BACKGROUND The COVID-19 pandemic has exposed major flaws in the PPE supply chain and healthcare facilities are at high risk of becoming overwhelmed beyond their capacity. Therefore, it is paramount to develop new technologies that can be leveraged as an early warning and detection system. OBJECTIVE In this study, Twitter sentiment analysis was utilized for two objectives. One was to determine if an increase in tweets pertaining to PPE shortages correspond to an actual increase in PPE shortages in long-term care facilities in California. The second objective was to apply the same methodology toward medical supply shortage prediction in the context of developing countries; namely, to determine if negative sentiment tweets pertaining to COVID-19 in Brazil and India correspond to a greater use of hospital Intensive Care Unit (ICU) beds in these countries. METHODS Two time series were created including frequency of negative sentiment tweets (extracted using VADER) and ground truth frequency of medical resource shortages or demands. Then, the Granger causality test was used to determine if the time series for tweets can be useful for forecasting medical resource shortages. RESULTS The sample size for the negative sentiment tweet analysis was 6,970 tweets pertaining to California, 24,1105 tweets for the analysis of ICU bed demand in Brazil, and 8,613,049 tweets for India. For California, the results of the Granger test were significant at lag 2 (P = 0.035) and lag 5 (P = 0.005). For Brazil, the Granger test was significant for six of the 25 regions which passed the Augmented-Dickey Fuller test: Amazonas (P = 0.039, lag 4), Bahia (P = 0.019, lag 1), Federal District (P = 0.010, lag 1), Espírito Santo (P = 0.006, lag 3), Roraima (P = 0.030, lag 1), and São Paulo (P = 0.013, lag 1). For India, the results of the Granger test were significant for ten of the 27 regions which passed the Augmented-Dickey Fuller test: Tripura (P = 0.020, lag 1), Gujarat (P = 0.023 , lag 3), West Bengal (P = 0.008, lag 2), Haryana (P = 0.045, lag 1), Bihar (P = 0.126, lag 1), Karnataka (P = 0.037, lag 3), Odisha (P = 0.048, lag 4), Andhra Pradesh (P = 0.037, lag 4), Jharkhand (P = 0.0004, lag 1), and Himachal Pradesh (P = 0.020, lag 3). CONCLUSIONS This study provides a novel approach for identifying regions of PPE shortage and high hospital bed demand by analyzing Twitter sentiment data given that Twitter can be used as a useful tool for rapidly organizing relief efforts. Natural language processing-driven Tweet extraction systems have the potential to be an effective method that allows for early detection of medical resource demand surges.

tRNAs undergo an extensive maturation process involving post-transcriptional modifications often associated with tRNA structural stability and promoting the native fold. Impaired post-transcriptional modification has been linked to human disease, likely through defects in translation, mitochondrial function, and increased susceptibility to degradation by various tRNA decay pathways. More recently, evidence has emerged that bacterial tRNA modification enzymes can act as tRNA chaperones to guide tRNA folding in a manner independent from catalytic activity. Here, we provide evidence that the fission yeast tRNA methyltransferase Trm1, which dimethylates nuclear- and mitochondrial-encoded tRNAs at G26, can also promote tRNA functionality in the absence of catalysis. We show that wild type and catalytic-dead Trm1 are active in an in vivo tRNA-mediated suppression assay and possess in vitro RNA folding activity, suggesting an alternate function as a tRNA chaperone. Further, we demonstrate crosstalk between Trm1 and the RNA chaperone La, with La binding to the 3′ end and body of nascent pre-tRNA inhibiting tRNA dimethylation in vivo and in vitro. Collectively, these results support the hypothesis for multi-functional tRNA modification enzymes that combine catalytic and non-catalytic activities to shape tRNA structure and function.

No abstract