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Introduction: The COVID-19 pandemic posed numerous challenges to patient care, including extensive PPE use, patient care in isolation rooms, inadequate numbers of intensivists particularly in rural communities, use of unfamiliar ventilators that would be partially remedied by the ability to remotely control lung ventilation. The goals of the project were to study the intended use, risk management, usability, cybersecurity for remote control of ventilators and demonstrate the use of a single interface for several different ventilators. Method(s): Clinical scenarios were developed including remote control of the ventilator from an antechamber of an isolation room, nursing station within the same ICU, and remote control from across the country. A risk analysis and was performed and a risk management plan established using the AAMI Consensus Report--Emergency Use Guidance for Remote Control of Medical Devices. A cybersecurity plan is in progress. Testing was done at the MDPNP laboratory. We worked with Nihon Kohden OrangeMed NKV-550, Santa Ana, CA, and Thornhill Medical MOVES SLC, Toronto, Canada. Both companies modified their devices to allow remote control by and application operating on DocBox's Apiary platform. Apiary is a commercially available ICE solution, DocBox Inc, Waltham, MA. An expert panel was created to provide guidance on the design of a single common, simple to use graphical user interface (GUI) for both ventilators. Manufacturers' ventilation modes were mapped to ISO 19223 vocabulary, data was logged using ISO/IEEE 11073-10101 terminology using AAMI 2700-2-1, Medical Devices and Medical Systems - Essential safety and performance requirements for equipment comprising the patient-centric integrated clinical environment (ICE): Part 2-1: Requirements for forensic data logging. Result(s): We demonstrated that both ventilators can be controlled and monitored using common user interface within an institution and across the country. Pressure and flow waveforms were available for the NKV-550 ventilator, and usual ventilator measurements were displayed in near-real time. The interface allowed changing FiO2, ventilation mode, respiratory rate, tidal volume, inspiratory pressure, and alarm settings. At times, increased network latency negatively affected the transmission of waveforms. Conclusion(s): We were able to demonstrate remote control of 2 ventilators with a common user interface. Further work needs to be done on cybersecurity, effects of network perturbations, safety of ventilator remote control, usability implications of having a common UI for different devices needs to be investigated.
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Nucleic acids, as a next generation of biotechnology drugs, not only can fundamentally treat diseases, but also own significant platform characteristics in view of technology and production. Therefore, nucleic acid-based drugs have broad clinical applications in biomedical fields. However, nucleic acids are degradable and unstable, and have very low intracellular delivery efficiency in vitro and in vivo, which greatly limits their applications. In recent years, ionizable lipid-based lipid nanoparticles have shown promising application potentials and have been successfully applied to COVID-19 (Coronavirus Disease 2019) vaccines in clinic. Lipid nanoparticles demonstrate high in vivo delivery efficiency and good safety profile due to their unique structural and physicochemical properties, which provides many possibilities for their clinical applications for nucleic acid delivery in the future. This review focused on the characteristics of nucleic acid drugs and their delivery barriers, and discussed the approved nucleic acid drugs to illustrate the key aspects of the success of their delivery carrier system. In addition, problems to be solved in the field were highlighted.Copyright © 2023, Chinese Pharmaceutical Association. All rights reserved.
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[Background] Sleep is closely related to immune function and human health, and adequate sleep is an important foundation for human health. [Objective] This study investigates the sleep status of the first-line medical staff in Wuhan in a fight against the coronavirus disease 2019 (COVID-19) outbreak, provides reference for improving the sleep quality of the first-line medical staff in public health emergencies. [Methods] Through convenience sampling, 112 medical workers (first-line group) who aided the COVID-19 fight in Wuhan and 134 medical staff (non-first-line group) who did not participate in the fight were selected. The Pittsburgh Sleep Quality Index (PSQI) was employed to collect data on the incidence of sleep disorders, time to fall asleep, duration of sleep, sleep efficiency, sleep disorders, use of sleep aid, and daytime functions. In addition, a self-made questionnaire was used to investigate the common concerns and time allocation characteristics of the first-line medical workers in the context of major infectious disease outbreaks. [Results] There were no significant differences between the two groups in demographic variables such as gender, age, job title, educational background, marriage status, number of children, and working years (P > 0.05). In the first-line group, 62 medical workers (55.36%) reported sleep disorders, while in the non-first-line group, 54 medical workers (40.30%) did;the difference was statistically significant (P=0.008). Among the seven components of the PSQI, the median sleep time (component 3) score of the first-line group was 1.5, which was higher than that of the non-first-line group (median 1.0) (P < 0.001);the median sleep efficiency (component 4) score of the first-line group was 1.0, which was higher than that of the non-first-line group (median 0) (P < 0.001). The actual sleep duration of the first-line group [(5.65+/-1.15) h] was lower than that of the non-first-line group [(7.00+/-1.40) h] (P < 0.001). The distributions of common concerns were different between the two group. The top three concerns were being infected (76.79%), exhausted (37.50%), and overloaded (27.68%) in the first-line group, and family members being infected (53.73%), being infected (45.52%), and child care (33.58%) in the non-first-line group. [Conclusion] The first-line medical team members report poor sleep quality, short sleep time, low sleep efficiency, sleep disorders, and many psychological concerns. It is necessary to take appropriate measures to improve their sleep quality.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.
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Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.
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During an emergency, timely and effective distribution of emergency supplies is critical in rescue. In the context of Covid-19, given the difficulties in distributing supplies to communities due to super infectious viruses, unmanned vehicle distribution is studied by taking into account the priority and satisfaction of communities to improve distribution safety and effectiveness of supplies. Furthermore, the influence of distribution time on the overall efficiency is also taken into account, thus ultimately establishing an unmanned distribution model with the shortest distribution time while meeting community satisfaction. The improved whale algorithm is used to solve the dual-objective model and compared with the basic whale optimization algorithm. The results show that the improved whale algorithm demonstrates better convergence, searchability, and stability. The constructed model can scientifically distribute daily necessities to communities while considering their priority and satisfaction. © 2022 IEEE.
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SARS-CoV-2 was originally discovered in China in late 2019 and is a member of the family of enveloped, single-strand RNA viruses known as Betacorona-virus in the Coronaviridae. Since then, it has spread throughout the entire world, generating the COVID-19 epidemic, which has a high infectivity and mortality rate. Nowadays, remdesivir and a few other neutralizing antibodies have been used extensively to treat COVID-19, and other medications are now being found to have anti-coronavirus properties both in vitro and in vivo. Remdesivir's therapeutic outcomes are debatable, though, and the world still needs new antiviral medications imminently. Quercetin and curcumin, both natural compounds derived from plants, may be an option for patients with COVID-19 as a kind of treatment. Molecular docking indicates the great ability of treating COVID-19, and the combination in use may be allowed based on similar mechanisms for treating SARS-CoV-2. This review aims to summarize the role of quercetin and curcumin acting as anti-coronavirus agents, point out the lack of clinical trials of their combined use, and emphasize the use of natural compounds in treating COVID-19. © 2023 SPIE.
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Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).
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COVID-19 has afflicted people's lives worldwide. Interleukin-6 (IL-6) is an important COVID-19 biomarker in human body fluids that can be used as a reference to monitor COVID-19 in real-time and therefore to reduce the risk of virus transmission. On the other hand, oseltamivir is a potential COVID-19 curing drug, but its overuse easily leads to hazardous side effects, calling for its real time monitoring in body fluids. For these purposes, a new yttrium metal-organic framework (Y-MOF) has been synthesized, in which the 5-(4-(imidazole-1-yl)phenyl)isophthalic linker contains a large aromatic backbone capable of strongly interacting with DNA sequences through pi-pi stacking interactions, which makes it appealing to build a unique sensor based on DNA functionalized MOFs. The MOF/DNA sequence hybrid luminescent sensing platform presents excellent optical properties associated with a high Forster resonance energy transfer (FRET) efficiency. Furthermore, to construct a dual emission sensing platform, a 5 '-carboxylfluorescein (FAM) labeled DNA sequence (S2) with a stem-loop structure that can specifically interact with IL-6 has been associated with the Y-MOF. The resulting Y-MOF@S2 exhibits an efficient ratiometric detection of IL-6 in human body fluids with an extremely high K-sv value 4.3 x 10(8) M-1 and a low detection limit (LOD) of 70 pM. Finally, the Y-MOF@S2@IL-6 hybrid platform allows the detection of oseltamivir with high sensitivity (K-sv value is as high as 5.6 x 10(5) M-1 and LOD is 54 nM), due to the fact that oseltamivir can disconnect the loop stem structure constructed by S2, leading to a strong quenching effect towards Y-MOF@S2@IL-6. The nature of the interactions between oseltamivir and Y-MOF has been elucidated using density functional theory calculations while the sensing mechanism for the dual detection of IL-6 and oseltamivir has been deciphered based on luminescence lifetime tests and confocal laser scanning microscopy.
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(Aim) COVID-19 has caused more than 2.28 million deaths till 4/Feb/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 based on chest CT images. (Methods) First, the two-dimensional fractional Fourier entropy was used to extract features. Second, a custom deep stacked sparse autoencoder (DSSAE) model was created to serve as the classifier. Third, an improved multiple-way data augmentation was proposed to resist overfitting. (Results) Our DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem (COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control). (Conclusion) Our method outperforms 10 state-of-the-art approaches. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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To comprehend the etiology and pathogenesis of many illnesses, it is essential to iden-tify disease-associated microRNAs (miRNAs). However, there are a number of challenges with cur-rent computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "iso-lated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.
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Neutrophils play an important role in infectious diseases by clearing pathogens in the early stages of the disease and damaging the surrounding tissues along with the disease progress. Low-density neutrophils (LDNs) are a crucial and distinct subpopulation of neutrophils. They are a mixture of activated and degranulated normal mature neutrophils and a considerable number of immature neutrophils prematurely released from the bone marrow. Additionally, they may be involved in the occurrence and development of diseases through the changes in phagocytosis, the generation of reactive oxygen species (ROS), the enhancement of the ability to produce neutrophils extracellular traps and immunosuppression. We summarizes the role of LDNs in the pathogenesis and their correlation with the severity of infectious diseases such as COVID-19, severe fever with thrombocytopenia syndrome (SFTS), AIDS, and tuberculosis.
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Background/Aims In April 2020 the British Society for Rheumatology (BSR) issued a risk stratification guide to identify patients at the highest risk of COVID-19 requiring shielding. This guidance was based on patients' age, comorbidities, and immunosuppressive therapies - including biologics that are not captured in primary care records. This meant rheumatologists needed to manually review outpatient letters to score patients' risk. The process required considerable clinician time, with shielding decisions not always transparently communicated. Our aim was to develop an automated shielding algorithm by text-mining outpatient letter diagnoses and medications, reducing the need for future manual review. Methods Rheumatology outpatient letters from Salford Royal Hospital, a large UK tertiary hospital, were retrieved between 2013-2020. The two most recent letters for each patient were extracted, created before 01.04.2020 when BSR guidance was published. Free-text diagnoses were processed using Intelligent Medical Objects software1 (Concept Tagger), which utilised interface terminology for each condition mapped to a SNOMED-CT code. We developed the Medication Concept Recognition tool (MedCore Named Entity Recognition) to retrieve medications type, dose, duration and status (active/past) at the time of the letter. The medication status was established based on the heading where they appeared (e.g. past medications, current medications), but incorporated additional information such as medication stop dates. The age, diagnosis and medication variables were then combined to output the BSR shielding score. The algorithm's performance was calculated using clinical review as the gold standard. Results To allow for the comparison with manual decisions, we focused on all 895 patients who were reviewed clinically. 64 patients (7.1%) had not consented for their data to be used for research as part of the national opt-out scheme. After removing duplicates, 803 patients were used to run the algorithm. 11,558 free-text diagnoses were extracted and mapped to SNOMED CT, with 15,003 free-text medications (that included past, present and any planned treatment). The automated shielding algorithm demonstrated a sensitivity of 80.3% (95% CI: 74.7, 85.2%) and specificity of 92.2% (95% CI: 89.7, 94.2%). Positive likelihood ratio was 10.3 (95% CI: 7.7, 13.7), negative likelihood ratio was 0.21 (95% CI: 0.16, 0.28), F1 score was 0.81. False positive rate was 7.9%, whilst false negative rate was 19.7%. Further evaluation of false positives/negatives revealed clinician interpretation of BSR guidance and misclassification of medications status were important contributing factors. Conclusion An automated algorithm for risk stratification has several advantages including reducing clinician time for manual review to allow more time for direct care, improving efficiency and transparently communicating decisions based on individual risk. With further development, it has the potential to be adapted for future public health initiatives that requires prompt automated review of hospital outpatient letters.
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Problem and Motivation. Medical device remote control technologies can enable remote experts to contribute to patient care during tele-critical care during public health emergencies like COVID-19 to address the shortage of local clinical expertise. The benefit of such technologies may be further amplified if one remote-control application can operate multiple interoperable medical devices (e.g. multiple types of ventilators or IV pumps) to support the typical diversity of deployed medical devices in one institution. However, due to the variation in capabilities of different makes/models of the same device type, this unified remote control capability requires the standardization of the data interfaces of similar devices to provide sufficient information about these devices to enable safe remote control. Method(s): Medical Device Interface Data Sheets (MDIDS) [1] can provide a useful tool for documenting current and future device interface requirements and capabilities. We examined several clinical use scenarios where externally controllable infusion pumps are used to support tele-critical care, based on which we generalized an MDIDS for remotely controllable infusion pumps. To validate this generic MDIDS, we cross-checked it with the capabilities of several externally controllable infusion pumps: the NeuroWave Accupump, Eitan Medical Sapphire, and the BD Alaris GH. Result(s): During the development of the generic remotely controllerable infusion pump MDIDS, we were able to identify the common and specific data elements that different infusion pumps need to provide at their data interfaces, considering the great diversity in these devices related to infusion mechanism, infusion programming methods, device alarms and alerts, and system settings. The resulting MDIDS includes over 100 data elements, many of which are essential for safety, including those common across different pump types (e.g., maximum settable infusion rate, occlusion alarm) and those specific to certain pump types (e.g., syringe size for syringe pumps). We developed the generic MDIDS as the theoretical basis and developed an application in our OpenICE open-source interoperability research platform [2] to remotely control the above three infusion pumps either via serial communication (representing controlling the infusion pump at a distance limited by a physical wired connection inside or outside the patient room) or across the Internet using the web extension service of OpenICE (representing situations where remote experts have no physical access to the patient). Conclusion. MDIDS for externally controllable medical devices can provide a solid basis to improve the safety and interoperability of medical device remote control technologies in the tele-critical care context. They can also benefit the research, development, and testing of physiological closed-loop control systems. We applied the MDIDS methodology to infusion pumps and ventilators to support the integration of these devices to the U.S. Army Telemedicine & Advanced Technology Research Center (TATRC) National Emergency Tele-Critical Care System.
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During the global efforts to prevent and control the COVID-19 pandemic, extensive research and development of SARS-CoV-2 vaccines using various technical approaches have taken place. Among these, vaccines based on adenovirus vector have gained substantial knowledge and experience in effectively combating potential emerging infectious diseases, while also providing novel ideas and methodologies for vaccine research and development (R&D). This comprehensive review focuses on the adenovirus vector technology platform in vaccine R&D, emphasizing the importance of mucosal immunity induced by adenoviral vector-based vaccine for COVID-19 prevention. Furthermore, it analyzes the key technical challenges and obstacles encountered in the development of vaccines based on the adenovirus vector technology platform, with the aim of providing valuable insights and references for researchers and professionals in related fields.
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Objective: To estimate the latent period and incubation period of Omicron variant infections and analyze associated factors. Methods: From January 1 to June 30, 2022, 467 infections and 335 symptomatic infections in five local Omicron variant outbreaks in China were selected as the study subjects. The latent period and incubation period were estimated by using log-normal distribution and gamma distribution models, and the associated factors were analyzed by using the accelerated failure time model (AFT). Results: The median (Q1, Q3) age of 467 Omicron infections including 253 males (54.18%) was 26 (20, 39) years old. There were 132 asymptomatic infections (28.27%) and 335 (71.73%) symptomatic infections. The mean latent period of 467 Omicron infections was 2.65 (95%CI: 2.53-2.78) days, and 98% of infections were positive for nucleic acid test within 6.37 (95%CI: 5.86-6.82) days after infection. The mean incubation period of 335 symptomatic infections was 3.40 (95%CI: 3.25-3.57) days, and 97% of them developed clinical symptoms within 6.80 (95%CI: 6.34-7.22) days after infection. The results of the AFT model analysis showed that compared with the group aged 18-49 years old, the latent period [exp(ß)=1.36 (95%CI: 1.16-1.60), P<0.001] and incubation period [exp(ß)=1.24 (95%CI: 1.07-1.45), P=0.006] of infections aged 0-17 years old were prolonged. The latent period [exp(ß)=1.38 (95%CI: 1.17-1.63), P<0.001] and the incubation period [exp(ß)=1.26 (95%CI: 1.06-1.48), P=0.007] of infections aged 50 years old and above were also prolonged. Conclusion: The latent period and incubation period of most Omicron infections are within 7 days, and age may be a influencing factor of the latent period and incubation period.