A Loop-Mediated Isothermal Amplification (Lamp)-Based Point-of-Care System for Rapid on-Site Clinical Detection of Sars-Cov-2 Viruses

In the ongoing COVID-19 pandemic, sensitive and rapid on-site detection of the SARS-CoV-2 coronavirus has been one of crucial objectives. A point-of-care (PoC) device called PATHPOD for quick, on-site detection of SARS-CoV-2 employing a real-time reverse-transcription loop-mediated isothermal amplification (RT-rLAMP) reaction on a polymer cartridge. The PATHPOD consists of a standalone device (weighing under 1.2 kg) and a cartridge, and can identify 10 distinct samples and 2 controls in less than 50 minutes. The PATHPOD PoC system is fabricated and clinically validated for the first time in this work © 2023 IEEE.

Mucociliary Transport Deficiency in Syrian Hamsters with SARS-CoV-2

RATIONALE: SARS-CoV-2 causes COVID-19 disease and infects respiratory epithelial cells, but how it affects ciliated cell function and the mucociliary transport apparatus is unknown. Abnormal mucociliary function could predispose to COVID-19 progression and/or secondary infection. Micro-optical coherence tomography (μOCT) is a novel method to simultaneously visualize and quantify the functional microanatomy of airways. Here, we established a hamster model of COVID-19 and evaluated their tracheas by μOCT. METHODS: Adult golden Syrian hamsters were inoculated intranasally with 3.2 × 105 (high dose, HD, N=4) or 3.2 × 104 (low dose, LD, N=4) plaque-forming units of SARS-CoV-2 (WA/1 strain). Clinical signs were monitored daily, nasal brushes collected intermittently, and hamsters were euthanized seven days (D7) after inoculation. Tracheas were imaged by μOCT, nasal washes and bronchial alveolar lavage fluid (BALF) from right lung lobes were collected for quantitation of viral load by qRT-PCR, and left lungs were inflated with and fixed in 10% neutral buffered formalin for histological analysis. Age-matched hamsters were used as uninfected controls (N=5). RESULTS: SARS-CoV-2 hamsters lost weight through D7 in dose-dependent fashion (-11% in HD vs. -4% in LD, p=0.02) and HD hamsters showed moderate lethargy. Nasal brushes on D4 and nasal washes on D7 contain 105-106 genome copies of virus while BALF on D7 was less than 104 genome copies and intermittently detected in LD. Histology demonstrated patchy and multifocal interstitial pneumonia (type II pneumocyte hyperplasia and mononuclear cell infiltrate), with ∼20% area affected in HD that was more variable in LD. Functional microanatomy of tracheas revealed diminished area of active ciliary beating (control 18 ± 2 vs. LD 7 ± 1%, p=0.0002, control vs. HD 9 ± 1%, p=0.001), reduced ciliary beat frequency (control 10.88 ± 0.70 vs. LD 8.83 ± 0.34 Hz, p=0.01, control vs. HD 8.26 ± 0.33 Hz, p=0.001), and decreased periciliary liquid depth (control 6.41 ± 0.18 vs. HD 5.61± 0.12 μ m, p=0.027). Mucociliary transport rate was diminished (control 0.84 ± 0.19 vs. LD 0.48 ± 0.16 vs. HD 0.37 ± 0.13 mm/min) although not statistically significant. Additional cohorts are in progress. CONCLUSION: SARS-Cov-2 infected hamsters exhibit reduced body weight, high viral load, and histopathological injury through 7 days. SARS-CoV-2 caused functional deficits of the mucociliary transport apparatus, consistent with early findings in COVID-19 patients (see Vijaykumar et al.). Abnormal ciliated cell function is important to SARS-CoV-2 pathogenesis, and may help monitor progression and represent a treatment opportunity for COVID-19.

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naive Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.