IMPACT OF SARS-CoV-2-MEDIATED CD4 T CELL ACTIVATION AND HIV DNA PERSISTANCE IN VIVO

Background: Antigen-driven CD4+ T cell proliferation is a proposed mechanism of HIV-1 reservoir persistence. We previously reported that SARSCoV- 2 infection leads to increased detectable low-level HIV-1 plasm RNA blips months after COVID-19, but the impact of SARS-CoV-2-mediated T cell activation on expansion of HIV-1 reservoirs is not known. We sought to identify if SARSCoV- 2 infection leads to expansion of preferentially HIV-infected CD4+ T cells in people with HIV (PWH) on ART. Method(s): Five PWH with samples collected prior to and approximately two months after SARS-CoV-2 infection were identified. We performed a surface activation induced marker (AIM) assay using a CD4-optimized overlapping SARS-CoV-2 peptide pool to measure OX40/CD137 expression following peptide stimulation and sorted CD4+ T cells based on surface marker expression. ddPCR quantification of genomic HIV-1 DNA was performed on sorted subsets. Result(s): We observed an increase in the frequency of SARS-CoV-2 AIM+ non-naive CD4+ T cells following COVID-19 in samples from 4 of 5 participants (mean AIM+ % 0.13 pre- vs 0.31 post). A large percentage of non-naive AIM+ CD4+ T cells expressed PD1 compared with total non-naive cells before (76% vs 36%) and after (65% vs 19%) COVID-19;PD1 expression was lower following SARS-CoV-2 in both AIM+ and AIM- CD4+ T cell subsets (although very few cells were AIM+ prior to COVID-19). HIV-1 DNA levels in non-naive AIM- CD4+ T cells prior to COVID-19 unexpectedly decreased following infection (mean 3,522 to 766 copies/106 cells). The numbers of AIM+ cells obtained by cell sorting were overall low ( 3,863 mean) and only one participant had detectable DNA in post-COVID AIM+ CD4+ T cells. However, a large majority of this participant's post-COVID AIM+ cells harbored HIV-1 DNA (0.89 copies per cell) whereas HIV DNA in their AIM- cells decreased from 8,387 to not detected following SARSCoV- 2 infection. No HIV-1 DNA was detected in the small number of AIM+ cells obtained prior to COVID-19 in this participant. Conclusion(s): COVID-19 in PWH led to a modest SARS-CoV-2-specific CD4+ cell response approximately two months following acute presentation. One participant may have preferentially expanded HIV-1-infected, SARS-CoV-2- specific CD4+ T cells following COVID-19 but studies involving larger numbers of participants and larger numbers of cells will be needed to fully understand the impact of SARS-CoV-2 on clonal expansion and HIV persistence.

CSR, monitoring cost and firm performance during COVID-19: balancing organizational legitimacy and agency cost

Purpose: COVID-19 induced uncertainty in the firms' business transactions, financial markets and product-market competition, causing a severe organizational legitimacy crisis. Using the organizational legitimacy perspective and agency theory, this paper aims to study the relationship between prior corporate social responsibility (CSR) activities, monitoring cost (MC) and firm performance. Design/methodology/approach: This study uses a quarterly panel (16,924 firm-quarter observations from 61 countries for CSR and 53,345 firm-quarter observations from 55 countries for MC) for 14 quarters from January 2018 to June 2021. This study uses panel fixed-effect regression models to estimate the effect of CSR activities and MC (measured as audit fees) on firm performance during the COVID-19 period. Findings: This study finds a U-shaped relationship between CSR and firm performance. This relationship is strengthened during COVID-19. In contrast, this study finds an inverted U-shaped relationship between firm MC and firm performance. However, this relationship is weakened during the pandemic. Originality/value: This study contributes to theory and practice on maintaining organizational legitimacy and reducing agency costs during the pandemic. This study shows that firms' prior legitimacy-gaining practices, such as CSR activities and MC, provide an opportunity to increase firm value. To balance agency costs and legitimacy benefits, firm managers also need to identify the optimal level of CSR activities and MC. © 2023, Emerald Publishing Limited.

A review on the effect of micro- and nano-plastics pollution on the emergence of antimicrobial resistance

The recent upsurge in the studies on micro/nano plastics and antimicrobial resistance genes has proven their deleterious effects on the environmental and human health. Till-date, there is a scarcity of studies on the in-teractions of these two factors and their combined influence. The interaction of microplastics has led to the formation of new plastics namely plastiglomerates, pyroplastics. and anthropoquinas. It has long been ignored that the occurrence of microplastics has become a breeding ground for the emergence of antimicrobial resistance genes. Evidently microplastics are also associated with the occurrence of other pollutants such as polyaromatic hydrocarbons and pesticides. The increased use of antibiotics (after Covid breakout) has further elevated the detrimental effects on human health. Therefore, this study highlights the relation of microplastics with antibiotic resistance generation. The factors such as uncontrolled use of antibiotics and negligent plastic consumption has been evaluated. Furthermore, the future research prospective was provided that can be helpful in correctly identifying the seriousness of the environmental occurrence of these pollutants.

Decompensation Prediction for Hospitalized COVID-19 Patients

Since the start of the COVID-19 pandemic, hospitals have been overwhelmed with the high number of ill and critically ill patients. The surge in ICU demand led to ICU wards running at full capacity, with no signs of demand falling. As a result, resource management of ICU beds and ventilators has been a bottleneck in providing adequate healthcare to those in need. Short-term ICU demand forecasts have become a critical tool for hospital administrators. Therefore, using the existing COVID-19 patient data, we build models to predict if a patient's health will deteriorate below safe thresholds to deem admission into ICU in the next 24 to 96 hours. We identify the most important clinical features responsible for the prediction and narrow down the health indicators to focus on, thereby assisting the hospital staff in increasing responsiveness. These models can help the hospital staff better forecast ICU demand in near real-time and triage patients for ICU admissions as per the risk of deterioration. Using a retrospective study with a dataset of 1411 COVID-19 patients from an actual hospital in the USA, we run experiments and find XGBoost performs the best among the models tested when tuning parameters for sensitivity (recall). The most important feature for the four prediction tasks is the maximum respiratory rate, but subsequent features in order of importance vary between models predicting ICU transfer in the next 24 to 48 hours and those predicting ICU transfer in the next 72 to 96 hours. © 2022 IEEE.

Reduce the Cold Start of COVID-19 In-hospital Mortality Prediction Models via Transfer Learning

At the beginning of the breakout of a new disease, the healthcare community almost always has little experience in treating patients of this kind. Similarly, due to insufficient patient records at the early stage of a pandemic, it is difficult to train an in-hospital mortality prediction model specific to the new disease. We call this the 'cold start' problem of mortality prediction models. In this paper, we aim to study the cold start problem of 3-days ahead COVID-19 mortality prediction models by the following two steps: (i) Train XGBoost [1] and logistic regression 3-days ahead mortality prediction models on MIMIC3, a publicly available ICU patient dataset [2];(ii) Apply those MIMIC3 models to COVID-19 patients and then use the prediction scores as a new feature to train COVID-19 3-days ahead mortality prediction models. Retrospective experiments are conducted on a real-world COVID-19 patient dataset(n = 1,287) collected in US from June 2020 to February 2021 with a mixed cohort of both ICU and Non-ICU patients. Since the dataset is imbalanced(death rate = 7.8%), we primarily focus on the relative improvement of AUPR. We trained models with and without MIMIC3 scores on the first 200, 400,..., 1000 patients respectively and then tested on the next 200 incoming patients. The results show a diminishing positive transfer effect of AUPR from 5.36% for the first 200 patients(death rate = 5.5%) to 3.58% for all 1,287 patients. Meanwhile the AUROC scores largely remain unchanged, regardless of the number of patients in the training set. What's more, the p-value of t-test suggests that the cold start problem disappears for a dataset larger than 600 COVID-19 patients. To conclude, we demonstrate the possibility of mitigating the cold start problem via the proposed method. © 2022 IEEE.

University Operations During a Pandemic: A Flexible Decision Analysis Toolkit

Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives;(ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society;(iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known;and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combinedwith ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota's Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.

Learning Student Interest Trajectory for MOOC Thread Recommendation

In recent years, Massive Open Online Courses (MOOCs) have witnessed immense growth in popularity. Now, due to the recent Covid19 pandemic situation, it is important to push the limits of online education. Discussion forums are primary means of interaction among learners and instructors. However, with growing class size, students face the challenge of finding useful and informative discussion forums. This problem can be solved by matching the interest of students with thread contents. The fundamental challenge is that the student interests drift as they progress through the course, and forum contents evolve as students or instructors update them. In our paper, we propose to predict future interest trajectories of students. Our model consists of two key operations: 1) Update operation and 2) Projection operation. Update operation models the interdependency between the evolution of student and thread using coupled Recurrent Neural Networks when the student posts on the thread. The projection operation learns to estimate future embedding of students and threads. For students, the projection operation learns the drift in their interests caused by the change in the course topic they study. The projection operation for threads exploits how different posts induce varying interest levels in a student according to the thread structure. Extensive experimentation on three real-world MOOC datasets shows that our model significantly outperforms other baselines for thread recommendation.

RKT: Relation-Aware Self-Attention for Knowledge Tracing

The world has transitioned into a new phase of online learning in response to the recent Covid19 pandemic. Now more than ever, it has become paramount to push the limits of online learning in every manner to keep flourishing the education system. One crucial component of online learning is Knowledge Tracing (KT). The aim of KT is to model student's knowledge level based on their answers to a sequence of exercises referred as interactions. Students acquire their skills while solving exercises and each such interaction has a distinct impact on student ability to solve a future exercise. This impact is characterized by 1) the relation between exercises involved in the interactions and 2) student forget behavior. Traditional studies on knowledge tracing do not explicitly model both the components jointly to estimate the impact of these interactions. In this paper, we propose a novel Relation-aware self-attention model for Knowledge Tracing (RKT). We introduce a relation-aware self-attention layer that incorporates the contextual information. This contextual information integrates both the exercise relation information through their textual content as well as student performance data and the forget behavior information through modeling an exponentially decaying kernel function. Extensive experiments on three real-world datasets, among which two new collections are released to the public, show that our model outperforms state-of-the-art knowledge tracing methods. Furthermore, the interpretable attention weights help visualize the relation between interactions and temporal patterns in the human learning process. © 2020 ACM.