And more combined toxicity studies are essential for MPs and pesticides risk assessment.Deltamethrin (Del), a commonly used broad-spectrum insecticide, has been reported to have a toxic effect on aquatic animals, but knowledge in freshwater prawns is limited. This study revealed that Del is highly toxic to Macrobrachium nipponens with the 24 h, 48 h, 72 h, and 96 h LC50 values to be 0.268, 0.165, 0.104, and 0.066 μg/L, respectively. To further investigate the toxic effect of Del in M. nipponense and the reversibility of damage, prawns were exposed to 0.05 μg/L Del for four days and then transferred into fresh water for seven days. Histopathological examination, oxidative stress, hepatopancreas function, respiration system, and immune system were analyzed through multiple biomarkers. Results showed that Del exposure caused severe histopathological damage to hepatopancreas and gill in M. nipponense, and the prominent decrease of acid phosphatase (ACP) and alkaline phosphatase (AKP) activity further enhanced the hepatopancreas damage; the accumulation of malonaldehyde (MDA) and hydrogen peroxide (H2O2), and the decrease of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activity, indicated severe oxidative stress caused by Del. Besides, Del exposure also induced remarkably increased lactic acid (LD) level, decreased lactate dehydrogenase (LDH) activity, and decreased expression of immune-related genes, which demonstrated the respiration disruption and immunosuppression caused by Del. After 7-day decontamination in freshwater, the indicator of hepatopancreas function (ACP and AKP activity) and respiration (LD level and LDH activity) improved to the control group level. However, the histopathological damage and the biomarker in oxidative stress and immune system did not recover to the initial level.Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and providing good care. Frequently, important information regarding a patient's medical timeline is buried in their Electronic Health Record (EHR) in the form of unstructured clinical notes. This results in care providers spending time reading notes in a patient's record in order to become familiar with their condition prior to developing a diagnosis or treatment plan. Valuable time could be saved if this information was readily accessible for searching and visualization for fast comprehension by the medical team. Clinical Natural Language Processing (NLP) is an area of research that aims to build computational methods to automatically extract medically relevant information from unstructured clinical texts. A key component of Clinical NLP is Temporal Reasoning, as understanding a patient's medical history relies heavily on the ability to identify, assimilate, and reason over temporal information. In this work, we review the current state of Temporal Reasoning in the clinical domain with respect to Clinical Timeline Extraction. While much progress has been made, the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Areas such as handling relative and implicit temporal expressions, both in normalization and in identifying temporal relationships, improving co-reference resolution, and building inter-operable timeline extraction tools that can integrate multiple types of data are in need of new and innovative solutions to improve performance on clinical data.The COVID-19 pandemic is continuing, and the innovative and efficient contributions of the emerging modern technologies to the pandemic responses are too early and cannot be completely quantified at this moment. Digital technologies are not a final solution but are the tools that facilitate a quick and effective pandemic response. In accordance, mobile applications, robots and drones, social media platforms (such as search engines, Twitter, and Facebook), television, and associated technologies deployed in tackling the COVID-19 (SARS-CoV-2) outbreak are discussed adequately, emphasizing the current-state-of-art. A collective discussion on reported literature, press releases, and organizational claims are reviewed. This review addresses and highlights how these effective modern technological solutions can aid in healthcare (involving contact tracing, real-time isolation monitoring/screening, disinfection, quarantine enforcement, syndromic surveillance, and mental health), communication (involving remote assistance, information sharing, and communication support), logistics, tourism, and hospitality. The study discusses the benefits of these digital technologies in curtailing the pandemic and 'how' the different sectors adapted to these in a shorter period. https://www.selleckchem.com/ Social media and television's role in ensuring global connectivity and serving as a common platform to share authentic information among the general public were summarized. The World Health Organization and Governments' role globally in-line with the prevention of propagation of false news, spreading awareness, and diminishing the severity of the COVID-19 was discussed. Furthermore, this collective review is helpful to investigators, health departments, Government organizations, and policymakers alike to facilitate a quick and effective pandemic response.Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system. Compared to an existing gold standard of manually collected data at our institution, CEDAR was statistically similar in most measures, including patient demographics and sepsis-related organ failure assessment (SOFA) scores. Additionally, CEDAR automated data extraction obviated the need for manual collection of 550 variables. Critically, during the spring 2020 COVID-19 surge in New York City, a modified version of CEDAR supported pandemic response efforts, including clinical operations and research. Other academic medical centers may find value in using the CEDAR method to automate data extraction from EHR systems to support ICU activities.