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CC180 clade dynamics do not universally explain Streptococcus pneumoniae serotype 3 persistence post-vaccine: a global comparative population genomics study

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eBioMedicine 2025年 117卷 105781页

作者： Stanley, Sydney Silva-Costa, Catarina Gomes-Silva, Joana Melo-Cristino, Jose Malley, Richard Ramirez, Mario Division of Infectious Diseases Boston Children's Hospital and Harvard Medical School Boston MA United States Computational Health Informatics Program Boston Children's Hospital and Harvard Medical School Boston MA United States Instituto de Microbiologia Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal

Background: Clonal complex 180 (CC180) is currently the major clone of serotype 3 Streptococcus pneumoniae (Spn) causing disease among children and adults worldwide. The 13-valent pneumococcal conjugate vaccine (PCV13) does not have significant efficacy against serotype 3 despite polysaccharide inclusion in the vaccine. It was hypothesized that PCV13 may effectively control Clade I of CC180 but that Clades III and IV are resistant, provoking a population shift that enables serotype 3 persistence. This has been observed in the United States, England, and Wales but not Spain. We tested this hypothesis further utilizing a dataset from Portugal to conduct our population genomics and molecular epidemiology comparative study. Methods: We performed whole-genome sequencing (WGS) of 501 serotype 3 strains from Portugal isolated from patients with pneumococcal infections between 1999 and 2020. The draft genomes underwent phylogenetic analyses, pangenome profiling, and a genome-wide association study (GWAS). We also completed antibiotic susceptibility testing and compiled over 2600 serotype 3 multilocus sequence type 180 (MLST180) WGSs to perform global comparative genomics. Findings: Relative to strains from all other lineages, CC180 Clades I, II, III, IV, and VI strains trend towards a decreased association with invasive disease cases compared to non-invasive pneumonia cases (binomial logistic regression, odds ratio or OR = 0.59, 95% confidence interval or CI = [0.34, 0.98], P = 0.046) and adult patients compared to paediatric patients (binomial logistic regression, OR = 0.34, 95% CI = [0.098, 0.92], P = 0.054). The serotype 3 CCs shifted post-PCV13 such that Clades I-VI comprise the majority of post-PCV13 lineages (binomial logistic regression, OR = 7.33, 95% CI = 4.36, 12.80, P < 0.0001), with Clade I representing 54% (220/404) of all post-PCV13 strains. As observed elsewhere, Clade I strains from Portugal are largely antibiotic-sensitive and carry the ΦOXC141 prophage. Ho

关键词： Clade I Invasive pneumococcal disease Molecular epidemiology PCV13 Serotype 3 Streptococcus pneumonia

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medical Knowledge-Guided Deep Learning for Mammographic Breast Density Classification

Medical Knowledge-Guided Deep Learning for Mammographic Brea...

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medical Imaging 2023: Computer-Aided Diagnosis

作者： Gao, Long Liu, Chang Arefan, Dooman Panigrahy, Ashok Zuley, Margarita L. Wu, Shandong Dept. of Radiology University of Pittsburgh PittsburghPA15213 United States Dept. of Bioengineering University of Pittsburgh PittsburghPA15213 United States Dept. of Biomedical Informatics University of Pittsburgh PittsburghPA15213 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15213 United States College of Computer National University of Defense Technology Changsha410073 China Magee-Womens Hospital University of Pittsburgh Medical Center PittsburghPA15213 United States

ISBN: (纸本)9781510660359

Classification of Breast Imaging Reporting and Data System (BI-RADS) breast density categories generally reflects the amount of dense/fibroglandular tissue in the breast. Studies have consistently shown that breast with higher density has a higher risk of developing breast cancer compared to breast with lower density. In this paper, we propose a novel end-to-end method, namely, medical Knowledge-guided Deep Learning (MKDL), for breast mammogram density classification. The principle behind MKDL lies in the fact that many breast image density classification tasks are partly or largely based on certain pre-known image features, such as image contrast and brightness. These pre-known features can be computationally represented and then leveraged as prior knowledge to facilitate more effective model learning and thus boost the classification performance. We designed specific knowledge-based transformations for breast density classification and showed that our model outperformed several state-of-the-art models. © 2023 SPIE.

关键词： Classification (of information)

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MedRep: medical Concept Representation for General Electronic Health Record Foundation Models

arXiv

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arXiv 2025年

作者： Kim, Junmo Lee, Namkyeong Kim, Jiwon Kim, Kwangsoo Interdisciplinary Program in Bioengineering Seoul National University Korea Republic of Dept. of Industrial and Systems Engineering KAIST Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University Korea Republic of Dept. of Transdisciplinary Medicine ICMIT Seoul National University Hospital Korea Republic of Center for Data Science Healthcare AI Research Institute Seoul National University Hospital Korea Republic of Dept. of Medicine College of Medicine Seoul National University Korea Republic of

Electronic health record (EHR) foundation models have been an area ripe for exploration with their improved performance in various medical tasks. Despite the rapid advances, there exists a fundamental limitation: Processing unseen medical codes out of the vocabulary. This problem limits the generality of EHR foundation models and the integration of models trained with different vocabularies. To deal with this problem, we propose MedRep for EHR foundation models based on the observational medical outcome partnership (OMOP) common data model (CDM), providing the integrated medical concept representations and the basic data augmentation strategy for patient trajectories. For concept representation learning, we enrich the information of each concept with a minimal definition through large language model (LLM) prompts and enhance the text-based representations through graph ontology of OMOP vocabulary. Trajectory augmentation randomly replaces selected concepts with other similar concepts that have closely related representations to let the model practice with the concepts out-of-vocabulary. Finally, we demonstrate that EHR foundation models trained with MedRep better maintain the prediction performance in external datasets. Our code implementation is publicly available at https://***/kicarussays/MedRep. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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The evaluation of pulmonary tuberculosis (TB) control program at Bandarharjo Community Health Center Semarang city in 2021

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AIP Conference Proceedings 2023年第1期2683卷

作者： Hargianti Dini Iswandari Sigit Sugiharto Harsono Harsono Medical Informatics Study Program Universitas Widya Husada Semarang (UWHS) Semarang Indonesia

Indonesia is the second country with the highest burden of tuberculosis (TB) worldwide after India. The discovery of TB sufferers in Semarang from 2015 to 2020 decreased, primarily due to the Covid19 pandemic since 2019. This study aimed to evaluate pulmonary TB control in health centres to find new patients using a systems approach. The research was conducted within a qualitative analysis. Data collection techniques were in-depth interviews, observation, document review, and focus group discussions. The results show that the Ministry of Health regulations guides TB program policies, the lack of health cadres for detecting TB patients, and the passive and active methods used to detect TB sufferers; there are still shortcomings. The process and planning at the health centre are under the guidelines. The mobilization-implementation of TB patient detection is good enough. Monitoring and evaluation in the detection of TB sufferers have not been going well. In addition, there is a problem with the referral system that is not good in Semarang City. The TB prevention program in detecting pulmonary TB patients in Semarang is running quite well even though the results have not been reached.

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Single-sequence protein structure prediction using language models from deep learning

Single-sequence protein structure prediction using language ...

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2021 AIChE Annual Meeting

作者： Chowdhury, Ratul Bouatta, Nazim Biswas, Surojit Rochereau, Charlotte Church, George M. Sorger, Peter K. Alquraishi, Mohammed Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Nabla Bio. Inc. United States Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New YorkNY United States Department of Systems Biology Columbia University New YorkNY United States

ISBN: (纸本)9781713852834

AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, three challenges remain: (i) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated, (ii) rapid exploration of designed structures, and (iii) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) able to predict protein structure from single protein sequences without use of MSAs. This deep learning system has two novel elements: a protein language model (AminoBERT) that uses a Transformer to learn latent structural information from millions of unaligned proteins and a geometric module that compactly represents Cabackbone geometry. RGN2 outperforms AlphaFold2 and RoseTTAFold (as well as trRosetta) on orphan proteins and is competitive with designed sequences, while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction. © 2021 American Institute of Chemical Engineers. All rights reserved.

关键词： Proteins

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm 1

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023

作者： Indriyono, Bonifacius Vicky Pamungkas, Natalinda Abbas, Fatima Hashim Mintorini, Ery Almohamadi, Mohammed Mahmood, Sarmad Nozad Alani, Sameer Alkhayyat, Ahmed H. Rachmawanto, Eko Hari University of Dian Nuswantoro Information System Kediri Indonesia Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq University of Dian Nuswantoro Visual Communication Design Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq University of Mashreq Research Center Medical Laboratories Techniques Baghdad Iraq College of Technical Engineering University of Mashreq the Islamic University Najaf Iraq University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments;they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD. © 2023 IEEE.

关键词： Energy efficiency

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Single-sequence protein structure prediction using a language model and deep learning (Oct, 10.1038/s41587-022-01432-w, 2022)

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NATURE BIOTECHNOLOGY 2022年第11期40卷 1692-1692页

作者： Chowdhury, Ratul Bouatta, Nazim Biswas, Surojit Floristean, Christina Kharkar, Anant Roy, Koushik Rochereau, Charlotte Ahdritz, Gustaf Zhang, Joanna Church, George M. Sorger, Peter K. AlQuraishi, Mohammed Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Nabla Bio Inc. Boston MA USA Department of Computer Science Columbia University New York NY USA Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New York NY USA Department of Systems Biology Columbia University New York NY USA Department of Systems Biology Harvard Medical School Boston MA USA

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

关键词： Computational biophysics Machine learning Protein structure predictions

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Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

arXiv

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arXiv 2023年

作者： Chen, Shan Li, Yingya Lu, Sheng Van, Hoang Aerts, Hugo J.W.L. Savova, Guergana K. Bitterman, Danielle S. Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women’s Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School BostonMA United States Ubiquitous Knowledge Processing Lab Technische Universität Darmstadt Darmstadt Germany Radiology and Nuclear Medicine GROW & CARIM Maastricht University Netherlands

Objective/Purpose: Recent advances in large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5, GPT-4) in biomedical tasks beyond question-answering. Materials/Methods: Because no patient data can be passed to the OpenAI API public interface, we evaluated model performance with 10,000+ samples as proxies for two fundamental tasks in the clinical domain – classification and reasoning. The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice. The second task is causal relation detection from the biomedical literature. We used 20% of the dataset for prompt development under the settings of zero- and few-shot with and without chain of thought (CoT). The most effective prompt from each setting was evaluated on the remaining 80%. We compared LLMs with models using simple features (bag-of-words (BoW)) with logistic regression, and fine-tuned BioBERT models. Results: Fine-tuning BioBERT yielded the best results for classification (F1 0.800-0.902) and reasoning (F1 0.851). Of the LLM approaches, few-shot CoT yielded the best results for classification (F1 0.671-0.770) and reasoning (F1 0.682), comparable to the BoW model (F1 0.602-0.753 and 0.675 for classification and reasoning, respectively). The total time needed to achieve the best LLM results was 78 hours, compared to 0.078 and 0.008 hours to develop the best-performing BioBERT and BoW models, respectively. Conclusions: Despite the excitement around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks remained the best strategy. The simple BoW model performed on par with the most complex LLM prompting. Prompt engineering required significant investment. © 2023, CC BY-SA.

关键词： medical applications

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A Unifying Model of Information Loss in Communication Across Populations

arXiv

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arXiv 2025年

作者： Kumar, Sagar Laber, Moritz Majumder, Maimuna S. Welles, Brooke Foucault Network Science Institute Northeastern University BostonMA United States Department of Communication Northeastern University BostonMA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States Complexity Science Hub Vienna Vienna Austria

Many of today’s most pressing issues require a more robust understanding of how information spreads in populations. Current models of information spread can be thought of as falling into one of two varieties: epidemiologically-inspired rumor spreading models, which do not account for the noisy nature of communication, or information theory-inspired communication models, which do not account for spreading dynamics in populations. The viral proliferation of misinformation and harmful messages seen both online and offline, however, suggests the need for a model that accounts for both noise in the communication process, as well as disease-like spreading dynamics. In this work, we leverage communication theory to meaningfully synthesize models of rumor spreading with models of noisy communication to develop a model for the noisy spread of structurally encoded messages. Furthermore, we use this model to develop a framework that allows us to consider not only the dynamics of messages, but the amount of information in the average message received by members of the population at any point in time. We find that spreading models and noisy communication models constitute the upper and lower bounds for the amount of information received, while our model fills the space in between. We conclude by considering our model and findings with respect to both modern communication theory and the current information landscape to glean important insights for the development of communication-based interventions to fight rising threats to democracy, public health, and social justice. Copyright © 2025, The Authors. All rights reserved.

关键词： Information theory

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Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry

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Chinese medical Journal 2023年第8期136卷 998-1000页

作者： Xueying Yang Lisheng Geng David Huang Kaiwen Li Hongqing Zhuang Jing Cai Ruijie Yang School of Physics Beihang UniversityBeijing 102206China Department of Radiation Oncology Peking University Third HospitalBeijing 100191China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China Medical Physics Graduate Program Duke Kunshan UniversityKunshanJiangsu 215316China Medical Management Department CAS Ion Medical Technology Co.Ltd.Beijing 100190China Department of Health Technology and Informatics The Hong Kong Polytechnic UniversityHong Kong 999077China

To the Editor:Due to the highly complex nature of intensity-modulated radiotherapy(IMRT)planning and delivery,patient specific quality assurance(PSQA)should be implemented to assure the reliability of treatment delive... 详细信息

关键词： IMRT treatment vivo

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