RESUMEN
Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization.
RESUMEN
Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization. Graphical
RESUMEN
BACKGROUND: Severe coronavirus disease 2019 (COVID-19) may require continuous administration of analgesics, sedatives, and muscle relaxants. Nafamostat has recently been reported as a therapeutic agent for COVID-19. However, there is a lack of information on the compatibility of nafamostat with the aforementioned drug classes. This study evaluated the physical compatibility of nafamostat with these drug classes. METHODS: Nafamostat was combined with 1-3 target drugs (fentanyl, morphine, midazolam, dexmedetomidine, and rocuronium). Fifteen physical compatibility tests were conducted. Nafamostat was dissolved in 5% glucose solution; the final concentration was 10 mg/mL. All other medications were diluted in 0.9% sodium chloride to obtain clinically relevant concentrations. The power of hydrogen (pH) of all medications was measured during each test. Compatibility tests were conducted with 4 test solutions in which nafamostat and the target drugs were compounded at equal volume ratios (1:1, 1:1:1, or 1:1:1:1). Visual appearance, turbidity, and pH were evaluated immediately after mixing and at 1 and 3 hours. Physical incompatibilities were defined as gross precipitation, cloudiness, appearance of the Tyndall effect, or a turbidity change of ≥0.5 nephelometric turbidity units (NTU) based on nafamostat. RESULTS: The mean pH of nafamostat was 3.13 ± 0.03. The combination of nafamostat, fentanyl, and dexmedetomidine had the highest pH (3.39 ± 0.01; 3 hours after mixing). All drugs were compatible with nafamostat until 3 hours after admixture, with a mean turbidity value of ≤0.03 NTU. CONCLUSIONS: Infusions combining nafamostat with the tested sedatives, analgesics, and muscle relaxants could be safely administered.