Linear spot Raman detector for the bi-channel immunochromatographic assay to simultaneously detect respiratory viruses

The rapid and accurate measurement of Raman signals on the test line (T-line) of surface-enhanced Raman scattering immunochromatography assay (SERS-ICA) strip is necessary for on-site detection based on SERS. Here, a novel linear spot Raman detector which can directly collect the SERS signals of the whole T-line on the dual-diagnostic SERS-ICA strip is developed for detection of multiple respiratory viruses via two magnetic SERS tags within 26min. The tags modified with two Raman reporter molecules can specifically recognize two viruses, thus enabling the simultaneous detection of both viruses on one T-line. Measured by the linear Raman detector, the limits of detection of the proposed dual-diagnostic SERS-ICA strip is as low as 870 and 263 copies/mL for severe acute respiratory syndrome coronavirus 2 and H1N1, respectively. The dual-diagnostic strip combined with the novel Raman detector herein are of great importance in the differential diagnosis of respiratory pathogens and show potential in the rapid and accurate diagnosis and control of emerging acute respiratory diseases.

Student teachers' perceptions of flipped classroom in EFL teacher education.

This paper aims to provide evidence on student teachers' perceptions of Flipped Classroom (FC) to help teacher educators (TEs) to make informed decisions about implementing FC and support student teachers to reflect on the value of FC in their teaching practice. FC, a pedagogical model requiring digital competence of students and teachers, has been a popular teaching approach for nearly two decades in K-12 and higher education. After the outbreak of Covid-19, more teachers have started to implement FC. In post-Covid-19, with the possibility of reusing video lectures made during the pandemic and the familiarity of digital skills to create digital lectures, a question for teachers is whether to continue with this approach. This paper follows an explanatory sequential mixed methods research approach. Insights from student teachers (STs) in the field of English as a foreign language (EFL) in Norway are the primary data, and surveys and focus group interviews are the main instruments to collect the data. FC's advantages and challenges perceived by STs are reported, and the possibility of STs becoming future flippers is explored. Findings from this paper indicate that STs would like to have more courses flipped in their studies, yet STs seem hesitant about flipping their courses in their teaching practice. STs also provide some practical suggestions on implementing the FC approach.

Simultaneously ultrasensitive and quantitative detection of influenza A virus, SARS-CoV-2, and respiratory syncytial virus via multichannel magnetic SERS-based lateral flow immunoassay.

Respiratory viruses usually induced similar clinical symptoms at early infection. Herein, we presented a multichannel surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-based LFA) using high-performance magnetic SERS tags for the simultaneous ultrasensitive detection of respiratory viruses, namely influenza A virus (H1N1), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and respiratory syncytial virus (RSV) in biological samples. As-prepared magnetic SERS tags can directly enrich and capture target viruses without pretreatment of samples, avoiding the interference of impurities in the samples as well as improving the sensitivity. With the capture-detection method, the detection limits of the proposed assay reached 85 copies mL-1, 8 pg mL-1, and 8 pg mL-1 for H1N1, SARS-CoV-2 and RSV, respectively. Moreover, the detection properties of the proposed method for target viruses in throat swab samples were verified, suggesting its remarkable potential for the early and rapid differential diagnosis of respiratory viruses.

Covid Rehabilitation in two Malaysian Tertiary Hospitals...American Congress of Rehabilitation Medicine (ACRM) Annual Conference, November 8-11, 2022, Chicago, Illinois

To investigate the severity of COVID-19 infections and complications experienced during inpatient admission. To study the patient outcomes after a 12 week outpatient rehabilitation program. A retrospective cohort study was conducted for a 12 month period from January 1st, 2021, to December 31st, 2021. COVID-19 patients who were referred to Rehabilitation Medicine team as inpatient or outpatient were included. Patient symptoms and outcome at 1 month and 3 month post initial contact were analysed. Sultan Ahmad Shah Medical Centre@IIUM) and Penang General Hospital are both tertiary hospitals and referral centers. 184 patients were selected for this study. By using IBM SPSS version 24, descriptive analysis was done to elaborate the demographic data and patient outcomes up to 3 months after the first contact with the Rehabilitation Medicine team. Pulmonary rehabilitation by telerehabilitation or face to face method. Residual symptom report and Medical Research Council Dyspnea Scale. The mean age of patients in the study was 51.8 years (SD 16.0). Out of 184 patients, majority were male (58.7%), from Malay ethnicity (74.5%) and hypertensive (51.6%). 25.5% of patients received telerehabilitation review. Most patients were classified as category 4 and 5 COVID-19 infection (78.8 %). During inpatient admission, many patients received treatment with steroids (77.2%) and antibiotics (69.0%). 30.5% of patients received antiviral treatment. Three commonest COVID-19 complications were pulmonary complications (59.8%), pressure injury (28.8%), and nosocomial infections (23.4%). Dyspnea and fatigue were the main reported symptoms during initial rehabilitation contact and all outpatient follow ups. The mean Medical Research Council Dyspnea scale improved from 2.7 at baseline to 1.7 at 3 months follow up. 32.1% of patients were diagnosed with Long COVID syndrome. Default rates increased from 26.1% at 1 month to 36.4% at 3 month follow up. COVID 19 patients who received inpatient rehabilitation input, followed by a 12 week outpatient follow up showed symptom improvement. Future studies need to establish long term outcomes of COVID-19 survivors in this region. All authors have no disclosures.

Covid Rehabilitation in two Malaysian Tertiary Hospitals

Research Objectives To investigate the severity of COVID-19 infections and complications experienced during inpatient admission. To study the patient outcomes after a 12 week outpatient rehabilitation program. Design A retrospective cohort study was conducted for a 12 month period from January 1st, 2021, to December 31st, 2021. COVID-19 patients who were referred to Rehabilitation Medicine team as inpatient or outpatient were included. Patient symptoms and outcome at 1 month and 3 month post initial contact were analysed. Setting Sultan Ahmad Shah Medical Centre@IIUM) and Penang General Hospital are both tertiary hospitals and referral centers. Participants 184 patients were selected for this study. By using IBM SPSS version 24, descriptive analysis was done to elaborate the demographic data and patient outcomes up to 3 months after the first contact with the Rehabilitation Medicine team. Interventions Pulmonary rehabilitation by telerehabilitation or face to face method. Main Outcome Measures Residual symptom report and Medical Research Council Dyspnea Scale. Results The mean age of patients in the study was 51.8 years (SD 16.0). Out of 184 patients, majority were male (58.7%), from Malay ethnicity (74.5%) and hypertensive (51.6%). 25.5% of patients received telerehabilitation review. Most patients were classified as category 4 and 5 COVID-19 infection (78.8 %). During inpatient admission, many patients received treatment with steroids (77.2%) and antibiotics (69.0%). 30.5% of patients received antiviral treatment. Three commonest COVID-19 complications were pulmonary complications (59.8%), pressure injury (28.8%), and nosocomial infections (23.4%). Dyspnea and fatigue were the main reported symptoms during initial rehabilitation contact and all outpatient follow ups. The mean Medical Research Council Dyspnea scale improved from 2.7 at baseline to 1.7 at 3 months follow up. 32.1% of patients were diagnosed with Long COVID syndrome. Default rates increased from 26.1% at 1 month to 36.4% at 3 month follow up. Conclusions COVID 19 patients who received inpatient rehabilitation input, followed by a 12 week outpatient follow up showed symptom improvement. Future studies need to establish long term outcomes of COVID-19 survivors in this region. Author(s) Disclosures All authors have no disclosures.

Deriving anti-epidemic policy from public sentiment: A framework based on text analysis with microblog data.

Microblog has become the "first scenario" under which the public learn about the epidemic situation and express their opinions. Public sentiment mining based on microblog data can provide a reference for the government's information disclosure, public sentiment guidance and formulation of epidemic prevention and control policy. In this paper, about 200,000 pieces of text data were collected from Jan. 1 to Feb. 26, 2020 from Sina Weibo, which is the most popular microblog website in China. And a public sentiment analysis framework suitable for Chinese-language scenarios was proposed. In this framework, a sentiment dictionary suitable for Chinese-language scenarios was constructed, and Baidu's Sentiment Analysis API was used to calculate the public sentiment indexes. Then, an analysis on the correlation between the public sentiment indexes and the COVID-19 case indicators was made. It was discovered that there is a high correlation between public sentiments and incidence trends, in which negative sentiment is of statistical significance for the prediction of epidemic development. To further explore the source of public negative sentiment, the topics of the public negative sentiment on Weibo was analyzed, and 20 topics in five categories were got. It is found that there is a strong linkage between the hot spots of public concern and the epidemic prevention and control policies. If the policies cover the hot spots of public concern in a timely and effective manner, the public negative sentiment will be effectively alleviated. The analytical framework proposed in this paper also applies to the public sentiment analysis and policy making for other major public events.

Optimization of Lipid Nanoparticles for saRNA Expression and Cellular Activation Using a Design-of-Experiment Approach.

Lipid nanoparticles (LNPs) are the leading technology for RNA delivery, given the success of the Pfizer/BioNTech and Moderna COVID-19 mRNA (mRNA) vaccines, and small interfering RNA (siRNA) therapies (patisiran). However, optimization of LNP process parameters and compositions for larger RNA payloads such as self-amplifying RNA (saRNA), which can have complex secondary structures, have not been carried out. Furthermore, the interactions between process parameters, critical quality attributes (CQAs), and function, such as protein expression and cellular activation, are not well understood. Here, we used two iterations of design of experiments (DoE) (definitive screening design and Box-Behnken design) to optimize saRNA formulations using the leading, FDA-approved ionizable lipids (MC3, ALC-0315, and SM-102). We observed that PEG is required to preserve the CQAs and that saRNA is more challenging to encapsulate and preserve than mRNA. We identified three formulations to minimize cellular activation, maximize cellular activation, or meet a CQA profile while maximizing protein expression. The significant parameters and design of the response surface modeling and multiple response optimization may be useful for designing formulations for a range of applications, such as vaccines or protein replacement therapies, for larger RNA cargoes.

Current Status and Future Perspectives on MRNA Drug Manufacturing.

The coronavirus disease of 2019 (COVID-19) pandemic launched an unprecedented global effort to rapidly develop vaccines to stem the spread of the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Messenger ribonucleic acid (mRNA) vaccines were developed quickly by companies that were actively developing mRNA therapeutics and vaccines for other indications, leading to two mRNA vaccines being not only the first SARS-CoV-2 vaccines to be approved for emergency use but also the first mRNA drugs to gain emergency use authorization and to eventually gain full approval. This was possible partly because mRNA sequences can be altered to encode nearly any protein without significantly altering its chemical properties, allowing the drug substance to be a modular component of the drug product. Lipid nanoparticle (LNP) technology required to protect the ribonucleic acid (RNA) and mediate delivery into the cytoplasm of cells is likewise modular, as are technologies and infrastructure required to encapsulate the RNA into the LNP. This enabled the rapid adaptation of the technology to a new target. Upon the coattails of the clinical success of mRNA vaccines, this modularity will pave the way for future RNA medicines for cancer, gene therapy, and RNA engineered cell therapies. In this review, trends in the publication records and clinical trial registrations are tallied to show the sharp intensification in preclinical and clinical research for RNA medicines. Demand for the manufacturing of both the RNA drug substance (DS) and the LNP drug product (DP) has already been strained, causing shortages of the vaccine, and the rise in development and translation of other mRNA drugs in the coming years will exacerbate this strain. To estimate demand for DP manufacturing, the dosing requirements for the preclinical and clinical studies of the two approved mRNA vaccines were examined. To understand the current state of mRNA-LNP production, current methods and technologies are reviewed, as are current and announced global capacities for commercial manufacturing. Finally, a vision is rationalized for how emerging technologies such as self-amplifying mRNA, microfluidic production, and trends toward integrated and distributed manufacturing will shape the future of RNA manufacturing and unlock the potential for an RNA medicine revolution.

Six-month follow-up of functional status in discharged patients with coronavirus disease 2019.

BACKGROUND: The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a 6-month follow-up of functional status of COVID-19 survivors. METHODS: We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a 6-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 4 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a 6-month follow-up after discharge. RESULTS: We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At 6-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95% CI 1.33-12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95% CI 1.06-10.81, p = 0.039). CONCLUSIONS: A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a 6-month follow-up; intervention strategies are required.

Rapid field determination of SARS-CoV-2 by a colorimetric and fluorescent dual-functional lateral flow immunoassay biosensor.

The rapid and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the early stage of virus infection can effectively prevent the spread of the virus and control the epidemic. Here, a colorimetric and fluorescent dual-functional lateral flow immunoassay (LFIA) biosensor was developed for the rapid and sensitive detection of spike 1 (S1) protein of SARS-CoV-2. A novel dual-functional immune label was fabricated by coating a single-layer shell formed by mixing 20 nm Au nanoparticles (Au NPs) and quantum dots (QDs) on SiO2 core to produce strong colorimetric and fluorescence signals and ensure good monodispersity and high stability. The colorimetric signal was used for visual detection and rapid screening of suspected SARS-CoV-2 infection on sites. The fluorescence signal was utilized for sensitive and quantitative detection of virus infection at the early stage. The detection limits of detecting S1 protein via colorimetric and fluorescence functions of the biosensor were 1 and 0.033 ng/mL, respectively. Furthermore, we evaluated the performance of the biosensor for analyzing real samples. The novel biosensor developed herein had good repeatability, specificity and accuracy, which showed great potential as a tool for rapidly detecting SARS-CoV-2.

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

ABSTRACT: In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.

Clinical Characteristics and Outcomes of COVID-19-Infected Cancer Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: Previous studies have indicated coronavirus disease 2019 (COVID-19) patients with cancer have a high fatality rate. METHODS: We conducted a systematic review of studies that reported fatalities in COVID-19 patients with cancer. A comprehensive meta-analysis that assessed the overall case fatality rate and associated risk factors was performed. Using individual patient data, univariate and multivariable logistic regression analyses were used to estimate odds ratios (OR) for each variable with outcomes. RESULTS: We included 15 studies with 3019 patients, of which 1628 were men; 41.0% were from the United Kingdom and Europe, followed by the United States and Canada (35.7%), and Asia (China, 23.3%). The overall case fatality rate of COVID-19 patients with cancer measured 22.4% (95% confidence interval [CI] = 17.3% to 28.0%). Univariate analysis revealed age (OR = 3.57, 95% CI = 1.80 to 7.06), male sex (OR = 2.10, 95% CI = 1.07 to 4.13), and comorbidity (OR = 2.00, 95% CI = 1.04 to 3.85) were associated with increased risk of severe events (defined as the individuals being admitted to the intensive care unit, or requiring invasive ventilation, or death). In multivariable analysis, only age greater than 65 years (OR = 3.16, 95% CI = 1.45 to 6.88) and being male (OR = 2.29, 95% CI = 1.07 to 4.87) were associated with increased risk of severe events. CONCLUSIONS: Our analysis demonstrated that COVID-19 patients with cancer have a higher fatality rate compared with that of COVID-19 patients without cancer. Age and sex appear to be risk factors associated with a poorer prognosis.

Six-month follow-up of functional status in discharged patients with coronavirus disease 2019 (preprint)

Background The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a six-month follow-up of functional status of COVID-19 survivors.Methods We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a six-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 5 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a six-month follow-up after discharge.Results We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At six-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95%CI 1.33 - 12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95%CI 1.06 - 10.81, p = 0.039).Conclusions A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a six-month follow-up; intervention strategies are required.

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.