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Ninety-six million people are symptomatically infected with Dengue globally every year. Under the current standard-of-care, up to 20% of Dengue patients may be hospitalized, while only 500,000 develop Dengue Haemorrhagic Fever (DHF) and require hospitalization. This leads to unnecessary overwhelming of hospitals in tropical countries during large Dengue epidemics, especially when healthcare systems are grappling with large numbers of COVID-19 patients. Our research team set out to discover biomarkers to prognosticate Dengue patients, and augment the infectious disease clinician's decision-making process to hospitalize Dengue patients. Host biomarkers with concentrations significantly different between pooled serum samples of Dengue Fever (DF) patients and DHF patients were identified using protein array. The prognostication capabilities of selected biomarkers were then validated over 283 adult Dengue patients recruited from three Singapore tertiary hospitals, prior to the diagnosis of DHF. Three biomarkers (A2M, CMA1 and VEGFA) were identified that provide independent prognostication value from one another, and from clinical parameters commonly monitored in Dengue patients. The combination of all three biomarkers was able to identify from as early as Day 1 after the onset of fever, DF patients whose conditions will deteriorate into DHF. The biomarkers are robust and able to predict DHF well when trained on different AI/ML algorithms (logistic regression, support vector machine, decision tree, random forest, AdaBoost and gradient boosting). When stacked, prediction models based on the biomarkers were able to predict DHF with 97.3% sensitivity, 92.7% specificity, 66.7% PPV, 99.6% NPV and an AUC of 0.978. To the best of our knowledge, our panel of three biomarkers offers the highest accuracy in prognosticating Dengue to date. Further studies are required to validate the biomarkers in different geographical settings and pilot their implementation as part of the standard-of-care workflow for Dengue patients. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Background: Acinetobacter baumannii can reside in humans without causing infection or symptoms but can opportunistically cause community and nosocomial infections. Few studies from Taiwan have used national-level data to investigate antibiotic resistance rates of A. baumannii infections in the intensive care units (ICUs) of medical centers. Aim(s): This study determined the number of infection sites of A. baumannii and the resistance rates of carbapenem-resistant A. baumannii (CRAB) infections in ICUs in Taiwan, and identified trends over time, variations of infection site, and factors associated with resistance. Method(s): This study used the database provided by Taiwan's Centers for Disease Control. Yearly, Taiwan Nosocomial Infections Surveillance System Surveys from 2008 to 2019 were analyzed, including data on the site of infection and resistance rates of A. baumannii and patient and hospital characteristics. Result(s): On average, 21 hospitals as medical center/year participated in the survey, and 6803 A. baumannii isolates were identified. All isolates were health care-related infections. The most frequent sites of infection were the urinary tract (50.6%), respiratory tract (19.6%), bloodstream (18.2%), surgical wounds (4.3%), and others (7.4%). Infection rates were the highest in the urinary tract in 2019 (63.6%;P < 0.001). On average, the rate of carbapenem resistance was 66.6% (95% confidence interval: 63.1-70.1) among ICU patients at medical centers. Considerable regional differences were observed, with the highest rates of resistance in the central regions. Higher resistance rates were observed between 2019 and 2020 COVID-19 pandemic (74.2%). Conclusion(s): This is the first report on the prevalence of health care-related A. baumannii infection in Taiwan in 2008-2019. Several invasive diseases, such as urinary tract infections, are associated with higher rates of carbapenem resistance. The resistance rate of CRAB in Taiwan is exceptionally high. The current big-data-derived findings may inform future surveillance and research efforts in Taiwan.Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.
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The marine transport companies have been experiencing intense competition with the supply increasing faster than the demand, making most of them face input congestion. This paper applies the input congestion data envelopment analysis (DEA) model proposed by Tone and Sahoo (2004) to compute efficiency scores and input congestions of 159 major marine transport companies in the world during 2010-2019. The inputs include em-ployees, total assets, and capital whereas the output is net sales. The base year for monetary values is 2010. It is found that the annual ratios of input-congested marine companies are between 20.9% and 65.7%, indicating that input congestion is not an unusual phenomenon among these companies. This paper also applies a BCG-like analysis to consider inefficiency and input congestion at the same time. The Mann-Whitney U test shows that most of the marine transport companies in Asia, Europe, and the Americas face both inefficiency and input congestion problems during the data period. This analysis is able to provide the implications for shipping companies to conduct more precise and efficient resource allocation and coordination in the post Covid-19 era.
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The devastating outbreaks of COVID-19 pandemic has negatively impacted social and economic sustainability of the world, particularly in routine services that require physical interactions, such as dining services. With the retrospective analysis via case study, we identified three cases in dining service from USA, Indonesia, Taiwan, respectively, and investigated their service systems with the Service-Dominant Logic to understand the interactions among actors and how they integrated resources to cope with the pandemic. We identified their resilient practices heavily relied on various types of social capital to quickly respond to demand shifts, reconnect value networks, and leverage ICTs for marketing and sales. These resilient practices could be used for guiding small and medium enterprises to cope with devastating unexpected crises, taking COVID-19 as an example. More cases collected and analyzed from corresponding regions in the follow-up study could further conclude a more general causal relationship in resilience toward the theory for resilience. © 2023 IEEE Computer Society. All rights reserved.
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One of the common practices during the COVID-19 pandemic is to work or study from home. This study aims to reexamine the factors affecting individual continuance intention of e-learning. During the pandemic, via a survey conducted in 2022, we assessed workers' continuance intention of e-learning from different sectors in Taiwan. This research brought motivations as mediators in continuance intention to e-learning. Through the statistical analysis, we identified the mediation effect of motivations based on the self-determination theory. The results show that autonomous motivation facilitates the learners' computer self-efficacy, the quality of the system and content toward continuance intention;controlled motivation could mediate the monetary award in influencing the continuance intention. The internalization of motivation is also an effective mediator. The obtained results not only add new knowledge of what affected the continuance intention of e-learning during the pandemic but also provide guidance for employers to allocate resources to boost e-learning after the pandemic. © 2023 IEEE Computer Society. All rights reserved.
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Aptamers are single-stranded DNA or RNA oligonucleotides that can selectively bind to a specific target. They are generally obtained by SELEX, but the procedure is challenging and time-consuming. Moreover, the identified aptamers tend to be insufficient in stability, specificity, and affinity. Thus, only a handful of aptamers have entered the practical use stage. Recently, computational approaches have demonstrated a significant capacity to assist in the discovery of high-performance aptamers. This review discusses the advances achieved in several aspects of computational tools in this field, as well as the new progress in machine learning and deep learning, which are used in aptamer identification and optimization. To illustrate these computationally aided processes, aptamer selections against SARS-CoV-2 are discussed in detail as a case study. We hope that this review will aid and motivate researchers to develop and utilize more computational techniques to discover ideal aptamers effectively. Copyright © 2022 Elsevier B.V.
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Objective To analyze the antibody against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of recovered patients with confirmed or suspected Coronavirus Disease 2019 (COVID-19) in Dawu County, Hubei Province, China 6 months after discharge from hospital. Methods A total of 166 serum samples were collected from the recovered patients with COVID-19 6 months after discharge from hospital and determined for neutralizing antibody by plaque reduction neutralization test (PRNT), and the neutralizing antibody titer (EC50) was calculated. The specific IgG antibody against SARS-CoV-2 was determined by ELISA, of which the relationship to the neutralizing antibody titer was analyzed by Pearson method. Results The positive rates of neutralizing antibody and specific IgG in the 166 samples were 68. 7% and 77. 7%, while those in samples from patients with typical symptoms were 82. 2%, and 90. 1%, those from confirmed patients without symptoms were 66. 7% and 88. 9%, and those from suspected patients were 44. 6% and 53. 6%, respectively. The Pearson relationship between neutralizing antibody titer and IgG titer was 93. 0%. Conclusion The antibody against SARS-CoV-2 still exists in patients with confirmed or suspected COVID-19 6 months after discharge from hospital, and the titer of neutralizing antibody is significandy related to that of specific IgG against SARS-CoV-2. Copyright © 2022 Changchun Institute of Biological Products. All rights reserved.
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BACKGROUND: It is difficult to make a lace-up surgical mask fit tightly to the face with conventional wearing methods because of the strings' poor tension, resulting in some air flowing through the gap. We introduced two feasible new wearing methods and obtained satisfactory experimental results. METHODS: The wearing of surgical masks by staff was investigated through observation and interview in operation rooms. The required time to don, close-fitting rates, and satisfaction of the conventional method and the two new recommended methods were counted and compared, according to the subjects' experience. The differences between the three wearing methods on the microbial contamination of the sterile area were explored in a mock operation. RESULTS: In the subjects' experience, the close-fitting rates were 47.0%, 92.0% and 100.0% in the conventional, Three Knots, and Elastic Band groups, respectively (P<0.001); the satisfaction scores evaluated by numerical rating scale from 0 to 10 were 5.06 ± 2.22, 6.89 ± 1.86 and 7.10 ± 1.72, respectively (P<0.001); the required times were 14.32 ± 2.20, 25.76 ± 5.13 and 27.37 ± 5.11 s, respectively (P<0.001). In the mock operation, there were significant differences between the conventional and Three Knots groups (37.5 (13) vs 18 (8) cfu, P<0.001), as well as between the conventional and Elastic Band groups (37.5 (13) vs 17(10) cfu, P<0.001). CONCLUSIONS: The recommended new wearing methods had the advantages of closer fit, higher satisfaction rates, were more comfortable, and resulted in lower contamination of the sterile area; however, the recommended two methods required more time.
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Masks , Operating Rooms , HumansABSTRACT
Coronavirus Disease 2019 (COVID-19) serology has an evolving role in the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, its use in hospitalized patients with acute respiratory symptoms remains unclear. Hospitalized patients with acute respiratory illness admitted to an isolation ward were recruited. All patients had negative nasopharyngeal swab polymerase chain reaction (PCR) for SARS-CoV-2. Serological studies using four separate assays (cPass: surrogate neutralizing enzyme-linked immunosorbent assay [ELISA]; Elecsys: N-antigen based chemiluminescent assay; SFB: S protein flow-based; epitope peptide-based ELISA) were performed on stored plasma collected from patients during the initial hospital stay, and a convalescent visit 4-12 weeks later. Of the 51 patients studied (aged 54, interquartile range 21-84; 62.7% male), no patients tested positive on the Elecsys or cPass assays. Out of 51 patients, 5 had antibodies detected on B-cell Epitope Assay and 3/51 had antibodies detected on SFB assay. These 8 patients with positive serological test to COVID-19 were more likely to have a high-risk occupation (p = 0.039), bacterial infection (p = 0.028), and neutrophilia (p = 0.013) during their initial hospital admission. Discrepant COVID-19 serological findings were observed among those with recent hospital admissions and bacterial infections. The positive serological findings within our cohort raise important questions about the interpretation of sero-epidemiology during the current pandemic.
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COVID-19 , SARS-CoV-2 , Antibodies, Viral , COVID-19/diagnosis , Enzyme-Linked Immunosorbent Assay , Female , Fever , Humans , Male , Pandemics , Polymerase Chain Reaction , SARS-CoV-2/geneticsABSTRACT
Autism Spectrum Disorder (ASD) exhibits social communication and social interaction disorders, and abnormal restrictive and repetitive behaviors. However, symptoms of infants less than 1-year-old are difficult to reliably predict subsequent diagnosis. Patients with mild ASD may not be discovered until school age, because schools have more opportunities for social activities. In addition, the therapist also needs to consider the labor cost. To provide effective treatment, it also needs to consume more resources. The current situation in Taiwan is that outlying islands and remote areas often have insufficient manpower for therapists. If VR technology can be applied, some of the problems may be solved. However, due to the global pandemic, COVID-19, early treatments or group treatments in many countries have been forced to stop. If VR technology can provide interpersonal interaction scenes, the training of ASD children can hardly be affected.This research uses Virtual Reality (VR) technology, combined with wearable multi-model sensing technology, including EEG, eye tracking, heart rate variability (HRV), and breath-sensing strap. Physiological signals and game performance data are collected while users are training, and integrate multiple evaluation scales such as ADOS, SRS, and CBCL. Statistical analysis of these data is performed to classify them through machine learning models to develop a VR assistance system that can be used to evaluate the diagnosis, severity, and social behavior treatment of ASD. This system presents assessment and therapy in a game-oriented way. In addition to enhancing the incentives for users to participate, it provides better training results than traditional training. It is also an effective and convenient tool for the therapist to use during evaluation and training. © 2021 IEEE.
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With the advance in automatic speech recognition, voice user interface has gained popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online communication due to its non-contact. Additionally, various ambient noise impedes the public applications of voice user interfaces due to the requirement of audio-only speech recognition methods for a high signal-to-noise ratio. In this paper, we present Wavoice, the first noise-resistant multi-modal speech recognition system that fuses two distinct voice sensing modalities, i.e., millimeter-wave (mmWave) signals and audio signals from a microphone, together. One key contribution is that we model the inherent correlation between mmWave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Furthermore, we elaborate on two novel modules into the neural attention mechanism for multi-modal signals fusion, and result in accurate speech recognition. Extensive experiments verify Wavoice's effectiveness under various conditions with the character recognition error rate below 1% in a range of 7 meters. Wavoice outperforms existing audio-only speech recognition methods with lower character error rate and word error rate. The evaluation in complex scenes validates the robustness of Wavoice. © 2021 ACM.
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The Covid-19 pandemic is impacting entire countries. One of them is having an impact on CO2 emissions in Indonesia and Japan. The research project is focused on the use of transportation and electricity by Japanese Literature students of BINUS and Wakayama University Japan students before and during the pandemic. Concepts and methodologies, looking at the situation before and during a pandemic, using quantitative methods and approaches based on carbon footprint analysis. This study provides results showing that the use of electronics and transportation which has CO2 emissions is drastically reduced both in Indonesia and in Japan. However, Japan has higher CO2 emissions when compared to Indonesia which is influenced by certain factors, such as transportation mileage.
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During the COVID-19 pandemic, countries applied trade restrictions to insulate their domestic markets from the world market. However, these trade policies could have amplified international market price fluctuations. This paper explores the effects of trade restrictions on international agricultural price volatility. A theoretical model is developed to quantify how trade policies amplify the initial shock. Using panel data covering 71 countries from January 2020 to July 2021, we examine empirically the effects of trade policies on world agricultural price volatility. The results show that trade distortions further induced volatility of world agricultural prices by around 22 percent during the COVID-19 pandemic. The multiplier effects are much more substantial in agricultural exporting countries than in importing countries. Large countries like China and the US could make significant contributions to stabilizing world prices by limiting the extent of unilateral trade policy interventions.
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BackgroundFamily planning health centers (FPHCs) provide low-income individuals in rural areas with essential primary care services, including STD prevention, testing, and treatment. Fifteen states have defunded FPHCs, causing thousands to be left without such services. This has accelerated in the COVID-19 era. We used a longitudinal analysis to examine the impact of FPHC closures in Iowa on gonorrhea and chlamydia incidence at the county level.MethodsIn 2017, Iowa defunded FPHCs, resulting in four clinic closures. This analysis uses Poisson regression for rates on gonorrhea and chlamydia incidence data from 2000–2018 to investigate if clinic closures were associated with reported gonorrhea and chlamydia cases. Newey-West standard errors are used to adjust for any time-related trends in STI incidence. Given the mid-year policy change, all data from 2017 was removed before running the Poisson regression model.ResultsWe examined 34,479 gonorrhea and 169,660 chlamydia cases. Iowans had 1.99 (95% CI: 1.67, 2.37) times the rate of gonorrhea after clinic closures compared to before closures. In 2018, people in counties with clinic closures were 1.71 (95% CI: 1.01,2.90) times more likely to be diagnosed with gonorrhea than residents in the counties without closures. Iowans had 1.05 (95% CI: 1.03, 1.07) times the rate of chlamydia after clinic closures compared to before closures. People in counties with closures were 1.37 (95% CI: 1.11, 1.69) times more likely to have chlamydia in 2018 as compared to those in counties without closures.ConclusionsDespite the fact that many STI diagnoses are made and reported by FPHCs, our data shows that reported gonorrhea and chlamydia cases increased in the year following clinic closures. This is consistent with delayed diagnoses and missed opportunities for providing essential STI services to vulnerable and under-served rural residents. Legislative action is urgently needed to ensure essential STI services for all.
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RATIONALE: The attempt at direct liberation from mechanical ventilation (MV) using open-circuit oxygen in the tracheostomized patients with prolonged MV might carry a substantial failure rate. Frequent switching between closed-circuit MV support and open-circuit oxygen, such as a T-piece during the liberation attempt, might also carry a risk of dispersing aerosol particles to the care environment, especially during the pandemic era. We aimed to analyze the feasibility and predictability of adding a screening test with automated tube compensation (ATC) or airway resistance compensation (ARC) mode before the attempt of an open-circuit oxygen trial. METHODS: We conducted a retrospective analysis of tracheostomized patients admitted to a medical center's dedicated weaning unit in Taiwan. Because of coronavirus's global pandemic, the unit implemented a universal process to apply ATC/ARC screening tests to patients since April 2020. Before starting the MV liberation trial with continuous spontaneous breathing via open-circuit oxygen (T-piece), the patients received a screening test consisting of 12 hours of ATC/ARC the first day followed by 72 hours of continuous ATC/ARC. Those who passed the test then proceeded to the open-circuit liberation trial. Those who failed the test would receive repeated ATC/ARC or open circuit trials. RESULTS: A total of 79 (51% of the admitted) patients, including 54 male, aged 69 [18-95] years, and averaged 32 days of MV before admission to the weaning unit, received the screening test (59 for ATC;20 for ARC) after MV settings reduction. Of the 79 patients, 48 (61%) succeeded in the test. Upon discharge from the unit, 49 (62%) were liberated from MV, and 43 (54%) were MV-free status 30 days after the ATC/ARC test. Of the 31 who failed ATC/ARC, 22 (71%) remained MV-dependent at 30 days after the ATC/ARC test. Therefore, the sensitivity, specificity, positive predictive value, and negative predictive values of the ATC/ARC test for outcomes of MV-liberation upon discharge from the unit were 90%, 87%, 92%, and 84%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive values of the ATC/ARC test for outcomes of MV-free status 30 days after the test were 93%, 78%, 83%, and 90%, respectively. CONCLUSION: Before proceeding with open-circuit oxygen, a screening test with ATC/ARC provides an optimal prediction for the 30-day outcomes of MV liberation.
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We use traditional and nontraditional data to measure the collapse and partial recovery of the US labor market from March to early July, contrast this downturn to previous recessions, and provide preliminary evidence on the effects of the policy response. For hourly workers at both small and large businesses, nearly all of the decline in employment occurred between March 14 and 28. It was driven by low-wage services, particularly the retail and leisure and hospitality sectors. A large share of the job losses in small businesses reflected firms that closed entirely, though many subsequently reopened. Firms that were already unhealthy were more likely to close and less likely to reopen, and disadvantaged workers were more likely to be laid off and less likely to return. Most laid-off workers expected to be recalled, and this was predictive of rehiring. Shelter-in-place orders drove only a small share of job losses. Last, states that received more small business loans from the Paycheck Protection Program and states with more generous unemployment insurance benefits had milder declines and faster recoveries. We find no evidence that high unemployment insurance replacement rates drove job losses or slowed rehiring.
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Background: Coronavirus Disease 2019 (COVID-19) has heightened the importance of emergency department (ED) providers in initiating advance care planning (ACP) conversations. Clarifying ACP helps ensure care is aligned with patients' wishes and can avoid unnecessary suffering. The objective was to determine the effect of a training for emergency providers on ACP conversations in the ED during the COVID-19 pandemic. We hypothesized that the intervention would increase the documentation of advance care planning in the emergency department. Methods: This was an observational pre-/post-interventional study at a southeastern academic emergency department. Palliative care physicians carried out a three-fold educational intervention for emergency medicine providers on April 1, 2020 including: (1) an evidence-based guide to COVID-19 risk stratification, (2) education on language to initiate ACP conversations, and (3) instructions on how to document ACP preferences in the electronic health record. A medical student was trained on manual chart review of COVID-infected patients. Data was then abstracted on patient demographics, placement of code status orders, documentation of a healthcare decision maker (HCDM), and documentation of ACP. Results: In total, 143 charts of confirmed COVID-19 patients were reviewed between March 26, 2020 and May 25, 2020. There were 28 patients in the pre-intervention period and 115 post-intervention. There was a roughly ∼25% increase in ACP-related activities among emergency providers in the post-intervention period (25.0% v 49.6% ACP activities noted in the pre-and post-intervention subjects, respectively ;p < 0.03). After adjustment for patient demographics, a non-significant trend towards increased ACP activity was observed (OR = 2.54, p = 0.08). Conclusion: We present preliminary evidence that a simple physician-facing educational intervention can improve ED-based goals of care conversation and documentation for patients infected with COVID-19.
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In the early stage of the outbreak of COVD-19 in Wuhan city at the end of 2019, the demand for medical protective equipment increased rapidly. Many problems emerged, such as poor quality and short supply of protective masks, shortage of medical supplies caused by long sterilization cycles, difficult treatment of the medical waste and wastewater of which might cause the secondary infection. Herein, three specific applications of nuclear technology were summarized to fight against the COVID-19. The first one is the nuclear pore membrane (NPM) produced by nuclear technology. It can be used to fabricate face masks with high filtration efficiency and good reusability after simple processing. Hence the problems of poor reusability and poor filtration effects of common disposable masks can be solved by using NPM technology. The second application is the radiation sterilization technology in the process of producing medical supplies such as masks and protective clothing. The long cycle of common sterilization technology makes the demand exceed supply whereas the radiation sterilization technology can greatly shorten the production cycle and increase output, which could alleviate the shortage of medical supplies in anti-epidemic process. The third application is the use of γ-ray, X-ray or high-energy electron to sterilize medical wastewater and garbage, among which massive of surviving coronavirus are still infectious. It can treat large-volume of wastewater rapidly with a broad vision of application. Due to these unique advantages, nuclear technology has been playing an important role in this epidemic prevention and control, it possesses broader development prospects in the future. © 2020, Science Press. All right reserved.