Timm Intemann, Knut Kaulke, Dennis-Kenji Kipker, Vanessa Lettieri, Christoph Stallmann, Carsten O. Schmidt, Lars Geidel, Martin Bialke, Christopher Hampf, Dana Stahl, Martin Lablans, Florens Rohde, Martin Franke, Klaus Kraywinkel, Joachim Kieschke, Sebastian Bartholomäus, Anatol-Fiete Näher, Galina Tremper, Mohamed Lambarki, Stefanie March, Fabian Prasser, Anna Christine Haber, Johannes Drepper, Irene Schlünder, Toralf Kirsten, Iris Pigeot, Ulrich Sax, Benedikt Buchner, Wolfgang Ahrens, Sebastian C. Semler
Record linkage means linking data from multiple sources. This approach enables the answering of scientific questions that cannot be addressed using single data sources due to limited variables. The potential of linked data for health research is enormous, as it can enhance prevention, treatment, and population health policies. Due the sensitivity of health data, there are strict legal requirements to prevent potential misuse. However, these requirements also limit the use of health data for research, thereby hindering innovations in prevention and care. Also, comprehensive Record linkage in Germany is often challenging due to lacking unique personal identifiers or interoperable solutions. Rather, the need to protect data is often weighed against the importance of research aiming at healthcare enhancements: for instance, data protection officers may demand the informed consent of individual study participants for data linkage, even when this is not mandatory. Furthermore, legal frameworks may be interpreted differently on varying occasions. Given both, technical and legal challenges, record linkage for health research in Germany falls behind the standards of other European countries. To ensure successful record linkage, case-specific solutions must be developed, tested, and modified as necessary before implementation. This paper discusses limitations and possibilities of various data linkage approaches tailored to different use cases in compliance with the European General Data Protection Regulation. It further describes requirements for achieving a more research-friendly approach to linking health data records in Germany. Additionally, it provides recommendations to legislators. The objective of this work is to improve record linkage for health research in Germany.
{"title":"Verbesserung des Record Linkage für die Gesundheitsforschung in Deutschland","authors":"Timm Intemann, Knut Kaulke, Dennis-Kenji Kipker, Vanessa Lettieri, Christoph Stallmann, Carsten O. Schmidt, Lars Geidel, Martin Bialke, Christopher Hampf, Dana Stahl, Martin Lablans, Florens Rohde, Martin Franke, Klaus Kraywinkel, Joachim Kieschke, Sebastian Bartholomäus, Anatol-Fiete Näher, Galina Tremper, Mohamed Lambarki, Stefanie March, Fabian Prasser, Anna Christine Haber, Johannes Drepper, Irene Schlünder, Toralf Kirsten, Iris Pigeot, Ulrich Sax, Benedikt Buchner, Wolfgang Ahrens, Sebastian C. Semler","doi":"arxiv-2312.10093","DOIUrl":"https://doi.org/arxiv-2312.10093","url":null,"abstract":"Record linkage means linking data from multiple sources. This approach\u0000enables the answering of scientific questions that cannot be addressed using\u0000single data sources due to limited variables. The potential of linked data for\u0000health research is enormous, as it can enhance prevention, treatment, and\u0000population health policies. Due the sensitivity of health data, there are\u0000strict legal requirements to prevent potential misuse. However, these\u0000requirements also limit the use of health data for research, thereby hindering\u0000innovations in prevention and care. Also, comprehensive Record linkage in\u0000Germany is often challenging due to lacking unique personal identifiers or\u0000interoperable solutions. Rather, the need to protect data is often weighed\u0000against the importance of research aiming at healthcare enhancements: for\u0000instance, data protection officers may demand the informed consent of\u0000individual study participants for data linkage, even when this is not\u0000mandatory. Furthermore, legal frameworks may be interpreted differently on\u0000varying occasions. Given both, technical and legal challenges, record linkage\u0000for health research in Germany falls behind the standards of other European\u0000countries. To ensure successful record linkage, case-specific solutions must be\u0000developed, tested, and modified as necessary before implementation. This paper\u0000discusses limitations and possibilities of various data linkage approaches\u0000tailored to different use cases in compliance with the European General Data\u0000Protection Regulation. It further describes requirements for achieving a more\u0000research-friendly approach to linking health data records in Germany.\u0000Additionally, it provides recommendations to legislators. The objective of this\u0000work is to improve record linkage for health research in Germany.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138741979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Albert B. Kao, Shoubhik Banerjee, Fritz Francisco, Andrew M. Berdahl
The past decade has witnessed a dramatically growing interest in collective intelligence - the phenomenon of groups having an ability to make more accurate decisions than isolated individuals. However, the vast majority of studies to date have focused, either explicitly or implicitly, on spatial decisions (e.g., potential nest sites, food patches, or migration directions). We highlight the equally important, but severely understudied, realm of temporal collective decision-making, i.e., decisions about when to perform an action. We argue that temporal collective decision making is likely to differ from spatial decision making in several crucial ways and probably involves different mechanisms, model predictions, and experimental outcomes. We anticipate that research focused on temporal decisions should lead to a radically expanded understanding of the adaptiveness and constraints of living in groups.
{"title":"Timing decisions as the next frontier for collective intelligence","authors":"Albert B. Kao, Shoubhik Banerjee, Fritz Francisco, Andrew M. Berdahl","doi":"arxiv-2312.02187","DOIUrl":"https://doi.org/arxiv-2312.02187","url":null,"abstract":"The past decade has witnessed a dramatically growing interest in collective\u0000intelligence - the phenomenon of groups having an ability to make more accurate\u0000decisions than isolated individuals. However, the vast majority of studies to\u0000date have focused, either explicitly or implicitly, on spatial decisions (e.g.,\u0000potential nest sites, food patches, or migration directions). We highlight the\u0000equally important, but severely understudied, realm of temporal collective\u0000decision-making, i.e., decisions about when to perform an action. We argue that\u0000temporal collective decision making is likely to differ from spatial decision\u0000making in several crucial ways and probably involves different mechanisms,\u0000model predictions, and experimental outcomes. We anticipate that research\u0000focused on temporal decisions should lead to a radically expanded understanding\u0000of the adaptiveness and constraints of living in groups.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138547236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores how the COVID-19 pandemic's economic impact has exacerbated nutritional health disparities among women. It sought to understand the effects of economic challenges on women's dietary choices and access to nutritious food across different socioeconomic groups. Using a mixed-methods approach, the research combined quantitative data from health and economic records with qualitative insights from interviews with diverse women. The study analyzed trends in nutritional health and economic factors before and after the pandemic and gathered personal accounts regarding nutrition and economic difficulties during this period. Findings showed a clear link between the economic downturn and deteriorating nutritional health, particularly in low-income and marginalized groups. These women reported decreased access to healthy foods and an increased dependence on less nutritious options due to budget constraints, leading to a decline in dietary quality. This trend was less evident in higher-income groups, highlighting stark disparities. The pandemic intensified pre-existing nutritional inequalities, with the most vulnerable groups facing greater adverse effects. However, community support and public health measures provided some relief. In summary, the pandemic's economic repercussions have indirectly impaired women's nutritional health, especially among the socioeconomically disadvantaged. This highlights the necessity for tailored nutritional interventions and economic policies focused on safeguarding women's health.
{"title":"Exploring the Relationship Between COVID-19 Induced Economic Downturn and Women's Nutritional Health Disparities","authors":"Alaa M. Sadeq","doi":"arxiv-2311.12080","DOIUrl":"https://doi.org/arxiv-2311.12080","url":null,"abstract":"This study explores how the COVID-19 pandemic's economic impact has\u0000exacerbated nutritional health disparities among women. It sought to understand\u0000the effects of economic challenges on women's dietary choices and access to\u0000nutritious food across different socioeconomic groups. Using a mixed-methods\u0000approach, the research combined quantitative data from health and economic\u0000records with qualitative insights from interviews with diverse women. The study\u0000analyzed trends in nutritional health and economic factors before and after the\u0000pandemic and gathered personal accounts regarding nutrition and economic\u0000difficulties during this period. Findings showed a clear link between the\u0000economic downturn and deteriorating nutritional health, particularly in\u0000low-income and marginalized groups. These women reported decreased access to\u0000healthy foods and an increased dependence on less nutritious options due to\u0000budget constraints, leading to a decline in dietary quality. This trend was\u0000less evident in higher-income groups, highlighting stark disparities. The\u0000pandemic intensified pre-existing nutritional inequalities, with the most\u0000vulnerable groups facing greater adverse effects. However, community support\u0000and public health measures provided some relief. In summary, the pandemic's\u0000economic repercussions have indirectly impaired women's nutritional health,\u0000especially among the socioeconomically disadvantaged. This highlights the\u0000necessity for tailored nutritional interventions and economic policies focused\u0000on safeguarding women's health.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"11 19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
eresa Zulueta-Coarasa, Florian Jug, Aastha Mathur, Josh Moore, Arrate Muñoz-Barrutia, Liviu Anita, Kola Babalola, Pete Bankhead, Perrine Gilloteaux, Nodar Gogoberidze, Martin Jones, Gerard J. Kleywegt, Paul Korir, Anna Kreshuk, Aybüke Küpcü Yoldaş, Luca Marconato, Kedar Narayan, Nils Norlin, Bugra Oezdemir, Jessica Riesterer, Norman Rzepka, Ugis Sarkans, Beatriz Serrano, Christian Tischer, Virginie Uhlmann, Vladimír Ulman, Matthew Hartley
Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data.
{"title":"MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis","authors":"eresa Zulueta-Coarasa, Florian Jug, Aastha Mathur, Josh Moore, Arrate Muñoz-Barrutia, Liviu Anita, Kola Babalola, Pete Bankhead, Perrine Gilloteaux, Nodar Gogoberidze, Martin Jones, Gerard J. Kleywegt, Paul Korir, Anna Kreshuk, Aybüke Küpcü Yoldaş, Luca Marconato, Kedar Narayan, Nils Norlin, Bugra Oezdemir, Jessica Riesterer, Norman Rzepka, Ugis Sarkans, Beatriz Serrano, Christian Tischer, Virginie Uhlmann, Vladimír Ulman, Matthew Hartley","doi":"arxiv-2311.10443","DOIUrl":"https://doi.org/arxiv-2311.10443","url":null,"abstract":"Artificial Intelligence methods are powerful tools for biological image\u0000analysis and processing. High-quality annotated images are key to training and\u0000developing new methods, but access to such data is often hindered by the lack\u0000of standards for sharing datasets. We brought together community experts in a\u0000workshop to develop guidelines to improve the reuse of bioimages and\u0000annotations for AI applications. These include standards on data formats,\u0000metadata, data presentation and sharing, and incentives to generate new\u0000datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and\u0000Accessibility) recommendations will accelerate the development of AI tools for\u0000bioimage analysis by facilitating access to high quality training data.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding how genes interact and relate to each other is a fundamental question in biology. However, current practices for describing these relationships, such as drawing diagrams or graphs in a somewhat arbitrary manner, limit our ability to integrate various aspects of the gene functions and view the genome holistically. To overcome these limitations, we need a more appropriate way to describe the intricate relationships between genes. Interestingly, category theory, an abstract field of mathematics seemingly unrelated to biology, has emerged as a powerful language for describing relations in general. We propose that category theory could provide a framework for unifying our knowledge of genes and their relationships. As a starting point, we construct a category of genes, with its morphisms abstracting various aspects of the relationships betweens genes. These relationships include, but not limited to, the order of genes on the chromosomes, the physical or genetic interactions, the signalling pathways, the gene ontology causal activity models (GO-CAM) and gene groups. Previously, they were encoded by miscellaneous networks or graphs, while our work unifies them in a consistent manner as a category. By doing so, we hope to view the relationships between genes systematically. In the long run, this paves a promising way for us to understand the fundamental principles that govern gene regulation and function.
{"title":"A Category of Genes","authors":"Yanying Wu","doi":"arxiv-2311.08546","DOIUrl":"https://doi.org/arxiv-2311.08546","url":null,"abstract":"Understanding how genes interact and relate to each other is a fundamental\u0000question in biology. However, current practices for describing these\u0000relationships, such as drawing diagrams or graphs in a somewhat arbitrary\u0000manner, limit our ability to integrate various aspects of the gene functions\u0000and view the genome holistically. To overcome these limitations, we need a more\u0000appropriate way to describe the intricate relationships between genes.\u0000Interestingly, category theory, an abstract field of mathematics seemingly\u0000unrelated to biology, has emerged as a powerful language for describing\u0000relations in general. We propose that category theory could provide a framework\u0000for unifying our knowledge of genes and their relationships. As a starting point, we construct a category of genes, with its morphisms\u0000abstracting various aspects of the relationships betweens genes. These\u0000relationships include, but not limited to, the order of genes on the\u0000chromosomes, the physical or genetic interactions, the signalling pathways, the\u0000gene ontology causal activity models (GO-CAM) and gene groups. Previously, they\u0000were encoded by miscellaneous networks or graphs, while our work unifies them\u0000in a consistent manner as a category. By doing so, we hope to view the\u0000relationships between genes systematically. In the long run, this paves a\u0000promising way for us to understand the fundamental principles that govern gene\u0000regulation and function.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"142 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biological data in digital form has become a, if not the, driving force behind innovations in biology, medicine, and the environment. No study and no model would be complete without access to digital data (including text) collected by others and available in public repositories. With this ascent in the fundamental importance of data for reproducible scientific progress has come a troubling paradox.
{"title":"The Biological Data Sustainability Paradox","authors":"Terence R. Johnson, Philip E. Bourne","doi":"arxiv-2311.05668","DOIUrl":"https://doi.org/arxiv-2311.05668","url":null,"abstract":"Biological data in digital form has become a, if not the, driving force\u0000behind innovations in biology, medicine, and the environment. No study and no\u0000model would be complete without access to digital data (including text)\u0000collected by others and available in public repositories. With this ascent in\u0000the fundamental importance of data for reproducible scientific progress has\u0000come a troubling paradox.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aram Akram Mohammed, Rasul Rafiq Aziz, Faraydwn Karim Ahmad, Ibrahim Maaroof Noori, Tariq Abubakr Ahmad
Study two cut patterns in hardwood cuttings of (Cydonia oblonga), (Punica granatum) and (Ficus carica). The cuttings have been cut either straight with different internode stub lengths [0 (just onto the basal node as control), 0.5, 1.0, 2.0 or 3.0 cm below the basal node], or slant with 45 degree angle for each length mentioned above (except the first length (0 cm). Effect of the basal cut directions on rooting percentage and other shoot and root characteristics were not significantly different, while the effect of slant cut pattern on one-side rooting at the basal margin observed in some quince cuttings but it was rarely observed in pomegranate and fig cuttings. Quince cuttings gave no different rooting percentage and other shoot and root characteristics significantly with different internode stub lengths. While, internode stub 1 and 2 cm in pomegranate cuttings, and 0 cm in fig cuttings gave the best rooting percentages 44.44% and 100%, respectively. Also, interaction effects of the two factors on rooting percentage and other shoot and root characteristics were just significantly different in pomegranate and fig cuttings. The best rooting capacity achieved in pomegranate cuttings (49.99%) in those were cut straightly at the base with 1 and 2 cm basal internode stub lengths, and fig cuttings straightly cut at the base with 0 and 1 cm basal internode stub lengths gave the highest rooting capacity (100%).
{"title":"Rooting capacity of hardwood cuttings of some fruit trees in relation to cutting pattern","authors":"Aram Akram Mohammed, Rasul Rafiq Aziz, Faraydwn Karim Ahmad, Ibrahim Maaroof Noori, Tariq Abubakr Ahmad","doi":"arxiv-2311.04953","DOIUrl":"https://doi.org/arxiv-2311.04953","url":null,"abstract":"Study two cut patterns in hardwood cuttings of (Cydonia oblonga), (Punica\u0000granatum) and (Ficus carica). The cuttings have been cut either straight with\u0000different internode stub lengths [0 (just onto the basal node as control), 0.5,\u00001.0, 2.0 or 3.0 cm below the basal node], or slant with 45 degree angle for\u0000each length mentioned above (except the first length (0 cm). Effect of the\u0000basal cut directions on rooting percentage and other shoot and root\u0000characteristics were not significantly different, while the effect of slant cut\u0000pattern on one-side rooting at the basal margin observed in some quince\u0000cuttings but it was rarely observed in pomegranate and fig cuttings. Quince\u0000cuttings gave no different rooting percentage and other shoot and root\u0000characteristics significantly with different internode stub lengths. While,\u0000internode stub 1 and 2 cm in pomegranate cuttings, and 0 cm in fig cuttings\u0000gave the best rooting percentages 44.44% and 100%, respectively. Also,\u0000interaction effects of the two factors on rooting percentage and other shoot\u0000and root characteristics were just significantly different in pomegranate and\u0000fig cuttings. The best rooting capacity achieved in pomegranate cuttings\u0000(49.99%) in those were cut straightly at the base with 1 and 2 cm basal\u0000internode stub lengths, and fig cuttings straightly cut at the base with 0 and\u00001 cm basal internode stub lengths gave the highest rooting capacity (100%).","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A higher Mediterranean diet score has been associated with lower likelihood of multiple sclerosis. However, evidence regarding its association with disease activity and progression is limited. Using data from the AusLong Study, we tested longitudinal associations (over 10 years follow-up) between the alternate Mediterranean diet score (aMED) and aMED-Red (including moderate consumption of unprocessed red meat) and time between relapses and disability measured by Expanded Disability Status Scale (EDSS) (n=132; 27 males, 105 females). We used covariate-adjusted survival analysis for time between relapses, and time series mixed-effects negative binomial regression for EDSS. After adjusting for covariates, both higher aMED (aHR=0.94, 95%CI: 0.90, 0.99, p=0.009) and higher aMED-Red (aHR=0.93, 95%CI: 0.89, 0.97, p=0.001) were associated with significantly longer time between relapses in females. Whether specific dietary components of a Mediterranean diet are important in relation to relapses merits further study.
{"title":"Higher Mediterranean diet score is associated with longer time between relapses in Australian females with multiple sclerosis","authors":"Hajar Mazahery, Alison Daly, Ngoc Minh Pham, Madeleine Stephens, Eleanor Dunlop, Anne-Louise Ponsonby, Ausimmune/AusLong Investigator Group, Lucinda J Black","doi":"arxiv-2311.01042","DOIUrl":"https://doi.org/arxiv-2311.01042","url":null,"abstract":"A higher Mediterranean diet score has been associated with lower likelihood\u0000of multiple sclerosis. However, evidence regarding its association with disease\u0000activity and progression is limited. Using data from the AusLong Study, we\u0000tested longitudinal associations (over 10 years follow-up) between the\u0000alternate Mediterranean diet score (aMED) and aMED-Red (including moderate\u0000consumption of unprocessed red meat) and time between relapses and disability\u0000measured by Expanded Disability Status Scale (EDSS) (n=132; 27 males, 105\u0000females). We used covariate-adjusted survival analysis for time between\u0000relapses, and time series mixed-effects negative binomial regression for EDSS.\u0000After adjusting for covariates, both higher aMED (aHR=0.94, 95%CI: 0.90, 0.99,\u0000p=0.009) and higher aMED-Red (aHR=0.93, 95%CI: 0.89, 0.97, p=0.001) were\u0000associated with significantly longer time between relapses in females. Whether\u0000specific dietary components of a Mediterranean diet are important in relation\u0000to relapses merits further study.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dan Stetson, Paul Labrousse, Hugh Russell, David Shera, Chris Abbosh, Brian Dougherty, J. Carl Barrett, Darren Hodgson, James Hadfield
Circulating tumour DNA (ctDNA) detection of molecular residual disease (MRD) in solid tumours correlates strongly with patient outcomes and is being adopted as a new clinical standard. ctDNA levels are known to correlate with tumor volume, and although the absolute levels vary across indication and histology, its analysis is driving the adoption of MRD. MRD assays must detect tumor when imaging cannot and, as such, require very high sensitivity to detect the low levels of ctDNA found after curative intent therapy. The minimum threshold is 0.01% Tumour Fraction but current methods like Archer and Signatera are limited by detection sensitivity resulting in some patients receiving a false negative call thereby missing out on earlier therapeutic intervention. Multiple vendors are increasing the number of somatic variants tracked in tumour-informed and personalized NGS assays, from tens to thousands of variants. Most recently, assays using other biological features of ctDNA, e.g methylation or fragmentome, have been developed at the LOD required for clinical utility. These uniformed, or tumour-naive and non-personalised assays may be more easily, and therefore more rapidly, adopted in the clinic. However, this rapid development in MRD assay technology results in significant challenges in benchmarking these new technologies for use in clinical trials. This is further complicated by the fact that previous reference materials have focused on somatic variants, and do not retain all of the epigenomic features assessed by newer technologies. In this Comments and Controversy paper, we detail what is known and what remains to be determined for optimal reference materials of MRD methods and provide opinions generated during three-years of MRD technology benchmarking in AstraZeneca Translational Medicine to help guide the community conversation.
{"title":"Next-generation MRD assays: do we have the tools to evaluate them properly?","authors":"Dan Stetson, Paul Labrousse, Hugh Russell, David Shera, Chris Abbosh, Brian Dougherty, J. Carl Barrett, Darren Hodgson, James Hadfield","doi":"arxiv-2311.00015","DOIUrl":"https://doi.org/arxiv-2311.00015","url":null,"abstract":"Circulating tumour DNA (ctDNA) detection of molecular residual disease (MRD)\u0000in solid tumours correlates strongly with patient outcomes and is being adopted\u0000as a new clinical standard. ctDNA levels are known to correlate with tumor\u0000volume, and although the absolute levels vary across indication and histology,\u0000its analysis is driving the adoption of MRD. MRD assays must detect tumor when\u0000imaging cannot and, as such, require very high sensitivity to detect the low\u0000levels of ctDNA found after curative intent therapy. The minimum threshold is\u00000.01% Tumour Fraction but current methods like Archer and Signatera are limited\u0000by detection sensitivity resulting in some patients receiving a false negative\u0000call thereby missing out on earlier therapeutic intervention. Multiple vendors\u0000are increasing the number of somatic variants tracked in tumour-informed and\u0000personalized NGS assays, from tens to thousands of variants. Most recently,\u0000assays using other biological features of ctDNA, e.g methylation or\u0000fragmentome, have been developed at the LOD required for clinical utility.\u0000These uniformed, or tumour-naive and non-personalised assays may be more\u0000easily, and therefore more rapidly, adopted in the clinic. However, this rapid\u0000development in MRD assay technology results in significant challenges in\u0000benchmarking these new technologies for use in clinical trials. This is further\u0000complicated by the fact that previous reference materials have focused on\u0000somatic variants, and do not retain all of the epigenomic features assessed by\u0000newer technologies. In this Comments and Controversy paper, we detail what is\u0000known and what remains to be determined for optimal reference materials of MRD\u0000methods and provide opinions generated during three-years of MRD technology\u0000benchmarking in AstraZeneca Translational Medicine to help guide the community\u0000conversation.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reinhard LaubenbacherDepartment of Medicine, University of Florida, Gainesville, FL, Fred AdlerDepartment of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, UT, Gary AnDepartment of Surgery, University of Vermont, Burlington, VT, Filippo CastiglioneBiotechnology Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates, Stephen EubankBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, Luis L. FonsecaDepartment of Medicine, University of Florida, Gainesville, FL, James GlazierDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Tomas HelikarDepartment of Biochemistry, University of Nebraska, Lincoln, NE, Marti Jett-TiltonU.S. Walter Reed Army Institute of Research, Silver Spring, MD, Denise KirschnerDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Paul MacklinDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Borna MehradDepartment of Medicine, University of Florida, Gainesville, FL, Beth MooreDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Virginia PasourU.S. Army Research Office, Research Triangle Park, NC, Ilya ShmulevichInstitute for Systems Biology, Seattle, WA, Amber SmithDepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, Isabel VoigtCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany, Thomas E. YankeelovDepartment of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, The University of Texas, Austin, TX, and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Tjalf ZiemssenCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany
Medical digital twins are computational models of human biology relevant to a given medical condition, which can be tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. If medical digital twins are to faithfully capture the characteristics of a patient's immune system, we need to answer many questions, such as: What do we need to know about the immune system to build mathematical models that reflect features of an individual? What data do we need to collect across the different scales of immune system action? What are the right modeling paradigms to properly capture immune system complexity? In February 2023, an international group of experts convened in Lake Nona, FL for two days to discuss these and other questions related to digital twins of the immune system. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.
{"title":"Forum on immune digital twins: a meeting report","authors":"Reinhard LaubenbacherDepartment of Medicine, University of Florida, Gainesville, FL, Fred AdlerDepartment of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, UT, Gary AnDepartment of Surgery, University of Vermont, Burlington, VT, Filippo CastiglioneBiotechnology Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates, Stephen EubankBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, Luis L. FonsecaDepartment of Medicine, University of Florida, Gainesville, FL, James GlazierDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Tomas HelikarDepartment of Biochemistry, University of Nebraska, Lincoln, NE, Marti Jett-TiltonU.S. Walter Reed Army Institute of Research, Silver Spring, MD, Denise KirschnerDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Paul MacklinDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Borna MehradDepartment of Medicine, University of Florida, Gainesville, FL, Beth MooreDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Virginia PasourU.S. Army Research Office, Research Triangle Park, NC, Ilya ShmulevichInstitute for Systems Biology, Seattle, WA, Amber SmithDepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, Isabel VoigtCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany, Thomas E. YankeelovDepartment of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, The University of Texas, Austin, TX, and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Tjalf ZiemssenCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany","doi":"arxiv-2310.18374","DOIUrl":"https://doi.org/arxiv-2310.18374","url":null,"abstract":"Medical digital twins are computational models of human biology relevant to a\u0000given medical condition, which can be tailored to an individual patient,\u0000thereby predicting the course of disease and individualized treatments, an\u0000important goal of personalized medicine. The immune system, which has a central\u0000role in many diseases, is highly heterogeneous between individuals, and thus\u0000poses a major challenge for this technology. If medical digital twins are to\u0000faithfully capture the characteristics of a patient's immune system, we need to\u0000answer many questions, such as: What do we need to know about the immune system\u0000to build mathematical models that reflect features of an individual? What data\u0000do we need to collect across the different scales of immune system action? What\u0000are the right modeling paradigms to properly capture immune system complexity?\u0000In February 2023, an international group of experts convened in Lake Nona, FL\u0000for two days to discuss these and other questions related to digital twins of\u0000the immune system. The group consisted of clinicians, immunologists,\u0000biologists, and mathematical modelers, representative of the interdisciplinary\u0000nature of medical digital twin development. A video recording of the entire\u0000event is available. This paper presents a synopsis of the discussions, brief\u0000descriptions of ongoing digital twin projects at different stages of progress.\u0000It also proposes a 5-year action plan for further developing this technology.\u0000The main recommendations are to identify and pursue a small number of promising\u0000use cases, to develop stimulation-specific assays of immune function in a\u0000clinical setting, and to develop a database of existing computational immune\u0000models, as well as advanced modeling technology and infrastructure.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"2006 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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