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Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).

The problem of corrosion in oil and gas pipelines is one of the major factors affecting the process safety and efficient sustainability development of the oil and gas industry. To gain a better understanding of global research trends and dynamics in the field of oil and gas pipeline corrosion and to advance the development of corrosion control technology, we conducted a literature review using a sample of 1,745 papers from the Web of Science (WOS) database published from 2002 to 2022. We employed a bibliometric analysis approach employed to investigate the distribution of publications over time, geographic regions, major organizations, major authors, journal cocitation, and literature cocitation, and to identify research hotspots and frontiers. The results revealed an exponential growth in the overall number of papers, with the most rapid increase occurring in the last 4 years. China, the US, Canada, the United Kingdom, and Brazil emerged as the most active countries in oil and natural gas pipeline corrosion research, and Mexico, Canada, and Australia also exhibited significant influence in the field. The journals Engineering Failure Analysis, Corrosion, and Corrosion Science had the highest number of publications and impact in this domain. Notably, Corrosion Science stood out as the most influential and highly regarded journal in the corrosion field. The fundamental theories and research framework in the realm of oil and natural gas pipeline corrosion have been primarily established, and a large number of research directions and frontier branches are emerging. The impact of flow parameters on corrosion, pipeline reliability assessment, and analysis of corrosion defects and failures are identified as the three main development paths in this field. In terms of research methodologies, machine learning techniques are becoming increasingly prevalent, with a growing number of studies adopting various machine learning methods. Among these methods, explainable deep learning is at the forefront of development in the field of oil and natural gas pipeline corrosion. [ABSTRACT FROM AUTHOR]

Integrative analysis based on quantitative representation of whole slide images (WSIs) in a large histology cohort may provide predictive models of clinical outcome. On one hand, the efficiency and effectiveness of such representation is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. On the other hand, perceptual interpretation/validation of important multivariate phenotypic signatures are often difficult due to the loss of visual information during feature transformation in hyperspace. To address these issues, we propose a novel approach for integrative analysis based on cellular morphometric context, which is a robust representation of WSI, with the emphasis on tumor architecture and tumor heterogeneity, built upon cellular level morphometric features within the spatial pyramid matching (SPM) framework. The proposed approach is applied to The Cancer Genome Atlas (TCGA) lower grade glioma (LGG) cohort, where experimental results (i) reveal several clinically relevant cellular morphometric types, which enables both perceptual interpretation/validation and further investigation through gene set enrichment analysis; and (ii) indicate the significantly increased survival rates in one of the cellular morphometric context subtypes derived from the cellular morphometric context.

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.

Myasthenia gravis (MG) is an autoimmune disease mediated by anti-acetylcholine receptor (AChR) antibodies and dependent on CD4 positive T lymphocytes (CD4+T) cells. MicroRNA (miRNA) is an endogenous small single-stranded non-coding RNA that involved in post-transcriptional regulation and plays an important role in differentiation and function of CD4+T cells. Increasing evidences have shown that changes of miRNAs happened in MG patients, which may participate in the occurrence and development of MG by regulating CD4+T cells, and miRNA may be a potential therapeutic target for MG. In this paper, relationship between miRNA and MG was reviewed in order to provide new ideas for further research on the pathogenesis and clinical diagnosis and treatment of MG. [ABSTRACT FROM AUTHOR]

Acid–base homeostasis is critical for proper physiological function and pathology. The SLC4 family of HCO3− transmembrane cotransporters is one of the HCO3− transmembrane transport carriers responsible for cellular pH regulation and the uptake or secretion of HCO3− in epithelial cells. NBCn1 (SLC4A7), an electroneutral Na+/HCO3− cotransporter, is extensively expressed in several tissues and functions as a cotransporter for net acid extrusion after cellular acidification. However, the expression and activity level of NBCn1 remain elusive. In addition, NBCn1 has been involved in numerous other cellular processes such as cell volume, cell death/survival balance, transepithelial transport, as well as regulation of cell viability. This review aims to give an inclusive overview of the most recent advances in the research of NBCn1, emphasizing the basic features, regulation, and tissue-specific physiology as well as the development and application of potent inhibitors of NBCn1 transporter in cancer therapy. Research and development of targeted therapies should be carried out for NBCn1 and its associated pathways. [ABSTRACT FROM AUTHOR]

Cardiac hypertrophy is defined as increased heart mass in response to increased hemodynamic requirements. Long-term cardiac hypertrophy, if not counteracted, will ultimately lead to heart failure. The incidence of heart failure is related to myocardial infarction, which could be salvaged by reperfusion and ultimately invites unfavorable myocardial ischemia-reperfusion injury. The Na+/H+ exchangers (NHEs) are membrane transporters that exchange one intracellular proton for one extracellular Na+. The first discovered NHE isoform, NHE1, is expressed almost ubiquitously in all tissues, especially in the myocardium. During myocardial ischemia-reperfusion, NHE1 catalyzes increased uptake of intracellular Na+, which in turn leads to Ca2+ overload and subsequently myocardial injury. Numerous preclinical research has shown that NHE1 is involved in cardiac hypertrophy and heart failure, but the exact molecular mechanisms remain elusive. The objective of this review is to demonstrate the potential role of NHE1 in cardiac hypertrophy and heart failure and investigate the underlying mechanisms. [ABSTRACT FROM AUTHOR]