Communications medicine最新文献

{"title":"A natural language processing approach to detect inconsistencies in death investigation notes attributing suicide circumstances","authors":"Song Wang, Yiliang Zhou, Ziqiang Han, Cui Tao, Yunyu Xiao, Ying Ding, Joydeep Ghosh, Yifan Peng","doi":"10.1038/s43856-024-00631-7","DOIUrl":"10.1038/s43856-024-00631-7","url":null,"abstract":"Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causing factors of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-circumstance attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify possible label errors. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. We measured annotation inconsistency by the degree of changes in the F-1 score. Our results show that incorporating the target state’s data into training the suicide-circumstance classifier brings an increase of 5.4% to the F-1 score on the target state’s test set and a decrease of 1.1% on other states’ test set. To conclude, we present an NLP framework to detect the annotation inconsistencies, show the effectiveness of identifying and rectifying possible label errors, and eventually propose an improvement solution to improve the coding consistency of human annotators. Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) contains the recording of individual suicide incidents taking place in the United States, and the contributing suicide circumstances. We used a computational method to check the accuracy of NVDRS records. Our method identified and rectified possible errors in labeling within the database. This method could be used to improve the label accuracy in the NVDRS database, enabling more accurate recording and study of suicide circumstances. Improved data recording of suicide circumstances could potentially be used to develop improved approaches to prevent suicide in the future. Wang et al. use a Natural Language Processing approach to detect suicide-circumstance annotation inconsistencies in death investigation notes. They identify possible label errors, show the effectiveness of identifying and rectifying possible label errors, and propose a coding consistency improvement solution.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00631-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving treatment of people with gastro-esophageal reflux disease refractory to proton pump inhibitors","authors":"Joachim Labenz, Sebastian F. Schoppmann","doi":"10.1038/s43856-024-00632-6","DOIUrl":"10.1038/s43856-024-00632-6","url":null,"abstract":"Proton pump inhibitors (PPIs) are the main treatment recommended and used for gastro-esophageal reflux disease (GERD). However, they fail to control symptoms in a substantial proportion of patients who have PPI-refractory GERD, which is defined as persistent symptoms attributable to objective findings of gastro-esophageal reflux. There remains a lack of dedicated guidelines to direct the management of these patients, some of whom could benefit greatly from surgical treatment. Too often patients remain long-term on ineffective treatment or stop treatment with lack of active review often resulting in their dissatisfaction going unnoticed. Also, concerns over efficacy and side effects of surgical procedures can be off-putting for both patients and physicians. It has been suggested that response to PPIs is predictive of surgical outcome. In this Perspective article we instead recommend that the key determinant should be whether symptoms are caused by GERD. We also discuss the traditional and newer surgical treatment options for people with PPI-refractory GERD. Labenz and Schoppmann discuss the approach to treatment for patients with gastro-esophageal reflux disease that is resistant to standard medical treatment with proton pump inhibitors. They highlight the scope of the problem and the principles of various treatment options with a focus on surgical options, in appropriate patients.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00632-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Antimicrobial drug pricing","authors":"Avaneesh Kumar Pandey, Nusrat Shafiq, Ashish Kumar Kakkar, Samir Malhotra, Beth Woods, Christopher Little, Tom Rhodes, Harriet Tuson, Zeshan Riaz, Tom Ashfield, Michael Corley, Ioannis Baltas","doi":"10.1038/s43856-024-00594-9","DOIUrl":"10.1038/s43856-024-00594-9","url":null,"abstract":"Despite the constant development of antimicrobial resistance (AMR), few new antimicrobials are currently becoming available clinically. Alternative approaches, such as different mechanisms to fund their use, are being explored to encourage development of new antimicrobials.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00594-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy","authors":"Nils Hentati Isacsson, Fehmi Ben Abdesslem, Erik Forsell, Magnus Boman, Viktor Kaldo","doi":"10.1038/s43856-024-00626-4","DOIUrl":"10.1038/s43856-024-00626-4","url":null,"abstract":"While psychological treatments are effective, a substantial portion of patients do not benefit enough. Early identification of those may allow for adaptive treatment strategies and improved outcomes. We aimed to evaluate the clinical usefulness of machine-learning (ML) models predicting outcomes in Internet-based Cognitive Behavioural Therapy, to compare ML-related methodological choices, and guide future use of these. Eighty main models were compared. Baseline variables, weekly symptoms, and treatment activity were used to predict treatment outcomes in a dataset of 6695 patients from regular care. We show that the best models use handpicked predictors and impute missing data. No ML algorithm shows clear superiority. They have a mean balanced accuracy of 78.1% at treatment week four, closely matched by regression (77.8%). ML surpasses the benchmark for clinical usefulness (67%). Advanced and simple models perform equally, indicating a need for more data or smarter methodological designs to confirm advantages of ML. While there are many therapy treatments that are effective for mental health problems some patients don’t benefit enough. Predicting whom might need more help can guide therapists to adjust treatments for better results. Computer methods are increasingly used for predicting the outcome of treatment, but studies vary widely in accuracy and methodology. We examined a variety of models to test performance. Those examined were based on a several factors: what data is chosen, how the data is managed, as well as type of mathematical equations and function used for prediction. When used on ~6500 patients, none of the computer methods tested stood out as the best. Simple models were as accurate as more advanced. Accuracy of prediction of treatment outcome was good enough to inform clinicians’ decisions, suggesting they may still be useful tools in mental health care. Hentati Isacsson et al. investigate and compare several data preprocessing and machine learning approaches to predict treatment outcomes in internet-delivered cognitive behavioural therapy. Despite indications that no algorithm or method examined shows clear superiority, results still suggest promise for clinical implementations.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting future hospital antimicrobial resistance prevalence using machine learning","authors":"Karina-Doris Vihta, Emma Pritchard, Koen B. Pouwels, Susan Hopkins, Rebecca L. Guy, Katherine Henderson, Dimple Chudasama, Russell Hope, Berit Muller-Pebody, Ann Sarah Walker, David Clifton, David W. Eyre","doi":"10.1038/s43856-024-00606-8","DOIUrl":"10.1038/s43856-024-00606-8","url":null,"abstract":"Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April–March) for 22 pathogen–antibiotic combinations (FY2016-2017 to FY2021-2022). Extreme Gradient Boosting (XGBoost) model predictions were compared to the previous value taken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability. Here we show that XGBoost models achieve the best predictive performance. Relatively limited year-to-year variability in AMR prevalence within Trust–pathogen–antibiotic combinations means previous value taken forwards also achieves a low mean absolute error (MAE), similar to or slightly higher than XGBoost. Using the difference between the previous two years taken forward or LTF performs consistently worse. XGBoost considerably outperforms all other methods in Trusts with a larger change in AMR prevalence from FY2020-2021 (last training year) to FY2021-2022 (held-out test set). Feature importance values indicate that besides historical resistance to the same pathogen–antibiotic combination as the outcome, complex relationships between resistance in different pathogens to the same antibiotic/antibiotic class and usage are exploited for predictions. These are generally among the top ten features ranked according to their mean absolute SHAP values. Year-to-year resistance has generally changed little within Trust–pathogen–antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions. Antibiotics play an important role in treating serious bacterial infections. However, with the increased usage of antibiotics, they are becoming less effective. In our study, we use machine learning to learn from past antibiotic resistance and usage in order to predict what resistance will look like in the future. Different hospitals across England have very different resistance levels, however, within each hospital, these levels remain stable over time. When larger changes in resistance occurred over time in individual hospitals, our methods were able to predict these. Understanding how much resistance there is in hospital populations, and what may occur in the future can help determine where resources and interventions should be directed. Vihta et al. use past hospital data including bloodstream infection cases, susceptibilities, and antimicrobial use to predict future resistance prevalence. Machine learning can improve the accuracy of predictions potentially impacting interventions.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00606-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing digital equity to promote online smoking cessation program engagement among rural adults: a randomized controlled pilot trial","authors":"Sydney Kelpin, Tabetha A. Brockman, Paul A. Decker, Antonia Young, Kasey Boehmer, Aaron Nguyen, Celia Kamath, Jennifer St. Sauver, Pamela S. Sinicrope, Pravesh Sharma, Rozalina McCoy, Summer Allen, Ming Huang, Joshua Pritchett, Dmitry Esterov, Michelle Lampman, Carolyn Petersen, Andrea Cheville, Christi A. Patten","doi":"10.1038/s43856-024-00624-6","DOIUrl":"10.1038/s43856-024-00624-6","url":null,"abstract":"Cigarette smoking prevalence is higher for rural than urban adults, yet digital access to cessation programming is reduced. We aim to investigate digital access interventions to promote engagement with an online evidence-based cessation treatment (EBCT) program among rural adults. This pilot trial used a pragmatic, three-arm, randomized, parallel-group design (ClinicalTrials.gov: NCT05209451). Inclusion criteria included being aged ≥18, Mayo Clinic Midwest patient, rural residency, and currently smokes cigarettes. All participants received an online, 12-week EBCT program and were randomized to receive one of three digital access interventions: print materials (control, n = 30); print materials + loaner iPad device with data plan coverage (n = 30); or print materials + loaner device + up to six, 15–20-minute motivational interviewing-based coaching calls to support technology needs (n = 30). A composite score of trial engagement (primary outcome) and self-reported smoking abstinence and use of EBCT resources (secondary outcomes) were assessed online at 4 and 12 weeks. Qualitative interviews were used to assess patient experience. Neither participants nor outcome assessors were blinded to group assignment. Results are reported for all 30 participants in each group. The average age of participants is 51.0 years and 61% are women. We show no significant arm differences for the trial engagement composite score (p = 0.30). We also find coaching support is significantly (p < 0.05) associated with enhanced smoking-related treatment response, including cigarette abstinence and use of EBCT resources, and participants reported positive experiences with the intervention. The coaching intervention to support technology needs is acceptable and shows preliminary evidence of its efficacy in smoking-related treatment response. Further studies could refine and implement the coaching intervention for trial engagement and long-term cessation. More adults smoke cigarettes in rural than urban adults, yet there is less access to effective digital tools to assist people to quit smoking in rural areas. We compared three strategies to promote engagement with an online program to encourage rural adults to quit smoking. The three interventions were: print materials; print materials plus a loaner iPad device with data plan coverage; or print materials and loaner device, plus up to six, 15–20-minute individualized coaching calls to support technology needs. We found that individuals receiving the coaching support had improved smoking-related outcomes. Our study provides a better understanding of the resources needed to support digital access in rural populations and the results could be used to improve approaches to encourage people living in rural areas to stop smoking. Kelpin et al. present results from a randomized controlled pilot trial comparing 3 digital access and literacy intervention strategies for enhancing access to online evidence-based smoking cessation treatm","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disease coverage of human genome-wide association studies and pharmaceutical research and development","authors":"María Gordillo-Marañón, Amand F. Schmidt, Alasdair Warwick, Chris Tomlinson, Cai Ytsma, Jorgen Engmann, Ana Torralbo, Rory Maclean, Reecha Sofat, Claudia Langenberg, Anoop D. Shah, Spiros Denaxas, Munir Pirmohamed, Harry Hemingway, Aroon D. Hingorani, Chris Finan","doi":"10.1038/s43856-024-00625-5","DOIUrl":"10.1038/s43856-024-00625-5","url":null,"abstract":"Despite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear. In this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts. We show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development. We produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication. The pharma industry has shown growing interest in the use of human genomic data to support drug development and reduce the risk of clinical-stage failure. We investigate the extent to which human diseases have been the subject of genetic studies, of pharmaceutical research and development, or both. We show that only a small proportion of all human diseases have an approved drug treatment and that less than half of all the diseases that are the subject of preclinical or clinical phase drug development have been investigated in genetic studies. In addition, approximately two-thirds of the diseases covered in genetic studies have yet to be investigated in drug development. These findings could help prioritize drug development efforts or genetic studies for diseases without effective treatments. Gordillo-Marañón et al. identify areas of convergence or divergence between genome-wide association studies (GWAS) and pharmaceutical research and development. They provide target-disease indication lists to prioritize drug development efforts in areas with genomic support, and diseases with few effective treatments that could benefit from GWAS.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A register-based cohort study on the effectiveness and Safety of anti-PCSK9 treatment in persons with hyperlipidemia","authors":"Michael Asger Andersen, Anne Helms Andreasen, Lia Evi Bang, Espen Jimenez-Solem, Tonny Studsgaard Petersen","doi":"10.1038/s43856-024-00611-x","DOIUrl":"10.1038/s43856-024-00611-x","url":null,"abstract":"Dyslipidemia is a known risk factor for cardiovascular disease. While statins are the primary treatment, some individuals require additional lipid-lowering therapies, such as proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Alirocumab and evolocumab have shown efficacy in reducing low-density lipoprotein cholesterol (LDL-C) levels and reduce the risk of major cardiovascular events (MACE) but have not been directly compared in clinical trials. This study aims to assess the effects of PCSK9 inhibitors on LDL-C levels and evaluate the impact of a mandated switch from alirocumab to evolocumab. Taking advantage of the mandated switch in PCSK9 treatment in Denmark, we conducted a register-based cohort study of 907 individuals with dyslipidemia treated with PCSK9 inhibitors in the Capital Region of Denmark from 2016 to 2022. We analyzed LDL-C levels, treatment retention, and MACE, adjusting for variables such as age, sex, dose, and concurrent lipid-lowering medications. We show that PCSK9 inhibitors treatment resulted in a 49% reduction in LDL-C levels. Following a mandated switch from alirocumab to evolocumab, no significant difference was observed in LDL-C levels or adverse clinical outcomes, including MACE. Treatment discontinuation was most likely within the first 100 days, and no significant difference in discontinuation rates was found between the two drugs. Our study demonstrates that both alirocumab and evolocumab are effective in significantly reducing LDL-C levels in individuals with dyslipidemia. The mandated switch from alirocumab to evolocumab did not result in significant changes in LDL-C or clinical outcomes, suggesting that these treatments can be used interchangeably. These findings support the clinical equivalence of the two PCSK9 inhibitors and may guide therapeutic decisions in lipid management. Andersen et al. evaluate the effectiveness and safety of PCSK9 inhibitors, alirocumab, and evolocumab, in a register-based cohort of 907 individuals with dyslipidemia. Their findings show a 49% reduction in LDL-C levels and no significant changes in clinical outcomes following a mandated switch from alirocumab to evolocumab. Dyslipidemia is a condition where there are unhealthy levels of fats, i.e. cholesterol (LDL-C and HDL-C), in the blood, which can increase the risk of heart disease. To help manage this, certain drugs called PCSK9 inhibitors, like alirocumab and evolocumab, can be used to lower cholesterol levels. Here, we studied 907 people in Denmark who were treated with these drugs between 2016 and 2022. We found that these drugs reduced LDL-C by almost half. During the study, some people were required to switch from one drug (alirocumab) to another (evolocumab), and this switch did not lead to any changes in cholesterol levels or increase the risk of heart problems or death. Our findings suggest that both alirocumab and evolocumab are safe and effective options for lowering cholesterol, and they can be used interchan","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twin simulation modelling shows that mass testing and local lockdowns effectively controlled COVID-19 in Denmark","authors":"Kaare Græsbøll, Rasmus Skytte Eriksen, Carsten Kirkeby, Lasse Engbo Christiansen","doi":"10.1038/s43856-024-00621-9","DOIUrl":"10.1038/s43856-024-00621-9","url":null,"abstract":"Following the COVID-19 pandemic, it is important to evaluate different mitigation strategies for future preparedness. Mass testing and local lockdowns were employed during the Alpha wave in Denmark, which led to ten times more tests than the typical European member state and incidence-based restrictions at the parish level. This study aims to quantify the effects of these interventions in terms of hospital admissions and societal freedom. This study assesses the effectiveness of these strategies via counterfactual scenarios using a detailed, individual-based simulation model that replicates the entire Danish population. The model considers multiple factors, including evolving societal restrictions, vaccination roll-out, seasonal influences, and varying intensities of PCR and antigen testing across different age groups and degree of completed vaccination. It also integrates adaptive human behavior in response to changes in incidences at the municipality and parish levels. The simulations show, that without mass testing in Denmark, there would have been a 150% increase in hospital admissions, and additional local lockdowns equivalent to 21 days of strict national lockdown. Without the policy of local lockdowns, hospitalizations would have increased by 50%. In conclusion, the combination of mass testing and local lockdowns likely prevented a large increase in hospitalizations while increasing overall societal freedom during the Alpha wave in Denmark. In future epidemics, mass testing and local lockdowns can likely prevent overwhelming healthcare systems in phases of high transmission and hospitalization risks. This study looked at how Denmark handled the COVID-19 pandemic, specifically focusing on mass testing and local lockdowns during the Alpha-wave. Compared to other European countries, Denmark conducted ten times more tests and implemented restrictions at the parish level based on local incidence. Using a detailed simulation model, the researchers explored what would have happened without these measures. Without mass testing, hospital admissions would have increased by 150%, and without local lockdowns, they would have gone up by 50%. Furthermore, mass testing prevented 21 days of strict national lockdown. In essence, mass testing and local lockdowns in Denmark prevented a substantial rise in hospitalizations while allowing more overall societal freedom. This highlights the importance of these strategies for future pandemic preparedness. Græsbøll et al. use a detailed, individual-based simulation model of SARS-CoV-2 transmission to evaluate mass testing and local lockdowns during the Alpha wave in Denmark in counterfactual scenarios. The model predicts that the combination of these measures prevented a large increase in hospitalizations while increasing overall societal freedom.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parasagittal dural volume correlates with cerebrospinal fluid volume and developmental delay in children with autism spectrum disorder","authors":"Nivedita Agarwal, Giulia Frigerio, Gloria Rizzato, Tommaso Ciceri, Elisa Mani, Fabiola Lanteri, Massimo Molteni, Roxana O. Carare, Letizia Losa, Denis Peruzzo","doi":"10.1038/s43856-024-00622-8","DOIUrl":"10.1038/s43856-024-00622-8","url":null,"abstract":"The parasagittal dura, a tissue that lines the walls of the superior sagittal sinus, acts as an active site for immune-surveillance, promotes the reabsorption of cerebrospinal fluid, and facilitates the removal of metabolic waste products from the brain. Cerebrospinal fluid is important for the distribution of growth factors that signal immature neurons to proliferate and migrate. Autism spectrum disorder is characterized by altered cerebrospinal fluid dynamics. In this retrospective study, we investigated potential correlations between parasagittal dura volume, brain structure volumes, and clinical severity scales in young children with autism spectrum disorder. We employed a semi-supervised two step pipeline to extract parasagittal dura volume from 3D-T2 Fluid Attenuated Inversion Recovery sequences, based on U-Net followed by manual refinement of the extracted parasagittal dura masks. Here we show that the parasagittal dura volume does not change with age but is significantly correlated with cerebrospinal fluid (p-value = 0.002), extra-axial cerebrospinal fluid volume (p-value = 0.0003) and severity of developmental delay (p-value = 0.024). These findings suggest that autism spectrum disorder children with severe developmental delay may have a maldeveloped parasagittal dura that potentially perturbs cerebrospinal fluid dynamics. Cerebrospinal fluid (CSF) is produced in the brain. It is a medium of transport for neural growth factors and waste products. CSF is drained out of the brain through multiple pathways, one of them being the recently identified parasagittal dura (PSD) which also plays a role in the immune system within the brain. We estimated the PSD volume in children with autism spectrum disorder (ASD) and found the volume was associated with the amount of CSF in the brain. We also found that the PSD volume is smaller in children who have severe forms of developmental delay. Our findings suggest problems in the development of the PSD could have in impact on brain development and waste removal in children with ASD. More research in this area could enable a better understanding of the underlying causes of ASD. Agarwal et al. apply a semi-supervised 2D U-Net to extract the volume of parasagittal dura in young kids with autism spectrum disorder. Positive correlation with cerebrospinal fluid volume and a negative correlation with severity of developmental delay suggests perturbed fluid dynamics.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}