气管上段腺样囊性癌的临床病理

目的　探讨气管上段腺样囊性癌的临床病理特征及诊断和鉴别诊断要点。方法　收集2000年1月至2014年2月在北京协和医院确诊的4例气管上段腺样囊性癌病例，通过光镜、免疫组织化学及组织化学染色分析其临床病理特征、免疫组织化学特点、诊断及鉴别诊断要点。结果　4例气管上段腺样囊性癌患者中，男1例，女3例，平均年龄47岁(38~57岁）；既往均无腺样囊性癌病史，1例患者既往有结节性甲状腺肿手术史。镜下检查4例均为筛状/管状型腺样囊性癌，3例累及甲状腺组织，3例累及神经组织，未见淋巴结受累。免疫组织化学染色示4例P16、CD117、BCL2、P63、SMA均阳性，Ki67指数平均8%，TTF1和P53均阴性；4例中基底膜样物胶原Ⅳ阳性；组织化学染色示AB/PAS阳性。术后均接受总剂量为48~56 Gy的放疗，随访6~120个月，平均72.5个月，1例术后96个月复发，3例随访期间无复发及转移。结论　气管上段腺样囊性癌是罕见的原发于气管的低度恶性肿瘤，肿瘤生长缓慢，就诊时多数已侵及甲状腺组织，需要与原发于甲状腺的恶性肿瘤相鉴别，特别是在甲状腺穿刺及术中冰冻检查时。结合电子喉气管镜下表现、典型的形态学及免疫组织化学和组织化学染色有助于准确诊断。手术难以切除干净，术后放疗对延缓疾病复发有一定帮助。     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Mast Cell Chymase and Kidney Disease

A sizable part (~2%) of the human genome encodes for proteases. They are involved in many physiological processes, such as development, reproduction and inflammation, but also play a role in pathology. Mast cells (MC) contain a variety of MC specific proteases, the expression of which may differ between various MC subtypes. Amongst these proteases, chymase represents up to 25% of the total proteins in the MC and is released from cytoplasmic granules upon activation. Once secreted, it cleaves the targets in the local tissue environment, but may also act in lymph nodes infiltrated by MC, or systemically, when reaching the circulation during an inflammatory response. MC have been recognized as important components in the development of kidney disease. Based on this observation, MC chymase has gained interest following the discovery that it contributes to the angiotensin-converting enzyme’s independent generation of angiotensin II, an important inflammatory mediator in the development of kidney disease. Hence, progress regarding its role has been made based on studies using inhibitors but also on mice deficient in MC protease 4 (mMCP-4), the functional murine counterpart of human chymase. In this review, we discuss the role and actions of chymase in kidney disease. While initially believed to contribute to pathogenesis, the accumulated data favor a more subtle view, indicating that chymase may also have beneficial actions.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis

Frequency band selection (FBS) in rotating machinery fault diagnosis aims to recognize frequency band location including a fault transient out of a full band spectrum, and thus fault diagnosis can suppress noise influence from other frequency components. Impulsiveness and cyclostationarity have been recently recognized as two distinctive signatures of a transient. Thus, many studies have focused on developing quantification metrics of the two signatures and using them as indicators to guide FBS. However, most previous studies almost ignore another aspect of FBS, i.e. health reference, which significantly affect FBS performance. To address this issue, this paper investigates importance of a health reference and recognize it as the third critical aspect in FBS. With help of the health reference, the frequency band where the fault transient exists could be located. A novel approach based on classification is proposed to integrate all three aspects (impulsiveness, cyclostationarity, and health reference) for FBS. Classification accuracy is developed as a novel indicator to select the most sensitive frequency band for rotating machinery fault diagnosis. The proposed method (coined by accugram) has been validated on benchmark and experiment datasets. Comparison results show its effectiveness and robustness over conventional envelope analysis, the kurtogram, and the infogram.     

Fair‐Weather Friends: Evidence of Lipoxin Dysregulation in Neurodegeneration

Lipoxins (LXs) are autacoids, specialized proresolving lipid mediators (SPMs) acting locally in a paracrine or autocrine fashion. They belong to a complex superfamily of dietary small polyunsaturated fatty acid (PUFA)–metabolites, which direct potent cellular responses to resolve inflammation and restore tissue homeostasis. Together, these SPM activities have been intensely studied in systemic inflammation and acute injury or infection, but less is known about LX signaling and activities in the central nervous system. LXs are derived from arachidonic acid, an omega‐6 PUFA. In addition to well‐established roles in systemic inflammation resolution, they have increasingly become implicated in regulating neuroinflammatory and neurodegenerative processes. In particular, chronic inflammation plays a central role in Alzheimer's disease (AD) etiology, and dysregulated LX production and activities have been reported in a variety of AD rodent models and clinical tissue samples, yet with complex and sometimes conflicting results. In addition, reduced LX production following retinal injury has been reported recently by the authors, and an intriguing direct neuronal activity promoting survival and homeostasis in retinal and cortical neurons is demonstrated. Here, the authors review and clarify this growing literature and suggest new research directions to further elaborate the role of lipoxins in neurodegeneration.     

microRNA在亨廷顿病中的研究进展

亨廷顿病(Huntington's disease, HD)是神经退行性疾病(neurodegenerative disorders)中一种单基因遗传病,由亨廷顿基因(the huntingtin gene, HTT)第一个外显子中的CAG三核苷酸序列重复扩增引起,尚无法治愈。HTT编码的蛋白产物被称为亨廷顿蛋白(Huntingtin protein, Htt)。突变亨廷顿蛋白(mutated huntingtin protein, mHtt)容易形成聚集体,具有毒性,导致一系列细胞学异常和神经元功能障碍。microRNA(miRNA)在基因转录后水平调控中起重要作用,其表达的改变与HD病理过程有关,成为治疗HD的潜在生物标志物。近年来,对一些特定miRNA在HD中调控机制及靶基因预测方面的研究可为HD提供潜在的治疗方法。本文就miRNA在HD中的相关研究进展进行综述。