Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve,Eastern Tibet Plateau

Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plateau,has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles.However,an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined.To provide this information,Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression,and two machine learning approaches–random forest and extreme gradient boosting,with 54 inventory plots.Regardless of forest type,Observed AGB of the Reserve varied from 61.7 to 475.1 Mg hawith an average of 180.6 Mg ha.Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches.The results highlight a potential way to improve the prediction of forest AGB in mountainous regions.Modelled AGB indicated a strong spatial variability.However,the modelled biomass varied greatly with global biomass products,indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required,particularly for areas with complex terrain to improve model accuracy.

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

基于Sentinel-2A/B时序数据与随机森林算法的农耕区土地利用分类

农耕区土地覆被信息是土地资源管理与规划的基础,在合理开发土地资源,调整土地利用结构以及土地动态监测等方面起着重要作用。由于农耕区土地类型复杂并且具有高异质性的特点,土地覆被信息提取的精度一直面临着挑战。因此,以Sentinel-2A/B多光谱遥感数据作为数据源,首先构建归一化植被指数(normalized difference vegetation index,NDVI)时序数据集和缨帽-湿度分量(tasseled cap wetness,TCW)时序数据集;其次,利用J-M(Jeffries-Matusita)距离对地物进行可分离性分析和挑选出NDVI和TCW最佳时序数据组合;最后,结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)、最大似然分类(maximum likelihood classification,MLC)3种分类算法以及利用单时相遥感数据对农耕区典型地物进行分类研究。研究结果表明:基于时间序列数据结合随机森林分类算法取得了较高的分类精度,其总体分类精度达到88.87%,Kappa系数达到0.8557,与利用单时相影像数据分类结果的精度相比分别提高了10.05百分点和0.2093,这充分说明利用时序数据结合RF分类算法在农耕地区能够有效提高典型地物的分类精度。

基于实测高光谱和Sentinel-2B影像的银川平原土壤盐分反演

土壤盐渍化是干旱半干旱区土壤资源损害、生境破坏和农业生产损失的重要影响因素,定量反演和监测盐渍化土壤,对防护土地生态安全具有重要意义.文章基于光谱变换筛选盐分特征波段和特征光谱指数,构建实测高光谱和Sentinel-2B影像的岭回归模型和偏最小二乘回归盐分反演模型,并以特征光谱指数为敏感参量进行星‒地光谱匹配,构建匹配后盐分反演模型,实现银川平原土壤盐分定量反演.结果表明,盐分指数3(Salinity index 3,S3)、强度指数1(Intensity index 1,Int1)和强度指数2(Intensity index 2,Int2)能够实现实测高光谱端元到多光谱像元尺度的匹配,有效地提升模型精度;经光谱匹配后构建的偏最小二乘模型精度最高(R2=0.721,RMSE=4.856 g·kg^(−1)).相比单独利用影像建模,其R2提升了0.309,均方根误差(Root Mean Square Error,RMSE)减小了2.085 g·kg^(−1).盐分反演结果与实地采样具有较好一致性,表明特征光谱指数可为不同尺度遥感数据间光谱匹配与联合,实现地表点到空间面尺度盐渍化定量监测,为土壤盐分监测提供理论借鉴和实践参考.

Assessment of the State of Forests Based on Joint Statistical Processing of Sentinel-2B Remote Sensing Data and the Data from Network of Ground-Based ICP-Forests Sample Plots

The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.

基于U-Net模型的多时相Sentinel-2A/B影像林分类型分类

[目的]基于多时相Sentinel-2A/B影像,探究深度学习模型在森林植被上的分类效果。[方法]以黑龙江省孟家岗林场为研究区,以多时相Sentinel-2A/B影像、数字高程模型(DEM)为数据源,通过各森林类别的JM距离,确定最佳单一时相。同时,构建多时相植被指数及红边指数特征(DVI、mNDVI、CIred-edge、NDre1)。采用支持向量机和优化的U-Net模型分别对单一时相+DEM和单一时相+DEM+多时相植被指数两种方案进行分类实验。[结果](1)在单一时相+DEM基础上,加入多时相植被指数后,U-Net模型精度为77.87%,比单一时相+DEM精度高6.67%;(2)U-Net模型的总体精度明显优于支持向量机,并且分类效果更好。同时,深度学习U-Net模型能够避免“椒盐”现象,分类结果更细腻。[结论]基于多时相Sentinel-2A/B影像,构建植被指数及红边指数时序特征,同时采用U-Net模型在一定程度上能够提高林分类型分类精度。

基于Sentinel-2A/B数据的火烧迹地提取方法研究

近年来随着全球气候变化加剧,世界各地森林火灾频发,不仅破坏了生态系统,还使各国人民的生命财产和生存环境遭受威胁。Sentinel-2数据红边波段和短波红外波段可以用于火烧迹地提取研究,本文选取火灾发生前和火灾发生后的Sentinel-2A/B时序数据,采用归一化植被指数(normalized difference vegetation index,NDVI)、修正的NDVI指数(NDVI_(red-edge1)、NDVI_(red-edge2)、NDVI_(red-edge3)、NDVI_(SWIR))和归一化燃烧指数(normalized burn ratio,NBR)分别提取美国加利福尼亚州天堂镇火烧迹地信息,分析不同方法提取火烧迹地信息的潜力与存在的问题,与官方公布的过火面积所转换的像元数比对,以探索Sentinel-2A/B数据的3个红边波段和2个短波红外波段提取火烧迹地的可行性。研究结果表明:(1)对火烧迹地提取,改进的NDVI_(red-edge1)、NDVI_(red-edge2)、NDVI_(SWIR)的精度略优于NDVI,其精度分别为92.85%、89.08%、90.14%。而改进后的NDVI_(red-edge3)精度为83.5%,低于NDVI;(2)基于短波红外波段的NBR及NDVI_(SWIR),精度分别达到了93.83%、90.14%。NBR以及基于NDVI改进后的NDVI_(red-edge1)和NDVI_(SWIR)可用于火烧迹地提取。

基于Sentinel-2B的油松冠层可燃物含水率反演研究

森林火灾的发生与植被冠层可燃物含水率的大小有着密切联系。利用高精度、大尺度、高效率的遥感影像反演获取植被冠层可燃物含水率对于有效防治森林火灾具有重要意义。油松由于其自身理化性质成为引发森林火灾的主要树种之一,以张家口崇礼区的油松为研究对象,基于Sentinel-2B遥感影像和油松含水率实测数据,建立了多个油松冠层可燃物含水率反演模型:一元线性回归模型、一元非线性回归模型和多元非线性回归模型,并利用决定系数(R 2)和均方根误差(RMSE)进行模型精度评价。结果表明,非线性模型总体上要优于线性模型;通过多个自变量因子建立的多元非线性模型能够更好地反映油松冠层可燃物含水率情况,模型反演精度更高,可以为植被冠层可燃物含水率反演模型方法选择提供一定的理论依据。

Evaluation of COC183B2 antibody targeting ovarian cancer by near-infrared fluorescence imaging

Objective: To evaluate the imaging potential of a novel near-infrared(NIR) probe conjugated to COC183 B2 monoclonal antibodies(MAb) in ovarian cancer(OC).Methods: The expression of OC183 B2 antigen in OC was determined by immunohistochemical(IHC) staining using tissue microarrays with the H-score system and immunofluorescence(IF) staining of tumor cell lines.Imaging probes with the NIR fluorescent dye cyanine 7(Cy7) conjugated to COC183 B2 Mab were chemically engineered. OC183 B2-positive human OC cells(SKOV3-Luc) were injected subcutaneously into BALB/c nude mice. Bioluminescent imaging(BLI) was performed to detect tumor location and growth. COC183 B2-Cy7 at 1.1,3.3, 10, or 30 μg were used for in vivo fluorescence imaging, and phosphate-buffered saline(PBS), free Cy7 dye and mouse isotype immunoglobulin G(IgG)-Cy7(delivered at the same doses as COC183 B2-Cy7) were used as controls.Results: The expression of OC183 B2 with a high H-score was more prevalent in OC tissue than fallopian tube(FT) tissue. Among 417 OC patients, the expression of OC183 B2 was significantly correlated with the histological subtype, histological grade, residual tumor size, relapse state and survival status. IF staining demonstrated that COC183 B2 specifically expressed in SKOV3 cells but not HeLa cells. In vivo NIR fluorescence imaging indicated that COC183 B2-Cy7 was mainly distributed in the xenograft and liver with optimal tumor-to-background(T/B)ratios in the xenograft at 30 μg dose. The highest fluorescent signals in the tumor were observed at 96 h postinjection(hpi). Ex vivo fluorescence imaging revealed the fluorescent signals mainly from the tumor and liver. IHC analysis confirmed that xenografts were OC183 B2 positive.Conclusions: COC183 B2 is a good candidate for NIR fluorescence imaging and imaging-guided surgery in OC.

CDX2、NF-κB和MUC2在Barrett食管中的表达及意义

目的研究尾型同源框转录因子-2(CDX2)、核因子-κB(NF-κB)和粘蛋白-2(MUC2)在Barrett食管(BE)中的表达及意义。方法采用随机对照法比较普通白光内镜及NBI模式下活检BE并肠化(IM)的检出率。选取BE(经NBI筛选)、正常食管黏膜及食管腺癌(EAC)采用免疫组化方法检测CDX2、NF-κB和MUC2的表达。结果 NBI组BE并IM的检出率为65.8%(25/38),显著高于普通内镜的34.2%(13/38),差异有统计学意义(P<0.05)。正常黏膜组、BE组和EAC组CDX2阳性率分别为0、73.3%(22/30)、33.3%(10/30);NF-κB阳性率依次为6.7%(2/30)、66.7%(20/30)、40.0%(12/30);MUC2阳性率依次为0、60.0%(18/30)、26.7%(8/30)。三组CDX2、NF-κB、MUC2的阳性率比较,差异均有统计学意义(P<0.05)。CDX2阳性率与NF-κB、MUC2阳性率呈正相关,r值分别为0.869、0.820。结论 NBI能有效提高BE并IM的病理检出率。CDX2、NF-κB、MUC2的激活可能导致BE肠上皮化生及后续恶变。

微钙化乳腺癌Cerb-B-2基因与X线征象的相关性分析

目的依据BI—RADS分类标准，对比分析Cerb-B-2基因阳性及阴性微钙化乳腺癌的x线征象与Cerb—B-2基因之间的相关性。方法对乳腺x线摄影中检出的以微钙化为主要病变的女性乳腺癌患者55例共55个病灶，根据免疫组织化学结果分为Cerb—B-2阳性组（22例）和Cerb—B-2阴性组（33例）。所有病例均行数字化乳腺x线摄影，由2～3名有经验的影像科医师阅读乳腺X线片并根据BI—RADS分类标准分析不同形态、分布的微钙化与Cerb—B-2基因表达之间的关系。结果Cerb—B-2基因表达阳性者其钙化形态多表现为细线样或细线样分支状，Cerb—B-2基因表达阴性者其钙化形态多表现为不定形或模糊不清的钙化；而微钙化的不同分布形式对Cerb—B-2基因表达差异无统计学意义。结论应用BI—RADS分类标准对微钙化乳腺癌进行规范化描述、分析有助于Cerb—B-2基因的预测判断，对乳腺癌的预后评估及临床治疗方案的选择提供了影像学依据。

原癌基因蛋白质C-erb B-2及增殖细胞核抗原在结肠癌中的定量检测及临床意义

目的 探讨原癌基因蛋白质 C- erb B- 2及增殖细胞核抗原 (PCNA )在结肠癌中含量及与结肠癌生物学行为的关系。 方法 利用免疫组织化学及自动化图像分析技术定量检测 C- erb B- 2及 PCNA在 10例正常肠粘膜 ,30例结肠腺瘤 ,5 3例结肠癌中的表达。 结果 阳性总面积、积分光密度、平均光密度、阳性率 1 、平均光密度 1及半定量测定的阳性率等 6个参数值均按正常肠粘膜→ 级不典型增生腺瘤→ 级不典型增生腺瘤→ 级不典型增生腺瘤→腺癌的顺序呈递变趋势 ,伴有淋巴结转移的腺癌组中 ,上述各参数值明显增高。 结论 C- erb B- 2及 PCNA一起可作为判断肿瘤的恶性程度及评价预后的指标 ,应用图像分析技术对结肠癌及癌前病变进行多指标多参数的计量分析 ,具有鉴别诊断的重要意义 ,为结肠癌的早期诊断提供客观依据。

基2-FFT输入分级截断算法在频域合成孔径超声成像中的研究

为提高超声频域成像算法的计算速度,提出一种应用于超声频域成像算法的基2-FFT输入分级截断算法。首先,借助于COMSOL多物理场仿真软件,建立钢件中含有孔缝缺陷的有限元模型进行声场仿真。仿真结果得到关于缺陷的回波信号,并通过PSM算法对频域内声场进行重建,得到成像区域的聚焦图像,和原始仿真信号的B扫图像相比效果更加直观且成像质量更好,验证了PSM算法的可行性。然后为了避免超声频域成像算法中二维傅里叶变换的冗余计算,进一步提出了支持任意非0值输入的基2-FFT输入分级截断算法。实验结果证明,基2-FFT输入分级截断算法比标准基2-FFT算法快27%,超声频域算法成像速度提高13%。

一种具有识别Hg^(2+)和ClO^(-)荧光探针的设计和应用

设计合成了一种含双酯基的1,2,3-三氮唑化合物,与罗丹明B酰肼结合生成了具有“开-关”性质的荧光探针(简称L_(2)),应用光谱学表征了L_(2)的物理化学参数。L_(2)分别在DMF/Tris-HCl(1:1,v/v,pH=6.0,20μmol/L)和MeOH(20μmol/L)溶液中对Hg^(2+)和ClO^(-)显示出高选择性和灵敏性;利用荧光和紫外光谱分别测定了L_(2)对19种金属离子和14种阴离子的光学性能。实验表明,Hg^(2+)和ClO^(-)的存在使得L_(2)在585 nm和576 nm均有一个新的发射峰出现;同时伴随着荧光强度明显的增强,溶液体系发生了裸眼能识别的显色变化,表明Hg^(2+)可以将罗丹明分子的酰肼闭环结构转换为开环结构,并以1:2的比例方式生成了一种新配合物,这也被质谱、工作曲线、核磁滴定和TD-DFT计算的结果所证实;L_(2)对Hg^(2+)和ClO^(-)的检测限分别为7.45 nmol/L和0.67μmol/L。此外,生物活性测定显示L_(2)对HeLa细胞有非常低的毒性,并且可用于HeLa细胞中Hg^(2+)和ClO^(-)的细胞成像,表明L_(2)在体内可进行微测定Hg^(2+)和ClO^(-)的巨大潜力。

基于Sentinel-2A卫星数据面向对象的水边线提取——以如东县为例

使用基于面向对象的方法提取水边线,B分量作为阈值分割条件,Sentinel-2A数据作为提取水边线的影像,通过多尺度分割与光谱差异分割组合的方式。对如东沿海的淤泥质海岸、交通围堤海岸和养殖围堤海岸3种不同类型的海岸水边线进行提取。通过提取的水边线与影像叠加,并对受潮汐影响较小的水边线做精度验证。总体来说,提取的水边线较为准确。水边线的快速准确提取,对监测海岸带动态变化具有重要意义。

多b值DWI联合T_(2)WI在前列腺疾病良恶性鉴别中的应用价值

目的探讨多b值弥散加权成像(DWI)联合T_(2)加权成像(T_(2)WI)在前列腺疾病良恶性鉴别中的应用价值。方法选取2017年3月至2021年2月收治的60例前列腺特异抗原(PSA)升高的患者作为研究对象,行多b值(600、1000、1500、2000 s/mm^(2))DWI及T_(2)WI,随后在经直肠超声引导下行12针的前列腺饱和穿刺。分析前列腺病理穿刺活检结果;比较前列腺癌和良性前列腺增生患者不同b值下DWI信号强度;分析不同b值DWI联合T_(2)WI对前列腺癌的诊断效能。结果39例患者诊断为前列腺癌,21例患者诊断为良性前列腺增生,1针穿刺阳性1例,2针穿刺阳性7例,3针及以上穿刺阳性31例。不同b值下,前列腺癌患者的DWI信号强度高于良性前列腺增生患者,差异具有统计学意义(P<0.05)。随着b值升高,DWI联合T_(2)WI对前列腺癌的诊断灵敏度升高;b值为2000 s/mm^(2)时,DWI联合T_(2)WI对前列腺癌的诊断灵敏度、特异度最高,为84.62%、80.95%。结论b值为2000 s/mm^(2)时,DWI联合T_(2)WI对前列腺癌的诊断灵敏度、特异度最高,临床应用价值高。

Usefulness of ultrasonography to assess the response to steroidal therapy for the rare case of type 2b immunoglobulin G4-related sclerosing cholangitis without pancreatitis:A case report

BACKGROUND A type 2b immunoglobulin G4(IgG4)-related sclerosing cholangitis(SC)without autoimmune pancreatitis is a rare condition with IgG4-SC.While the variety of the imaging modalities have tested its usefulness in diagnosing the IgG4-SC,however,the usage of ultrasonography for the assessment of the response to steroidal therapy on the changes of bile duct wall thickness have not been reported in the condition.Therefore,the information of our recent case and reported cases have been summarized.CASE SUMMARY We report the case of an 82-year-old Japanese man diagnosed with isolated IgG4-related SC based on the increase of serum IgG4,narrowing of the bile duct,its wall thickness,no complication of autoimmune pancreatitis,and IgG4 positive inflammatory cell infiltration to the wall with the fibrotic changes.The cholangiogram revealed type 2b according to the classification.Corticosteroid treatment showed a favorable effect,with the smooth decrease in serum IgG4 and the improvement of the bile duct wall thickness.CONCLUSION As isolated type 2b,IgG4-SC is rare,the images,histological findings,and clinical course of our case will be helpful for physicians to diagnose and treat the new cases appropriately.

Drug-induced sarcoidosis-like reaction three months after BNT162b2 mRNA COVID-19 vaccination:A case report and review of literature

BACKGROUND A 70-year-old man with hepatitis C virus-related recurrent hepatocellular carcinoma was admitted for further diagnosis of a 1 cm iso-hyperechoic nodule in segment(S)5.CASE SUMMARY Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging(EOB-MRI)revealed the nodule in S5 with a defect at the hepatobiliary phase,hyperintensity on diffusion weighted imaging(DWI)and hypointensity on apparent diffusion coefficient(ADC)map.Contrast-enhanced computed tomography revealed hypervascularity at the early phase,and delayed contrast-enhancement was observed at the late phase.Contrast-enhanced ultrasound(US)revealed incomplete defect at the late vascular phase.Inflammatory liver tumor,lymphoproliferative disease,intrahepatic cholangiocarcinoma(small duct type)and bile duct adenoma were suspected through the imaging studies.US guided biopsy,however,showed a noncaseating hepatic sarcoid-like epithelioid granuloma(HSEG),and histopathological analysis disclosed spindle shaped epithelioid cells harboring Langhans-type multinucleated giant cells.One month after admission,EOB-MRI signaled the disappearance of the defect at the hepatobiliary phase,of hyperintensity on DWI,of hypointensity on ADC map,and no stain at the early phase.CONCLUSION That the patient had received BNT162b2 messenger RNA(mRNA)coronavirus disease 2019 vaccination 3 mo before the occurrence of HSEG,and that its disappearance was confirmed 4 mo after mRNA vaccination suggested that the drug-induced sarcoidosis-like reaction(DISR)might be induced by the mRNA vaccination.Fortunately,rechallenge of drug-induced DISR with the third mRNA vaccination was not confirmed.