Pub Date : 2024-10-21DOI: 10.1038/s43856-024-00643-3
Junyu Wang, Nikolaos Nikolaou, Matthias an der Heiden, Christopher Irrgang
Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels. Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate change scenarios. We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014–2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7–14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions. Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning. Heat is becoming a major cause of preventable deaths during the summer. We developed a computer model to estimate heat-related deaths at specific times and in different districts. Using this model for Germany, we estimate that over the past decade (2014–2023), around 48,000 deaths were heat-related, with most occurring during heatwaves. For example, a heatwave from July 7–14, 2023, contributed to 1100 out of 3900 heat-related deaths that year. Our model also suggests that, without adaptation, heat-related deaths in Germany could increase remarkably due to climate change. The insights from our model can help identify areas at high risk and support long-term public health planning to reduce the impact of heatwaves. Wang et al. developed a multi-scale machine learning model with high spatial and temporal resolution to estimate heat-related mortality in Germany. The model indicates that 48,000 deaths between 2014 and 2023 were heat related, and, without adaptation, climate change could increase heat-related mortality by 2.5 to 9 times by 2100.
{"title":"High-resolution modeling and projection of heat-related mortality in Germany under climate change","authors":"Junyu Wang, Nikolaos Nikolaou, Matthias an der Heiden, Christopher Irrgang","doi":"10.1038/s43856-024-00643-3","DOIUrl":"10.1038/s43856-024-00643-3","url":null,"abstract":"Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels. Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate change scenarios. We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014–2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7–14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions. Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning. Heat is becoming a major cause of preventable deaths during the summer. We developed a computer model to estimate heat-related deaths at specific times and in different districts. Using this model for Germany, we estimate that over the past decade (2014–2023), around 48,000 deaths were heat-related, with most occurring during heatwaves. For example, a heatwave from July 7–14, 2023, contributed to 1100 out of 3900 heat-related deaths that year. Our model also suggests that, without adaptation, heat-related deaths in Germany could increase remarkably due to climate change. The insights from our model can help identify areas at high risk and support long-term public health planning to reduce the impact of heatwaves. Wang et al. developed a multi-scale machine learning model with high spatial and temporal resolution to estimate heat-related mortality in Germany. The model indicates that 48,000 deaths between 2014 and 2023 were heat related, and, without adaptation, climate change could increase heat-related mortality by 2.5 to 9 times by 2100.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482297","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}
Pub Date : 2024-10-19DOI: 10.1038/s43856-024-00640-6
Prakasini Satapathy, Muhammad Aaqib Shamim, Bijaya K. Padhi, Aravind P. Gandhi, Mokanpally Sandeep, Tarun Kumar Suvvari, Jogender Kumar, Gunjeet Kaur, Joshuan J. Barboza, Patricia Schlagenhauf, Ranjit Sah
{"title":"Author Correction: Mpox virus infection in women and outbreak sex disparities: A Systematic Review and Meta-analysis","authors":"Prakasini Satapathy, Muhammad Aaqib Shamim, Bijaya K. Padhi, Aravind P. Gandhi, Mokanpally Sandeep, Tarun Kumar Suvvari, Jogender Kumar, Gunjeet Kaur, Joshuan J. Barboza, Patricia Schlagenhauf, Ranjit Sah","doi":"10.1038/s43856-024-00640-6","DOIUrl":"10.1038/s43856-024-00640-6","url":null,"abstract":"","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-1"},"PeriodicalIF":5.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482295","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}
In the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied “desmoplastic” tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics. We developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable, and non-responsive tumors to chemotherapy, immunotherapy, or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized. We present strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes. This approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes. Voutouri, Englezos et al. present a convolutional attention model utilizing ultrasound elastography for predicting chemo-immunotherapy responses in mouse tumors. Through training optimization on a large number of images, this approach highlights the potential of combining shear wave elastography with deep learning to enhance personalized cancer treatment. In personalized cancer treatment, it is important to understand that not all tumors respond the same way to therapy. While some patients may benefit from a particular treatment, others may not, leading to different outcomes. This study focuses on predicting how tumors will respond to a combination of chemotherapy and immunotherapy. Specifically, we looked at difficult-to-treat tumors with very stiff structures. These tumors can be softened with certain drugs making them more responsive to treatment. We developed a computer method to analyze medical images that measure the stiffness of tumors. Our method was trained on a large set of tumor images and was able to predict how well a tumor would respond to treatment. Overall, this approach could improve personalized cancer treatment using non-invasive medical imaging to predi
背景：在个性化癌症治疗时代，了解肿瘤的内在异质性至关重要。尽管一些患者对某种治疗方法反应良好，但另一些患者可能无法从中获益，从而导致标准疗法的疗效参差不齐。这项研究的重点是预测肿瘤对化疗免疫疗法的反应，探索肿瘤力学和医学成像作为预测性生物标志物的潜力。我们对 "去瘤细胞 "肿瘤进行了广泛研究，这种肿瘤的特点是基质致密且非常僵硬，给治疗带来了巨大挑战。通过机械治疗药物的药理干预，可以恢复此类肿瘤增加的硬度：方法：我们开发了一种基于剪切波弹性成像（SWE）图像的深度学习方法，该方法涉及一个用注意力模块增强的卷积神经网络（CNN）模型。该模型被开发并评估为一种预测性生物标记物，用于检测肿瘤对化疗、免疫疗法或机械疗法联合用药后的反应性、稳定性和非反应性。我们从之前的实验中获得了 630 个肿瘤的 1365 幅 SWE 图像数据集，并将其用于训练和成功评估我们的方法。SWE 与深度学习模型相结合，在疾病诊断和肿瘤分类方面取得了可喜的成果，但其在治疗前预测肿瘤反应方面的潜力尚未得到充分发挥：我们提出了强有力的证据，证明将 SWE 衍生的生物标记物与自动肿瘤分割算法相结合可实现准确的肿瘤检测和治疗效果预测：结论：这种方法可以通过提供非侵入性、可靠的疗效预测来加强个性化癌症治疗。
{"title":"A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models","authors":"Chrysovalantis Voutouri, Demetris Englezos, Constantinos Zamboglou, Iosif Strouthos, Giorgos Papanastasiou, Triantafyllos Stylianopoulos","doi":"10.1038/s43856-024-00634-4","DOIUrl":"10.1038/s43856-024-00634-4","url":null,"abstract":"In the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied “desmoplastic” tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics. We developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable, and non-responsive tumors to chemotherapy, immunotherapy, or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized. We present strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes. This approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes. Voutouri, Englezos et al. present a convolutional attention model utilizing ultrasound elastography for predicting chemo-immunotherapy responses in mouse tumors. Through training optimization on a large number of images, this approach highlights the potential of combining shear wave elastography with deep learning to enhance personalized cancer treatment. In personalized cancer treatment, it is important to understand that not all tumors respond the same way to therapy. While some patients may benefit from a particular treatment, others may not, leading to different outcomes. This study focuses on predicting how tumors will respond to a combination of chemotherapy and immunotherapy. Specifically, we looked at difficult-to-treat tumors with very stiff structures. These tumors can be softened with certain drugs making them more responsive to treatment. We developed a computer method to analyze medical images that measure the stiffness of tumors. Our method was trained on a large set of tumor images and was able to predict how well a tumor would respond to treatment. Overall, this approach could improve personalized cancer treatment using non-invasive medical imaging to predi","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-13"},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482294","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}
Pub Date : 2024-10-16DOI: 10.1038/s43856-024-00619-3
Westyn Branch-Elliman, Melissa Zeynep Ertem, Richard E. Nelson, Anseh Danesharasteh, David Berlin, Lloyd Fisher, Elissa M. Schechter-Perkins
During the 2021–22 academic year, Massachusetts supported several in-school testing programs to facilitate in-person learning. Additionally, COVID-19 vaccines became available to all school-aged children and many were infected with SARS-CoV-2. There are limited studies evaluating the impacts of these testing programs on SARS-CoV-2 cases in elementary and secondary school settings. The aim of this state-wide, retrospective cohort study was to assess the impact of testing programs and immunity on SARS-CoV-2 case rates in elementary and secondary students. Community-level vaccination and cumulative incidence rates were combined with data about participation in and results of in-school testing programs (test-to-stay, pooled surveillance testing). School-level impacts of surveillance testing programs on SARS-CoV-2 cases in students were estimated using generalized estimating equations within a target trial emulation approach stratified by school type (elementary/middle/high). Impacts of immunity and vaccination were estimated using random effects linear regression. Here we show that among N = 652,353 students at 2141 schools participating in in-school testing programs, surveillance testing is associated with a small but measurable decrease in in-school positivity rates. During delta, pooled testing positivity rates are higher in communities with higher cumulative incidence of infection. During omicron, when immunity from prior infection became more prevalent, the effect reversed, such that communities with lower burden of infection during the earlier phases of the pandemic had higher infection rates. Testing programs are an effective strategy for supporting in-person learning. Fluctuating levels of immunity acquired via natural infection or vaccination are a major determinant of SARS-CoV-2 cases in schools. During the height of the Covid-19 pandemic, multiple strategies were used to enable students to participate in in-person elementary and secondary schools. Little is known about the overall impact of prior immunity and in-person testing programs on the ability to maintain protection from Covid-19 in schools. This study, conducted in Massachusetts during the 2021-2022 academic year, found that community immunity gained through prior infection or vaccination, combined with testing strategies including testing programs to monitor infection and test to-stay modified quarantine programs, were safe and effective for allowing in-person learning. These data can be used to shape policy about in-school practices during future respiratory virus pandemics. Branch-Elliman et. al assess the impact of testing programs and immunity on SARS-CoV-2 case rates in elementary and secondary students in Massachusetts. They find that testing strategies are an effective intervention for supporting in-person learning and that immunity acquired from natural infection or vaccination mitigate COVID cases in schools.
{"title":"Impacts of testing and immunity acquired through vaccination and infection on covid-19 cases in Massachusetts elementary and secondary students","authors":"Westyn Branch-Elliman, Melissa Zeynep Ertem, Richard E. Nelson, Anseh Danesharasteh, David Berlin, Lloyd Fisher, Elissa M. Schechter-Perkins","doi":"10.1038/s43856-024-00619-3","DOIUrl":"10.1038/s43856-024-00619-3","url":null,"abstract":"During the 2021–22 academic year, Massachusetts supported several in-school testing programs to facilitate in-person learning. Additionally, COVID-19 vaccines became available to all school-aged children and many were infected with SARS-CoV-2. There are limited studies evaluating the impacts of these testing programs on SARS-CoV-2 cases in elementary and secondary school settings. The aim of this state-wide, retrospective cohort study was to assess the impact of testing programs and immunity on SARS-CoV-2 case rates in elementary and secondary students. Community-level vaccination and cumulative incidence rates were combined with data about participation in and results of in-school testing programs (test-to-stay, pooled surveillance testing). School-level impacts of surveillance testing programs on SARS-CoV-2 cases in students were estimated using generalized estimating equations within a target trial emulation approach stratified by school type (elementary/middle/high). Impacts of immunity and vaccination were estimated using random effects linear regression. Here we show that among N = 652,353 students at 2141 schools participating in in-school testing programs, surveillance testing is associated with a small but measurable decrease in in-school positivity rates. During delta, pooled testing positivity rates are higher in communities with higher cumulative incidence of infection. During omicron, when immunity from prior infection became more prevalent, the effect reversed, such that communities with lower burden of infection during the earlier phases of the pandemic had higher infection rates. Testing programs are an effective strategy for supporting in-person learning. Fluctuating levels of immunity acquired via natural infection or vaccination are a major determinant of SARS-CoV-2 cases in schools. During the height of the Covid-19 pandemic, multiple strategies were used to enable students to participate in in-person elementary and secondary schools. Little is known about the overall impact of prior immunity and in-person testing programs on the ability to maintain protection from Covid-19 in schools. This study, conducted in Massachusetts during the 2021-2022 academic year, found that community immunity gained through prior infection or vaccination, combined with testing strategies including testing programs to monitor infection and test to-stay modified quarantine programs, were safe and effective for allowing in-person learning. These data can be used to shape policy about in-school practices during future respiratory virus pandemics. Branch-Elliman et. al assess the impact of testing programs and immunity on SARS-CoV-2 case rates in elementary and secondary students in Massachusetts. They find that testing strategies are an effective intervention for supporting in-person learning and that immunity acquired from natural infection or vaccination mitigate COVID cases in schools.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00619-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443638","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}
Pub Date : 2024-10-16DOI: 10.1038/s43856-024-00614-8
Zeeshan H. Syedain, Matthew Lahti, Gurumurthy Hiremath, James Berry, John P. Carney, Jill Schappa Faustich, Tate Shannon, Andrea Rivera, Hadi Wiputra, Zhitian Shi, Richard Bianco, Robroy MacIver, John E. Mayer, Robert T. Tranquillo
Growth is the holy grail of tissue implants in pediatrics. No vascular graft currently in use for surgical repairs of congenital heart defects has somatic growth capacity. Biologically-engineered grafts (6 mm) grown from donor ovine fibroblasts in a sacrificial fibrin gel were implanted into the left pulmonary branch of 3-month old lambs for 3, 6, and 18 months. A control group of Propaten® PTFE grafts was implanted for 6 months. The engineered grafts exhibit extensive site-appropriate recellularization after only 3 months and near-normal increase of diameter from the preimplant value of 6 mm to 12.9 mm and also a doubling of length from 6.0 mm to 13.0 mm at 6 months (n = 3) associated with apparent somatic graft growth (collagen content increase of 265% over 18-month, n = 2), along with excellent hemodynamics and no calcification, in contrast to the Propaten® grafts. The left-right flow distribution is nearly 50–50 for the engineered grafts at 6 months (n = 3) compared to about 20–80 for the Propaten® grafts (n = 3), which have less than one-half the diameter, a 6-fold higher pressure gradient, and stunted vascular development downstream of the graft. The engineered grafts exhibit a stable diameter over months 12–18 when the lambs become adult sheep (n = 2). This study supports the use of these regenerative grafts with somatic growth capacity for clinical trial in patients born with a unilateral absent pulmonary artery branch, and it shows their potential for improving development of the downstream pulmonary vasculature. Blood vessel implants that are currently used to repair heart defects at birth do not grow with the child. This means that children need to have multiple open heart surgeries to replace implants with larger implants as they grow. We grew implants from a donor sheep’s skin cells, and then completely removed the cells from the graft. We then implanted the grafts in 3-month old lambs. The lambs’ cells repopulated the implants and the implants increased in size as the lambs grew. Further experiments are required first, but our preliminary findings suggest that using a similar implant in children could improve the quality of life of children with heart defects by avoiding the need for them to have multiple surgeries to replace implants as the child grows. Syedain et al. evaluate growth of biologically-engineered grafts grown from donor ovine fibroblasts in a sacrificial fibrin gel implanted into the left pulmonary branch of 3-month old lambs. The grafts exhibit extensive site-appropriate recellularization and increase in diameter and length until the lambs reach adulthood.
{"title":"Evaluation of an engineered vascular graft exhibiting somatic growth in lambs to model repair of absent pulmonary artery branch","authors":"Zeeshan H. Syedain, Matthew Lahti, Gurumurthy Hiremath, James Berry, John P. Carney, Jill Schappa Faustich, Tate Shannon, Andrea Rivera, Hadi Wiputra, Zhitian Shi, Richard Bianco, Robroy MacIver, John E. Mayer, Robert T. Tranquillo","doi":"10.1038/s43856-024-00614-8","DOIUrl":"10.1038/s43856-024-00614-8","url":null,"abstract":"Growth is the holy grail of tissue implants in pediatrics. No vascular graft currently in use for surgical repairs of congenital heart defects has somatic growth capacity. Biologically-engineered grafts (6 mm) grown from donor ovine fibroblasts in a sacrificial fibrin gel were implanted into the left pulmonary branch of 3-month old lambs for 3, 6, and 18 months. A control group of Propaten® PTFE grafts was implanted for 6 months. The engineered grafts exhibit extensive site-appropriate recellularization after only 3 months and near-normal increase of diameter from the preimplant value of 6 mm to 12.9 mm and also a doubling of length from 6.0 mm to 13.0 mm at 6 months (n = 3) associated with apparent somatic graft growth (collagen content increase of 265% over 18-month, n = 2), along with excellent hemodynamics and no calcification, in contrast to the Propaten® grafts. The left-right flow distribution is nearly 50–50 for the engineered grafts at 6 months (n = 3) compared to about 20–80 for the Propaten® grafts (n = 3), which have less than one-half the diameter, a 6-fold higher pressure gradient, and stunted vascular development downstream of the graft. The engineered grafts exhibit a stable diameter over months 12–18 when the lambs become adult sheep (n = 2). This study supports the use of these regenerative grafts with somatic growth capacity for clinical trial in patients born with a unilateral absent pulmonary artery branch, and it shows their potential for improving development of the downstream pulmonary vasculature. Blood vessel implants that are currently used to repair heart defects at birth do not grow with the child. This means that children need to have multiple open heart surgeries to replace implants with larger implants as they grow. We grew implants from a donor sheep’s skin cells, and then completely removed the cells from the graft. We then implanted the grafts in 3-month old lambs. The lambs’ cells repopulated the implants and the implants increased in size as the lambs grew. Further experiments are required first, but our preliminary findings suggest that using a similar implant in children could improve the quality of life of children with heart defects by avoiding the need for them to have multiple surgeries to replace implants as the child grows. Syedain et al. evaluate growth of biologically-engineered grafts grown from donor ovine fibroblasts in a sacrificial fibrin gel implanted into the left pulmonary branch of 3-month old lambs. The grafts exhibit extensive site-appropriate recellularization and increase in diameter and length until the lambs reach adulthood.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-12"},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00614-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439131","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}
Pub Date : 2024-10-14DOI: 10.1038/s43856-024-00631-7
Song Wang, Yiliang Zhou, Ziqiang Han, Cui Tao, Yunyu Xiao, Ying Ding, Joydeep Ghosh, Yifan Peng
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.
{"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":" ","pages":"1-13"},"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}
Pub Date : 2024-10-14DOI: 10.1038/s43856-024-00632-6
Joachim Labenz, Sebastian F. Schoppmann
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.
质子泵抑制剂（PPI）是胃食管反流病（GERD）推荐和使用的主要治疗方法。然而，在 PPI 难治性胃食管反流病患者中，有相当一部分人的症状无法得到控制，而 PPI 难治性胃食管反流病的定义是由于胃食管反流的客观发现而导致的持续症状。目前仍然缺乏专门的指南来指导这些患者的治疗，其中一些患者可能会从手术治疗中获益匪浅。患者往往长期接受无效治疗或停止治疗，缺乏积极的复查，导致他们的不满情绪被忽视。此外，手术治疗的疗效和副作用也会让患者和医生感到不安。有人认为，对 PPIs 的反应可预测手术结果。在这篇 "视角 "文章中，我们建议关键的决定因素应该是症状是否由胃食管反流病引起。我们还讨论了针对 PPI 难治性胃食管反流患者的传统和新型手术治疗方案。Labenz 和 Schoppmann 讨论了对质子泵抑制剂标准药物治疗产生耐药性的胃食管反流病患者的治疗方法。他们强调了问题的范围和各种治疗方案的原则，并重点介绍了适合患者的手术方案。
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Pub Date : 2024-10-11DOI: 10.1038/s43856-024-00594-9
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
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.
{"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":" ","pages":"1-5"},"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}
Pub Date : 2024-10-10DOI: 10.1038/s43856-024-00626-4
Nils Hentati Isacsson, Fehmi Ben Abdesslem, Erik Forsell, Magnus Boman, Viktor Kaldo
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.
背景：虽然心理治疗很有效，但相当一部分患者并没有从中获得足够的益处。及早发现这些患者，可以采取适应性治疗策略，改善治疗效果。我们旨在评估机器学习（ML）模型预测基于互联网的认知行为疗法结果的临床实用性，比较与 ML 相关的方法选择，并指导这些模型的未来使用：比较了 80 个主要模型。基线变量、每周症状和治疗活动用于预测6695名常规护理患者数据集的治疗结果：结果：我们发现，最好的模型是使用手工挑选的预测因子并对缺失数据进行补偿。没有一种 ML 算法显示出明显的优越性。它们在治疗第四周的平均平衡准确率为 78.1%，与回归法（77.8%）相差无几：结论：ML 超过了临床实用性基准（67%）。高级模型和简单模型表现相当，这表明需要更多的数据或更智能的方法设计来证实 ML 的优势。
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Pub Date : 2024-10-10DOI: 10.1038/s43856-024-00606-8
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
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.
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