chome Chuo-ku Osaka 541-8564 Japan. takazawa@ono.co.jp. Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine Kyoto Japan. takazawa@ono.co.jp. Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine Kyoto Japan.

In the original article in the section "Application to the Motivating Example", Greenberg's trial data have been deleted from the historical data.

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A New Time-Aware Collaborative Filtering Intelligent Recommendation System

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Computers, Materials & Continua 2019年第8期61卷 849-859页

作者： Weijin Jiang Jiahui Chen Yirong Jiang Yuhui Xu Yang Wang Lina Tan Guo Liang Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Hunan University of Technology and BusinessChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China Tonghua Normal University Tonghua134002China School of Bioinformatics University of MinnesotaTwin CitiesUSA

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project ***,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more *** the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large *** experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

关键词： Fuzzy clustering time weight attenuation function Collaborative filtering method recommendation algorithm

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Trajectories of macrophage ontogeny and reprogramming in cancer

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iscience 2025年第5期28卷 112498页

作者： Duval, Florent Lourenco, Joao Hicham, Mehdi Boivin, Gaël Guichard, Alan Wyser-Rmili, Celine Fournier, Nadine Mansouri, Nahal De Palma, Michele Swiss Institute for Experimental Cancer Research (ISREC) School of Life Sciences Swiss Federal Institute of Technology in Lausanne (EPFL) Lausanne Switzerland Agora Cancer Research Center Lausanne Switzerland Swiss Cancer Center Léman (SCCL) Switzerland Translational Data Science (TDS) Facility Swiss Institute of Bioinformatics (SIB) Lausanne Switzerland Division of Pulmonary Medicine Department of Medicine Lausanne University Hospital (CHUV) University of Lausanne (UNIL) Lausanne Switzerland

Tumor-associated macrophages (TAMs) often manifest immunosuppressive and tumor-promoting phenotypes contributing to immunotherapy resistance. Dicer1 inactivation in TAMs (D^KO) prompts their immunostimulatory activation, enabling effective immunotherapy in mouse cancer models. Single-cell RNA sequencing (scRNA-seq) analysis revealed interferon-γ (IFNγ)-dependent immunostimulatory programming of the tumor microenvironment in D^KO mice. In tumors of wild-type mice and patients with cancer, dynamic inferences on macrophage ontogeny by pseudotime analysis identified trajectories associated with monocyte-to-macrophage differentiation, progression into the cell cycle, and transition from immunostimulatory (M1-like) to immunosuppressive and protumoral (M2-like) states. Dicer1 inactivation interfered with this trajectory and stalled TAMs at an intermediate state, impeding immunosuppressive and M2-like TAM development. This reprogramming translated into enhanced response to antiangiogenic immunotherapy in an orthotopic lung cancer model. Cycling/M2-like macrophages are conserved in mouse and human cancers and are enriched in patients with poor response to immunotherapy, making them a more selective therapeutic target than the bulk of TAMs. © 2025 The Author(s)

关键词： Cancer Immune response Microenvironment Transcriptomics

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Prediction of oral food challenge outcomes via ensemble learning

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Informatics in Medicine Unlocked 2023年 36卷

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann Arbor MI United States Department of Internal Medicine University of Michigan Ann Arbor MI United States Department of Pediatrics University of Michigan Ann Arbor MI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI United States Michigan Institute for Data Science University of Michigan Ann Arbor MI United States Department of Emergency Medicine University of Michigan Ann Arbor MI United States Department of Computer Science and Engineering University of Michigan Ann Arbor MI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann Arbor MI United States Mary H. Weiser Food Allergy Center University of Michigan Ann Arbor MI United States Department of Computer Science Queens College City University of New York New York NY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. © 2022 The Author(s)

关键词： Ensemble learning Learning Using Concave and Convex Kernels Machine learning Oral food challenges SHAP

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

arXiv

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arXiv 2024年

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024, CC BY-NC-ND.

关键词： Neurons

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING

arXiv

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arXiv 2023年

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2023, CC BY-NC-SA.

关键词： Maximum principle

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