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Wearing a face mask is strongly advised to prevent the spread of the virus causing the COVID-19 pandemic, though masks have produced a tremendous amount of waste. As masks contain polypropylene and other plastics products, total degradation is not achievable, and masks may remain in the form of microplastics for several years in the environment. Therefore, this urgent issue ought to be addressed by properly handling waste face masks to limit their environmental impact. In relation to this goal, a novel application of recycled mask fiber (MF) derived from COVID-19 single-use surgical face masks (i.e., shredded mask fiber-SMF and cut mask fiber-CMF) has been undertaken. Eighteen mortar mixes (9 for water and 9 for 10% CO2 concentration curing) were fabricated at 0%, 0.5%, 1.0%, 1.5%, and 2.0% of both SMF and CMF by volume of ordinary Portland cement-based mortar. The compressive strength, flexural strength, ultrasonic pulse velocity, shrinkage, carbonation degree, permeable voids, and water absorption capabilities were assessed. The outcomes reveal that the compressive strength decreased with an increased percentage of MFs due to increased voids of the mixes with MFs as compared to a control mix. In contrast, significantly higher flexural strength was noted for the mortar with MFs, which is augmented with an increased percentage of MFs. Furthermore, the inclusion of MFs decreased the shrinkage of the mortar compared to the control mix. It was also found that MFs dramatically diminished the water absorption rate compared to the control mix, which reveals that MFs can enhance the durability of the mortar. © 2023 Elsevier Ltd
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Background: Patients on OCR have attenuated antibody, but largely intact T-cell responses to COVID-19 vaccination. Little is known about durability of post-vaccine responses in OCR-treated patients. Objective(s): To examine antibody and cellular responses to mRNA COVID-19 vaccines (Pfizer, BioNTech/Moderna) in Ocrelizumab (OCR)-treated MS patients over 24-week period. Method(s): MS patients on OCR were recruited from NYU (New York City) and Rocky Mountain at CU (Denver) MS Centers. Antibody responses to SARS-CoV-2 spike proteins were assessed with multiplex bead-based (MBI) immunoassays, and cellular responses to SARS-CoV-2 Spike protein with ELISpot and activation induced marker (AIM) panel in a Cytek Aurora full-spectrum flow cytometry platform. Data on samples collected pre-vaccine and 4-, 12-, 24-weeks post 2-doses and 4-, 12-weeks post-third dose will be presented. Result(s): 40/61 enrollees (age 38.3+/-10.9;77.5% female;57.5% non-white) had 24-week post-vaccination data and 9 patients had 4-week post 3rd dose data. Antibody response increased from prevaccine level of 972.0 U/mL to 6307.4 U/mL at week-4 (p=0.0002), then decreased to 4633.8 u/mL at week-12 (26% decrease from week-4, p=0.1377), and further to 2878.4 u/mL at week-24 (37% decrease from week-12, p value=0.109). Spikespecific IFNgamma T-cell responses by ELIspot were 125.7 SFU/106 cells pre-vaccine, increased to 362.9 SFU/106 cells at week-4 (p=0.009), then to 511.5 SFU/106 cells at week-12 (40.9% increase relative to 4-week time-point, p=0.8474), and remained elevated at 501.7 SFU/106 cells at week-24 (p=0.7393, 1.9% compared to week 12). 4-week post 3rd dose, Ab level increased to 5094.8 U/mL (189.9% compared to pre-3rd dose, p =0.076) and IFNgamma responses to 1253.3 SFU/106 cells (484.5% increase, p=0.037). Conclusion(s): Antibody responses to 2-series vaccine peaked at 4 weeks and trended downward thereafter, while cellular responses were sustained at 24 weeks. Third-dose resulted in marked increases in both antibody and T-cell responses 4-weeks. Expanded analyses, including in-depth immunophenotyping and 12-week post 3rd vaccination responses will be presented.
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Objective: To compare humoral and cellular responses to COVID-19 vaccines in 400 consecutive MS patients who were on Ocrelizumab ('OCR') and other disease-modifying therapies ('nonOCR') at the time of vaccination. Background: Peripheral B-cell depletion with anti-CD20 therapies, attenuates humoral responses to vaccines, but less is known about cellular responses. Design/Methods: Consecutive MS patients from NYU MS Care Center were invited to participate if they completed COVID-19 vaccination ≥6 weeks previously. Immune testing included anti-spike RBD antibody (Elecsys Anti-SARS-CoV-2) (Roche Diagnostics);multiepitope bead-based immunoassays (MBI) of antibody-responses to SARS-COV-2 spike proteins (threshold of 'positivity'was chosen as 2 SD below non-OCR mean);T-cell responses to SARSCoV-2 Spike protein using IFNγ enzyme-linked immune-absorbent spot (Invitrogen) and TruCulture (Myriad RBM) assays;high dimensional immunophenotyping;live virus immunofluorescence-based microneutralization assay. Results: Antibody and T cell data was available on 145/355 patients enrolled to date (mean age: 40.0 years;75% female;48% non-white;39% on OCR;12% with prior COVID-19 infection;vaccines: 58% Pfizer/BioNTech, 36% Moderna and 6% Johnson&Johnson;median vaccine-tosample time: 93 (+/-32) days). In OCR, Elecsys Anti-SARS-CoV-2 Ab titers were detected in 30/63 (48%;mean antibody titer in log scale: 1.63) and in non-OCR - in 78/81 (96%, mean Ab titer in log scale: 2.83;p<0.0001). In OCR, antibody response by MBI were detected in 41/57 (72%, mean level in log scale: 3.09) and in non OCR - in 68/72 (94%, mean level in log scale: 4.08;p<0.001). Neutralizing antibodies were detected in 10/42 (38%) of OCR and 24/43 (56%) of non-OCR (p=0.1). T-cell activation based on induced IFNg secretion (TruCulture) was observed in 50/64 (78%) OCR and 43/81 (53%) non-OCR (p=0.002). Conclusions: Preliminary results suggest robust vaccine-specific T-cell immune response to SARS-CoV2 vaccines in B-cell depleted patients, but markedly attenuated antibody responses. Final results of pre-planned multivariable analyses stratified by DMT class and high-dimensional immunophenotyping will be presented.
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Objective: To evaluate the frequency of infusion-related reactions (IRRs) and PROs following administration of ocrelizumab (OCR) as a 2-hour home infusion. Background: Home-based infusion of multiple sclerosis (MS) drugs may be a safe and convenient treatment during the SARS-CoV-2 pandemic. Design/Methods: 100 MS patients from Rocky Mountains MS Center who fulfill these criteria: 18-55 years;relapsing or primary progressive MS;completed first 600-mg dose of OCR;had neurologist-approved-therapy-monitoring labs;resided in area with 911 access;completed PROs in English;and no >/Grade 3 IRR in prior infusions. Patients completed majority of study visits in home or via telehealth. Primary outcome is IRRs with common terminology criteria for adverse events (CTCAE) collected at the infusion visit, 24 hours post-infusion, and 2 weeks post infusion via telehealth. Patients were asked to compare their home infusion vs last OCR infusion using PROs measuring infusion experience, nurse responsiveness and confidence in receiving a home infusion. Standard statistical methods were used for proportions and change scores. Results: Currently 51/100 patients have received a home infusion. Mean age of 42.5 years (SD +/ - 8.34);73% female;89% white;96% with relapsing MS;mean MS duration 8.8 years;3.3 years on OCR. Only 15.70% (95% CI: (7.02%, 28.59%) experienced an IRR, all classified as Grade 1. CTCAEs were self-reported in 82.35% of patients. Most common by occurrence were fatigue (n=21), itching (n=19), headache/migraine (n=10) and tingling (n=9). No SAEs were reported. These PROs showed improvements pre vs post home infusion (range 1-5, higher is better): nurses explained things clearly (pre=3.78, post=3.94;p=0.01);confidence in nurses administering infusion (pre=4.40, post=4.67;p=0.02);felt safe and respected during infusion (pre=4.45, post=4.69;p=0.03);felt comfortable in surroundings (pre=3.98;post=4.65;p<0.0001);worries about safety and AEs decreased (pre=3.75;post=4.16;p=0.008). Conclusions: Interim analysis of OCR home infusion safety and experience is encouraging.
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BACKGROUND: Evidence about the impact of the COVID-19 pandemic on the mental health of specific subpopulations- such as university students-is needed as communities prepare for future waves. AIMS: To study the association of proximity of COVID-19 with symptoms of anxiety and depression in university students. METHODS: This trend study analyzed weekly cross-sectional surveys of probabilistic samples of students from the University of British Columbia for 13 weeks through the first wave of COVID-19. The main variable assessed was propinquity of COVID-19, defined as "knowing someone who tested positive for COVID-19", which was specified at different levels: knowing someone anywhere globally, in Canada, in Vancouver, in their course, or at home. Proximity was included in multivariable linear regressions to assess its association with primary outcomes, including 30-day symptoms of anxiety and/or depression. RESULTS: Of 1,388 respondents (adjusted response rate=50%), 5.6% knew someone with COVID-19 in Vancouver, 0.8% in their course, and 0.3% at home. Ten percent were overwhelmed and unable to access help. Knowing someone in Vancouver was associated with an 11 percentage-point increase in the probability of 30-day anxiety symptoms (SE=0,05;p<=0,05), moderated by gender, with a significant interaction of the exposure and being female (coefficient= 20(SE=0,09), p<=0,05). No association was found with depressive symptoms. CONCLUSION: Propinquity of COVID-19 cases may increase the likelihood of anxiety symptoms in students, particularly amongst men. Most students report coping well, but additional supports are needed for an emotionally overwhelmed minority who report being unable to access help.
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The COVID-19 pandemic is a worldwide crisis with impacts that are both devastating and inequitable as effects often fall hardest on communities that are already suffering from economic, social, and political disparities. Interpretable machine learning (IML) offers the possibility for detailed understanding of this and similar disease outbreaks, allowing subject matter experts to explore the data more thoroughly and find patterns and connections that might otherwise remain hidden. As an active area of research in artificial intelligence, IML has great significance yet numerous technical challenges to overcome. In this paper, we focus on approximating epidemic curves using an interpretable artificial neural network. This is a first step toward a flexible and interpretable modeling framework that we plan to use to study impacts of various demographic, socioeconomic, and other factors on disease outbreaks. We tap into a substantial but little-known collection of IML studies in nonlinear function approximation from engineering mechanics, where domain knowledge including visually observable features of the data is systematically sorted and directly utilized in the initialization of sigmoidal neural networks leading to training success and good generalization. After an introductory review of existing work, we present a feasibility study on approximating a particular epidemic curve leading to a promising result. © 2020 IEEE.
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Graph is a natural representation encoding both the features of the data samples and relationships among them. Analysis with graphs is a classic topic in data mining and many techniques have been proposed in the past. In recent years, because of the rapid development of data mining and knowledge discovery, many novel graph analytics algorithms have been proposed and successfully applied in a variety of areas. The goal of this tutorial is to summarize the graph analytics algorithms developed recently and how they have been applied in healthcare. In particular, our tutorial will cover both the technical advances and the application in healthcare. On the technical aspect, we will introduce deep network embedding techniques, graph neural networks, knowledge graph construction and inference, graph generative models and graph neural ordinary differential equation models. On the healthcare side, we will introduce how these methods can be applied in predictive modeling of clinical risks (e.g., chronic disease onset, in-hospital mortality, condition exacerbation, etc.) and disease subtyping with multi-modal patient data (e.g., electronic health records, medical image and multi-omics), knowledge discovery from biomedical literature and integration with data-driven models, as well as pharmaceutical research and development (e.g., de-novo chemical compound design and optimization, patient similarity for clinical trial recruitment and pharmacovigilance). We will conclude the whole tutorial with a set of potential issues and challenges such as interpretability, fairness and security. In particular, considering the global pandemic of COVID-19, we will also summarize the existing research that have already leveraged graph analytics to help with the understanding the mechanism, transmission, treatment and prevention of COVID-19, as well as point out the available resources and potential opportunities for future research. © 2020 Owner/Author.
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Objectives : In this era of big data, new technologies such as big data and cloud computing have played a great role in production and life. As far as the outbreak of epidemic is concerned, obtaining accurate and detailed outbreak information is a crucial step to prevent the spread of the outbreak. So the Bayesian algorithm is used to provide a theoretical and feasible solution for the control of epidemic spread. Methods : The main core idea of Bayesian prediction model is to predict the possibility of the cause of an event through conditional probability. According to professional data, symptoms of infection with the new coronavirus include fever, fatigue, dry cough, diarrhea, and difficulty breathing. Here we select the first five as the conditions for the study of the Bayesian model, and whether they are infected with the virus as a result. Based on the above conditions, n samples are selected, and the attribute values include body temperature, degree of fatigue, dry cough, diarrhea, and dyspnea. Because the value of the temperature attribute in the original data obtained in the actual data collection is numerical. To facilitate the study, we need to specify 37 degrees and above as the fever, and convert the numerical data of the body temperature into a binary discrete type. Here, the infected virus is recorded as A , and the non-infected virus is recorded as A . Assume that the physical condition of a person in a certain area is x 1 x 2 x 3 x 4 x 5 , then the conditional probability of his infection with the virus is recorded P ( A;x 1 x 2 x 3 x 4 x 5 ). For such a joint distribution probability, according to Bayes' theorem: P ( A ) P ( A ;B ) = P ( B ) P ( B ;A ), we can get the conditional probability that someone is infected with the virus P(A;x1x2x3x4x5)= P(A) P(A)+P(A) . Discussion : Compared with the traditional method of directly determining whether a person is infected with a virus by using the Bayesian model, the main improvement lies in the fact that during this extraordinary period, the traditional statistical method is very sensitive to the symptoms caused by the virus infection, and it is easy to ignore the area. difference. Various adverse symptoms are not necessarily caused by the virus. However, the Bayesian algorithm considers the situation of virus infection in various regions and determines whether a person is infected with the virus by calculating the probability that the symptom occurs due to the virus. Therefore, this method has caught the focus in practical application, and it is also a more reasonable method of epidemic forecast in practice. Conclusions : The use of big data to predict and warn of infectious diseases will surely become a research hotspot in the field of disease control. Big data analysis technology does not have fixed algorithms and models. It is necessary to develop algorithms and models that are suitable for the needs of the business under specific business and needs. This paper uses Bayesian theory to predict the probability of individual infection in a certain area, which is theoretically feasible and efficient. However, due to the complexity of the epidemic, a large amount of data, and the fast update speed, there will also be errors in the predicted values.