Effectiveness of Atypical Antipsychotic Drugs in Patients with Alzheimer's Disease

BACKGROUND Second-generation (atypical) antipsychotic drugs are widely used to treat psychosis, aggression, and agitation in patients with Alzheimer's disease, but their benefits are uncertain and concerns about safety have emerged. We assessed the effectiveness of atypical antipsychotic drugs in outpatients with Alzheimer's disease. METHODS In this 42-site, double-blind, placebo-controlled trial, 421 outpatients with Alzheimer's disease and psychosis, aggression, or agitation were randomly assigned to receive olanzapine (mean dose, 5.5 mg per day), quetiapine (mean dose, 56.5 mg per day), risperidone (mean dose, 1.0 mg per day), or placebo. Doses were adjusted as needed, and patients were followed for up to 36 weeks. The main outcomes were the time from initial treatment to the discontinuation of treatment for any reason and the number of patients with at least minimal improvement on the Clinical Global Impression of Change (CGIC) scale at 12 weeks. RESULTS There were no significant differences among treatments with regard to the time to the discontinuation of treatment for any reason: olanzapine (median, 8.1 weeks), quetiapine (median, 5.3 weeks), risperidone (median, 7.4 weeks), and placebo (median, 8.0 weeks) (P=0.52). The median time to the discontinuation of treatment due to a lack of efficacy favored olanzapine (22.1 weeks) and risperidone (26.7 weeks) as compared with quetiapine (9.1 weeks) and placebo (9.0 weeks) (P=0.002). The time to the discontinuation of treatment due to adverse events or intolerability favored placebo. Overall, 24% of patients who received olanzapine, 16% of patients who received quetiapine, 18% of patients who received risperidone, and 5% of patients who received placebo discontinued their assigned treatment owing to intolerability (P=0.009). No significant differences were noted among the groups with regard to improvement on the CGIC scale. Improvement was observed in 32% of patients assigned to olanzapine, 26% of patients assigned to quetiapine, 29% of patients assigned to risperidone, and 21% of patients assigned to placebo (P=0.22). CONCLUSIONS Adverse effects offset advantages in the efficacy of atypical antipsychotic drugs for the treatment of psychosis, aggression, or agitation in patients with Alzheimer's disease

The role of TG2 in regulating S100A4-mediated mammary tumour cell migration

The importance of S100A4, a Ca2+-binding protein, in mediating tumour cell migration, both intracellularly and extracellularly, is well documented. Tissue transglutaminase (TG2) a Ca2+-dependent protein crosslinking enzyme, has also been shown to enhance cell migration. Here by using the well characterised non-metastatic rat mammary R37 cells (transfected with empty vector) and highly metastatic KP1 cells (R37 cells transfected with S100A4), we demonstrate that inhibition of TG2 either by TG2 inhibitors or transfection of cells with TG2 shRNA block S100A4-accelerated cell migration in the KP1cells and in R37 cells treated with exogenous S100A4. Cell migration was also blocked by the treatment with the non-cell permeabilizing TG2 inhibitor R294, in the human breast cancer cell line MDA-MB-231 (Clone 16, which has a high level of TG2 expression). Inhibition was paralleled by a decrease in S100A4 polymer formation. co-immunoprecipitation and Far Western blotting assays and cross-linking assays showed not only the direct interaction between TG2 and S100A4, but also confirmed S100A4 as a substrate for TG2. Using specific functional blocking antibodies, a targeting peptide and a recombinant protein as a competitive treatment, we revealed the involvement of syndecan-4 and a5ß1 integrin co-signalling pathways linked by activation of PKCa in this TG2 and S100A4-mediated cell migration. We propose a mechanism for TG2-regulated S100A4-related mediated cell migration, which is dependent on TG2 crosslinking

A systematic review and meta-analysis to determine the contribution of mr imaging to the diagnosis of foetal brain abnormalities In Utero.

OBJECTIVES: This systematic review was undertaken to define the diagnostic performance of in utero MR (iuMR) imaging when attempting to confirm, exclude or provide additional information compared with the information provided by prenatal ultrasound scans (USS) when there is a suspicion of foetal brain abnormality. METHODS: Electronic databases were searched as well as relevant journals and conference proceedings. Reference lists of applicable studies were also explored. Data extraction was conducted by two reviewers independently to identify relevant studies for inclusion in the review. Inclusion criteria were original research that reported the findings of prenatal USS and iuMR imaging and findings in terms of accuracy as judged by an outcome reference diagnosis for foetal brain abnormalities. RESULTS: 34 studies met the inclusion criteria which allowed diagnostic accuracy to be calculated in 959 cases, all of which had an outcome reference diagnosis determined by postnatal imaging, surgery or autopsy. iuMR imaging gave the correct diagnosis in 91 % which was an increase of 16 % above that achieved by USS alone. CONCLUSION: iuMR imaging makes a significant contribution to the diagnosis of foetal brain abnormalities, increasing the diagnostic accuracy achievable by USS alone. KEY POINTS: • Ultrasound is the primary modality for monitoring foetal brain development during pregnancy • iuMRI used together with ultrasound is more accurate for detecting foetal brain abnormalities • iuMR imaging is most helpful for detecting midline brain abnormalities • The moderate heterogeneity of reviewed studies may compromise findings

Systems and algorithms for wireless sensor networks based on animal and natural behavior

In last decade, there have been many research works about wireless sensor networks (WSNs) focused on improving the network performance as well as increasing the energy efficiency and communications effectiveness. Many of these new mechanisms have been implemented using the behaviors of certain animals, such as ants, bees, or schools of fish.These systems are called bioinspired systems and are used to improve aspects such as handling large-scale networks, provide dynamic nature, and avoid resource constraints, heterogeneity, unattended operation, or robustness, amongmanyothers.Therefore, thispaper aims to studybioinspired mechanisms in the field ofWSN, providing the concepts of these behavior patterns in which these new approaches are based. The paper will explain existing bioinspired systems in WSNs and analyze their impact on WSNs and their evolution. In addition, we will conduct a comprehensive review of recently proposed bioinspired systems, protocols, and mechanisms. Finally, this paper will try to analyze the applications of each bioinspired mechanism as a function of the imitated animal and the deployed application. Although this research area is considered an area with highly theoretical content, we intend to show the great impact that it is generating from the practical perspective.Sendra, S.; Parra Boronat, L.; Lloret, J.; Khan, S. (2015). Systems and algorithms for wireless sensor networks based on animal and natural behavior. International Journal of Distributed Sensor Networks. 2015:1-19. doi:10.1155/2015/625972S1192015Iram, R., Sheikh, M. I., Jabbar, S., & Minhas, A. A. (2011). Computational intelligence based optimization in wireless sensor network. 2011 International Conference on Information and Communication Technologies. doi:10.1109/icict.2011.5983561Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. 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Ant-Based Transmission Range Assignment Scheme for Energy Hole Problem in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 8(12), 290717. doi:10.1155/2012/290717Riva, G., & Finochietto, J. M. (2012). Pheromone-based In-Network Processing for Wireless Sensor Network Monitoring Systems. Network Protocols and Algorithms, 4(4). doi:10.5296/npa.v4i4.2206Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Kim, J.-Y., Sharma, T., Kumar, B., Tomar, G. S., Berry, K., & Lee, W.-H. (2014). Intercluster Ant Colony Optimization Algorithm for Wireless Sensor Network in Dense Environment. International Journal of Distributed Sensor Networks, 10(4), 457402. doi:10.1155/2014/457402Dressler, F., & Akan, O. B. (2010). A survey on bio-inspired networking. 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The International Conference on Information Network 2012. doi:10.1109/icoin.2012.6164367Chen, G., Guo, T.-D., Yang, W.-G., & Zhao, T. (2006). An improved ant-based routing protocol in Wireless Sensor Networks. 2006 International Conference on Collaborative Computing: Networking, Applications and Worksharing. doi:10.1109/colcom.2006.361893Okdem, S., & Karaboga, D. (2006). Routing in Wireless Sensor Networks Using Ant Colony Optimization. First NASA/ESA Conference on Adaptive Hardware and Systems (AHS’06). doi:10.1109/ahs.2006.63Salehpour, A.-A., Mirmobin, B., Afzali-Kusha, A., & Mohammadi, S. (2008). An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization. 2008 International Conference on Innovations in Information Technology. doi:10.1109/innovations.2008.4781748Wen, Y., Chen, Y., & Pan, M. (2008). Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics. Journal of Zhejiang University-SCIENCE A, 9(4), 531-538. doi:10.1631/jzus.a071382Liao, W.-H., Kao, Y., & Wu, R.-T. (2011). Ant colony optimization based sensor deployment protocol for wireless sensor networks. Expert Systems with Applications, 38(6), 6599-6605. doi:10.1016/j.eswa.2010.11.079Pavai, K., Sivagami, A., & Sridharan, D. (2009). Study of Routing Protocols in Wireless Sensor Networks. 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies. doi:10.1109/act.2009.133Juan, L., Chen, S., & Chao, Z. (2007). Ant System Based Anycast Routing in Wireless Sensor Networks. 2007 International Conference on Wireless Communications, Networking and Mobile Computing. doi:10.1109/wicom.2007.603Wang, C., & Lin, Q. (2008). Swarm intelligence optimization based routing algorithm for Wireless Sensor Networks. 2008 International Conference on Neural Networks and Signal Processing. doi:10.1109/icnnsp.2008.4590326Jiang, H., Wang, M., Liu, M., & Yan, J. (2012). A quantum-inspired ant-based routing algorithm for WSNs. Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD). doi:10.1109/cscwd.2012.6221881Okazaki, A. M., & Frohlich, A. A. (2011). Ant-based Dynamic Hop Optimization Protocol: A routing algorithm for Mobile Wireless Sensor Networks. 2011 IEEE GLOBECOM Workshops (GC Wkshps). doi:10.1109/glocomw.2011.6162356Hui, X., Zhigang, Z., & Xueguang, Z. (2009). A Novel Routing Protocol in Wireless Sensor Networks Based on Ant Colony Optimization. 2009 International Conference on Environmental Science and Information Application Technology. doi:10.1109/esiat.2009.460AbdelSalam, H. S., & Olariu, S. (2012). BEES: BioinspirEd backbonE Selection in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 23(1), 44-51. doi:10.1109/tpds.2011.100Da Silva Rego, A., Celestino, J., dos Santos, A., Cerqueira, E. C., Patel, A., & Taghavi, M. (2012). BEE-C: A bio-inspired energy efficient cluster-based algorithm for data continuous dissemination in Wireless Sensor Networks. 2012 18th IEEE International Conference on Networks (ICON). doi:10.1109/icon.2012.6506592Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. N. (2012). Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), 965-997. doi:10.1007/s10462-012-9342-2Antoniou, P., Pitsillides, A., Blackwell, T., & Engelbrecht, A. (2009). Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks. 2009 IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2009.4983153Ruihua, Z., Zhiping, J., Xin, L., & Dongxue, H. (2011). Double cluster-heads clustering algorithm for wireless sensor networks using PSO. 2011 6th IEEE Conference on Industrial Electronics and Applications. doi:10.1109/iciea.2011.5975688Kulkarni, R. V., Venayagamoorthy, G. K., & Cheng, M. X. (2009). Bio-inspired node localization in wireless sensor networks. 2009 IEEE International Conference on Systems, Man and Cybernetics. doi:10.1109/icsmc.2009.5346107Kulkarni, R. V., & Venayagamoorthy, G. K. (2010). Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 663-675. doi:10.1109/tsmcc.2010.2049649Xin Song, Cuirong Wang, Wang, J., & Bin Zhang. (2010). A hierarchical routing protocol based on AFSO algorithm for WSN. 2010 International Conference On Computer Design and Applications. doi:10.1109/iccda.2010.5541265Gao, X. Z., Wu, Y., Zenger, K., & Huang, X. (2010). A Knowledge-Based Artificial Fish-Swarm Algorithm. 2010 13th IEEE International Conference on Computational Science and Engineering. doi:10.1109/cse.2010.49Wang, L., & Ma, L. (2011). A hybrid artificial fish swarm algorithm for Bin-packing problem. Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. doi:10.1109/emeit.2011.6022829Yiyue, W., Hongmei, L., & Hengyang, H. (2012). Wireless Sensor Network Deployment Using an Optimized Artificial Fish Swarm Algorithm. 2012 International Conference on Computer Science and Electronics Engineering. doi:10.1109/iccsee.2012.453Yang, X.-S. (2010). A New Metaheuristic Bat-Inspired Algorithm. Studies in Computational Intelligence, 65-74. doi:10.1007/978-3-642-12538-6_6Goyal, S., & Patterh, M. S. (2013). Performance of BAT Algorithm on Localization of Wireless Sensor Network. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 6(3), 351-358. doi:10.24297/ijct.v6i3.4481Krishnanand, K. N., & Ghose, D. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3), 209-222. doi:10.3233/mgs-2006-2301Apostolopoulos, T., & Vlachos, A. (2011). Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem. International Journal of Combinatorics, 2011, 1-23. doi:10.1155/2011/523806Liao, W.-H., Kao, Y., & Li, Y.-S. (2011). A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Systems with Applications, 38(10), 12180-12188. doi:10.1016/j.eswa.2011.03.053Sun, Y., Jiang, Q., & Zhang, K. (2012). A clustering scheme for Reachback Firefly Synchronicity in wireless sensor networks. 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content. doi:10.1109/icnidc.2012.6418705Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Termite-Hill. International Journal of Swarm Intelligence Research, 3(4), 1-22. doi:10.4018/jsir.2012100101KumarE, S., S. M., K., & Kumar B. P., V. (2014). Clustering Protocol for Wireless Sensor Networks based on Rhesus Macaque (Macaca mulatta) Animal's Social Behavior. International Journal of Computer Applications, 87(8), 20-27. doi:10.5120/15229-3754Breza, M., & McCann, J. A. (2008). Lessons in Implementing Bio-inspired Algorithms on Wireless Sensor Networks. 2008 NASA/ESA Conference on Adaptive Hardware and Systems. doi:10.1109/ahs.2008.72Aziz, N. A. B. A., Mohemmed, A. W., & Sagar, B. S. D. (2007). Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization. 2007 International Conference on Intelligent and Advanced Systems. doi:10.1109/icias.2007.4658528Falcon, R., Li, X., Nayak, A., & Stojmenovic, I. (2012). A harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots. 2012 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2012.6363859Antoniou, P., & Pitsillides, A. (2010). A bio-inspired approach for streaming applications in wireless sensor networks based on the Lotka–Volterra competition model. Computer Communications, 33(17), 2039-2047. doi:10.1016/j.comcom.2010.07.020Benahmed, K., Merabti, M., & Haffaf, H. (2012). Inspired Social Spider Behavior for Secure Wireless Sensor Networks. International Journal of Mobile Computing and Multimedia Communications, 4(4), 1-10. doi:10.4018/jmcmc.2012100101Alrajeh, N. A., & Lloret, J. (2013). Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(10), 351047. doi:10.1155/2013/351047Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic Algorithm for Hierarchical Wireless Sensor Networks. Journal of Networks, 2(5). doi:10.4304/jnw.2.5.87-97Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks. Fourth International Conference on Information Technology (ITNG’07). doi:10.1109/itng.2007.97Ferentinos, K. P., & Tsiligiridis, T. A. (2007). Adaptive design optimization of wireless sensor networks using genetic algorithms. Computer Networks, 51(4), 1031-1051. doi:10.1016/j.comnet.2006.06.013Jia, J., Chen, J., Chang, G., & Tan, Z. (2009). Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Computers & Mathematics with Applications, 57(11-12), 1756-1766. doi:10.1016/j.camwa.2008.10.036Nan, G.-F., Li, M.-Q., & Li, J. (2007). Estimation of Node Localization with a Real-Coded Genetic Algorithm in WSNs. 2007 International Conference on Machine Learning and Cybernetics. doi:10.1109/icmlc.2007.4370265Saleem, K., Fisal, N., Abdullah, M. S., Zulkarmwan, A. B., Hafizah, S., & Kamilah, S. (2009). Proposed Nature Inspired Self-Organized Secure Autonomous Mechanism for WSNs. 2009 First Asian Conference on Intelligent Information and Database Systems. doi:10.1109/aciids.2009.75Jabbari, A., & Lang, W. (2010). Advanced Bio-inspired Plausibility Checking in a Wireless Sensor Network Using Neuro-immune Systems: Autonomous Fault Diagnosis in an Intelligent Transportation System. 2010 Fourth International Conference on Sensor Technologies and Applications. doi:10.1109/sensorcomm.2010.24Ponnusamy, V., & Abdullah, A. (2010). Biologically Inspired (Botany) Mobile Agent Based Self-Healing Wireless Sensor Network. 2010 Sixth International Conference on Intelligent Environments. doi:10.1109/ie.2010.46Li, J., Cui, Z., & Shi, Z. (2012). An Improved Artificial Plant Optimization Algorithm for Coverage Problem in WSN. Sensor Letters, 10(8), 1874-1878. doi:10.1166/sl.2012.2627Sendra, S., Llario, F., Parra, L., & Lloret, J. (2014). Smart Wireless Sensor Network to Detect and Protect Sheep and Goats to Wolf Attacks. Recent Advances in Communications and Networking Technology, 2(2), 91-101. doi:10.2174/22117407112016660012Sendra, S., Granell, E., Lloret, J., & Rodrigues, J. J. P. C. (2013). Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People. Mobile Networks and Applications, 19(3), 287-302. doi:10.1007/s11036-013-0445-zGarcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682-1690. doi:10.1049/iet-com.2010.0654Sendra, S., Lloret, J., Rodrigues, J. J. P. C., & Aguiar, J. M. (2013). Underwater Wireless Communications in Freshwater at 2.4 GHz. IEEE Communications Letters, 17(9), 1794-1797. doi:10.1109/lcomm.2013.072313.131214Lloret, J., Sendra, S., Ardid, M., & Rodrigues, J. J. P. C. (2012). Underwater Wireless Sensor Communications in the 2.4 GHz ISM Frequency Band. Sensors, 12(4), 4237-4264. doi:10.3390/s12040423

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study.

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level.Comment: 24 pages, 5 figure

Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages.Comment: 27 pages, 3 figure