诺如病毒急性胃肠炎流行特征及病原学特征分析

目的:分析诺如病毒急性胃肠炎的流行特征及病原学特征。方法:选取2021年2月—2023年1月于重庆市永川区儿童医院就诊的疑似诺如病毒引起的急性肠胃炎患者156例作为研究对象,分析诺如病毒流行病学及病原学特点并进行治疗。结果:诺如病毒阳性者发病月份前三位为11月、10月、12月。学校暴发人数最多,占比67.95%。0~9岁儿童最多,占比57.02%。GⅡ型株占比73.68%(84/114),GⅠ型株占比26.32%(30/114)。治疗后,患者中性粒细胞、白细胞、红细胞、血红蛋白、中性粒细胞百分比水平低于治疗前,淋巴细胞百分比、单核细胞百分比、嗜酸性粒细胞百分比水平高于治疗前,差异有统计学意义(P<0.05)。结论:诺如病毒儿童、学校感染相对密集,GⅡ基因型居多,临床需不断提高实验室检测质量,掌握其病原学特征,以提高疫情防控质量。

淋巴瘤所致冷凝集素综合征3例临床回顾性分析并文献复习

目的回顾性分析单中心淋巴瘤所致冷凝集素综合征的临床表现、实验室检查特征及预后。方法收集2011年12月-2018年11月我院收治的3例淋巴瘤所致冷凝集素综合征患者的病历资料,分析其临床特征、实验室检查结果及生存情况。结果3例淋巴瘤所致冷凝集素综合征患者中包括2例女性,1例男性,中位年龄33(24~73)岁,2例以贫血症状急性加重就诊,1例以指端坏死就诊,3例患者均有明确诱发加重因素;中位血红蛋白52(49~88)g·L^-1,中位冷凝集素滴度1∶512(1∶64~1∶1024);2例患者继发于弥漫大B细胞淋巴瘤,1例继发于淋巴浆细胞淋巴瘤/华氏巨球蛋白血症,均弥漫侵犯骨髓,免疫表型均表达CD20+B细胞淋巴瘤,轻链限制性表达。2例患者行R-CHOP方案化疗6个疗程,其中1例化疗2个疗程后评估达PR,4个疗程后病情进展,6个疗程后死亡,生存时间6个月;1例化疗1个疗程后评估达PR,2个疗程后达CR,生存时间23个月(存活至今)。另1例患者无治疗意愿,仅坏死指端局部保暖治疗,局部症状有所改善,生存时间16个月(存活至今)。结论冷凝集素综合征具有对冷接触相关的特殊临床表现,寒冷、感染、创伤、手术等均可诱发加重溶血,应尽量避免诱发加重因素并积极治疗,其长期疗效和预后取决于其原发疾病的治疗。

辣木茎叶营养成分及多酚提取物体外抗氧化研究

以辣木茎叶为原料,分析了辣木茎叶中粗蛋白、粗脂肪、粗纤维、粗灰分、无氮浸出物、钙、总磷及多酚含量,并提取了辣木茎叶多酚提取物,以DPPH自由基、ABTS自由基、羟自由基清除率以及金属螯合能力等指标评价辣木茎叶多酚提取物体外抗氧化能力。结果表明:辣木茎叶中粗蛋白含量为(17.98±0.87)%,粗脂肪含量为(2.71±0.17)%,粗纤维含量为(27.74±0.02)%,无氮浸出物含量为(35.56±0.96)%,粗灰分含量为(9.01±0.15)%,钙含量为(1.76±0.02)%,总磷含量为(0.62±0.01)%,多酚含量为(4.01±0.01)%。辣木茎叶多酚有较强的清除DPPH自由基的能力,浓度0.50 mg/mL时清除能力最强为91.08%,对羟基自由基的清除能力优于BHT,浓度1.25 mg/mL时,辣木茎叶多酚和BHT对羟自由基的清除率分别为97.50%和13.87%;辣木茎叶多酚对ABTS自由基表也现出了较强的清除能力,浓度1.25 mg/mL时,辣木茎叶多酚和BHT对ABTS自由基清除率分别为97.80%和99.18%;辣木茎叶多酚也具有一定的金属螯合能力,浓度在0.75 mg/mL时,辣木茎叶多酚金属螯合能力达到78.99%,而EDTA的金属螯合能力最高98.03%。

甲型H1N1流感患儿血常规、血清淀粉样蛋白A及C反应蛋白水平

目的:分析甲型H1N1流感(简称"甲流")患儿血常规、血清淀粉样蛋白A(serum amyloid A,SA A)及C反应蛋白(Creactiveprotein,CRP)水平,为初步诊断儿童甲流提供帮助。方法:选择138例甲流患儿(A组)、126例普通流感患儿(B组)和120名健康对照儿童(C组),统计分析白细胞(white blood cells,WBC)计数、血小板(platelet,PLT)计数、平均血小板体积(mean plateletv o l u m e, M P V)、中性粒细胞百分比、淋巴细胞百分比、单核细胞百分比以及全血S A A和C R P水平。三组间计量资料比较采用F检验,两两组间计量资料比较采用L SD检验。结果:3组W B C计数差异无统计学意义(F=2.698, P>0.05); A组PLT计数较B组和C组显著降低(F=6.598,P <0. 0 5); A组M P V水平与B, C两组相比,差异有统计学意义(F=5. 9 8 9, P <0. 0 5)。采用L S D检验进行两两组间比较:A组PLT计数显著低于B组(q=6.26,P<0.05),但与C组相比差异无统计学意义(q=3.02,P>0.05);A组MPV水平显著低于C组(q=6.23,P<0.05),B组MPV水平也显著低于C组(q=6.84,P<0.05),但A组与B组MPV水平无显著差异(q=1.06,P>0.05)。三组中性粒细胞含量、单核细胞含量、淋巴细胞含量差异均有统计学意义(F值分别为5.364,5.261,6.169,均P <0.05)。A组淋巴细胞和中性粒细胞含量与C组比较差异无统计学意义(q值分别为2.36、 1.94,均P>0.05),但A组单核细胞百分比明显高于B组和C组,差异有统计学意义(q值分别为6.29,6. 1 0,均P <0. 0 5)。A组S A A水平与B组和C组比较,差异有统计学意义(F=8. 1 9 8, P <0. 0 5)。A组与B组SAA水平差异有统计学意义(q=6.97,P<0.05);A,B两组SAA水平均显著高于C组,差异有统计学意义(q值分别为6.99、6.07,均P<0.05)。A组CRP水平与B组和C组比较,差异有统计学意义(F=7.654,P<0.05)。A组与B组间CRP水平差异无统计学意义(q=1.94,P>0.05);但A,B两组C R P水平均显著高于C组,差异有统计学意义(q值分别为6. 4 8, 6. 6 1,均P <0. 0 5)。通过RO C分析得知,SA A诊断甲流的RO C曲线下面积(AUC)为0.823(95%CI0.701～0.944),最佳诊断临界值为121.34mg/L;CRP诊断甲流的AUC为0.904(95%CI0.814～0.994),最佳诊断临界值为11.06mg/L。结论:甲流患儿血常规中单核细胞比例、SA A和CRP水平增高,而PLT总数低于普通流感患儿,这可为初步鉴别普通流感和甲流提供参考。

Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO_2 in China

Observations of atmospheric carbon dioxide (CO2 ) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO2 can be analyzed and modeled by geostatistical methods, and CO2 values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO2 , and does not consider temporal variation in the satellite-observed CO2 data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO2 in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO2 data sets in China. Prediction of monthly CO2 using the spatiotemporal variogram model and spacetime kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient (r2 ), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO2 than those from the spatial-only approach.

A comparison of atmospheric CO_2 concentration GOSAT-based observations and model simulations

Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2concentration between satellite observations and model simulations in China is larger than that in the US and the globe.This inconsistency is related to the GOSAT retrieval error of CO2 caused by the interference among input parameters of satellite retrieval algorithm,and the uncertainty of driving parameters in GEOS-Chem model.