Pub Date : 2024-08-28DOI: 10.1101/2024.08.28.24312729
Kirstin I Oliveira Roster, Minttu M Ronn, Heather Elder, Thomas Gift, Kathleen Roosevelt, Joshua A Salomon, Katherine Hsu, Yonatan Grad
Antimicrobial resistance (AMR) is a serious public health threat. Neisseria gonorrhoeae has developed resistance to all antibiotics recommended for treatment and reports of reduced susceptibility to ceftriaxone, the last-line treatment, are increasing. Since many asymptomatic infections remain undiagnosed and most diagnosed infections do not undergo antibiotic susceptibility testing, surveillance systems may underestimate the number of ceftriaxone non-susceptible infections. There is an urgent need for better interpretation and use of surveillance data to estimate the prevalence of resistance. In this modeling study, we simulated the spread of a new strain of ceftriaxone non-susceptible gonorrhea in a population of men who have sex with men as well as heterosexual men and women. We compared scenarios with varying strain characteristics and surveillance capacity. For each scenario, we estimated (i) the number of undetected infections by the time the non-susceptible strain was discovered and (ii) the likelihood of strain persistence in the absence of newly reported cases. Upon detection of one ceftriaxone non-susceptible isolate, the undetected disease burden was an estimated 5.4 infections with substantial uncertainty (0-18 infections, 95% uncertainty interval). In the absence of additional reports of ceftriaxone non-susceptible infections over the subsequent 180 days, the estimate declined to 2.5 infections with a narrower uncertainty interval (0-10 infections). The likelihood of ongoing transmission also declined from 66% (26-86%) at first detection to 2% (0-10%) after 180 days. Enhanced monitoring of the magnitude and trends of AMR is a key priority to reduce the burden of resistance and extend the useful lifespan of last-line antibiotics.
{"title":"Estimating the undetected burden of infections and the likelihood of strain persistence of drug-resistant Neisseria gonorrhoeae","authors":"Kirstin I Oliveira Roster, Minttu M Ronn, Heather Elder, Thomas Gift, Kathleen Roosevelt, Joshua A Salomon, Katherine Hsu, Yonatan Grad","doi":"10.1101/2024.08.28.24312729","DOIUrl":"https://doi.org/10.1101/2024.08.28.24312729","url":null,"abstract":"Antimicrobial resistance (AMR) is a serious public health threat. Neisseria gonorrhoeae has developed resistance to all antibiotics recommended for treatment and reports of reduced susceptibility to ceftriaxone, the last-line treatment, are increasing. Since many asymptomatic infections remain undiagnosed and most diagnosed infections do not undergo antibiotic susceptibility testing, surveillance systems may underestimate the number of ceftriaxone non-susceptible infections. There is an urgent need for better interpretation and use of surveillance data to estimate the prevalence of resistance. In this modeling study, we simulated the spread of a new strain of ceftriaxone non-susceptible gonorrhea in a population of men who have sex with men as well as heterosexual men and women. We compared scenarios with varying strain characteristics and surveillance capacity. For each scenario, we estimated (i) the number of undetected infections by the time the non-susceptible strain was discovered and (ii) the likelihood of strain persistence in the absence of newly reported cases. Upon detection of one ceftriaxone non-susceptible isolate, the undetected disease burden was an estimated 5.4 infections with substantial uncertainty (0-18 infections, 95% uncertainty interval). In the absence of additional reports of ceftriaxone non-susceptible infections over the subsequent 180 days, the estimate declined to 2.5 infections with a narrower uncertainty interval (0-10 infections). The likelihood of ongoing transmission also declined from 66% (26-86%) at first detection to 2% (0-10%) after 180 days. Enhanced monitoring of the magnitude and trends of AMR is a key priority to reduce the burden of resistance and extend the useful lifespan of last-line antibiotics.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189811","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 : 2024-08-28DOI: 10.1101/2024.08.27.24312676
Julia A. Messina, Jennifer L. Saullo, Steven Wolf, Yanhong Li, Helen Tang, Lauren B. Hill, Gena G. Foster, Michael Grant, Samantha M. Thomas, Rachel A. Miller, Barbara D. Alexander, Mitchell Horwitz, Anthony D. Sung
The impact of multiple episodes of clinically significant cytomegalovirus infection (csCMVi) on clinical outcomes during pre-emptive CMV monitoring in allogeneic hematopoietic stem cell transplant recipients (HCT) is not well understood.
{"title":"Impact of Multiple Episodes of Cytomegalovirus Infection on Patient Outcomes in Allogeneic Hematopoietic Stem Cell Transplant Using a Pre-emptive Monitoring Strategy","authors":"Julia A. Messina, Jennifer L. Saullo, Steven Wolf, Yanhong Li, Helen Tang, Lauren B. Hill, Gena G. Foster, Michael Grant, Samantha M. Thomas, Rachel A. Miller, Barbara D. Alexander, Mitchell Horwitz, Anthony D. Sung","doi":"10.1101/2024.08.27.24312676","DOIUrl":"https://doi.org/10.1101/2024.08.27.24312676","url":null,"abstract":"The impact of multiple episodes of clinically significant cytomegalovirus infection (csCMVi) on clinical outcomes during pre-emptive CMV monitoring in allogeneic hematopoietic stem cell transplant recipients (HCT) is not well understood.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189812","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}
COVID-19 mortality rates have varied dramatically across the globe. Yet the reasons behind these disparities remain poorly understood. While recent research has linked gut microbes to these variations, the role of oral bacteria, a main port of entry for the coronavirus, remains unexplored. We investigated the relationship between oral microbiota and COVID-19 mortality rates across eight countries. Raw sequencing data of 16S rRNA regions from oral microbiota in 244 healthy subjects from eight countries were obtained from public databases. We employed a generalized linear model (GLM) to predict COVID-19 mortality rates using oral microbiota composition. GLM revealed that high abundances of hydrogen sulfide (H₂S)-producing bacteria, particularly Treponema, predicted low COVID-19 mortality rates with a markedly low p-value. Unsupervised clustering using a combination of LIGER and t-SNE yielded four oral microbiome "orotypes." Orotypes enriched in H₂S-producing bacteria coincided with lower mortality rates, while orotypes harboring Haemophilus or Rothia were associated with increased vulnerability. To validate our findings, we analyzed influenza mortality data from the same countries, observing similar protective trends. Our findings suggest that oral bacteria-produced H₂S may serve as a critical initial defense against SARS-CoV-2 infection. H₂S from oral bacteria may prevent infection through antiviral activity, blocking ACE2 receptors, suppressing cytokines, and boosting antioxidants. This highlights the oral microbiome's role in COVID-19 outcomes and suggests new preventive and therapeutic strategies.
{"title":"Hydrogen Sulfide (H₂S)-Producing Oral Bacteria May Protect Against COVID-19","authors":"Meghalbahen Vaishnani, Anupama Modi, kshipra Chauhan, Bhavin Parekh","doi":"10.1101/2024.08.07.24311606","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311606","url":null,"abstract":"COVID-19 mortality rates have varied dramatically across the globe. Yet the reasons behind these disparities remain poorly understood. While recent research has linked gut microbes to these variations, the role of oral bacteria, a main port of entry for the coronavirus, remains unexplored. We investigated the relationship between oral microbiota and COVID-19 mortality rates across eight countries. Raw sequencing data of 16S rRNA regions from oral microbiota in 244 healthy subjects from eight countries were obtained from public databases. We employed a generalized linear model (GLM) to predict COVID-19 mortality rates using oral microbiota composition. GLM revealed that high abundances of hydrogen sulfide (H₂S)-producing bacteria, particularly Treponema, predicted low COVID-19 mortality rates with a markedly low p-value. Unsupervised clustering using a combination of LIGER and t-SNE yielded four oral microbiome \"orotypes.\" Orotypes enriched in H₂S-producing bacteria coincided with lower mortality rates, while orotypes harboring Haemophilus or Rothia were associated with increased vulnerability. To validate our findings, we analyzed influenza mortality data from the same countries, observing similar protective trends. Our findings suggest that oral bacteria-produced H₂S may serve as a critical initial defense against SARS-CoV-2 infection. H₂S from oral bacteria may prevent infection through antiviral activity, blocking ACE2 receptors, suppressing cytokines, and boosting antioxidants. This highlights the oral microbiome's role in COVID-19 outcomes and suggests new preventive and therapeutic strategies.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929950","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 : 2024-08-07DOI: 10.1101/2024.08.07.24311593
Fernanda Goncalves Ferreira Salvador, Claudia C J Duarte, Mayumi D Wakimoto, Luis V Lapão, Henrique M C Silveira, Claudia Maria Valete
Background Neglected Tropical Diseases (NTDs) represent a global public health problem with great socioeconomic impact. Using digital health technologies to provide medical care remotely can be an important strategy for reducing inequalities in access but is challenging in low-and middle-income settings and geographically isolated areas. The objective of the current review was to identify and summarize international evidence on the use of telemedicine for clinical care of patients with NTD around the world based on a scoping review protocol. Methodology/Principal Findings A total of 422 articles were extracted from the databases MEDLINE/PubMed, Web of Science and Scopus, and after remove 129 duplicates, 285 studies were excluded because they did not meet at least one of the eligibility criteria. A total of 8 articles were included for data collection, published between 2006 and 2023, half of them after 2021 (n=4). Fifty percent of the studies (n=4) were focused on dermatological diseases, like leprosy and leishmaniosis, and the other diseases found were dengue (n=2), trachoma (n=1) and cysticercosis (n=1). Most of telemedicine interventions in NTD took place in South America (n=5), with emphasis on Brazil (n=2). Studies that evaluated the accuracy of remote diagnosis demonstrated good effectiveness for leprosy, trachoma and complications of neurocysticercosis. There was a significant reduction in the need for specialized in-person medical consultations with telemedicine for the care of dengue fever and some dermatological NTDs; and an improvement in the quality of clinical monitoring of cutaneous leishmaniasis using mobile health was related. Conclusions/Significance This scoping review mapped the existing evidence on telemedicine applied at clinical care for NTD. Although we observed a small recent increase in academic research on the theme after COVID-19 pandemic, there is a gap in recommendations for the clinical management of NTDs through telemedicine, especially for synchronous approaches, revealing that this resource is still largely underutilized.
{"title":"Telemedicine and clinical care for Neglected Tropical Diseases in a post-pandemic world: where are we on the way to mitigate global inequities? A scoping review","authors":"Fernanda Goncalves Ferreira Salvador, Claudia C J Duarte, Mayumi D Wakimoto, Luis V Lapão, Henrique M C Silveira, Claudia Maria Valete","doi":"10.1101/2024.08.07.24311593","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311593","url":null,"abstract":"Background Neglected Tropical Diseases (NTDs) represent a global public health problem with great socioeconomic impact. Using digital health technologies to provide medical care remotely can be an important strategy for reducing inequalities in access but is challenging in low-and middle-income settings and geographically isolated areas. The objective of the current review was to identify and summarize international evidence on the use of telemedicine for clinical care of patients with NTD around the world based on a scoping review protocol. Methodology/Principal Findings A total of 422 articles were extracted from the databases MEDLINE/PubMed, Web of Science and Scopus, and after remove 129 duplicates, 285 studies were excluded because they did not meet at least one of the eligibility criteria. A total of 8 articles were included for data collection, published between 2006 and 2023, half of them after 2021 (n=4). Fifty percent of the studies (n=4) were focused on dermatological diseases, like leprosy and leishmaniosis, and the other diseases found were dengue (n=2), trachoma (n=1) and cysticercosis (n=1). Most of telemedicine interventions in NTD took place in South America (n=5), with emphasis on Brazil (n=2). Studies that evaluated the accuracy of remote diagnosis demonstrated good effectiveness for leprosy, trachoma and complications of neurocysticercosis. There was a significant reduction in the need for specialized in-person medical consultations with telemedicine for the care of dengue fever and some dermatological NTDs; and an improvement in the quality of clinical monitoring of cutaneous leishmaniasis using mobile health was related. Conclusions/Significance This scoping review mapped the existing evidence on telemedicine applied at clinical care for NTD. Although we observed a small recent increase in academic research on the theme after COVID-19 pandemic, there is a gap in recommendations for the clinical management of NTDs through telemedicine, especially for synchronous approaches, revealing that this resource is still largely underutilized.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929957","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 : 2024-08-07DOI: 10.1101/2024.08.05.24311344
Marc-Antoine Guery, Sukai Ceesay, Sainabou Drammeh, Fatou K Jaiteh, Umberto D'Alessandro, Teun Bousema, David J Conway, Antoine Claessens
Understanding the genetic diversity and transmission dynamics of Plasmodium falciparum, the causative agent of malaria, is crucial for effective control and elimination efforts. In some endemic regions, malaria is highly seasonal with no or little transmission during up to 8 months, yet little is known about how seasonality affects the parasite population genetics. Here we conducted a longitudinal study over 2.5 year on 1516 participants in the Upper River Region of The Gambia. With 425 P. falciparum genetic barcodes genotyped from asymptomatic infections, we developed an identity by descent (IBD) based pipeline and validated its accuracy using 199 parasite genomes. Genetic relatedness between isolates revealed a highly recombinatorial genetic diversity, suggesting continuous recombination among parasites rather than the dominance of specific strains. However, isolates from the same household were six-fold more likely to be genetically related compared to those from other villages. Seasonal patterns influenced genetic relatedness, with a notable increase of parasite differentiation during high transmission. Yet chronic infections presented exceptions, including one individual who had a continuous infection by the same parasite genotype for at least 18 months. Our findings highlight the burden of asymptomatic chronic malaria carriers and the importance of characterising the parasite genetic population at the community-level. Most importantly, reactive approaches for malaria elimination should not be limited to acute malaria cases but be broadened to households of asymptomatic carriers.
{"title":"Household clustering and seasonal genetic variation of Plasmodium falciparum at the community-level in The Gambia","authors":"Marc-Antoine Guery, Sukai Ceesay, Sainabou Drammeh, Fatou K Jaiteh, Umberto D'Alessandro, Teun Bousema, David J Conway, Antoine Claessens","doi":"10.1101/2024.08.05.24311344","DOIUrl":"https://doi.org/10.1101/2024.08.05.24311344","url":null,"abstract":"Understanding the genetic diversity and transmission dynamics of Plasmodium falciparum, the causative agent of malaria, is crucial for effective control and elimination efforts. In some endemic regions, malaria is highly seasonal with no or little transmission during up to 8 months, yet little is known about how seasonality affects the parasite population genetics. Here we conducted a longitudinal study over 2.5 year on 1516 participants in the Upper River Region of The Gambia. With 425 P. falciparum genetic barcodes genotyped from asymptomatic infections, we developed an identity by descent (IBD) based pipeline and validated its accuracy using 199 parasite genomes. Genetic relatedness between isolates revealed a highly recombinatorial genetic diversity, suggesting continuous recombination among parasites rather than the dominance of specific strains. However, isolates from the same household were six-fold more likely to be genetically related compared to those from other villages. Seasonal patterns influenced genetic relatedness, with a notable increase of parasite differentiation during high transmission. Yet chronic infections presented exceptions, including one individual who had a continuous infection by the same parasite genotype for at least 18 months. Our findings highlight the burden of asymptomatic chronic malaria carriers and the importance of characterising the parasite genetic population at the community-level. Most importantly, reactive approaches for malaria elimination should not be limited to acute malaria cases but be broadened to households of asymptomatic carriers.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929949","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 : 2024-08-07DOI: 10.1101/2024.08.07.24311618
ZhenNan Wang, ChaoMei Liu
Transformer models have achieved excellent results in various tasks, primarily due to the self-attention mechanism. We explore using self-attention for detecting coronavirus sequences in high-throughput sequencing data, offering a novel approach for accurately identifying emerging and highly variable coronavirus strains. Coronavirus and human genome data were obtained from the Genomic Data Commons (GDC) and the National Genomics Data Center (NGDC) databases. After preprocessing, a simulated high-throughput sequencing dataset of coronavirus-infected samples was constructed. This dataset was divided into training, validation, and test datasets. The self-attention-based model was trained on the training datasets, tested on the validation and test datasets, and SARS-CoV-2 genome data were collected as an independent test datasets. The results showed that the self-attention-based model outperformed traditional bioinformatics methods in terms of performance on both the test and the independent test datasets, with a significant improvement in computation speed. The self-attention-based model can sensitively and rapidly detect coronavirus sequences from high-throughput sequencing data while exhibiting excellent generalization ability. It can accurately detect emerging and highly variable coronavirus strains, providing a new approach for identifying such viruses.
{"title":"Self-attention based deep learning model for predicting the coronavirus sequences from high-throughput sequencing data","authors":"ZhenNan Wang, ChaoMei Liu","doi":"10.1101/2024.08.07.24311618","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311618","url":null,"abstract":"Transformer models have achieved excellent results in various tasks, primarily due to the self-attention mechanism. We explore using self-attention for detecting coronavirus sequences in high-throughput sequencing data, offering a novel approach for accurately identifying emerging and highly variable coronavirus strains. Coronavirus and human genome data were obtained from the Genomic Data Commons (GDC) and the National Genomics Data Center (NGDC) databases. After preprocessing, a simulated high-throughput sequencing dataset of coronavirus-infected samples was constructed. This dataset was divided into training, validation, and test datasets. The self-attention-based model was trained on the training datasets, tested on the validation and test datasets, and SARS-CoV-2 genome data were collected as an independent test datasets. The results showed that the self-attention-based model outperformed traditional bioinformatics methods in terms of performance on both the test and the independent test datasets, with a significant improvement in computation speed. The self-attention-based model can sensitively and rapidly detect coronavirus sequences from high-throughput sequencing data while exhibiting excellent generalization ability. It can accurately detect emerging and highly variable coronavirus strains, providing a new approach for identifying such viruses.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"191 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929947","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 : 2024-08-07DOI: 10.1101/2024.08.06.24311559
Hagit Kopel, Van Hung Nguyen, Alina N Bogdanov, Isabelle Winer, Catherine Boileau, Thierry Ducruet, Ni Zeng, Jessamine P Winer-Jones, Daina B Esposito, Mary Bausch-Jurken, Ekkehard Beck, Machaon Bonafede, James A. Mansi
This retrospective cohort study evaluated the relative vaccine effectiveness (rVE) of two bivalent (Original/Omicron BA.4/BA.5) vaccines mRNA-1273.222 versus BNT162b2 Bivalent in preventing COVID-19-related outcomes in adults with underlying medical conditions associated with increased risk for severe COVID-19. In a linked EHR-claims dataset, US adults (≥ 18 years) with ≥ 1 underlying medical condition of interest who received either bivalent vaccine between August 31, 2022, and February 28, 2023, were identified. Inverse probability of treatment weighting was used to adjust for cohort differences. Cohorts were followed up for COVID-19-related hospitalizations and outpatient encounters until May 31, 2023. Hazard ratios and rVEs were estimated using Cox regression. Subgroup analyses were performed on individuals with pre-specified comorbid conditions. 757,572 mRNA-1273.222 and 1,204,975 BNT162b2 Bivalent recipients were identified. The adjusted rVE over a median follow-up of 198 days was 10.9% (6.2%-15.2%) against COVID-19-related hospitalization and 3.2% (1.7%-4.7%) against COVID-19-related outpatient encounters. rVE estimates for COVID-19 hospitalizations among subgroups with comorbid conditions were: diabetes 15.1% (8.7%-21.0%), cardiovascular disease 14.7% (9.0%-20.1%), chronic lung disease 11.9% (5.1%-18.2%), immunocompromised 15.0% (7.2%-22.2%), chronic kidney disease 8.4% (0.5%-15.7%). Overall, among adults with underlying medical conditions, mRNA-1273.222 was more effective than BNT162b2 Bivalent, especially in preventing COVID-19-related hospitalizations.
{"title":"Comparative Effectiveness of the Bivalent (Original/Omicron BA.4/BA.5) mRNA COVID-19 Vaccines mRNA-1273.222 and BNT162b2 Bivalent in Adults With Underlying Medical Conditions in the United States","authors":"Hagit Kopel, Van Hung Nguyen, Alina N Bogdanov, Isabelle Winer, Catherine Boileau, Thierry Ducruet, Ni Zeng, Jessamine P Winer-Jones, Daina B Esposito, Mary Bausch-Jurken, Ekkehard Beck, Machaon Bonafede, James A. Mansi","doi":"10.1101/2024.08.06.24311559","DOIUrl":"https://doi.org/10.1101/2024.08.06.24311559","url":null,"abstract":"This retrospective cohort study evaluated the relative vaccine effectiveness (rVE) of two bivalent (Original/Omicron BA.4/BA.5) vaccines mRNA-1273.222 versus BNT162b2 Bivalent in preventing COVID-19-related outcomes in adults with underlying medical conditions associated with increased risk for severe COVID-19.\u0000In a linked EHR-claims dataset, US adults (≥ 18 years) with ≥ 1 underlying medical condition of interest who received either bivalent vaccine between August 31, 2022, and February 28, 2023, were identified. Inverse probability of treatment weighting was used to adjust for cohort differences. Cohorts were followed up for COVID-19-related hospitalizations and outpatient encounters until May 31, 2023. Hazard ratios and rVEs were estimated using Cox regression. Subgroup analyses were performed on individuals with pre-specified comorbid conditions.\u0000757,572 mRNA-1273.222 and 1,204,975 BNT162b2 Bivalent recipients were identified. The adjusted rVE over a median follow-up of 198 days was 10.9% (6.2%-15.2%) against COVID-19-related hospitalization and 3.2% (1.7%-4.7%) against COVID-19-related outpatient encounters. rVE estimates for COVID-19 hospitalizations among subgroups with comorbid conditions were: diabetes 15.1% (8.7%-21.0%), cardiovascular disease 14.7% (9.0%-20.1%), chronic lung disease 11.9% (5.1%-18.2%), immunocompromised 15.0% (7.2%-22.2%), chronic kidney disease 8.4% (0.5%-15.7%).\u0000Overall, among adults with underlying medical conditions, mRNA-1273.222 was more effective than BNT162b2 Bivalent, especially in preventing COVID-19-related hospitalizations.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929948","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}
Introduction: Sepsis remains a critical healthcare challenge worldwide, demanding prompt identification and treatment to improve patient outcomes. Given the absence of a definitive gold standard diagnostic test, there is an imperative need for adjunct diagnostic tools to aid in early sepsis detection and guide effective treatment strategies. This study introduces a novel 3-step model to identify and classify sepsis, integrating current knowledge and clinical guidelines to enhance diagnostic precision. Methods: This longitudinal observational study was conducted at a tertiary care teaching hospital in northern India. Adult patients admitted with suspected sepsis underwent screening using predefined criteria. The 3-step model consisted of Step 1, assessing dysregulated host response using a National Early Warning Score-2 (NEWS-2) score of ≥6; Step 2, evaluating risk factors for infection; and Step 3, confirming infection presence through clinical, supportive, or confirmatory evidence. Patients were categorized into Asepsis, Possible sepsis, Probable sepsis, or Confirmed sepsis at various intervals during hospitalization. Results: A total of 230 patients were included. Initial categorization on Day 1 showed 13.0% in Asepsis, 35.2% in Possible sepsis, 51.3% in Probable sepsis, and 0.4% in confirmed sepsis. By Day 7, shifts were observed with 49.7% in Asepsis, 9.5% in Possible sepsis, 25.4% in Probable sepsis, and 15.4% in confirmed sepsis. At discharge or death, categories were 60.4% Asepsis, 5.2% Possible sepsis, 21.7% Probable sepsis, and 12.6% Confirmed sepsis. Transitions between categories were noted throughout hospitalisation, demonstrating the dynamic nature of sepsis progression and response to treatment. Conclusion: The 3-step model effectively stratifies sepsis status over hospitalization, facilitating early identification and classification of septic patients. This approach holds promise for enhancing diagnostic accuracy, guiding clinical decision-making, and optimizing antibiotic stewardship practices. Further validation across diverse patient cohorts and healthcare settings is essential to confirm its utility and generalizability.
{"title":"3-STEP MODEL- AN EXPLORATIVE NOVEL APPROACH TO CLASSIFY SEPSIS: A LONGITUDINAL OBSERVATIONAL STUDY","authors":"Jaideep Pilania, Prasan Kumar Panda, Ananya Das, Udit Chauhan, Ravi Kant","doi":"10.1101/2024.08.07.24311597","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311597","url":null,"abstract":"Introduction: Sepsis remains a critical healthcare challenge worldwide, demanding prompt identification and treatment to improve patient outcomes. Given the absence of a definitive gold standard diagnostic test, there is an imperative need for adjunct diagnostic tools to aid in early sepsis detection and guide effective treatment strategies. This study introduces a novel 3-step model to identify and classify sepsis, integrating current knowledge and clinical guidelines to enhance diagnostic precision.\u0000Methods: This longitudinal observational study was conducted at a tertiary care teaching hospital in northern India. Adult patients admitted with suspected sepsis underwent screening using predefined criteria. The 3-step model consisted of Step 1, assessing dysregulated host response using a National Early Warning Score-2 (NEWS-2) score of ≥6; Step 2, evaluating risk factors for infection; and Step 3, confirming infection presence through clinical, supportive, or confirmatory evidence. Patients were categorized into Asepsis, Possible sepsis, Probable sepsis, or Confirmed sepsis at various intervals during hospitalization.\u0000Results: A total of 230 patients were included. Initial categorization on Day 1 showed 13.0% in Asepsis, 35.2% in Possible sepsis, 51.3% in Probable sepsis, and 0.4% in confirmed sepsis. By Day 7, shifts were observed with 49.7% in Asepsis, 9.5% in Possible sepsis, 25.4% in Probable sepsis, and 15.4% in confirmed sepsis. At discharge or death, categories were 60.4% Asepsis, 5.2% Possible sepsis, 21.7% Probable sepsis, and 12.6% Confirmed sepsis. Transitions between categories were noted throughout hospitalisation, demonstrating the dynamic nature of sepsis progression and response to treatment.\u0000Conclusion: The 3-step model effectively stratifies sepsis status over hospitalization, facilitating early identification and classification of septic patients. This approach holds promise for enhancing diagnostic accuracy, guiding clinical decision-making, and optimizing antibiotic stewardship practices. Further validation across diverse patient cohorts and healthcare settings is essential to confirm its utility and generalizability.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929872","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 : 2024-08-07DOI: 10.1101/2024.08.07.24311590
Keith James Fraser, Laurence Cibrelus, Jennifer Horton, Chiori Kodama, J. Erin Staples, Katy A.M. Gaythorpe
The importation of arbovirus diseases into countries where they are not currently endemic is a global concern, driven by human movement and direct and indirect climate change effects. In the World Health Organization Eastern Mediterranean region, three countries - the Republic of Djibouti, the Federal Republic of Somalia, and the Republic of Yemen - are currently considered to be at potential or moderate risk for yellow fever outbreaks, and an assessment for outbreak potential in the event of importation was sought. Djibouti and Somalia share land borders and significant cross-border movement with countries where yellow fever is endemic, while Yemen is currently experiencing a crisis which has severely impacted healthcare infrastructure, and has already seen suspected outbreaks of other similar arboviruses such as dengue, chikungunya and West Nile. Here we present a mathematical modelling assessment of the risk of introduction and propagation of yellow fever in Djibouti, Somalia and Yemen. This modelling has two components: projecting the risk of importation of infectious individuals into individual administrative regions of the countries of interest, and the use of a dynamic yellow fever model to model yellow fever virus transmission within the same regions. We present results showing that certain regions of Djibouti, Somalia and Yemen are at higher risk than others for yellow fever outbreaks, with the risk being higher in some areas such as the western coastal regions of Yemen (an area that has experienced recent outbreaks of other arboviruses), regions of Somalia bordering both the Federal Democratic Republic of Ethiopia and the Republic of Kenya, and Djibouti City.
{"title":"Yellow fever outbreak potential in Djibouti, Somalia and Yemen","authors":"Keith James Fraser, Laurence Cibrelus, Jennifer Horton, Chiori Kodama, J. Erin Staples, Katy A.M. Gaythorpe","doi":"10.1101/2024.08.07.24311590","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311590","url":null,"abstract":"The importation of arbovirus diseases into countries where they are not currently endemic is a global concern, driven by human movement and direct and indirect climate change effects. In the World Health Organization Eastern Mediterranean region, three countries - the Republic of Djibouti, the Federal Republic of Somalia, and the Republic of Yemen - are currently considered to be at potential or moderate risk for yellow fever outbreaks, and an assessment for outbreak potential in the event of importation was sought. Djibouti and Somalia share land borders and significant cross-border movement with countries where yellow fever is endemic, while Yemen is currently experiencing a crisis which has severely impacted healthcare infrastructure, and has already seen suspected outbreaks of other similar arboviruses such as dengue, chikungunya and West Nile.\u0000Here we present a mathematical modelling assessment of the risk of introduction and propagation of yellow fever in Djibouti, Somalia and Yemen. This modelling has two components: projecting the risk of importation of infectious individuals into individual administrative regions of the countries of interest, and the use of a dynamic yellow fever model to model yellow fever virus transmission within the same regions.\u0000We present results showing that certain regions of Djibouti, Somalia and Yemen are at higher risk than others for yellow fever outbreaks, with the risk being higher in some areas such as the western coastal regions of Yemen (an area that has experienced recent outbreaks of other arboviruses), regions of Somalia bordering both the Federal Democratic Republic of Ethiopia and the Republic of Kenya, and Djibouti City.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929946","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 : 2024-08-06DOI: 10.1101/2024.08.05.24311512
Abebe A. Fola, Ilinca I. Ciubotariu, Jack Dorman, Mulenga C. Mwenda, Brenda Mambwe, Conceptor Mulube, Rachael Kasaro, Moonga B. Hawela, Busiku Hamainza, John M. Miller, Jeffrey A. Bailey, William J. Moss, Daniel Bridge, Giovanna Carpi
The emergence of antimalarial drug resistance is a major threat to malaria control and elimination. Using whole genome sequencing of 282 P. falciparum samples collected during the 2018 Zambia National Malaria Indicator Survey, we determined the prevalence and spatial distribution of known and candidate antimalarial drug resistance mutations. High levels of genotypic resistance were found across Zambia to pyrimethamine, with over 94% (n=266) of samples having the Pfdhfr triple mutant (N51I, C59R, and S108N), and sulfadoxine, with over 84% (n=238) having the Pfdhps double mutant (A437G and K540E). In northern Zambia, 5.3% (n=15) of samples also harbored the Pfdhps A581G mutation. Although 29 mutations were identified in Pfkelch13, these mutations were present at low frequency (<2.5%), and only three were WHO-validated artemisinin partial resistance mutations: P441L (n=1, 0.35%), V568M (n=2, 0.7%) and R622T (n=1, 0.35%). Notably, 91 (32%) of samples carried the E431K mutation in the Pfatpase6 gene, which is associated with artemisinin resistance. No specimens carried any known mutations associated with chloroquine resistance in the Pfcrt gene (codons 72-76). P. falciparum strains circulating in Zambia were highly resistant to sulfadoxine and pyrimethamine but remained susceptible to chloroquine and artemisinin. Despite this encouraging finding, early genetic signs of developing artemisinin resistance highlight the urgent need for continued vigilance and expanded routine genomic surveillance to monitor these changes.
{"title":"National genomic profiling of Plasmodium falciparum antimalarial resistance in Zambian children participating in the 2018 Malaria Indicator Survey","authors":"Abebe A. Fola, Ilinca I. Ciubotariu, Jack Dorman, Mulenga C. Mwenda, Brenda Mambwe, Conceptor Mulube, Rachael Kasaro, Moonga B. Hawela, Busiku Hamainza, John M. Miller, Jeffrey A. Bailey, William J. Moss, Daniel Bridge, Giovanna Carpi","doi":"10.1101/2024.08.05.24311512","DOIUrl":"https://doi.org/10.1101/2024.08.05.24311512","url":null,"abstract":"The emergence of antimalarial drug resistance is a major threat to malaria control and elimination. Using whole genome sequencing of 282 <em>P. falciparum</em> samples collected during the 2018 Zambia National Malaria Indicator Survey, we determined the prevalence and spatial distribution of known and candidate antimalarial drug resistance mutations. High levels of genotypic resistance were found across Zambia to pyrimethamine, with over 94% (n=266) of samples having the <em>Pfdhfr</em> triple mutant (N51<strong>I</strong>, C59<strong>R</strong>, and S108<strong>N</strong>), and sulfadoxine, with over 84% (n=238) having the <em>Pfdhps</em> double mutant (A437<strong>G</strong> and K540<strong>E</strong>). In northern Zambia, 5.3% (n=15) of samples also harbored the <em>Pfdhps</em> A581<strong>G</strong> mutation. Although 29 mutations were identified in <em>Pfkelch13</em>, these mutations were present at low frequency (<2.5%), and only three were WHO-validated artemisinin partial resistance mutations: P441<strong>L</strong> (n=1, 0.35%), V568<strong>M</strong> (n=2, 0.7%) and R622<strong>T</strong> (n=1, 0.35%). Notably, 91 (32%) of samples carried the E431<strong>K</strong> mutation in the <em>Pfatpase6</em> gene, which is associated with artemisinin resistance. No specimens carried any known mutations associated with chloroquine resistance in the <em>Pfcrt</em> gene (codons 72-76). <em>P. falciparum</em> strains circulating in Zambia were highly resistant to sulfadoxine and pyrimethamine but remained susceptible to chloroquine and artemisinin. Despite this encouraging finding, early genetic signs of developing artemisinin resistance highlight the urgent need for continued vigilance and expanded routine genomic surveillance to monitor these changes.","PeriodicalId":501509,"journal":{"name":"medRxiv - Infectious Diseases","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929952","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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