Combined hepatocellular-cholangiocarcinoma:An update on epidemiology,classification,diagnosis and management

Background:Combined hepatocellular-cholangiocarcinoma(CHC)is a rare subtype of primary hepatic malignancies,with variably reported incidence between 0.4%–14.2%of primary liver cancer cases.This study aimed to systematically review the epidemiological,clinicopathological,diagnostic and therapeutic data for this rare entity.Data sources:We reviewed the literature of diagnostic approach of CHC with special reference to its clinical,molecular and histopathological characteristics.Additional analysis of the recent literature in order to evaluate the results of surgical and systemic treatment of this entity has been accomplished.Results:The median age at CHC’s diagnosis appears to be between 50 and 75 years.Evaluation of tumor markers[alpha fetoprotein(AFP),carbohydrate antigen 19–9(CA19–9)and carcinoembryonic antigen(CEA)]along with imaging patterns provides better opportunities for CHC’s preoperative diagnosis.Reported clinicopathologic prognostic parameters possibly correlated with increased tumor recurrence and grimmer survival odds include advanced age,tumor size,nodal and distal metastases,vascular and regional organ invasion,multifocality,decreased capsule formation,stem-cell features verification and increased GGT as well as CA19–9 and CEA levels.In case of inoperable or recurrent disease,combinations of cholangiocarcinoma-directed systemic agents display superior results over sorafenib.Liver-directed methods,such as transarterial chemoembolization(TACE),percutaneous ethanol injection(PEI),hepatic arterial infusion chemotherapy(HAIC),radioembolization and ablative therapies,demonstrate inferior efficacy than in cases of hepatocellular carcinoma(HCC)due to CHC’s common hypovascularity.Conclusions:CHC demonstrates an overlapping clinical and biological pattern between its malignant ingredients.Natural history of the disease seems to be determined by the predominant tumor element.Gold standard for diagnosis is histology of surgical specimens.Regarding therapeutic interventions,major hepatectomy is acknowledged as the cornerstone of treatment whereas minor hepatectomy and liver transplantation may be applied in patients with advanced cirrhosis.Despite all therapeutic attempts,prognosis of CHC remains dismal.

Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

Combined hepatocellular cholangiocarcinoma: Controversies to be addressed

Combined hepatocellular cholangiocarcinoma(CHC) accounts for 0.4%-14.2% of primary liver cancer cases and possesses pathological features of both hepatocellular carcinoma and cholangiocarcinoma. Since this disease was first described and classified in 1949, the classification of CHC has continuously evolved. The latest definition and classification of CHC by the World Health Organization is based on the speculation that CHC arises from hepatic progenitor cells. However, there is no evidence demonstrating the common origin of different components of CHC. Furthermore, the definition of CHC subtypes is still ambiguous and the identification of CHC subtype when a single tumor contains many components has remained unresolved. In addition, there is no summary on the newly recognized histopathology features or the contribution of CHC components to prognosis and outcome of this disease. Here we provide a review of the current literature to address these questions.

Classification of features selected through Optimum Index Factor (OIF) for improving classification accuracy

The present investigation was performed to determine if the features selected through Optimum Index Factor （OIF） could provide improved classification accuracy of the various categories on the satellite images of the individual years as well as stacked images of two different years as compared to all the features considered together. Further, in order to determine if there occurs increase in the classification accuracy of the different categories with corresponding increase in the OIF values of the features extracted from both the individual years＇ and stacked images, we performed linear regression between the producer＇s accuracy （PA） of the various categories with the OIF values of the different combinations of the features. The investigations demonstrated that there occurs significant improvement in the PA of two impervious categories viz. moderate built-up and low density built-up determined from the classification of the bands and principal components associated with the highest OIF value as compared to all the bands and principal components for both the individual years＇ and stacked images respectively. Regression analyses exhibited positive trends between the regression coeffi- cients and OIF values for the various categories determined for the individual years＇ and stacked images respectively signifying the prevalence of direct relationship between the increase in the information content with corresponding increase in the OIF values. The research proved that features extracted through OIF from both the individual years＇ and stacked images are capable of providing significantly improved PA as compared to all the features pooled together.

Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.

Clinical features,histology,and histogenesis of combined hepatocellular-cholangiocarcinoma

Combined hepatocellular-cholangiocarcinoma(CHC) is a rare tumor with poor prognosis,with incidence ranging from 1.0%-4.7% of all primary hepatic tumors.This entity will be soon renamed as hepato-cholangiocarcinoma.The known risk factors for hepatocellular carcinoma(HCC) have been implicated for CHC including viral hepatitis and cirrhosis.It is difficult to diagnose this tumor pre-operatively.The predominant histologic component within the tumor largely determines the predominant radiographic features making it a difficult distinction.Heterogeneous and overlapping imaging features of HCC and cholangiocarcinoma should raise the suspicion for CHC and multiple core biopsies(from different areas of tumor) are recommended before administering treatment.Serum tumor markers CA19-9 and alpha-fetoprotein can aid in the diagnosis,but it remains a challenging diagnosis prior to resection.There is sufficient data to support bipotent hepatic progenitor cells as the cell of origin for CHC.The current World Health Organization classification categorizes two main types of CHC based on histo-morphological features:Classical type and CHC with stem cell features.Liver transplant is one of the available treatment modalities with other management options including transarterial chemoembolization,radiofrequency ablation,and percutaneous ethanol injection.We present a review paper on CHC highlighting the risk factors,origin,histological classification and therapeutic modalities.

顾及特征离散程度的SEaTH特征优化选择方法

特征选择是面向对象信息提取的关键步骤之一。本文针对分离阈值(separability and threshold,SEaTH)这一特征选择方法在实际应用中存在的不足,例如未考虑特征值的离散程度,仅利用J-M距离评判单一特征,特征间可能存在较强相关性,以及无法有效确定出分类顺序,提出了一种改进的SEaTH算法(optimized SEaTH,OPSEaTH)。OPSEaTH算法首先在J-M距离基础上构建了一类特征评价指标(E值),有效解决了特征值的离散度问题;然后,基于E值构建出特征组合评价指标(C_(e)值),可有效评估得到每种地物的最佳特征组合并自动确定出地物的分类顺序;最后基于eCognition等分类器可完成对地物对象的最终有效分类。利用高分二号遥感影像数据对本文方法进行了测试,并将结果分别与SEaTH算法、DPC、OIF和最近邻分类器的分类结果进行了对比,结果表明:OPSEaTH算法不仅能有效降低特征维数、优化特征空间,还能够对分类顺序进行自动化合理确定,总体精度和Kappa系数及其他精度指标,均显著优于基于SEaTH算法的特征选择结果。本文方法无论从特征降维效果、分类结果精度还是计算效率方面均优于DPC、OIF和最近邻分类器结果。OPSEaTH是一种更优的特征选择方法。

新疆天然草原分类系统调整合并的探讨

【目的】草地类型的划分是草地经营管理中的基础性工作,是人们有效保护、科学开发、合理利用和培育草地的理论基础。为了简化原有繁杂的分类系统,紧紧围绕林业、草原和国家公园“三位一体”融合发展的总体要求,着眼新疆草地资源特点,统筹推进“山水林田湖草沙”系统治理,提出新的草地分类系统和标准势在必行。【方法】本文参照2016年农业部《草地分类》行业标准,结合现阶段林草融合机制与草原生态修复治理的需求,草地类的名称与全国草地分类系统及农业部《草地分类》行业标准保持基本一致,并与国土三调土地分类标准相协调,取消亚类和组的分类;型的命名减少优势种数量的原则进行归并;面积小于平均值、仅分布局部地区、代表性不强的型归并到临近草地型或者删除;具有地方特色植物种类组成的特有型保留。【结果】新疆原天然草地分类系统的11类687个型,调整归并为9类106个型。【结论】新疆原天然草地分类系统及草地资源资料是30多年前形成的,之后未系统地开展全疆草地调查,实际的草地类、型或分布范围可能已经发生了一些变化。本分类系统具有较强的实用性和可操作性,为推动草地资源本底数据的更新和数字化管理提供技术依据。

内蒙古白云鄂博某低品位含铁岩矿磁悬浮联合分级流程工业试验研究

白云鄂博铁矿石储量巨大,缓解了国家铁矿石对外依存度过高的问题,同时也助力了包钢集团生产发展。但是在采矿过程中,产生了大量TFe品位低于20%的低品位含铁岩矿。对该类矿石,采用传统连续磨矿-磁选流程,在细磨至-0.074 mm粒级含量95%情况下,铁精矿品位最高只有60.8%,无法达到合格铁精矿质量要求;如果采用浮选流程,选矿经济性能不佳,故该部分矿石一直进行堆放未得到应用。本文通过对原矿性质进行分析,引入新型磁悬浮精选机作为磁选精选设备,同时设计三条全磁工艺流程进行工业试验,均可得到TFe品位64.5%以上的合格铁精矿,同时铁精矿中杂质元素K、Na、Si含量远低于浮选工艺流程。其中,自主开发的阶段磨选-磁悬浮联合分级分选流程,在细磨至-0.074 mm粒级含量95%情况下,最终可得到铁精矿TFe品位64.5%、磁性铁回收率95.07%的优良技术指标,分选效率高,效果好。现场应用后可实现白云鄂博低品位含铁矿经济性绿色利用,经济效益显著,同时极大地延长了白云鄂博铁资源的供给年限,对国家自有铁矿石产能接续、矿山自身可持续发展及资源绿色利用具有深远意义。

基于图像分类网络的非侵入式负荷辨识算法的运算成本优化

目前基于图像分类网络的非侵入式负荷辨识算法可达到较高的辨识准确率,但存在较严重的参数冗余,引发了不必要的运算成本。对此类算法的运算成本进行优化,提出一种基于灰色编码的设备特征组合方法,以减少算法中设备特征的参数冗余;然后使用轻量级图像分类网络ZFNet构建设备辨识模型,并引入Inception模块来减少模型中卷积层输出的参数冗余,同时基于仿真实验结果对模型中全连接层的结构和参数进行适应性调整,以减少模型的参数冗余,最后使用PLAID数据集进行算例分析。结果表明:相比于同类算法,所提算法在设备特征的参数量上减少了66.7%~67.5%,在模型的参数量上减少了90%~97.1%,在整体运算量上的变动为-91.7%~6.1%。

小断面土石组合地质条件下TBM施工围岩可掘性分级识别

围岩可掘性分级以及识别研究对隧道掘进机(TBM)高效率施工及智能化控制意义重大。依托南水北调安阳市西部调水工程TBM施工实际数据,利用掘进性能综合指标单位贯入度推力(FPI)、单位贯入度扭矩(TPI)建立了小断面土石组合地质条件下TBM施工围岩可掘性分级标准;提出了PCA-RF模型对围岩可掘性分级进行识别,并与BP、SVR和RF模型进行了比较讨论。结果表明:①建立的小断面土石组合围岩TBM施工可掘性分级标准是适用的,克服了土石组合围岩下传统围岩分类方法的局限性;②小断面土石组合围岩TBM施工可掘性分级PCA-RF识别模型的识别准确率达到了98.3%,高于BP、SVR和RF模型,可以满足工程施工需要。

农村医养结合服务分级整合体系构建——基于M村的经验研究

随着人口老龄化进程的加快,我国农村养老问题日益突出。医养结合被广泛认为是积极应对人口老龄化的重要举措。基于整体性治理理论的分级整合分析框架,结合京郊M村的医养结合实践,本研究构建了纵横交织的农村医养结合服务分级整合体系,以期为相关研究提供理论与实践参考。在纵向上,需求、供给和整合三个程度分级子系统揭示了医养结合的运行机制。在横向上,自主型、辅助型和依附型三种医养结合的供给方式可有效满足农村养老需求。因此,农村医养结合服务分级整合体系的构建为有效解决我国农村养老问题提供了一个可行路径。

船山诗词中的酒意象

全面研究船山诗词,可以发现其中有丰富的酒意象。船山诗词酒意象分为直接酒意象和间接酒意象。内容组合包括酒意象与自然意象组合、酒意象与人事意象组合。组合方式可以分为并列式组合、递进式组合、对比式组合。组合的语法结构包括动宾结构、偏正结构。酒意象的作用包括寄托情思、体现审美。

ICF功能障碍组合在康复住院患者功能评估中的信度研究

目的探讨国际功能、残疾和健康分类(International Classification of Functioning,Disability,and Health,ICF)功能障碍组合(ICF generic-30 set)30个类目在康复科住院患者功能评估中的评估者间和评估者内信度,为其临床应用提供依据。方法选取宿迁市第一人民医院神经康复科的90例患者纳入本研究。所有患者在入院及出院2个时间点接受同一功能评估者的ICF功能障碍组合的评估,以进行评估者内信度分析。随机抽取30例患者同时接受2名ICF功能评估者的评估,以进行评估者间重测信度分析。以Cronbach’sα系数检验量表的内部一致性,以组内相关系数(intra-group correlation coefficient,ICC)检验评估者间信度和评估者内信度。结果ICF功能障碍组合及身体功能和活动与参与两个分量表的Cronbach’sα系数分别为0.941、0.717、0.942。评估者间信度分析显示量表总分及身体功能和活动与参与两个分量表总分的ICC分别为0.998、0.971、0.999(P<0.01),评估者间信度佳;评估者内信度分析显示量表总分及身体功能和活动与参与两个分量表总分的ICC分别为0.988、0.917、0.991(P<0.01),除b640性功能不适用,b130精力和驱力功能ICC为0.558、b280痛觉ICC为0.409,其他组合条目的ICC在0.838~0.986(P<0.01),评估者内信度佳;90例患者的ICF功能障碍组合量表30个类目入、出院比较除b640性功能不适用,其余差异均有统计学意义(P<0.05)。结论ICF功能障碍组合在神经康复科患者功能评估应用中具有良好的内部一致性及评估者间和评估者内信度。

外周血白细胞分类和PCT、CRP、肺炎支原体抗体水平对儿童肺炎支原体合并细菌性感染肺炎的早期诊断价值

目的探究外周血白细胞分类和降钙素原(Procalcitonin,PCT)、C-反应蛋白(C-reaction protein,CRP)、肺炎支原体抗体水平对儿童肺炎支原体合并细菌性感染性肺炎的早期诊断价值。方法选取2020年7月—2023年3月于北京市昌平区中西医结合医院接受治疗的500例肺炎支原体合并细菌性感染肺炎患儿为研究组,另取同期于北京市昌平区中西医结合医院接受治疗的100例病毒感染性肺炎患儿为病例对照组,选择同期体格检查的100例健康儿童为对照组,分别检测其血样中PCT、CRP、肺炎支原体抗体IgM、外周血白细胞分类指标(白细胞计数、中性粒细胞百分数、淋巴细胞百分数、单核细胞百分数)水平,对比两组儿童上述指标差异,采用绘制受试者操作曲线(Receiver operating characteris,ROC)的方式分别计算上述指标对肺炎支原体合并细菌性感染肺炎的诊断价值,并计算其曲线下面积(area under curv,AUC)。结果研究组患儿的血样PCR、CRP以及肺炎支原体抗体IgM水平均显著高于病例对照组和对照组儿童(P<0.05);研究组患儿外周血白细胞计数、中性粒细胞百分数和单核细胞百分数均显著高于病例对照组和对照组儿童,淋巴细胞百分数显著低于对照组儿童(P<0.05);通过绘制ROC曲线并计算得知,PCT、CRP以及肺炎支原体抗体IgM对肺炎支原体合并细菌性感染肺炎的诊断曲线下面积(AUC)分别为0.934(95%CI=0.900~0.968,P<0.001)、0.928(95%CI=0.880~0.975,P<0.001)、0.780(95%CI=0.712~0.848,P<0.001);外周血白细胞分类中的白细胞计数、中性粒细胞百分数、淋巴细胞百分数以及单核细胞百分数对肺炎支原体合并细菌性感染肺炎的诊断AUC分别为0.957(95%CI=0.925~0.988,P<0.001)、0.768(95%CI=0.700~0.836,P<0.001)、0.670(95%CI=0.595~0.746,P<0.001)、0.765(95%CI=0.698~0.832,P<0.001)。结论肺炎支原体合并细菌性感染肺炎患儿血样PCT、CRP、肺炎支原体抗体IgM均较健康儿童出现显著升高,白细胞分类水平也出现明显变化,上述指标在肺炎支原体合并细菌性感染肺炎患儿早期诊断中具有较好的应用价值。

A New Combinational Terminology for Geothermal Systems

A geothermal resource can be defined as a reservoir inside the Earth from which heat can be extracted economically Geothermal resources are classified on the basis of different aspects, such as heat source, heat transfer, reservoir tem perature, physical state, commercial utilization and geological settings. Unfortunately most of the current classifications that are used for geothermal systems are not complete. So, a combinational terminology of geological and tempera ture-based classifications would be more complete. This terminology can explain all geological situations, temperature and physical state of geothermal reservoir altogether. According to geological settings, in combinational terminology (from left to right), the class of geothermal resource’s name would be placed at first, then the physical state of reservoir (Liquid-dominated or Two-phase or Vapor-dominated) would be written and finally the class of the geothermal reser voir which is related to its temperature, is written.

基于双向椭圆局部二值模式的环境声音分类

针对目前机器学习算法在环境声音分类准确率不高,训练速度慢的问题,提出了基于双向椭圆局部二值模式的环境声音分类方法。设计了双向椭圆局部二进制模式的音频信号特征提取方法,采用3×5信号邻域增加时长影响,并使用邻域左右两列整体平均值分别代替椭圆左右顶点像素,减少噪音干扰,提高对噪音的鲁棒性,使用整个邻域的平均值代替中心像素,并采用双向局部特征均衡顺序权重,在上述特征基础上增加VAR算子,反应局部特征差异强度,之后将这些特征与梅尔频率倒谱系数(MFCC)、伽玛频率倒谱系数(GFCC)和色度特征(Chromagram)融合。采用经典机器学习算法,如支持向量机(SVM)、随机森林(RF)和k近邻(kNN),结合融合特征,在ESC-10和ESC-50数据集上进行评估,两种数据集的分类准确度分别达到了90.9%和66.7%。

联合收获机知识库数据多表联合查询方法研究

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