爱知病毒(AiV)在全球范围内导致胃肠炎,通过受污染的贝类和水传播。AiV对常见灭活过程的抗性/耐受性以及缺乏市售疫苗使得有必要研究其热灭活动力学。这项研究通过线性和Weibull模型使用2ml无菌玻璃小瓶评估了AiV在细胞培养基中的热灭活。在50、54和58°C的水浴中进行三次AiV滴度为7个对数菌斑形成单位(PFU)/ml的热处理,最长90分钟。每个稀释的一式两份的空斑测定用于确定感染性病毒滴度。AiV在50±1°C时的线性模型D值(±=标准误差)(出现时间=68s),54±0.7°C(130s),58±0.6°C(251s)为43.3±4.23(R2=0.40,RMSE=0.56),5.69±0.28(R2=0.80,RMSE=0.43),和1.20±0.63分钟(R2=0.69,RMSE=0.39),分别,线性模型z值为5.14±0.39°C(R2=0.99,RMSE=0.08)。对于相同的温度,Weibull模型td=1值为20.98±8.8(R2=0.62,RMSE=0.46,α(尺度参数)=2.30,β(形状参数)=0.38),3.84±0.69(R2=0.85,RMSE=0.38,α=1.08,β=0.66),0.87±0.10min(R2=0.80,RMSE=0.32,α=0.22,β=0.61),分别为z值(使用Td=1)为5.79±0.22°C(R2=1.0,RMSE=0.03)。对于具有较高R2和较低RMSE值的对数减少对时间的Weibull模型获得了更好的拟合。AiV灭活参数的应用有助于降低AiV爆发的风险。
Aichi virus (AiV) that results in gastroenteritis worldwide, is spread through contaminated shellfish and water. The resistance/tolerance of AiV to common inactivation processes along with the absence of commercially available vaccines makes it necessary to study its thermal inactivation kinetics. This research evaluated the heat inactivation of AiV in cell-culture media using 2-ml sterile glass vials by the linear and Weibull models. Heat treatments of AiV titers of 7 log plaque forming units (PFU)/ml were conducted thrice in a water-bath at 50, 54, and 58 °C for up to 90 min. Plaque assays for each dilution in duplicate were used to determine infectious virus titers. Linear model D-values for AiV at 50 ± 1 °C (± = standard error) (come-up time = 68 s), 54 ± 0.7 °C (130 s), and 58 ± 0.6°C (251 s) were 43.3 ± 4.23 (R2 = 0.40, RMSE = 0.56), 5.69 ± 0.28 (R2 = 0.80, RMSE = 0.43), and 1.20 ± 0.63 min (R2 = 0.69, RMSE = 0.39), respectively, and the linear model z-value was 5.14 ± 0.39°C (R2 = 0.99, RMSE = 0.08). For the same temperatures, the Weibull model td = 1 values were 20.98 ± 8.8 (R2 = 0.62, RMSE = 0.46, α (scale parameter) = 2.30, β (shape parameter) = 0.38), 3.84 ± 0.69 (R2 = 0.85, RMSE = 0.38, α = 1.08, β = 0.66), and 0.87 ± 0.10 min (R2 = 0.80, RMSE = 0.32, α = 0.22, β = 0.61), respectively and the z-value (using Td = 1 ) was 5.79 ± 0.22 °C (R2 = 1.0, RMSE = 0.03). A better fit was obtained with the Weibull model for log reductions versus time with higher R2 and lower RMSE values. Application of AiV inactivation parameters can help reduce the risk of AiV outbreaks.