下一代诊断病理学

随着人类基因组测序、生物大数据信息分析、分子病理检测和人工智能辅助病理诊断等技术进步及其应用, 临床医学发展迈向精准诊疗时代。这一时代背景下, 传统诊断病理学迎来前所未有的历史机遇, 正在向"下一代诊断病理学(next-generation diagnostic pathology)"迈进。下一代诊断病理学以病理形态和临床信息为诊断基础, 以分子检测与生物信息分析、智慧制样与流程质控、智能诊断与远程会诊、病灶活体可视化与"无创"病理诊断等创新前沿交叉技术为主要特征, 以多组学和跨尺度整合诊断为病理报告内容, 实现对疾病的"最后诊断", 并预测疾病演进和结局、建议治疗方案和评估治疗反应, 形成新的疾病诊断"金标准"。未来, 需要激发病理学科创新活力, 加快下一代诊断病理学成熟和应用, 重塑病理学科理论和技术体系, 发挥诊断病理学在疾病"防、诊、治、养"等过程中的重要作用, 促进临床医学进一步发展, 服务健康中国战略。     

In silico screening and analysis of single-nucleotide polymorphic variants of the ABCC2 gene affecting Dubin–Johnson syndrome

Background and Study AimsDubin–Johnson syndrome (DJS) is a benevolent genetic disorder of the liver with autosomal inheritance. It is a rare disorder characterized by an increase in conjugated bilirubin and anomaly in coproporphyrin clearance. DJS is caused by deleterious mutations in the ABCC2 gene. A polymorphism in the ABCC2 gene causes malfunctions in its ability to regulate the efflux of different organic anions, such as bilirubin, from hepatocytes to the canaliculi. Multidrug resistance protein 2 (MRP2) encoded by the ABCC2 gene is one of the main regulators of the export of bilirubin to respective sites. ABCC2 gene mutations have widely drawn attention in the pathology of DJS in various populations.Patients and MethodsThe ABCC2 gene was subjected to the National Center for Biotechnology Information (NCBI) database in 2020, and non-synonymous single-nucleotide polymorphisms (nsSNPs) and variants in untranslated regions were studied using different computational servers. SIFT, Protein variation effect analyzer, and PolyPhen-2 were used to retrieve the damaging Single-nucleotide polymorphisms (SNPs); PhD-SNP, SNPs&GO, and Protein Analysis Through Evolutionary Relationships were used to predict the association of nsSNPs with DJS; Mutation3D illustrated the location of variants in the protein; SNAP2, MutPred2, ELASPIC, and HOPE were used to predict the structural and functional effects of these mutations on MRP2; and I-mutant 3.0 and MuPro were used to determine the effects of polymorphism on the function of MRP2.ResultsIn this study, 18,947 SNPs were screened from the NCBI database, followed by a series of refinement of variants using online available servers. We concluded that 41 ABCC2 gene variants are vital etiological candidates for DJS in humans. These 41 variants had highly damaging effects on the MRP2 protein, which may lead to deficient transportation capacity, thereby affecting the efflux of bilirubin across the canalicular membrane.ConclusionIn silico tools are an alternative approach for predicting the target SNPs. Hence, previously unreported variants can be considered strong etiological candidates for diseases related to MRP2.     

Bioinformatics Analysis of Key Genes and Pathways for Medulloblastoma as a Therapeutic Target

Introduction: One of the major challenges in cancer treatment is the lack of specific and accurate treatment incancer. Data analysis can help to understand the underlying molecular mechanism that leads to better treatment.Increasing availability and reliability of DNA microarray data leads to increase the use of these data in a variety ofcancers. This study aimed at applying and evaluating microarray data analyzing, identification of important pathwaysand gene network for medulloblastoma patients to improve treatment approaches especially target therapy. Methods:In the current study, Microarray gene expression data (GSE50161) were extracted from Geo datasets and then analyzedby the affylmGUI package to predict and investigate upregulated and downregulated genes in medulloblastoma. Then,the important pathways were determined by using software and gene enrichment analyses. Pathways visualizationand network analyses were performed by Cytoscape. Results: A total number of 249 differentially expressed genes(DEGs) were identified in medulloblastoma compared to normal samples. Cell cycle, p53, and FoxO signaling pathwayswere indicated in medulloblastoma, and CDK1, CCNB1, CDK2, and WEE1 were identified as some of the importantgenes in the medulloblastoma. Conclusion: Identification of critical and specific pathway in any disease, in our casemedulloblastoma, can lead us to better clinical management and accurate treatment and target therapy.     

A Simple Computational Theory of General Collective Intelligence

Researchers have recently demonstrated that group performance across tasks tends to be correlated, motivating the use of a single metric for the general collective intelligence of groups akin to general intelligence metrics for individuals. High general collective intelligence is achieved when a group performs well across a wide variety of tasks. A number of factors have been shown to be predictive of general collective intelligence, but there is sparse formal theory explaining the presence of correlations across tasks, betraying a fundamental gap in our understanding of what general collective intelligence is measuring. Here, we formally argue that general collective intelligence arises from groups achieving commitment to group goals, accurate shared beliefs, and coordinated actions. We then argue for the existence of generic mechanisms that help groups achieve these cognitive alignment conditions. The presence or absence of such mechanisms can potentially explain observed correlations in group performance across tasks. Under our view, general collective intelligence can be conceived as measuring group performance on classes of tasks that have particular combinations of cognitive alignment requirements.     

A1 asynchrony,a potential risk factor for the rupture of anterior communicating artery aneurysms: A computational fluid dynamics study

Introduction and objectivesThe anterior communicating complex is one the most common locations for aneurysm development. It receives blood from both carotid circulations and the effect of synchrony on the arrival of blood flow has not been previously studied. The objective of this study was to compare the asynchrony conditions of the A1 pulse and its effects on the haemodynamic conditions of anterior communicating artery (ACoA) aneurysms.Materials and methodsFrom 2008 to 2017, 54 anterior communicating artery aneurysms treated at our centre were included in the study. Computational fluid dynamics (CFD) techniques were employed and simulations consisted of complete conditions of synchrony and introducing a delay of 0.2 s in the non-dominant A1 artery. Time-averaged wall shear stress (TAWSS), low shear area (LSA), A1 diameter and ACoA angles were measured.ResultsThe difference in the LSA in conditions of synchrony and asynchrony resulted in a broad range of positive and negative values. The symmetry index (p = 0.04) and A1/A2 angle on the dominant artery (p = 0.04) were associated with changes in LSA.ConclusionsIn asynchrony, LSA increased in the absence of A1 asymmetry and low A1/A2 angles, potentially increasing the risk of aneurysm rupture in this location.     

Interrogating the microenvironmental landscape of tumors with computational image analysis approaches

The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.     

Evaluating optimized temporal patterns of spinal cord stimulation (SCS)

BackgroundTemporal patterns of stimulation represent a novel dimension for improving the efficacy of spinal cord stimulation to treat chronic neuropathic pain.ObjectiveWe hypothesized that nonregular temporal patterns of stimulation designed using a computational model would be superior to conventional stimulation at constant frequencies or completely random patterns of stimulation.MethodsUsing a computational model of the dorsal horn network and an optimization algorithm based on biological evolution, we designed an optimized pattern of spinal cord stimulation with comparable efficacy and increased efficiency relative to constant frequency (CF) stimulation. We evaluated the effect of different temporal patterns on individual neurons recorded in the dorsal horn of urethane-anesthetized rats.ResultsThe optimized pattern and 50 Hz CF stimulation produced greater inhibition of spontaneously firing neurons recorded in vivo than random 50 Hz stimulation or a pattern designed intentionally with poor fitness. Spinal Cord Stimulation (SCS) led to significant changes in the firing patterns of recorded units, and stimulation patterns that generated significant inhibition also tended to reduce entropy and regularize the firing patterns of units, suggesting that patterns of dorsal horn neuron activity may be important for pain perception in addition to the firing rate.ConclusionsThese results demonstrate that the computational model can be used as a tool for optimizing stimulation parameters and suggest that optimized temporal patterns may increase the efficacy of spinal cord stimulation.