Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

基于网络药理学和分子对接技术探讨黄芪干预腹膜纤维化的机制

目的 运用网络药理学方法及分子对接技术探讨黄芪干预腹膜纤维化的可能机制。方法 利用中药系统药理学数据库及分析平台(TCMSP)检索黄芪的主要化学成分及靶点,并补充文献报道相关药理作用的成分作为潜在活性成分。以"peritoneal fibrosis"为关键词分别在OMIM、Genecards获取目前已知的与腹膜纤维化相关的疾病靶点,后取两者的交集靶点;对交集基因通过STRING数据库与Cytoscape 3.7.2软件构建"药物-成分-靶点-疾病"网络及蛋白互作(PPI)网络并筛选核心网络。基于R软件使用Bioconductor生物信息软件对核心靶点进行GO及KEGG富集分析,最终采用AutoDock软件将主要有效成分与核心靶点进行分子对接,得出其结合能力。结果 筛选出20个黄芪活性成分及文献报道有相关药理作用4个, 457药物作用靶点,与674个腹膜纤维化病靶点取交集,得到86个共同靶点。GO功能富集分析提示黄芪拮抗腹膜纤维化主要参与了蛋白激酶B信号转导的调节、细胞对化学的应激反应、炎症反应的调节等通路; KEGG通路富集分析主要涉及调控肿瘤、磷脂酰肌醇-3-羟激酶-蛋白激酶B(PI3K-Akt)、晚期糖基化终末产物/晚期糖基化终末产物受体(AGE-RAGE)、人类巨细胞病毒感染、HIF-1信号通路等;分子对接结果显示关键靶点与活性成分具有较好的结合能力。结论 黄芪治疗腹膜纤维化的分子机制,可能与抑制炎症及氧化应激反应、调节多种信号通路等相关。     

Replicative senescence of human dermal fibroblasts affects structural and functional aspects of the Golgi apparatus

It is well recognized that the world population is ageing rapidly. Therefore, it is important to understand ageing processes at the cellular and molecular levels to predict the onset of age‐related diseases and prevent them. Recent research has focused on the identification of ageing biomarkers, including those associated with the properties of the Golgi apparatus. In this context, Golgi‐mediated glycosylation of proteins has been well characterized. Additionally, other studies show that the secretion of many compounds, including pro‐inflammatory cytokines and extracellular matrix–degrading enzymes, is modified during ageing, resulting in physical and functional skin degradation. Since the Golgi apparatus is a central organelle of the secretory pathway, we investigated its structural organization in senescent primary human dermal fibroblasts using confocal and electron microscopy. In addition, we monitored the expression of Golgi‐related genes in the same cells. Our data showed a marked alteration in the Golgi morphology during replicative senescence. In contrast to its small and compact structure in non‐senescent cells, the Golgi apparatus exhibited a large and expanded morphology in senescent fibroblasts. Our data also demonstrated that the expression of many genes related to Golgi structural integrity and function was significantly modified in senescent cells, suggesting a relationship between Golgi apparatus function and ageing.     

直肠癌淋巴结转移的深度神经网络评判研究进展

正目前,临床上对于直肠癌常用的影像评估方法有MRI、螺旋CT、PET-CT、直肠腔内超声(ERUS)等。而MRI作为首选检查方式,对肿瘤位置、浸润深度、淋巴结转移、血管侵犯、环周切缘及周围器官侵犯等方面的评估均具有明显优势~([1-2])。通过MRI诊断淋巴结的方法通常是影像科医师逐层浏览每一幅图像,从中识别淋巴结的形状、界限及密度来判断,这种传统方式耗时较长且存在主观偏倚,导致     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

How structural and functional MRI can inform dual-site tACS parameters: A case study in a clinical population and its pragmatic implications

BackgroundAbnormalities in frontoparietal network (FPN) were observed in many neuropsychiatric diseases including substance use disorders. A growing number of studies are using dual-site-tACS with frontoparietal synchronization to engage this network. However, a computational pathway to inform and optimize parameter space for frontoparietal synchronization is still lacking. In this case study, in a group of participants with methamphetamine use disorders, we proposed a computational pathway to extract optimal electrode montage while accounting for stimulation intensity using structural and functional MRI.MethodsSixty methamphetamine users completed an fMRI drug cue-reactivity task. Four main steps were taken to define electrode montage and adjust stimulation intensity using 4x1 high-definition (HD) electrodes for a dual-site-tACS; (1) Frontal seed was defined based on the maximum electric fields (EF) predicted by simulation of HD montage over DLPFC (F3/F4 in EEG 10–10), (2) frontal seed-to-whole brain context-dependent correlation was calculated to determine connected regions to frontal seeds, (3) center of connected cluster in parietal cortex was selected as a location for placing the second set of HD electrodes to shape the informed montage, (4) individualized head models were used to determine optimal stimulation intensity considering underlying brain structure. The informed montage was compared to montages with large electrodes and classic frontoparietal HD montages (F3-P3/F4-P4) in terms of tACS-induced EF and ROI-to-ROI task-based/resting-state connectivity.ResultsCompared to the large electrodes, HD frontoparietal montages allow for a finer control of the spatial peak fields in the main nodes of the FPN at the cost of lower maximum EF (large-pad/HD: max EF[V/m] = 0.37/0.11, number of cortical sub-regions that EF exceeds 50% of the max = 77/13). For defining stimulation targets based on EF patterns, using group-level head models compared to a single standard head model results in comparable but significantly different seed locations (6.43 mm Euclidean distance between the locations of the frontal maximum EF in standard-space). As expected, significant task-based/resting-state connections were only found between frontal-parietal locations in the informed montage. Cue-induced craving score was correlated with frontoparietal connectivity only in the informed montage (r = ?0.24). Stimulation intensity in the informed montage, and not in the classic HD montage, needs 40% reduction in the parietal site to reduce the disparity in EF between stimulation sites.ConclusionThis study provides some empirical insights to montage and dose selection in dual-site-tACS using individual brain structures and functions and proposes a computational pathway to use head models and functional MRI to define (1) optimum electrode montage for targeting FPN in a context of interest (drug-cue-reactivity) and (2) proper transcranial stimulation intensity.