Detection of Antibodies for COVID-19 from Reflectance Spectrum Using Supervised Machine Learning

Since the coronavirus disease 2019 occurred, the lateral flow immunoassay (LFIA) test strip has become a global testing tool for convenience and low cost. However, some studies have shown that LFIA strips perform poorly compared to other professional testing methods. This paper proposes a new method to improve the accuracy of LFIA strips using spectral signals. A spectrochip module is applied to disperse the reflected light from the LFIA strips. The obtained spectral signals will be used for supervised machine learning. After training, the trained model has 93.8% accuracy compared to the standard test. This result indicated that the evaluation method based on the spectrum of LFIA strips could enhance the detection performance. © 2022 IEEE.

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment (PPE) donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19. However, the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers??? PPE use in the field of healthcare. In this paper, we construct a dress codes dataset for medical staff under the epidemic. And based on this, we propose a PPE donning automatic detection approach using deep learning. With the participation of health care personnel, we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment, and an effective and robust dataset is constructed with a total of 5233 preprocessed images. Starting from the task???s dual requirements for speed and accuracy, we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety. Experimental results show that compared with three typical deep -learning-based detection models, our method achieves a relatively optimal balance while ensuring high detection accuracy (84.14%), with faster processing time (42.02 ms) after the average analysis of 17 classes of PPE donning situation. Overall, this research focuses on the automatic detection of worker safety protection for the first time in healthcare, which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

Retrospective Multihospital Prevalence Study of COVID-19 Acute Neurological Manifestations

Objective: We aim to investigate the prevalence, characteristics and outcomes of COVID-19 patients with neurological manifestations Background: To date, SARS-CoV2 has infected 213 million population worldwide. It is a multisystem disease affecting primarily the respiratory system, but neurological manifestations have been increasingly described in the literature. Design/Methods: Consecutive patients diagnosed with SARS-CoV2 admitted to 5 hospitals in Detroit Medical Center from March 3rd, 2020-May 1st, 2020 were included. Basic demographics and clinical manifestations were included. Relevant laboratory findings and neuroimaging were reported. Results: 413 patients were included in the study. Patients' demographics were as follows: mean age-66 years, 212 (51%) male, 346 (87%) African-American. 219(53%) patients had neurological symptoms at presentation, 32 patients presented purely with neurological symptoms. Other symptoms at onset include-respiratory 312(76%), constitutional 250(61%) and gastrointestinal 104(25%). 121(29%) patients were admitted to ICU, mean days from admission to ICU was 3.14 days. Incidence of neurological presentations were as follows: Encephalopathy 191(46.25%), myalgia 51(12.35%), headache 27 (6.54%), vertigo 20 (4.84%), hypogeusia 14 (3.39%), anosmia 12 (2.9%), stroke 13(3.14%), seizure 11 (2.9%). For patients with encephalopathy, median GCS at the onset of encephalopathy was 13 with IQR4. 94 (49.21%) of these patients were admitted to ICU;53(27.75%) were without coexisting toxic, metabolic or hypoxic factors contributing to encephalopathy. For patients with stroke, 12 patients presented with acute ischemic stroke, 2 with hemorrhagic conversion and 1 patient had cerebral venous sinus thrombosis. Characteristics of stroke were as follows: 8-multiple vascular territory, 11-cryptogenic etiology, 3-concurrent thromboembolic event. Median D-dimer was 5.76mg/LFEU(IQR3.74) and fibrinogen 550mg/dl(IQR 2.1). 2 patients received thrombolysis and 1 underwent thrombectomy. Mortality was 77%, Modified Rankin Scale (MRS)at baseline was 0-2 and all except 1 patient had MRS of 4-6 on discharge. Conclusions: Neurological manifestation is common amongst patients with SARS-CoV-2. Presence of encephalopathy or stroke confers an increased risk of mortality and morbidity.

NEUROPILIN-1 MEDIATES SARS-CoV-2 INFECTION of ASTROCYTES PROMOTING NEURON DYSFUNCTION

Background: SARS-CoV-2 primarily infects the lung but may also damage other organs including the brain, heart, kidney, and intestine. Central nervous system (CNS) disorders include loss of smell and taste, headache, delirium, acute psychosis, seizures, and stroke. Pathological loss of gray matter occurs in SARS-CoV-2 infection but it is unclear whether this is due to direct viral infection, indirect effects associated with systemic inflammation, or both. Methods: We used iPSC-derived brain organoids and primary human astrocytes from cerebral cortex to study direct SARS-CoV-2 infection, as confirmed by Spike and Nucleocapsid immunostaining and RT-qPCR. siRNAs, blocking antibodies, and small molecule inhibitors were used to assess SARS-CoV-2 receptor candidates. Bulk RNA-seq, DNA methylation seq, and Nanostring GeoMx digital spatial profiling were utilized to identify virus-induced changes in host gene expression. Results: Astrocytes were robustly infected by SARS-CoV-2 in brain organoids while neurons and neuroprogenitor cells supported only low-level infection. Based on siRNA knockdowns, Neuropilin-1, not ACE2, functioned as the primary receptor for SARS-CoV-2 in astrocytes. The endolysosomal two-pore channel protein, TPC, also facilitated infection likely through its regulatory effects on endocytosis. Other alternative receptors, including the AXL tyrosine kinase, CD147, and dipeptidyl protease 4 (DPP4), did not function as SARS-CoV-2 receptors in astrocytes. SARS-CoV-2 infection dynamically induced type I, II, and III interferons, and genes involved in Toll-like receptor signaling, MDA5 and RIG-I sensing of double-stranded RNA, and production of inflammatory cytokines. Genes activating apoptosis were also increased. Down-regulated genes included those involved in water, ion and lipid transport, synaptic transmission, and formation of cell junctions. Epigenetic analyses revealed transcriptional changes related to DNA methylation states, particularly decreased DNA methylation in interferon-related genes. Long-term viral infection of brain organoids resulted in progressive neuronal degeneration and death. Conclusion: Our findings support a model where SARS-CoV-2 infection of astrocytes produces a panoply of changes in the expression of genes regulating innate immune signaling and inflammatory responses. Deregulation of these genes in astrocytes produces a microenvironment within the CNS that ultimately disrupts normal neuron function, promoting neuronal cell death and CNS deficits.

Visualization Analysis of Spatiotemporal Data of COVID-19

Aiming at the problem of COVID-19 epidemic data visualization, this paper proposes a spatiotemporal visualization analysis method based on the technology of scraping crawler and visualization, and carries on the visualization analysis and research, intuitively shows the development and change of the epidemic situation in different countries and regions, and excavates its spatiotemporal variation rules. Firstly, we use scrapy crawler framework to collect COVID-19 epidemic data;then, the collected data were cleaned and processed to construct a spatiotemporal data set of COVID-19 epidemic;finally, pyecharts is used to analyze the dataset data visually. The results showed the changes and trends of epidemic situation in different countries and regions, and provided reference for epidemic prevention and control. © 2021 IEEE.

Modeling the COVID-19 Epidemic in PR China

This study is to investigate the impacts of the strategies against COVID-19 epidemic in China, so as to provide a solid reference to control its spread in the world. A two-stage dynamics transmission model is proposed using 'lockdown of Wuhan city' as the time line. The first stage is a SEIR derived model that considers the contagious of the exposed ones. It simulates the COVID-19 epidemic in Hubei Province before 'lockdown of Wuhan city'. The second stage is the new transmission dynamics model proposed in this paper and referred to as SEIRQH. It takes into account the influence over the COVID-19 epidemic from the series of strategies taken by Chinese government, such as travel restriction, contact tracing, centralized treatment, the asymptomatic infected patients, hospitalized patients and so on. It simulates the COVID-19 epidemic in China after 'lockdown of Wuhan city'. The least square method is used to estimate the parameters of the SEIR derived model and the SEIRQH model based on the collected data of COVID-19 from Hubei Province and the mainland of China before April 30, 2020. The experimental results found that the SEIR derived model simulates the actual data in Hubei Province before 'lockdown of Wuhan city', and the basic reproduction number of COVID-19 epidemic in Hubei Province is 3.2035. The SEIRQH model simulates the number of the hospitalized persons of COVID-19 in Hubei Province and the mainland of China after the 'lockdown of Wuhan city' perfectly. The control reproductive number is 0.11428 and 0.09796 in Hubei Province and the mainland of China, respectively. Our two-stage dynamics transmission model simulates the COVID-19 epidemic in China, especially our SEIRQH model simulates the actual data very well. The strategies taken by Chinese government are effective, and plays significant role in preventing the spread of COVID-19 epidemic in China. This study gives the reference to World Health Organization and other countries against the COVID-19 epidemic. © 2021 IEEE.

A low-cost, programmable, and multi-functional droplet printing system for low copy number SARS-CoV-2 digital PCR determination

Droplet digital polymerase chain reaction (ddPCR) is a rapidly developing technology used for accurate, quantitative analysis of rare samples. However, ddPCR has only been implemented in research field but rarely in clinical trials due to its relatively high cost and negative user experiences compared with qPCR. We developed a novel programmable on-demand droplet generator based on a microfluidic adaptive printing system (MAP-ddPCR) to create a cost-effective ddPCR process. This process easily produces low-volume, spot-on-demand droplet dispensing and data analysis using simple equipment and workflow. Compared with the existing droplet generation systems that rely on surface-assistant, the proposed MAP system generates a variety of droplet arrays on regular non-surface-treated glass slides. This system directly processes PCR and performs data analysis without requiring a secondary droplets transfer. The static and independent properties of each droplet dramatically avoid cross-contamination during PCR, provide the opportunity to trace droplets in real-time and simplify the analysis. We demonstrated that the MAP-ddPCR produces reliable data using gradient concentrations of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in human genomic cDNA. These concentrations were further verified by quantitative PCR (qPCR). In addition, a very low viral load of SARS-CoV-2 was precisely detected and quantified by the MAP-ddPCR system. Finally, this system is affordable and simpler to integrate compared to other more expensive commercial digital PCR methods. Therefore, the proposed MAP-ddPCR system is expected to have a significant impact on market applications. © 2021 Elsevier B.V.

Acute Encephalopathy in COVID-19 patients Early Experience from an Inner-City Hospital

Objective: We aim to investigate the prevalence, characteristics and outcomes of COVID-19 patients with encephalopathy. Other neurological manifestations of COVID-19 were described. Background: SARS-CoV-2 has rapidly spread worldwide and has now affected more than 30 million people. Although respiratory symptoms are the primary clinical manifestations of COVID-19, neurological manifestations of COVID-19 are increasingly recognized. Encephalopathy is reported as a common neurological presentation of COVID-19. The characteristics of patients with COVID-19 associated encephalopathy, including potential confounding toxic/metabolic/hypoxic factors has not been explored. Design/Methods: We retrospectively reviewed all patients consulted to the neurology service at the Detroit Medical Centre, from March 3 , 2020 to May 1 2020 who were tested positive for SARS-COV2. Clinical and laboratory data were recorded. Characteristics of encephalopathic COVID-19 patients with or without confounders were compared. Statistical analysis was performed using SPSS. Results: 49 patients were included, 40 patients (81.6%) had encephalopathy, of whom 21 patients (52.5%) had no confounders. Most common confounders were hypoxia and uremia. Patients with confounders were more likely to have dementia at baseline (p=0.049), significantly elevated inflammatory markers-C-reactive protein (P=0.02), white blood cell count(p=0.019), Ddimer(p=0.015). They were also less likely to be discharged home (p=0.009). Overall mortality is high in patient with encephalopathy (65%). 5 patients had embolic strokes, 5 had new onset seizures and 2 patients had pleocytosis on cerebrospinal fluid examination. Conclusions: We found a high prevalence of COVID-19 associated encephalopathy, independent of confounders. COVID-19 associated encephalopathy can be attributed to stroke, seizure, meningoencephalitis or idiopathic. This early report is part of an ongoing study with a larger cohort of all COVID-19 patients that continue to be admitted to our center to investigate underlying etiological mechanisms of encephalopathy, including long-term follow up of these patients.