Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311719
B. K. Mengistu, T. Alemayehu, T. H. Mengesha, M. M. Ali
Background: Staphylococcus aureus colonizing the nasal cavity is a potential source of infections. Vancomycin is a mainstay for treating invasive infections caused by penicillin and methicillin-resistant S. aureus (MRSA). Some reports indicate the emergence of vancomycin-resistant S. aureus (VRSA) making it a high-priority pathogen that needs attention. There is a limited report on the epidemiology of VRSA and vancomycin-intermediate S. aureus (VISA) from the Sidama regional state. Objective: The objective of this study was to determine VRSA and VISA among S. aureus colonizing patients admitted at Hawassa University Comprehensive Specialized Hospital (HUCSH), associated factors, and antimicrobial susceptibility profile. Methods: A hospital-based prospective cross-sectional study was conducted from April to June 2023. Socio-demographic and clinical data were collected using an interviewer-administered questionnaire. Nasal swabs were collected from 378 admitted patients. Identification of S. aureus was made using standard bacteriological methods. VRSA was determined by the Epsilometer test (E-test). The antimicrobial susceptibility profile was determined according to the Kirby-Bauer disk diffusion method. Data was analyzed using SPSS version 22. A p<0.05 was taken as a cut point to determine a statistically significant association. Results: Out of the total 92 S. aureus isolated 12 (13.04%), 27(29.3%), 15(16.3%) were VRSA, VISA, and MRSA respectively. The carriage rate of VRSA and VISA among admitted patients were 12(3.2%) with 95% CI: 1.7%-5.5% and 27(7.14%) with 95% CI: 4.8%-10.2% respectively. The overall nasal carriage rate of S. aureus and MRSA was 92(24.3%) with 95% CI: 20.1%-29% and 15(3.97%) with 95% CI: 2.2%-6.5% respectively. Of the VRSA isolates, 11(91.7%) were susceptible to tigecycline. Forty (43.5%) of S. aureus were positive for inducible clindamycin resistance. Participants with a history of hospitalization at the intensive care unit were 37 times more likely to be colonized with VRSA (p=0.001). Participants who have domestic animals were 22 times more likely to be colonized with VRSA (p=0.021). Conclusions: This study indicated a high proportion of VRSA and VISA among S. aureus isolated from hospitalized patients in the study area. More than 80% of VRSA were susceptible to tigecycline. History of hospitalization at the intensive care unit and having domestic animals at home could increase the odds of VRSA colonization.
{"title":"Nasal colonizing vancomycin-resistant and intermediate Staphylococcus aureus among admitted patients","authors":"B. K. Mengistu, T. Alemayehu, T. H. Mengesha, M. M. Ali","doi":"10.1101/2024.08.12.24311719","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311719","url":null,"abstract":"Background: Staphylococcus aureus colonizing the nasal cavity is a potential source of infections. Vancomycin is a mainstay for treating invasive infections caused by penicillin and methicillin-resistant S. aureus (MRSA). Some reports indicate the emergence of vancomycin-resistant S. aureus (VRSA) making it a high-priority pathogen that needs attention. There is a limited report on the epidemiology of VRSA and vancomycin-intermediate S. aureus (VISA) from the Sidama regional state. Objective: The objective of this study was to determine VRSA and VISA among S. aureus colonizing patients admitted at Hawassa University Comprehensive Specialized Hospital (HUCSH), associated factors, and antimicrobial susceptibility profile. Methods: A hospital-based prospective cross-sectional study was conducted from April to June 2023. Socio-demographic and clinical data were collected using an interviewer-administered questionnaire. Nasal swabs were collected from 378 admitted patients. Identification of S. aureus was made using standard bacteriological methods. VRSA was determined by the Epsilometer test (E-test). The antimicrobial susceptibility profile was determined according to the Kirby-Bauer disk diffusion method. Data was analyzed using SPSS version 22. A p<0.05 was taken as a cut point to determine a statistically significant association. Results: Out of the total 92 S. aureus isolated 12 (13.04%), 27(29.3%), 15(16.3%) were VRSA, VISA, and MRSA respectively. The carriage rate of VRSA and VISA among admitted patients were 12(3.2%) with 95% CI: 1.7%-5.5% and 27(7.14%) with 95% CI: 4.8%-10.2% respectively. The overall nasal carriage rate of S. aureus and MRSA was 92(24.3%) with 95% CI: 20.1%-29% and 15(3.97%) with 95% CI: 2.2%-6.5% respectively. Of the VRSA isolates, 11(91.7%) were susceptible to tigecycline. Forty (43.5%) of S. aureus were positive for inducible clindamycin resistance. Participants with a history of hospitalization at the intensive care unit were 37 times more likely to be colonized with VRSA (p=0.001). Participants who have domestic animals were 22 times more likely to be colonized with VRSA (p=0.021). Conclusions: This study indicated a high proportion of VRSA and VISA among S. aureus isolated from hospitalized patients in the study area. More than 80% of VRSA were susceptible to tigecycline. History of hospitalization at the intensive care unit and having domestic animals at home could increase the odds of VRSA colonization.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"9 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919468","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}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311617
G. Cozier, ‡. M. Gardner, ‡. S. Craft, Martine Skumlien, Jack Spicer, Rachael An-drews, Alexander Power, Tom S F Haines, Richard Bowman, Amy E. Manley, Peter Sunderland, Oliver B. Sutcliffe, Stephen M. Husbands, Lindsey Hines, Gillian Taylor, Tom P Freeman, Jennifer Scott, Christopher R. Pudney
Synthetic cannabinoids (SCs), colloquially spice or K2, are the most common drug to be found in prisons in the UK, where they are associated with nearly half of non-natural deaths. In the community, SCs are associated with poly-drug users who are also likely to be homeless. People who use SCs report debilitating side effects and withdrawal symptoms, coupled with dependence. Until now, SC use was believed to be largely restricted to prison and homeless populations. However, media reporting in the UK has increasingly identified cases of children collapsing in schools, which are claimed to be as-sociated with vaping and putatively the vaping of a drug, variously reported as tetrahydrocannabinol (THC) 'synthetic cannabis' or 'spice'. We therefore conducted the first study to identify and quantity SCs in e-cigarettes routinely collected from schools. We sampled 27 schools from geographically distinct regions of England, representing a very broad range of social metrics (free school meals, persistent absenteeism, and SEN). The material was sampled by self-submission by individual schools of e-cigarettes seized during normal school operation and transferred to us for analysis via local po-lice forces. We found a remarkably consistent picture where SCs were detected in 17.5 % of all e-cigarettes sampled, and in 21 of 27 (78 %) of all sampled schools. Moreover, the percentage of SC e-cigarettes positively correlated with a metric of social deprivation, the fraction of pupils eligible for free school meals. The SC positive e-cigarettes were almost entire-ly found in e-cigarette liquid bottles and refillable e-cigarette devices, with very few identified in single use e-cigarette products. Within the positive samples we found an average SC concentration of 1.03 mg/mL with a maximum of 3.6 mg mL-1. In contrast to the high prevalence of SCs, few samples contained THC (1.6 %). We suggest that pupils are being sold SC e-cigarettes as 'cannabis' and may be unaware they are consuming (and sometimes supplying) considerably more harmful drugs. Our findings are immediately crucial to policy policing and healthcare in the UK as well as to educational bodies and schools.
{"title":"Synthetic cannabinoids consumed via e-cigarettes in English schools","authors":"G. Cozier, ‡. M. Gardner, ‡. S. Craft, Martine Skumlien, Jack Spicer, Rachael An-drews, Alexander Power, Tom S F Haines, Richard Bowman, Amy E. Manley, Peter Sunderland, Oliver B. Sutcliffe, Stephen M. Husbands, Lindsey Hines, Gillian Taylor, Tom P Freeman, Jennifer Scott, Christopher R. Pudney","doi":"10.1101/2024.08.12.24311617","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311617","url":null,"abstract":"Synthetic cannabinoids (SCs), colloquially spice or K2, are the most common drug to be found in prisons in the UK, where they are associated with nearly half of non-natural deaths. In the community, SCs are associated with poly-drug users who are also likely to be homeless. People who use SCs report debilitating side effects and withdrawal symptoms, coupled with dependence. Until now, SC use was believed to be largely restricted to prison and homeless populations. However, media reporting in the UK has increasingly identified cases of children collapsing in schools, which are claimed to be as-sociated with vaping and putatively the vaping of a drug, variously reported as tetrahydrocannabinol (THC) 'synthetic cannabis' or 'spice'. We therefore conducted the first study to identify and quantity SCs in e-cigarettes routinely collected from schools. We sampled 27 schools from geographically distinct regions of England, representing a very broad range of social metrics (free school meals, persistent absenteeism, and SEN). The material was sampled by self-submission by individual schools of e-cigarettes seized during normal school operation and transferred to us for analysis via local po-lice forces. We found a remarkably consistent picture where SCs were detected in 17.5 % of all e-cigarettes sampled, and in 21 of 27 (78 %) of all sampled schools. Moreover, the percentage of SC e-cigarettes positively correlated with a metric of social deprivation, the fraction of pupils eligible for free school meals. The SC positive e-cigarettes were almost entire-ly found in e-cigarette liquid bottles and refillable e-cigarette devices, with very few identified in single use e-cigarette products. Within the positive samples we found an average SC concentration of 1.03 mg/mL with a maximum of 3.6 mg mL-1. In contrast to the high prevalence of SCs, few samples contained THC (1.6 %). We suggest that pupils are being sold SC e-cigarettes as 'cannabis' and may be unaware they are consuming (and sometimes supplying) considerably more harmful drugs. Our findings are immediately crucial to policy policing and healthcare in the UK as well as to educational bodies and schools.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"10 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919703","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}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311851
Ali N. M. Gubran¹ Naif, Mohammed Al-Haidary², Marwa Faisal, M. Bajubair³, Afrah Mohsen, Ali Algibary³, Manal Galeb, Mhmad Ali³, Marwa Fuad, Othman Ali³, Naif Mohammed Al-Haidary
Background: Intestinal parasite infection is a significant public health problem worldwide. This study aimed to determine the prevalence of intestinal parasites among primary school children, identify the most common types of parasites, and identify the risk factors contributing to infection in Aden, Yemen. Methodology/Principal Findings: An analytical cross-sectional study was conducted on 201 school children in Aden, Yemen. Stool specimens were collected and tested using direct methods (saline and iodine preparations) and sedimentation concentration techniques. Data analysis was performed using SPSS (Version 21) with p <= 0.05 considered statistically significant. The overall prevalence of intestinal parasites was 47.3%; 35.8% had a single parasite and 11.5% had multiple parasites. Higher rates were observed among female schoolchildren (51.2%), children whose mothers had primary education (51.3%), secondary education (50%), housewives (48.5%), and children aged >9 years (50%). The most predominant parasite was Entamoeba histolytica/dispar (36.3%). There was no significant association between the identified risk factors and intestinal parasitic infections. Conclusions/Significance: The prevalence rate of intestinal parasites is high in Aden, Yemen, with Entamoeba histolytica/dispar being the most dominant parasite. The highest rates were found among female schoolchildren, those whose mothers were housewives with primary or secondary education, and children aged >9 years.
{"title":"Intestinal Parasites among Primary School Children in Aden, Yemen","authors":"Ali N. M. Gubran¹ Naif, Mohammed Al-Haidary², Marwa Faisal, M. Bajubair³, Afrah Mohsen, Ali Algibary³, Manal Galeb, Mhmad Ali³, Marwa Fuad, Othman Ali³, Naif Mohammed Al-Haidary","doi":"10.1101/2024.08.12.24311851","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311851","url":null,"abstract":"Background: Intestinal parasite infection is a significant public health problem worldwide. This study aimed to determine the prevalence of intestinal parasites among primary school children, identify the most common types of parasites, and identify the risk factors contributing to infection in Aden, Yemen. Methodology/Principal Findings: An analytical cross-sectional study was conducted on 201 school children in Aden, Yemen. Stool specimens were collected and tested using direct methods (saline and iodine preparations) and sedimentation concentration techniques. Data analysis was performed using SPSS (Version 21) with p <= 0.05 considered statistically significant. The overall prevalence of intestinal parasites was 47.3%; 35.8% had a single parasite and 11.5% had multiple parasites. Higher rates were observed among female schoolchildren (51.2%), children whose mothers had primary education (51.3%), secondary education (50%), housewives (48.5%), and children aged >9 years (50%). The most predominant parasite was Entamoeba histolytica/dispar (36.3%). There was no significant association between the identified risk factors and intestinal parasitic infections. Conclusions/Significance: The prevalence rate of intestinal parasites is high in Aden, Yemen, with Entamoeba histolytica/dispar being the most dominant parasite. The highest rates were found among female schoolchildren, those whose mothers were housewives with primary or secondary education, and children aged >9 years.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"7 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919760","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}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311872
Masab A. Mansoor, Dba, MD Kashif Ansari
Background: Early detection of mental health crises is crucial for timely intervention and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multi-modal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over a 12-month period. Performance was evaluated using standard metrics and validated against expert psychiatric assessment. Results: The AI model demonstrated high accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges including privacy concerns, potential stigmatization, and cultural biases need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration with existing mental health services, and development of personalized, culturally-sensitive models. Keywords: artificial intelligence, mental health, crisis detection, social media analysis, early intervention
{"title":"Early Detection of Mental Health Crises through AI-Powered Social Media Analysis: A Prospective Observational Study","authors":"Masab A. Mansoor, Dba, MD Kashif Ansari","doi":"10.1101/2024.08.12.24311872","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311872","url":null,"abstract":"Background: Early detection of mental health crises is crucial for timely intervention and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multi-modal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over a 12-month period. Performance was evaluated using standard metrics and validated against expert psychiatric assessment. Results: The AI model demonstrated high accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges including privacy concerns, potential stigmatization, and cultural biases need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration with existing mental health services, and development of personalized, culturally-sensitive models. Keywords: artificial intelligence, mental health, crisis detection, social media analysis, early intervention","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"27 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919029","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}
Effective utilization of academic literature is crucial for Machine Reading Comprehension to generate actionable scientific knowledge for wide real-world applications. Recently, Large Language Models (LLMs) have emerged as a powerful tool for distilling knowledge from scientific articles, but they struggle with the issues of reliability and verifiability. Here, we propose LORE, a novel unsupervised two-stage reading methodology with LLM that models literature as a knowledge graph of verifiable factual statements and, in turn, as semantic embeddings in Euclidean space. Applied to PubMed abstracts for large-scale understanding of disease-gene relationships, LORE captures essential information of gene pathogenicity. Furthermore, we demonstrate that modeling a latent pathogenic flow in the semantic embedding with supervision from the ClinVar database leads to a 90% mean average precision in identifying relevant genes across 2,097 diseases. Finally, we have created a disease-gene relation knowledge graph with predicted pathogenicity scores, 200 times larger than the ClinVar database.
{"title":"LORE: A Literature Semantics Framework for Evidenced Disease-Gene Pathogenicity Prediction at Scale","authors":"P.-H. Li, Y.-Y. Sun, H.-F. Juan, C.-Y. Chen, H.-K. Tsai, J.-H. Huang","doi":"10.1101/2024.08.10.24311801","DOIUrl":"https://doi.org/10.1101/2024.08.10.24311801","url":null,"abstract":"Effective utilization of academic literature is crucial for Machine Reading Comprehension to generate actionable scientific knowledge for wide real-world applications. Recently, Large Language Models (LLMs) have emerged as a powerful tool for distilling knowledge from scientific articles, but they struggle with the issues of reliability and verifiability. Here, we propose LORE, a novel unsupervised two-stage reading methodology with LLM that models literature as a knowledge graph of verifiable factual statements and, in turn, as semantic embeddings in Euclidean space. Applied to PubMed abstracts for large-scale understanding of disease-gene relationships, LORE captures essential information of gene pathogenicity. Furthermore, we demonstrate that modeling a latent pathogenic flow in the semantic embedding with supervision from the ClinVar database leads to a 90% mean average precision in identifying relevant genes across 2,097 diseases. Finally, we have created a disease-gene relation knowledge graph with predicted pathogenicity scores, 200 times larger than the ClinVar database.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919044","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}
Pub Date : 2024-08-11DOI: 10.1101/2024.08.11.24311809
Adam B. Smith, Darren C. Greenwood, Paul Williams, Joseph Kwon, Stavros Petrou, Mike Horton, Thomas Osborne, Ruairidh Milne, Locomotion Consortium, Manoj Sivan
Background: Long Covid (LC) is a clinical syndrome of persistent, fluctuating symptoms subsequent to COVID-19 infection with a prevalence global estimate of many millions of cases. LC has significant detrimental effects on health-related quality of life (HRQoL), activities of daily living (ADL), and work productivity. Condition-specific patient-reported outcome measures (PROMs), such as the modified Covid-19 Yorkshire Rehabilitation Scale (C19-YRSm), have been developed to capture the impact of LC. However, these do not provide health utility data required for cost-utility analyses of LC interventions. The aim of this study was therefore to derive a mapping algorithm for the C19-YRSm to enable health utilities to be generated from this PROM. Methods: Data were collected from a large study evaluating LC services in the UK. A total of 1434 people with LC had completed both the C19-YRSm and the EQ-5D-5L on the same day. The EQ-5D-5L responses were then converted to EQ-5D-3L utility scores. Correlation and linear regression analyses were applied to determine items from the C19-YRSm and covariates for inclusion in the algorithm. Model fit, mean differences across the range of EQ-5D-3L scores (-0.59 to 1), and Bland-Altman plots were used to evaluate the algorithm. Responsiveness (standardised response mean; SRM) of the mapped utilities was also investigated on a subset of participants with repeat assessments (N=85). Results: There was a strong level of association between 8 items and 2 domains on the C19-YRSm with the EQ-5D single-item dimensions. These related to joint pain, muscle pain, anxiety, depression, walking/moving around, personal care, ADL, and social role, as well as Overall Health and Other Symptoms. Model fit was good (R2 = 0.7). The mean difference between the actual and mapped scores was < 0.10 for the range from 0 to 1 indicating a good degree of targeting for positive values of the EQ-5D-3L. The SRM for the mapped EQ-5D-3L health utilities (based on the C19-YRSm) was 0.37 compared to 0.17 for the observed EQ-5D-3L utility scores, suggesting the mapped EQ-5D-3L is more responsive to change. Conclusions: We have developed a simple, responsive, and robust mapping algorithm to enable EQ-5D-3L health utilities to be generated from 10 items of the C19-YRSm. This mapping algorithm will facilitate economic evaluations of interventions, treatment, and management of people with LC, as well as further helping to describe and characterise patients with LC irrespective of any treatment and interventions.
{"title":"Health-related quality of life in Long COVID: Mapping the condition-specific C19-YRSm measure onto the EQ-5D-5L","authors":"Adam B. Smith, Darren C. Greenwood, Paul Williams, Joseph Kwon, Stavros Petrou, Mike Horton, Thomas Osborne, Ruairidh Milne, Locomotion Consortium, Manoj Sivan","doi":"10.1101/2024.08.11.24311809","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311809","url":null,"abstract":"Background: Long Covid (LC) is a clinical syndrome of persistent, fluctuating symptoms subsequent to COVID-19 infection with a prevalence global estimate of many millions of cases. LC has significant detrimental effects on health-related quality of life (HRQoL), activities of daily living (ADL), and work productivity. Condition-specific patient-reported outcome measures (PROMs), such as the modified Covid-19 Yorkshire Rehabilitation Scale (C19-YRSm), have been developed to capture the impact of LC. However, these do not provide health utility data required for cost-utility analyses of LC interventions. The aim of this study was therefore to derive a mapping algorithm for the C19-YRSm to enable health utilities to be generated from this PROM. Methods: Data were collected from a large study evaluating LC services in the UK. A total of 1434 people with LC had completed both the C19-YRSm and the EQ-5D-5L on the same day. The EQ-5D-5L responses were then converted to EQ-5D-3L utility scores. Correlation and linear regression analyses were applied to determine items from the C19-YRSm and covariates for inclusion in the algorithm. Model fit, mean differences across the range of EQ-5D-3L scores (-0.59 to 1), and Bland-Altman plots were used to evaluate the algorithm. Responsiveness (standardised response mean; SRM) of the mapped utilities was also investigated on a subset of participants with repeat assessments (N=85). Results: There was a strong level of association between 8 items and 2 domains on the C19-YRSm with the EQ-5D single-item dimensions. These related to joint pain, muscle pain, anxiety, depression, walking/moving around, personal care, ADL, and social role, as well as Overall Health and Other Symptoms. Model fit was good (R2 = 0.7). The mean difference between the actual and mapped scores was < 0.10 for the range from 0 to 1 indicating a good degree of targeting for positive values of the EQ-5D-3L. The SRM for the mapped EQ-5D-3L health utilities (based on the C19-YRSm) was 0.37 compared to 0.17 for the observed EQ-5D-3L utility scores, suggesting the mapped EQ-5D-3L is more responsive to change. Conclusions: We have developed a simple, responsive, and robust mapping algorithm to enable EQ-5D-3L health utilities to be generated from 10 items of the C19-YRSm. This mapping algorithm will facilitate economic evaluations of interventions, treatment, and management of people with LC, as well as further helping to describe and characterise patients with LC irrespective of any treatment and interventions.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919765","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}
Pub Date : 2024-08-11DOI: 10.1101/2024.08.10.24311686
Francesco La Rosa, Jonadab Dos, Santos Silva, Emma Dereskewicz, A. Invernizzi, Noa Cahan, Julia Galasso, Nadia Garcia, Robin Graney, Sarah Levy, Gaurav Verma, P. Balchandani, Daniel S Reich, Megan Horton, H. Greenspan, James Sumowski, M. B. Cuadra, Erin S Beck
Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.
{"title":"BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis","authors":"Francesco La Rosa, Jonadab Dos, Santos Silva, Emma Dereskewicz, A. Invernizzi, Noa Cahan, Julia Galasso, Nadia Garcia, Robin Graney, Sarah Levy, Gaurav Verma, P. Balchandani, Daniel S Reich, Megan Horton, H. Greenspan, James Sumowski, M. B. Cuadra, Erin S Beck","doi":"10.1101/2024.08.10.24311686","DOIUrl":"https://doi.org/10.1101/2024.08.10.24311686","url":null,"abstract":"Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"16 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919251","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}
Pub Date : 2024-08-11DOI: 10.1101/2024.08.10.24311791
T. Cai, Q. Pan, Y. Tao, L. Yang, C. Nangia, A. Rajendrakumar, Y. Huang, Y. Shao, Y. Ye, T. Dottorini, M. Haque, C. N. Palmer, W. Meng
Purpose: Diabetic retinopathy (DR), a complication affecting the eyes, is associated with diabetes. This study aims to identify genetic variants associated with DR in patients with type 1 diabetes in the UK Biobank cohort (n = 1,004). Methods: A genome-wide association study (GWAS) was conducted to identify significant genetic variants of DR in type 1 diabetes. The findings are set to undergo validation during the replication and meta-analysis stages by using six cohorts: African American, European, FinnGen, GoSHARE, GoDARTS and Caucasian Australians. Results: In a locus, top single nucleotide polymorphism (SNP) rs184619214 in CCDC7 reached a GWAS significance level (p = 6.38 x 10-9) and rs79853754 in ITGB1 (p = 3.24 x 10-8), with both genes being adjacent to each other. The SNP-based heritability was estimated to be 31.09%. Rs184619214 was replicated and reached statistical significance (p < 5.0 x 10-8) in the meta-analysis stage. Pathway analysis revealed that ITGB1 is involved in the generation of biomolecules that impact the progression of DR. PheWAS analysis revealed that osteoarthritis (OA) of the hip was significantly associated with most of the SNPs of the locus. Mendelian Randomization further confirmed an association between OA and DR. Conclusions: Our study has identified a novel genomic risk locus associated with DR in type 1 diabetes, located in the intergenic region between the CCDC7 and ITGB1 genes, providing insights for DR researchers. Keywords: Diabetic retinopathy; genome-wide association study; meta-analysis; Phenome-Wide Association Study; type 1 diabetes
目的：糖尿病视网膜病变（DR）是一种影响眼睛的并发症，与糖尿病有关。本研究旨在确定英国生物库队列中 1 型糖尿病患者（n = 1,004 人）中与 DR 相关的基因变异。研究方法进行了一项全基因组关联研究（GWAS），以确定 1 型糖尿病患者中与 DR 有关的重要遗传变异。研究结果将在复制和荟萃分析阶段通过六个队列进行验证：非裔美国人、欧洲人、FinnGen、GoSHARE、GoDARTS 和澳大利亚高加索人。结果在一个位点上，CCDC7的顶级单核苷酸多态性（SNP）rs184619214达到了GWAS显著性水平（p = 6.38 x 10-9），ITGB1的顶级单核苷酸多态性（SNP）rs79853754达到了GWAS显著性水平（p = 3.24 x 10-8），这两个基因彼此相邻。基于 SNP 的遗传率估计为 31.09%。在荟萃分析阶段，Rs184619214得到了复制，并达到了统计学显著性（p < 5.0 x 10-8）。通路分析表明，ITGB1 参与了影响 DR 进展的生物分子的生成。PheWAS分析表明，髋关节骨性关节炎（OA）与该基因座的大多数SNP显著相关。孟德尔随机化进一步证实了 OA 与 DR 之间的关联。结论：我们的研究发现了一个与1型糖尿病DR相关的新基因组风险位点，该位点位于CCDC7和ITGB1基因之间的基因间区，为DR研究人员提供了新的见解。关键词：糖尿病视网膜病变糖尿病视网膜病变；全基因组关联研究；荟萃分析；表型全关联研究；1 型糖尿病
{"title":"Genome-wide association studies found CCDC7 and ITGB1 associated with diabetic retinopathy","authors":"T. Cai, Q. Pan, Y. Tao, L. Yang, C. Nangia, A. Rajendrakumar, Y. Huang, Y. Shao, Y. Ye, T. Dottorini, M. Haque, C. N. Palmer, W. Meng","doi":"10.1101/2024.08.10.24311791","DOIUrl":"https://doi.org/10.1101/2024.08.10.24311791","url":null,"abstract":"Purpose: Diabetic retinopathy (DR), a complication affecting the eyes, is associated with diabetes. This study aims to identify genetic variants associated with DR in patients with type 1 diabetes in the UK Biobank cohort (n = 1,004). Methods: A genome-wide association study (GWAS) was conducted to identify significant genetic variants of DR in type 1 diabetes. The findings are set to undergo validation during the replication and meta-analysis stages by using six cohorts: African American, European, FinnGen, GoSHARE, GoDARTS and Caucasian Australians. Results: In a locus, top single nucleotide polymorphism (SNP) rs184619214 in CCDC7 reached a GWAS significance level (p = 6.38 x 10-9) and rs79853754 in ITGB1 (p = 3.24 x 10-8), with both genes being adjacent to each other. The SNP-based heritability was estimated to be 31.09%. Rs184619214 was replicated and reached statistical significance (p < 5.0 x 10-8) in the meta-analysis stage. Pathway analysis revealed that ITGB1 is involved in the generation of biomolecules that impact the progression of DR. PheWAS analysis revealed that osteoarthritis (OA) of the hip was significantly associated with most of the SNPs of the locus. Mendelian Randomization further confirmed an association between OA and DR. Conclusions: Our study has identified a novel genomic risk locus associated with DR in type 1 diabetes, located in the intergenic region between the CCDC7 and ITGB1 genes, providing insights for DR researchers. Keywords: Diabetic retinopathy; genome-wide association study; meta-analysis; Phenome-Wide Association Study; type 1 diabetes","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"14 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919286","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}
Pub Date : 2024-08-11DOI: 10.1101/2024.08.10.24311789
M. F. Vinueza Veloz, L. Bhatta, P. R. Jones, M. S. Tesli, G. Davey Smith, N. M. Davies, B. M. Brumpton, O. E. Naess
Importance: Observational studies have demonstrated consistent protective effects of higher educational attainment (EA) on the risk of suffering mental health conditions (MHC). Determining whether these beneficial effects are causal is challenging given the potential role of dynastic effects and demographic factors (assortative mating and population structure) in this association. Objective: To evaluate to what extent the relationship between EA and various MHC is independent from dynastic effects and demographic factors. Design: Within-sibship Mendelian randomization (MR) study. Setting: One-sample MR analyses included participants data from the Trondelag Health Study (HUNT, Norway) and UK Biobank (United Kingdom). For two-sample MR analyses we used summary statistics from publicly available genome-wide-association-studies. Participants: 61 880 siblings (27 507 sibships). Exposure: Years of education. Main outcomes: Scores for symptoms of anxiety, depression and neuroticism using the Hospital Anxiety Depression Scale (HADS), the 7-item Generalized Anxiety Disorder Scale (GAD-7), the 9-item Patient Health Questionnaire (PHQ-9), and the Eysenck Personality Questionnaire, as well as self-reported consumption of psychotropic medication. Results: One standard deviation (SD) unit increase in years of education was associated with a lower symptom score of anxiety (-0.20 SD [95%CI: -0.26, -0.14]), depression (-0.11 SD [-0.43, 0.22]), neuroticism (-0.30 SD [-0.53, -0.06]), and lower odds of psychotropic medication consumption (OR: 0.60 [0.52, 0.69]). Estimates from the within-sibship MR analyses showed some attenuation, which however were suggestive of a causal association (anxiety: -0.17 SD [-0.33, -0.00]; depression: -0.18 SD [-1.26, 0.89]; neuroticism: -0.29 SD [-0.43, -0.15]); psychotropic medication consumption: OR, 0.52 [0.34, 0.82]). Conclusions and Relevance: Associations between EA and MHC in adulthood, although to some extend explained by dynastic effects and demographic factors, overall remain robust, indicative of a causal effect. However, larger studies are warranted to improve statistical power and further validate our conclusions.
{"title":"Educational attainment and mental health conditions: a within-sibship Mendelian randomization study","authors":"M. F. Vinueza Veloz, L. Bhatta, P. R. Jones, M. S. Tesli, G. Davey Smith, N. M. Davies, B. M. Brumpton, O. E. Naess","doi":"10.1101/2024.08.10.24311789","DOIUrl":"https://doi.org/10.1101/2024.08.10.24311789","url":null,"abstract":"Importance: Observational studies have demonstrated consistent protective effects of higher educational attainment (EA) on the risk of suffering mental health conditions (MHC). Determining whether these beneficial effects are causal is challenging given the potential role of dynastic effects and demographic factors (assortative mating and population structure) in this association. Objective: To evaluate to what extent the relationship between EA and various MHC is independent from dynastic effects and demographic factors. Design: Within-sibship Mendelian randomization (MR) study. Setting: One-sample MR analyses included participants data from the Trondelag Health Study (HUNT, Norway) and UK Biobank (United Kingdom). For two-sample MR analyses we used summary statistics from publicly available genome-wide-association-studies. Participants: 61 880 siblings (27 507 sibships). Exposure: Years of education. Main outcomes: Scores for symptoms of anxiety, depression and neuroticism using the Hospital Anxiety Depression Scale (HADS), the 7-item Generalized Anxiety Disorder Scale (GAD-7), the 9-item Patient Health Questionnaire (PHQ-9), and the Eysenck Personality Questionnaire, as well as self-reported consumption of psychotropic medication. Results: One standard deviation (SD) unit increase in years of education was associated with a lower symptom score of anxiety (-0.20 SD [95%CI: -0.26, -0.14]), depression (-0.11 SD [-0.43, 0.22]), neuroticism (-0.30 SD [-0.53, -0.06]), and lower odds of psychotropic medication consumption (OR: 0.60 [0.52, 0.69]). Estimates from the within-sibship MR analyses showed some attenuation, which however were suggestive of a causal association (anxiety: -0.17 SD [-0.33, -0.00]; depression: -0.18 SD [-1.26, 0.89]; neuroticism: -0.29 SD [-0.43, -0.15]); psychotropic medication consumption: OR, 0.52 [0.34, 0.82]). Conclusions and Relevance: Associations between EA and MHC in adulthood, although to some extend explained by dynastic effects and demographic factors, overall remain robust, indicative of a causal effect. However, larger studies are warranted to improve statistical power and further validate our conclusions.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"1 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920053","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}
Pub Date : 2024-08-11DOI: 10.1101/2024.08.10.24311686
Francesco La Rosa, Jonadab Dos, Santos Silva, Emma Dereskewicz, A. Invernizzi, Noa Cahan, Julia Galasso, Nadia Garcia, Robin Graney, Sarah Levy, Gaurav Verma, P. Balchandani, Daniel S Reich, Megan Horton, H. Greenspan, James Sumowski, M. B. Cuadra, Erin S Beck
Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.
{"title":"BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis","authors":"Francesco La Rosa, Jonadab Dos, Santos Silva, Emma Dereskewicz, A. Invernizzi, Noa Cahan, Julia Galasso, Nadia Garcia, Robin Graney, Sarah Levy, Gaurav Verma, P. Balchandani, Daniel S Reich, Megan Horton, H. Greenspan, James Sumowski, M. B. Cuadra, Erin S Beck","doi":"10.1101/2024.08.10.24311686","DOIUrl":"https://doi.org/10.1101/2024.08.10.24311686","url":null,"abstract":"Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919766","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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