medRxiv : the preprint server for health sciences最新文献

{"title":"The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes.","authors":"G L Barlow, C M Schürch, S S Bhate, D Phillips, A Young, S Dong, H A Martinez, G Kaber, N Nagy, S Ramachandran, J Meng, E Korpos, J A Bluestone, G P Nolan, P L Bollyky","doi":"10.1101/2023.03.15.23287145","DOIUrl":"10.1101/2023.03.15.23287145","url":null,"abstract":"<p><p>In autoimmune Type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans - islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 ⁺ T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA ⁺ T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9197159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Keyphrase Identification Using Minimal Labeled Data with Hierarchical Contexts and Transfer Learning.","authors":"Rohan Goli, Keerthana Komatineni, Shailesh Alluri, Nina Hubig, Hua Min, Yang Gong, Dean F Sittig, Lior Rennert, David Robinson, Paul Biondich, Adam Wright, Christian Nøhr, Timothy Law, Arild Faxvaag, Aneesa Weaver, Ronald Gimbel, Xia Jing","doi":"10.1101/2023.01.26.23285060","DOIUrl":"10.1101/2023.01.26.23285060","url":null,"abstract":"<p><strong>Background: </strong>Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. Ontology construction is traditionally a manual effort by human domain experts, and the newly advanced natural language processing techniques, such as KP identification, can be a critical complementary automatic part of building ontology. However, KP identification requires human expertise, consensus, and contextual understanding for data labeling.</p><p><strong>Methods: </strong>This paper presents a semi-supervised KP identification framework (long short-term memory-based encoders and the conditional random fields -based decoder models, BiLSTM-CRF) using minimal human labeled data based on hierarchical attention (i.e., at word, sentence, and abstract levels) over the documents and domain adaptation. We created synthetic labels for initial training and human-labeled data for fine-tuning. We also tested different options during NLP preprocessing and ML training to optimize the ML pipeline.</p><p><strong>Results: </strong>Our method outperforms the prior neural architectures by learning through synthetic labels for initial training, document-level contextual learning, language modeling, and fine-tuning with limited gold standard label data. After comparison, we found that the BIO encoding schema performed slightly better than Blue, and domain adaptation techniques can improve the quality of synthetic labels. In addition, document-level context, pre-trained LM, and pre-trained WE all contributed to better model performance in our tasks. Add 2 to 4 human-labeled documents for every 100 synthetic labeled documents improves the model performance without exhausting human-labeled documents too quickly.</p><p><strong>Conclusions: </strong>To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify KPs, which is trained on limited human labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging, and light-weighted deep learning models play an important role in real-time KP identification as a complementary approach to human experts' effort.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b9/97/nihpp-2023.01.26.23285060v2.PMC10246160.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10009443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Efficacy Prediction for EHR-based Emulated Trials in Repurposing Heart Failure Therapies.","authors":"Nansu Zong, Shaika Chowdhury, Shibo Zhou, Sivaraman Rajaganapathy, Yue Yu, Liewei Wang, Qiying Dai, Pengyang Li, Xiaoke Liu, Suzette J Bielinski, Jun Chen, Yongbin Chen, James R Cerhan","doi":"10.1101/2023.05.25.23290531","DOIUrl":"10.1101/2023.05.25.23290531","url":null,"abstract":"<p><strong>Introduction: </strong>The High mortality rates associated with heart failure (HF) have propelled the strategy of drug repurposing, which seeks new therapeutic uses for existing, approved drugs to enhance the management of HF symptoms effectively. An emerging trend focuses on utilizing real-world data, like EHR, to mimic randomized controlled trials (RCTs) for evaluating treatment outcomes through what are known as emulated trials (ET). Nonetheless, the intricacies inherent in EHR data-comprising detailed patient histories in databases, the omission of certain biomarkers or specific diagnostic tests, and partial records of symptoms-introduce notable discrepancies between EHR data and the stringent standards of RCTs. This gap poses a substantial challenge in conducting an ET to accurately predict treatment efficacy.</p><p><strong>Objective: </strong>The objective of this research is to predict the efficacy of drugs repurposed for HF in randomized trials by leveraging EHR in ET.</p><p><strong>Methods: </strong>We proposed an ET framework to predict drug efficacy, integrating target prediction based on biomedical databases with statistical analysis using EHR data. Specifically, we developed a novel target prediction model that learns low-dimensional representations of drug molecules, protein sequences, and diverse biomedical associations from a knowledge graph. Additionally, we crafted strategies to improve the prediction by considering the interactions between HF drugs and biological factors in the context of HF prognostic markers.</p><p><strong>Results: </strong>Our validation of the drug-target prediction model against the BETA benchmark demonstrated superior performance, with an average AUCROC of 97.7%, PRAUC of 97.4%, F1 score of 93.1%, and a General Score of 96.1%, surpassing existing baseline algorithms. Further analysis of our ET framework on identifying 17 repurposed drugs-derived from 266 phase 3 HF RCTs-using data from 59,000 patients at the Mayo Clinic highlighted the framework's remarkable predictive accuracy. This analysis took into account various factors such as biological variables (e.g., gender, age, ethnicity), HF medications (e.g., ACE inhibitors, Beta-blockers, ARBs, Loop Diuretics), types of HF (HFpEF and HFrEF), confounders, and prognostic markers (e.g., NT-proBNP, bUn, creatinine, and hemoglobin). The ET framework significantly improved the accuracy compared to the baseline efficacy analysis that utilized EHR data. Notably, the best results were improved in AUC-ROC from 75.71% to 93.57% and in PRAUC from 78.66% to 90.34%, compared to the baseline models.</p><p><strong>Conclusion: </strong>Our study presents an ET framework that significantly enhances drug efficacy emulation by integrating EHR-based analysis with target prediction. We demonstrated substantial success in predicting the efficacy of 17 HF drugs repurposed for phase 3 RCTs, showcasing the framework's potential in advancing HF treatment strategies.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b0/45/nihpp-2023.05.25.23290531v1.PMC10312819.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9754104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel autoantibody targets identified in patients with autoimmune hepatitis (AIH) by PhIP-Seq reveals pathogenic insights.","authors":"Arielle Klepper, James Asaki, Andrew F Kung, Sara E Vazquez, Aaron Bodansky, Anthea Mitchell, Sabrina A Mann, Kelsey Zorn, Isaac Avila-Vargas, Swathi Kari, Melawit Tekeste, Javier Castro, Briton Lee, Maria Duarte, Mandana Khalili, Monica Yang, Paul Wolters, Jennifer Price, Emily Perito, Sandy Feng, Jacquelyn J Maher, Jennifer C Lai, Christina Weiler-Normann, Ansgar W Lohse, Joseph DeRisi, Michele Tana","doi":"10.1101/2023.06.12.23291297","DOIUrl":"10.1101/2023.06.12.23291297","url":null,"abstract":"<p><strong>Background and aims: </strong>Autoimmune hepatitis (AIH) is a severe disease characterized by elevated immunoglobin levels. However, the role of autoantibodies in the pathophysiology of AIH remains uncertain.</p><p><strong>Methods: </strong>Phage Immunoprecipitation-Sequencing (PhIP-seq) was employed to identify autoantibodies in the serum of patients with AIH (<i>n</i> = 115), compared to patients with other liver diseases (metabolic associated steatotic liver disease (MASH) <i>n</i> = 178, primary biliary cholangitis (PBC), <i>n</i> = 26, or healthy controls, <i>n</i> = 94).</p><p><strong>Results: </strong>Logistic regression using PhIP-seq enriched peptides as inputs yielded a classification AUC of 0.81, indicating the presence of a predictive humoral immune signature for AIH. Embedded within this signature were disease relevant targets, including SLA/LP, the target of a well-recognized autoantibody in AIH, disco interacting protein 2 homolog A (DIP2A), and the relaxin family peptide receptor 1 (RXFP1). The autoreactive fragment of DIP2A was a 9-amino acid stretch nearly identical to the U27 protein of human herpes virus 6 (HHV-6). Fine mapping of this epitope suggests the HHV-6 U27 sequence is preferentially enriched relative to the corresponding DIP2A sequence. Antibodies against RXFP1, a receptor involved in anti-fibrotic signaling, were also highly specific to AIH. The enriched peptides are within a motif adjacent to the receptor binding domain, required for signaling and serum from AIH patients positive for anti-RFXP1 antibody was able to significantly inhibit relaxin-2 singling. Depletion of IgG from anti-RXFP1 positive serum abrogated this effect.</p><p><strong>Conclusions: </strong>These data provide evidence for a novel serological profile in AIH, including a possible functional role for anti-RXFP1, and antibodies that cross react with HHV6 U27 protein.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3f/66/nihpp-2023.06.12.23291297v2.PMC10312872.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9754091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic insights into the association between inflammatory bowel disease and Alzheimer's disease.","authors":"Lu Zeng, Charles C White, David A Bennett, Hans-Ulrich Klein, Philip L De Jager","doi":"10.1101/2023.04.17.23286845","DOIUrl":"10.1101/2023.04.17.23286845","url":null,"abstract":"<p><p>Myeloid cells, including monocytes, macrophages, microglia, dendritic cells and neutrophils are a part of innate immune system, playing a major role in orchestrating innate and adaptive immune responses. Both Alzheimer's disease (AD) and inflammatory bowel disease (IBD) susceptibility loci are enriched for genes expressed in myeloid cells, but it is not clear whether these myeloid risk factors are shared between the two diseases. Leveraging results of genome-wide association studies, we investigated the causal effect of IBD (including ulcerative colitis (UC) and Crohn's disease (CD)) variants on AD and its endophenotypes. Microglia and monocyte expression Quantitative Trait Locus (eQTLs) were used to examine the functional consequences of IBD and AD variants. Our results revealed distinct sets of genes and pathways of AD and IBD susceptibility loci. Specifically, AD loci are enriched for microglial eQTLs, while IBD loci are enriched for monocyte eQTLs. However, we also found that genetically determined IBD is associated with a protective effect against AD (p<0.03). Yet, a genetic propensity for the CD subtype is associated with increased amyloid accumulation (beta=7.14, p-value=0.02) and susceptibility to AD. Susceptibility to UC was associated with increased deposition of TDP-43 (beta=7.58, p-value=6.11×10^-4). The relation of these gastrointestinal inflammatory disease to AD is therefore complex; while the different subsets of susceptibility variants preferentially affect different myeloid cell subtypes, there do appear to be certain shared pathways and the possible protective effect of IBD susceptibility on the risk of AD which may provide therapeutic insights.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/39/e6/nihpp-2023.04.17.23286845v1.PMC10153331.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9459545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum.","authors":"David S M Lee, Kathleen M Cardone, David Y Zhang, Noah L Tsao, Sarah Abramowitz, Pranav Sharma, John S DePaolo, Mitchell Conery, Krishna G Aragam, Kiran Biddinger, Ozan Dilitikas, Lily Hoffman-Andrews, Renae L Judy, Atlas Khan, Iftikhar Kulo, Megan J Puckelwartz, Nosheen Reza, Benjamin A Satterfield, Pankhuri Singhal, Zoltan P Arany, Thomas P Cappola, Eric Carruth, Sharlene M Day, Ron Do, Christopher M Haggarty, Jacob Joseph, Elizabeth M McNally, Girish Nadkarni, Anjali T Owens, Daniel J Rader, Marylyn D Ritchie, Yan V Sun, Benjamin F Voight, Michael G Levin, Scott M Damrauer","doi":"10.1101/2023.07.16.23292724","DOIUrl":"10.1101/2023.07.16.23292724","url":null,"abstract":"<p><p>Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, which affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (P-value < 5×10^-8). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes (<i>MYBPC3</i>, <i>BAG3</i>) and in regulators of lipoprotein (<i>LPL</i>) and glucose metabolism (<i>GIPR</i>, <i>GLP1R</i>). These signals are enriched in myocyte and adipocyte cell types and can be clustered into 5 broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity, and arrhythmias. Gene burden studies across three biobanks (PMBB, UKB, AOU), including 27,208 individuals with HF and 349,126 without, uncover exome-wide significant (P-value < 1.57×10^-6) associations for HF and rare predicted loss-of-function (pLoF) variants in <i>TTN</i>, <i>MYBPC3</i>, <i>FLNC, and BAG3.</i> Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, while common variant heritability (4.3%, 95% CI 3.9-4.7%) is more diffusely spread throughout the genome. Finally, we show that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. Together, these findings provide a genetic link between dysregulated metabolism and HF, and suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ec/53/nihpp-2023.07.16.23292724v3.PMC10371173.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9945525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TGIRT-seq of Inflammatory Breast Cancer Tumor and Blood Samples Reveals Widespread Enhanced Transcription Impacting RNA Splicing and Intronic RNAs in Plasma.","authors":"Dennis Wylie, Xiaoping Wang, Jun Yao, Hengyi Xu, Elizabeth A Ferrick-Kiddie, Toshiaki Iwase, Savitri Krishnamurthy, Naoto T Ueno, Alan M Lambowitz","doi":"10.1101/2023.05.26.23290469","DOIUrl":"10.1101/2023.05.26.23290469","url":null,"abstract":"<p><p>Inflammatory breast cancer (IBC) is the most aggressive and lethal breast cancer subtype but lacks unequivocal genomic differences or robust biomarkers that differentiate it from non-IBC. Here, Thermostable Group II intron Reverse Transcriptase RNA-sequencing (TGIRT-seq) revealed myriad differences in tumor samples, Peripheral Blood Mononuclear Cells (PBMCs), and plasma that distinguished IBC from non-IBC patients and healthy donors across all tested receptor-based subtypes. These included numerous differentially expressed protein-coding gene and non-coding RNAs in all three sample types, a granulocytic immune response in IBC PBMCs, and over-expression of antisense RNAs, suggesting wide-spread enhanced transcription in both IBC tumors and PBMCs. By using TGIRT-seq to quantitate Intron-exon Depth Ratios (IDRs) and mapping reads to both genome and transcriptome reference sequences, we developed methods for parallel analysis of transcriptional and post-transcriptional gene regulation. This analysis identified numerous differentially and non-differentially expressed protein-coding genes in IBC tumors and PBMCs with high IDRs, the latter reflecting rate-limiting RNA splicing that negatively impacts mRNA production. Mirroring gene expression differences in tumors and PBMCs, over-represented protein-coding gene RNAs in IBC patient plasma were largely intronic RNAs, while those in non-IBC patients and healthy donor plasma were largely mRNA fragments. Potential IBC biomarkers in plasma included T-cell receptor pre-mRNAs and intronic, LINE-1, and antisense RNAs. Our findings provide new insights into IBC and set the stage for monitoring disease progression and response to treatment by liquid biopsy. The methods developed for parallel transcriptional and post-transcriptional gene regulation analysis have potentially broad RNA-seq and clinical applications.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5f/b3/nihpp-2023.05.26.23290469v1.PMC10312853.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10122265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities.","authors":"Min Gu Kwak, Lingchao Mao, Zhiyang Zheng, Yi Su, Fleming Lure, Jing Li","doi":"10.1101/2023.08.24.23294574","DOIUrl":"10.1101/2023.08.24.23294574","url":null,"abstract":"<p><p>Early detection of Alzheimer's Disease (AD) is crucial for timely interventions and optimizing treatment outcomes. Despite the promise of integrating multimodal neuroimages such as MRI and PET, handling datasets with incomplete modalities remains under-researched. This phenomenon, however, is common in real-world scenarios as not every patient has all modalities due to practical constraints such as cost, access, and safety concerns. We propose a deep learning framework employing cross-modal Mutual Knowledge Distillation (MKD) to model different sub-cohorts of patients based on their available modalities. In MKD, the multimodal model (e.g., MRI and PET) serves as a teacher, while the single-modality model (e.g., MRI only) is the student. Our MKD framework features three components: a Modality-Disentangling Teacher (MDT) model designed through information disentanglement, a student model that learns from classification errors and MDT's knowledge, and the teacher model enhanced via distilling the student's single-modal feature extraction capabilities. Moreover, we show the effectiveness of the proposed method through theoretical analysis and validate its performance with simulation studies. In addition, our method is demonstrated through a case study with Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, underscoring the potential of artificial intelligence in addressing incomplete multimodal neuroimaging datasets and advancing early AD detection.</p><p><strong>Note to practitioners—: </strong>This paper was motivated by the challenge of early AD diagnosis, particularly in scenarios when clinicians encounter varied availability of patient imaging data, such as MRI and PET scans, often constrained by cost or accessibility issues. We propose an incomplete multimodal learning framework that produces tailored models for patients with only MRI and patients with both MRI and PET. This approach improves the accuracy and effectiveness of early AD diagnosis, especially when imaging resources are limited, via bi-directional knowledge transfer. We introduced a teacher model that prioritizes extracting common information between different modalities, significantly enhancing the student model's learning process. This paper includes theoretical analysis, simulation study, and real-world case study to illustrate the method's promising potential in early AD detection. However, practitioners should be mindful of the complexities involved in model tuning. Future work will focus on improving model interpretability and expanding its application. This includes developing methods to discover the key brain regions for predictions, enhancing clinical trust, and extending the framework to incorporate a broader range of imaging modalities, demographic information, and clinical data. These advancements aim to provide a more comprehensive view of patient health and improve diagnostic accuracy across various neurodegenerative diseases.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/70/04/nihpp-2023.08.24.23294574v1.PMC10473798.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10213310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.","authors":"Hellen Lesmann, Alexander Hustinx, Shahida Moosa, Hannah Klinkhammer, Elaine Marchi, Pilar Caro, Ibrahim M Abdelrazek, Jean Tori Pantel, Merle Ten Hagen, Meow-Keong Thong, Rifhan Azwani Binti Mazlan, Sok Kun Tae, Tom Kamphans, Wolfgang Meiswinkel, Jing-Mei Li, Behnam Javanmardi, Alexej Knaus, Annette Uwineza, Cordula Knopp, Tinatin Tkemaladze, Miriam Elbracht, Larissa Mattern, Rami Abou Jamra, Clara Velmans, Vincent Strehlow, Maureen Jacob, Angela Peron, Cristina Dias, Beatriz Carvalho Nunes, Thainá Vilella, Isabel Furquim Pinheiro, Chong Ae Kim, Maria Isabel Melaragno, Hannah Weiland, Sophia Kaptain, Karolina Chwiałkowska, Miroslaw Kwasniewski, Ramy Saad, Sarah Wiethoff, Himanshu Goel, Clara Tang, Anna Hau, Tahsin Stefan Barakat, Przemysław Panek, Amira Nabil, Julia Suh, Frederik Braun, Israel Gomy, Luisa Averdunk, Ekanem Ekure, Gaber Bergant, Borut Peterlin, Claudio Graziano, Nagwa Gaboon, Moisés Fiesco-Roa, Alessandro Mauro Spinelli, Nina-Maria Wilpert, Prasit Phowthongkum, Nergis Güzel, Tobias B Haack, Rana Bitar, Andreas Tzschach, Agusti Rodriguez-Palmero, Theresa Brunet, Sabine Rudnik-Schöneborn, Silvina Noemi Contreras-Capetillo, Ava Oberlack, Carole Samango-Sprouse, Teresa Sadeghin, Margaret Olaya, Konrad Platzer, Artem Borovikov, Franziska Schnabel, Lara Heuft, Vera Herrmann, Renske Oegema, Nour Elkhateeb, Sheetal Kumar, Katalin Komlosi, Khoushoua Mohamed, Silvia Kalantari, Fabio Sirchia, Antonio F Martinez-Monseny, Matthias Höller, Louiza Toutouna, Amal Mohamed, Amaia Lasa-Aranzasti, John A Sayer, Nadja Ehmke, Magdalena Danyel, Henrike Sczakiel, Sarina Schwartzmann, Felix Boschann, Max Zhao, Ronja Adam, Lara Einicke, Denise Horn, Kee Seang Chew, Choy Chen Kam, Miray Karakoyun, Ben Pode-Shakked, Aviva Eliyahu, Rachel Rock, Teresa Carrion, Odelia Chorin, Yuri A Zarate, Marcelo Martinez Conti, Mert Karakaya, Moon Ley Tung, Bharatendu Chandra, Arjan Bouman, Aime Lumaka, Naveed Wasif, Marwan Shinawi, Patrick R Blackburn, Tianyun Wang, Tim Niehues, Axel Schmidt, Regina Rita Roth, Dagmar Wieczorek, Ping Hu, Rebekah L Waikel, Suzanna E Ledgister Hanchard, Gehad Elmakkawy, Sylvia Safwat, Frédéric Ebstein, Elke Krüger, Sébastien Küry, Stéphane Bézieau, Annabelle Arlt, Eric Olinger, Felix Marbach, Dong Li, Lucie Dupuis, Roberto Mendoza-Londono, Sofia Douzgou Houge, Denisa Weis, Brian Hon-Yin Chung, Christopher C Y Mak, Hülya Kayserili, Nursel Elcioglu, Ayca Aykut, Peli Özlem Şimşek-Kiper, Nina Bögershausen, Bernd Wollnik, Heidi Beate Bentzen, Ingo Kurth, Christian Netzer, Aleksandra Jezela-Stanek, Koen Devriendt, Karen W Gripp, Martin Mücke, Alain Verloes, Christian P Schaaf, Christoffer Nellåker, Benjamin D Solomon, Markus M Nöthen, Ebtesam Abdalla, Gholson J Lyon, Peter M Krawitz, Tzung-Chien Hsieh","doi":"10.1101/2023.06.06.23290887","DOIUrl":"10.1101/2023.06.06.23290887","url":null,"abstract":"<p><p>The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2b/fe/nihpp-2023.06.06.23290887v1.PMC10371103.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9934770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exome sequencing of 20,979 individuals with epilepsy reveals shared and distinct ultra-rare genetic risk across disorder subtypes.","authors":"Siwei Chen, Bassel W Abou-Khalil, Zaid Afawi, Quratulain Zulfiqar Ali, Elisabetta Amadori, Alison Anderson, Joe Anderson, Danielle M Andrade, Grazia Annesi, Mutluay Arslan, Pauls Auce, Melanie Bahlo, Mark D Baker, Ganna Balagura, Simona Balestrini, Eric Banks, Carmen Barba, Karen Barboza, Fabrice Bartolomei, Nick Bass, Larry W Baum, Tobias H Baumgartner, Betül Baykan, Nerses Bebek, Felicitas Becker, Caitlin A Bennett, Ahmad Beydoun, Claudia Bianchini, Francesca Bisulli, Douglas Blackwood, Ilan Blatt, Ingo Borggräfe, Christian Bosselmann, Vera Braatz, Harrison Brand, Knut Brockmann, Russell J Buono, Robyn M Busch, S Hande Caglayan, Laura Canafoglia, Christina Canavati, Barbara Castellotti, Gianpiero L Cavalleri, Felecia Cerrato, Francine Chassoux, Christina Cherian, Stacey S Cherny, Ching-Lung Cheung, I-Jun Chou, Seo-Kyung Chung, Claire Churchhouse, Valentina Ciullo, Peggy O Clark, Andrew J Cole, Mahgenn Cosico, Patrick Cossette, Chris Cotsapas, Caroline Cusick, Mark J Daly, Lea K Davis, Peter De Jonghe, Norman Delanty, Dieter Dennig, Chantal Depondt, Philippe Derambure, Orrin Devinsky, Lidia Di Vito, Faith Dickerson, Dennis J Dlugos, Viola Doccini, Colin P Doherty, Hany El-Naggar, Colin A Ellis, Leon Epstein, Meghan Evans, Annika Faucon, Yen-Chen Anne Feng, Lisa Ferguson, Thomas N Ferraro, Izabela Ferreira Da Silva, Lorenzo Ferri, Martha Feucht, Madeline C Fields, Mark Fitzgerald, Beata Fonferko-Shadrach, Francesco Fortunato, Silvana Franceschetti, Jacqueline A French, Elena Freri, Jack M Fu, Stacey Gabriel, Monica Gagliardi, Antonio Gambardella, Laura Gauthier, Tania Giangregorio, Tommaso Gili, Tracy A Glauser, Ethan Goldberg, Alica Goldman, David B Goldstein, Tiziana Granata, Riley Grant, David A Greenberg, Renzo Guerrini, Aslı Gundogdu-Eken, Namrata Gupta, Kevin Haas, Hakon Hakonarson, Garen Haryanyan, Martin Häusler, Manu Hegde, Erin L Heinzen, Ingo Helbig, Christian Hengsbach, Henrike Heyne, Shinichi Hirose, Edouard Hirsch, Chen-Jui Ho, Olivia Hoeper, Daniel P Howrigan, Donald Hucks, Po-Chen Hung, Michele Iacomino, Yushi Inoue, Luciana Midori Inuzuka, Atsushi Ishii, Lara Jehi, Michael R Johnson, Mandy Johnstone, Reetta Kälviäinen, Moien Kanaan, Bulent Kara, Symon M Kariuki, Josua Kegele, Yeşim Kesim, Nathalie Khoueiry-Zgheib, Jean Khoury, Chontelle King, Karl Martin Klein, Gerhard Kluger, Susanne Knake, Fernando Kok, Amos D Korczyn, Rudolf Korinthenberg, Andreas Koupparis, Ioanna Kousiappa, Roland Krause, Martin Krenn, Heinz Krestel, Ilona Krey, Wolfram S Kunz, Gerhard Kurlemann, Ruben I Kuzniecky, Patrick Kwan, Maite La Vega-Talbott, Angelo Labate, Austin Lacey, Dennis Lal, Petra Laššuthová, Stephan Lauxmann, Charlotte Lawthom, Stephanie L Leech, Anna-Elina Lehesjoki, Johannes R Lemke, Holger Lerche, Gaetan Lesca, Costin Leu, Naomi Lewin, David Lewis-Smith, Gloria Hoi-Yee Li, Calwing Liao, Laura Licchetta, Chih-Hsiang Lin, Kuang-Lin Lin, Tarja Linnankivi, Warren Lo, Daniel H Lowenstein, Chelsea Lowther, Laura Lubbers, Colin H T Lui, Lucia Inês Macedo-Souza, Rene Madeleyn, Francesca Madia, Stefania Magri, Louis Maillard, Lara Marcuse, Paula Marques, Anthony G Marson, Abigail G Matthews, Patrick May, Thomas Mayer, Wendy McArdle, Steven M McCarroll, Patricia McGoldrick, Christopher M McGraw, Andrew McIntosh, Andrew McQuillan, Kimford J Meador, Davide Mei, Véronique Michel, John J Millichap, Raffaella Minardi, Martino Montomoli, Barbara Mostacci, Lorenzo Muccioli, Hiltrud Muhle, Karen Müller-Schlüter, Imad M Najm, Wassim Nasreddine, Samuel Neaves, Bernd A Neubauer, Charles R J C Newton, Jeffrey L Noebels, Kate Northstone, Sam Novod, Terence J O'Brien, Seth Owusu-Agyei, Çiğdem Özkara, Aarno Palotie, Savvas S Papacostas, Elena Parrini, Carlos Pato, Michele Pato, Manuela Pendziwiat, Page B Pennell, Slavé Petrovski, William O Pickrell, Rebecca Pinsky, Dalila Pinto, Tommaso Pippucci, Fabrizio Piras, Federica Piras, Annapurna Poduri, Federica Pondrelli, Danielle Posthuma, Robert H W Powell, Michael Privitera, Annika Rademacher, Francesca Ragona, Byron Ramirez-Hamouz, Sarah Rau, Hillary R Raynes, Mark I Rees, Brigid M Regan, Andreas Reif, Eva Reinthaler, Sylvain Rheims, Susan M Ring, Antonella Riva, Enrique Rojas, Felix Rosenow, Philippe Ryvlin, Anni Saarela, Lynette G Sadleir, Barış Salman, Andrea Salmon, Vincenzo Salpietro, Ilaria Sammarra, Marcello Scala, Steven Schachter, André Schaller, Christoph J Schankin, Ingrid E Scheffer, Natascha Schneider, Susanne Schubert-Bast, Andreas Schulze-Bonhage, Paolo Scudieri, Lucie Sedláčková, Catherine Shain, Pak C Sham, Beth R Shiedley, S Anthony Siena, Graeme J Sills, Sanjay M Sisodiya, Jordan W Smoller, Matthew Solomonson, Gianfranco Spalletta, Kathryn R Sparks, Michael R Sperling, Hannah Stamberger, Bernhard J Steinhoff, Ulrich Stephani, Katalin Štěrbová, William C Stewart, Carlotta Stipa, Pasquale Striano, Adam Strzelczyk, Rainer Surges, Toshimitsu Suzuki, Mariagrazia Talarico, Michael E Talkowski, Randip S Taneja, George A Tanteles, Oskari Timonen, Nicholas John Timpson, Paolo Tinuper, Marian Todaro, Pınar Topaloglu, Meng-Han Tsai, Birute Tumiene, Dilsad Turkdogan, Sibel Uğur-İşeri, Algirdas Utkus, Priya Vaidiswaran, Luc Valton, Andreas van Baalen, Maria Stella Vari, Annalisa Vetro, Markéta Vlčková, Sophie von Brauchitsch, Sarah von Spiczak, Ryan G Wagner, Nick Watts, Yvonne G Weber, Sarah Weckhuysen, Peter Widdess-Walsh, Samuel Wiebe, Steven M Wolf, Markus Wolff, Stefan Wolking, Isaac Wong, Randi von Wrede, David Wu, Kazuhiro Yamakawa, Zuhal Yapıcı, Uluc Yis, Robert Yolken, Emrah Yücesan, Sara Zagaglia, Felix Zahnert, Federico Zara, Fritz Zimprich, Milena Zizovic, Gábor Zsurka, Benjamin M Neale, Samuel F Berkovic","doi":"10.1101/2023.02.22.23286310","DOIUrl":"10.1101/2023.02.22.23286310","url":null,"abstract":"<p><p>Identifying genetic risk factors for highly heterogeneous disorders like epilepsy remains challenging. Here, we present the largest whole-exome sequencing study of epilepsy to date, with >54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets, and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies, generalized and focal epilepsies, while most other gene discoveries are subtype-specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single nucleotide/short indel-, copy number-, and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9466375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}