Multiplex and Ultrasensitive Detection of Severe Acute Respiratory Syndrome Coronavirus 2 and Influenza A/B Nucleocapsid Proteins Based on Nanometer-Sized Core-Shell Superparamagnetic Antifouling Probes

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A (Flu A), and influenza B (Flu B) show similar clinical symptoms, such as cough, fever, and dyspnea, but patients infected by these viruses should be treated differently. The rapid and accurate diagnosis of infections caused by SARS-CoV-2, Flu A or Flu B is critical during the influenza season. Herein, we synthesized core-shell magnetic particles (MNPs) with excellent antifouling properties and applied them in the MNP-based immunochromatographic test (MICT) for simultaneous detection of SARS-CoV-2, Flu A, and Flu B nucleocapsid(N) proteins in 20 min. Two kinds of carboxyl -modified MNPs, MNP@pMBAA and MNP@Si-SA, were prepared and evaluated as probes in the MICT. Among them, the MNP@ pMBAA showed lower nonspecific adsorption of proteins and low background noise in the application in MICTs. Particularly, the MNP@pMBAA50 bead-based MICT strip exhibited the highest signal-to-noise ratio for SARS-CoV-2 N protein detection with a limit of detection (LOD) of 0.072 ng/mL. Moreover, the proposed MICT strip demonstrated a minimal cross-reactivity and a broad linear dynamic detection range under a magnetic assay reader in the simultaneous detection of SARS-CoV-2, Flu A, and Flu B N proteins with relative LOD values of 0.0086, 0.012, and 0.018 ng/mL, respectively. The results demonstrated that the synthesized MNPs showed great potential for use as MICT probes for sensitive and multiplex detection of biomarkers in the development of point-of-care testing systems.

Multiplex and Ultrasensitive Detection of Severe Acute Respiratory Syndrome Coronavirus 2 and Influenza A/B Nucleocapsid Proteins Based on Nanometer-Sized Core-Shell Superparamagnetic Antifouling Probes

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A (Flu A), and influenza B (Flu B) show similar clinical symptoms, such as cough, fever, and dyspnea, but patients infected by these viruses should be treated differently. The rapid and accurate diagnosis of infections caused by SARS-CoV-2, Flu A or Flu B is critical during the influenza season. Herein, we synthesized core-shell magnetic particles (MNPs) with excellent antifouling properties and applied them in the MNP-based immunochromatographic test (MICT) for simultaneous detection of SARS-CoV-2, Flu A, and Flu B nucleocapsid(N) proteins in 20 min. Two kinds of carboxyl-modified MNPs, MNP@pMBAA and MNP@Si-SA, were prepared and evaluated as probes in the MICT. Among them, the MNP@pMBAA showed lower nonspecific adsorption of proteins and low background noise in the application in MICTs. Particularly, the MNP@pMBAA50 bead-based MICT strip exhibited the highest signal-to-noise ratio for SARS-CoV-2 N protein detection with a limit of detection (LOD) of 0.072 ng/mL. Moreover, the proposed MICT strip demonstrated a minimal cross-reactivity and a broad linear dynamic detection range under a magnetic assay reader in the simultaneous detection of SARS-CoV-2, Flu A, and Flu B N proteins with relative LOD values of 0.0086, 0.012, and 0.018 ng/mL, respectively. The results demonstrated that the synthesized MNPs showed great potential for use as MICT probes for sensitive and multiplex detection of biomarkers in the development of point-of-care testing systems. © 2023 American Chemical Society.

The Impact of COVID-19 on the CS Student Learning Experience How the Pandemic has Shaped the Educational Landscape

Students have experienced incredible shifts in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic pressed on, students have had to repeatedly adapt to the continuously evolving educational landscape. This working group builds upon foundations and structure created by a 2021 ITiCSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That working group identified the incorporation of some pandemic-induced changes into future teaching practices. This working group examines the existing literature and insights gained from responses to a multi-national survey to explore the new student experience emerging from the continuously evolving teaching practices catalyzed by the global pandemic. Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We investigate how the changes within the COVID-Affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-Affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their educational methods going forward. © 2022 ACM.

A Template Matching Based Cough Detection Algorithm Using IMU Data From Earbuds

Coughing is a common symptom across different clinical conditions and has gained further relevance in the past years due to the COVID-19 pandemic. An automated cough detection for continuous health monitoring could be developed using Earbud, a wearable sensor platform with audio and inertial measurement unit (IMU) sensors. Though several previous works have investigated audio-based automated cough detection, audio-based methods can be highly power-consuming for wearable sensor applications and raise privacy concerns. In this work, we develop IMU-based cough detection using a template matching-based algorithm. IMU provides a low-power privacy-preserving solution to complement audio-based algorithms. Similarly, template matching has low computational and memory needs, suitable for on-device implementations. The proposed method uses feature transformation of IMU signal and unsupervised representative template selection to improve upon our previous work. We obtained an AUC (AUC-ROC) of 0.85 and 0.83 for cough detection in a lab-based dataset with 45 participants and a controlled free-living dataset with 15 participants, respectively. These represent an AUC improvement of 0.08 and 0.10 compared to the previous work. Additionally, we conducted an uncontrolled free-living study with 7 participants where continuous measurements over a week were obtained from each participant. Our cough detection method achieved an AUC of 0.85 in the study, indicating that the proposed IMU-based cough detection translates well to the varied challenging scenarios present in free-living conditions. © 2022 IEEE.

COVID-VIT: Classification of Covid-19 from 3D CT chest images based on vision transformer model

This paper presents an explainable deep learning network to classify COVID from non-COVID based on 3D CT lung images. It applies a subset of the data for MIA-COV19 challenge through the development of 3D form of Vision Transformer deep learning architecture. The data comprise 1924 subjects with 851 being diagnosed with COVID, among them 1,552 being selected for training and 372 for testing. While most of the data volume are in axial view, there are a number of subjects' data are in coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D data based classification is investigated, in this competition, 2D axial-view images remains the main focus. Two deep learning methods are studied, which are vision transformer (ViT) based on attention models and DenseNet that is built upon conventional convolutional neural network (CNN). Initial evaluation results indicates that ViT performs better than DenseNet with F1 scores being 0.81 and 0.72 respectively. (Codes are available at GitHub at https://github.com/xiaohong1/COVID-ViT). This paper illustrates that vision transformer performs the best in comparison to the other current state of the art approaches in classification of COVID from CT lung images. © 2022 IEEE.

Social isolation promotes mammary cancer recurrence, increases IL6/JAK/STAT3 signaling and causes mitochondrial dysfunction in tamoxifen treated rats, and a traditional herbal mixture reverses all these changes

Social isolation is associated with increased risk and mortality from many diseases, such as breast cancer. Socially isolated breast cancer survivors have a 43% higher risk of recurrence and a 64% higher risk of breast cancer-specific mortality than socially integrated survivors. Since Covid-19 has dramatically increased the incidence of social isolation, it is important to determine if social isolation affects the response to endocrine therapy and/or recurrence after the therapy is completed. Since previous studies indicate that social isolation increases circulating inflammatory cytokines, we investigated if an anti-inflammatory herbal mixture Jaeumkanghwa-tang (JGT) prevents the adverse effects of social isolation on breast cancer mortality. Estrogen receptor positive mammary tumors were initiated with 7,12-dimethylbenz[a]anthracene. When a rat developed a palpable mammary tumor, it was either socially isolated (SI) by housing it singly or a rat was allowed to remain group-housed (GH). Tamoxifen (340ppm via diet) or tamoxifen + JGT (500ppm via drinking water) started when the first mammary tumor reached a size of 11 mm in diameter. Tamoxifen administration ended when a complete response to this therapy had lasted for 9 weeks (corresponds to 5 years in women). During tamoxifen therapy, social isolation non-significantly reduced the rate of complete responses to 21%, from 31% in GH group (p>0.05). After the therapy was completed, SI significantly increased local mammary tumor recurrence (p<0.001;45% GH vs 75% SI). RNAseq analysis was performed in the mammary glands. Gene set enrichment analysis (GSEA) of transcriptome showed that the increased recurrence risk in socially isolated rats was associated with an enrichment of IL6/JAK/STAT3 signaling: this result was confirmed in the tumors. In addition, oxidative phosphorylation (OXPHOS) pathway was suppressed: the suppressed genes included those involved in mitochondrial pyruvate transport and conversion of pyruvate to acetyl CoA as well as genes in the TCA cycle and mediating electron transport in mitochondrial complexes I-IV. Social isolation also increased the expression of inflammatory receptor for advanced glycation end-products (RAGE) (p≤0.05). Consumption of an anti-inflammatory JGT inhibited IL6/JAK/STAT3 signaling, upregulated OXPHOS signaling and prevented the increased risk of mammary cancer recurrence in socially isolated animals. The percentage of recurrences in the SI rats dropped from 75% without JGT to 22% with JGT (p<0.001). Breast cancer mortality among socially isolated survivors may be most effectively prevented by focusing on the period following endocrine therapy using tools that inhibit IL6/JAK/STAT3 inflammatory cytokine signaling and correct disrupted OXPHOS and mitochondrial dysfunction.

COVID-19, Students and the New Educational Landscape

Students have experienced incredible shifts in the in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic presses on, students have had to repeatedly adapt to the continuously evolving educational landscape as this global health crisis forced an "unprecedented global shift within higher education in the ways that we communicate with and educate students". This working group builds upon foundations and structure created by a 2021 ITICSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That Working Group identified the incorporation of some pandemic-induced changes into future teaching practices. In this Working Group, we explore existing literature regarding the student experience in response to the evolving teaching practices catalyzed by COVID-19). Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We explore how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their practices going forward. © 2022 Owner/Author.

COUGHTRIGGER: EARBUDS IMU BASED COUGH DETECTION ACTIVATOR USING AN ENERGY-EFFICIENT SENSITIVITY-PRIORITIZED TIME SERIES CLASSIFIER

Persistent coughs are a major symptom of respiratory-related diseases. Increasing research attention has been paid to detecting coughs using wearables, especially during the COVID-19 pandemic. Microphone is most widely used sensor to detect coughs. However, the intense power consumption needed to process audio hinders continuous audio-based cough detection on battery-limited commercial wearables, such as earbuds. We present CoughTrigger, which utilizes a lower-power sensor, inertial measurement unit (IMU), in earbuds as a cough detection activator to trigger a higher-power sensor for audio processing and classification. It runs all-the-time as a standby service with minimal battery consumption and triggers the audio-based cough detection when a candidate cough is detected from IMU. Besides, the use of IMU brings the benefit of improved specificity of cough detection. Experiments are conducted on 45 subjects and CoughTrigger achieved 0.77 AUC score. We also validated its effectiveness on free-living data and through on-device implementation. © 2022 IEEE

COVID-CBR: A Deep Learning Architecture Featuring Case-Based Reasoning for Classification of COVID-19 from Chest X-Ray Images

Background and Objectives: This study aims to assist rapid accurate diagnosis of COVID-19 based on chest x-ray (CXR) images to provide supplementary information, leading to screening program for early detection of COVID-19 based on CXR images by developing an interpretable, robust and performant AI system. Methods: A case-based reasoning approach built upon autoencoder deep learning architecture is applied to classify COVID-19 from other non-COVID-19 as well as normal subjects from chest x-ray images. The system integrates the interpretation and decision-making together by producing a set of profiles that in appearance resemble the training samples and hence explain the outcome of classifications. Three classes are studied, which are COVID-19 (n=250), other non-COVID-19 diseases (NCD) (n=384), including TB and ARDS, and normal (n=327). Results: This COVID-CBR system sustains the average sensitivity and specificity of 93.1±3.58% and 96.1±4.10% respectively for classification of these three classes. In comparison with the current state of the art, including COVID-Net, VGG-16 and other explainable AI systems, the developed COVID-CBR system appears to perform similar or better when classifying multi-class categories. Conclusion: This paper presents a case-based reasoning deep learning system for detection of COVID19 from chest x-ray images. Comparison with several state of the art systems is conducted. Although the improvement tends to be marginal, especially for VGG-16, the novelty of this work manifests its interpretable feature building upon case-based reasoning, leading to revealing this viral insight and hence ascertaining more effective treatment and drugs while maintaining being transparent. Furthermore, different from several other current explainable networks that highlight key regions or the points of an input that activate the network, i.e. heat maps, this work is constructed upon whole training images, i.e. case-based, whereby each training image belongs to one of the case clusters. © 2021 IEEE.

The impact of COVID-19 on diabetes management for adolescents with T1D and their parents

Introduction: For families with type 1 diabetes (T1D), anxiety from the COVID-19 pandemic may be elevated due to potential for increased vulnerability. Objectives: We aimed to describe the impact of the pandemic on adolescents with T1D and their parents. Methods: In a 2-site (Seattle WA, Houston TX USA) clinical trial of a psychosocial intervention targeting stress/resilience, adolescents 13-18 years old with T1D ≥ 1 year and diabetes distress (PAID-T ≥30) were enrolled with a parent. Using a mixed-methods approach, participants enrolled August 2020-June 2021 completed a survey about the pandemic, including an open-ended question about how COVID impacted T1D management. Survey responses were summarized using frequencies and percentages, and associations between variables were assessed by Chi-squared tests. A1C was extracted from clinical records. Results: Adolescents (n=122) were 56% female, 80% White race, 18% Hispanic, mean A1c = 8.5±2.1%. Parents (n=102) were 79% White, 14% Hispanic, 61% college graduate, 67% reporting annual household income ≥75K USD. 10% of adolescents reported history of COVID-19 infection, 51% had a family member/other important person diagnosed, and 12% had a family member/other important person die from COVID-19 complications. 49% of parents reported loss of job or salary reduction. 29% of adolescents and 33% of parents reported significant struggle to manage T1D during the pandemic (Table 1). Adolescents who reported more difficulty with T1D management were more likely to have A1C ≥ 8%, p<.01. Qualitative themes indicated perceived positive, negative, and neutral effects of the pandemic on: T1D self-care, exercise, food, mental health, telehealth, and motivation. Conclusions: Discussing how the pandemic impacted families' T1D management may be an important focus for clinicians, especially for adolescents with above-target A1C. Strategies to improve resilience for ongoing and future stress may be of value. (Table Presented).

A magnetic nanoparticle labeled immunochromatography kit for sars-cov-2 infection diagnosis

The novel coronavirus disease (COVID-19) is breaking out and spreading rapidly around the world. There is an urgent need for an accurate and rapid detection method to quickly find infected patients and asymptomatic carriers in order to prevent the spread of the severe acute respiratory syndrome coronavirus [SARS-CoV-2]. In this paper, we designed a test strip which used the principle of double antigen sandwich. Fe3O4 magnetic nanobeads are firstly coupled with specific antibodies, and the S protein of the new coronavirus is used as the coating antigen to capture specific antibodies against the new coronavirus, which is used to detect the virus nucleoprotein of specific antibodies in clinical samples. At the same time, Fe3O4 magnetic nanobeads have unique magnetic properties, which can be used to generate different types of detection signals and simplify the detection process. These results can be judged by color changes and magnetic changes at the test and control lines. Compared with the traditional method, this test strip of Fe3O4 magnetic nanobeads has high sensitivity and can qualitatively detect samples within 15 minutes. The magnetic performance of the magnetic nanobeads can be used to improve the sensitivity of the strip in our further research and product development.

Designing a novel nano-vaccine against SARS-CoV-2

The new coronavirus SARS-CoV-2 has become a global pandemic, which has had a huge impact on the lives of people around the world and has caused huge impacts and losses on global economic development. To now, there is still no effective drug or therapy against coronavirus. A large number of studies have shown that vaccines are the ultimate weapon to eliminate major infectious diseases. The development of new vaccines against new coronaviruses is the best way to prevent new coronavirus infections. In this study, we developed a new vaccine against the new coronavirus by combining our self-developed nano adjuvant loaded with carnosine graphene oxide adjuvant with loaded with CpG molecule and RBD protein antigen. Our results showed that this vaccine can produce high titer anti-SARS-CoV-2 RBD antibody neutralizing SARS-CoV-2 in mice within 2 weeks.

A Rapid and high-sensitive real-time reverse transcription-polymerase chain reaction assay used for the detection of severe acute respiratory syndrome coronavirus 2

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a public health emergency of international concern. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is widely used as the gold standard method for the diagnosis of SARS-CoV-2 infection. However, the reliability of current real-time RT-PCR assays is questioned due to some false-negative reports. In this study, we improved the real-time RT-PCR method based on three target regions (ORF1ab, E, and N) of SARS-CoV-2. Results showed that real-time RT-PCR assays herein could complete detection within one hour after viral RNA preparation and had high sensitivity down to 5 copies of viral RNA. In addition, six clinical specimens were detected to evaluate the availability of this method. Among them, four samples were 3-plex SARS-CoV-2 positive and two were negative by real-time RT-PCR. The sensitivity was 100% (4/4), and specificity was 100% (2/2). These results demonstrate that we develop a rapid and high-sensitive real-time RT-PCR method for SARS-CoV-2 detection, which will be a powerful tool for COVID-19 identification and for monitoring suspected patients.