Artificial intelligence for renal cancer: From imaging to histology and beyond

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation

Interferon regulatory factor 8-deficiency determines massive neutrophil recruitment but T cell defect in fast growing granulomas during tuberculosis

Following Mycobacterium tuberculosis (Mtb) infection, immune cell recruitment in lungs is pivotal in establishing protective immunity through granuloma formation and neogenesis of lymphoid structures (LS). Interferon regulatory factor-8 (IRF-8) plays an important role in host defense against Mtb, although the mechanisms driving anti-mycobacterial immunity remain unclear. In this study, IRF-8 deficient mice (IRF-8−/−) were aerogenously infected with a low-dose Mtb Erdman virulent strain and the course of infection was compared with that induced in wild-type (WT-B6) counterparts. Tuberculosis (TB) progression was examined in both groups using pathological, microbiological and immunological parameters. Following Mtb exposure, the bacterial load in lungs and spleens progressed comparably in the two groups for two weeks, after which IRF-8−/− mice developed a fatal acute TB whereas in WT-B6 the disease reached a chronic stage. In lungs of IRF-8−/−, uncontrolled growth of pulmonary granulomas and impaired development of LS were observed, associated with unbalanced homeostatic chemokines, progressive loss of infiltrating T lymphocytes and massive prevalence of neutrophils at late infection stages. Our data define IRF-8 as an essential factor for the maintenance of proper immune cell recruitment in granulomas and LS required to restrain Mtb infection. Moreover, IRF-8−/− mice, relying on a common human and mouse genetic mutation linked to susceptibility/severity of mycobacterial diseases, represent a valuable model of acute TB for comparative studies with chronically-infected congenic WT-B6 for dissecting protective and pathological immune reactions

B Cell: T Cell Interactions Occur within Hepatic Granulomas during Experimental Visceral Leishmaniasis

Hepatic resistance to Leishmania donovani infection in mice is associated with the development of granulomas, in which a variety of lymphoid and non-lymphoid populations accumulate. Although previous studies have identified B cells in hepatic granulomas and functional studies in B cell-deficient mice have suggested a role for B cells in the control of experimental visceral leishmaniasis, little is known about the behaviour of B cells in the granuloma microenvironment. Here, we first compared the hepatic B cell population in infected mice, where ≈60% of B cells are located within granulomas, with that of naïve mice. In infected mice, there was a small increase in mIgMlomIgD+ mature B2 cells, but no enrichment of B cells with regulatory phenotype or function compared to the naïve hepatic B cell population, as assessed by CD1d and CD5 expression and by IL-10 production. Using 2-photon microscopy to quantify the entire intra-granuloma B cell population, in conjunction with the adoptive transfer of polyclonal and HEL-specific BCR-transgenic B cells isolated from L. donovani-infected mice, we demonstrated that B cells accumulate in granulomas over time in an antigen-independent manner. Intra-vital dynamic imaging was used to demonstrate that within the polyclonal B cell population obtained from L. donovani-infected mice, the frequency of B cells that made multiple long contacts with endogenous T cells was greater than that observed using HEL-specific B cells obtained from the same inflammatory environment. These data indicate, therefore, that a subset of this polyclonal B cell population is capable of making cognate interactions with T cells within this unique environment, and provide the first insights into the dynamics of B cells within an inflammatory site

Comparative tissue transcriptomics reveal prompt inter-organ communication in response to local bacterial kidney infection

<p>Abstract</p> <p>Background</p> <p>Mucosal infections elicit inflammatory responses via regulated signaling pathways. Infection outcome depends strongly on early events occurring immediately when bacteria start interacting with cells in the mucosal membrane. Hitherto reported transcription profiles on host-pathogen interactions are strongly biased towards <it>in vitro </it>studies. To detail the local <it>in vivo </it>genetic response to infection, we here profiled host gene expression in a recent experimental model that assures high spatial and temporal control of uropathogenic <it>Escherichia coli </it>(UPEC) infection within the kidney of a live rat.</p> <p>Results</p> <p>Transcriptional profiling of tissue biopsies from UPEC-infected kidney tissue revealed 59 differentially expressed genes 8 h post-infection. Their relevance for the infection process was supported by a Gene Ontology (GO) analysis. Early differential expression at 3 h and 5 h post-infection was of low statistical significance, which correlated to the low degree of infection. Comparative transcriptomics analysis of the 8 h data set and online available studies of early local infection and inflammation defined a core of 80 genes constituting a "General tissue response to early local bacterial infections". Among these, 25% were annotated as interferon-γ (IFN-γ) regulated. Subsequent experimental analyses confirmed a systemic increase of IFN-γ in rats with an ongoing local kidney infection, correlating to splenic, rather than renal <it>Ifng </it>induction and suggested this inter-organ communication to be mediated by interleukin (IL)-23. The use of comparative transcriptomics allowed expansion of the statistical data handling, whereby relevant data could also be extracted from the 5 h data set. Out of the 31 differentially expressed core genes, some represented specific 5 h responses, illustrating the value of comparative transcriptomics when studying the dynamic nature of gene regulation in response to infections.</p> <p>Conclusion</p> <p>Our hypothesis-free approach identified components of infection-associated multi-cellular tissue responses and demonstrated how a comparative analysis allows retrieval of relevant information from lower-quality data sets. The data further define marked representation of IFN-γ responsive genes and a prompt inter-organ communication as a hallmark of an early local tissue response to infection.</p

Attenuated T Cell Responses to a High-Potency Ligand In Vivo

According to this study, the strongest T cell receptor ligands in vitro do not necessarily induce the strongest T cell responses in vivo, suggesting that vaccine designers may need to reconsider their strategies

Male/Female Is Not Enough: Adding Measures of Masculinity and Femininity to General Population Surveys

Survey research and sociological theory each provide insights into how and why people and groups act, think, and feel. Sociological theories identify what concepts are important for understanding and representing the social world. That is, sociological theories inform what to measure in surveys, and, to a certain extent, how to measure it. Survey research permits sociologists to carefully specify what is to be measured vis a vis sociological theory, setting surveys apart as a social research tool. It is this level of specification of concepts and measures that allow surveys to provide continued value at a time when “big data” proliferate. High quality survey measurement and estimation is necessary for sociologists to evaluate sociological theory among generalizable samples with well-developed questions, leading to further refinement and improvement of the theory and improved understanding of the social world. High quality surveys also provide insights into where sociological theories fail and where they must be adjusted for different subgroups, as well as basic insights into the prevalence of outcomes of interest. Together, sociological theory and survey methods produce insights about society that can inform decision-making and social policy. This mutually reinforcing relationship between sociological theory and survey methods requires sociological theory to evolve from insights obtained using survey methods and survey measurement to evolve with advances in in sociological theory. The measurement of sex and gender in surveys is one area where the development of survey measures has not kept pace with sociological theory and empirical, largely qualitative, findings. Contemporary gender theory sees sex and gender as separate concepts, both of which are important for understanding behaviors and outcomes. Yet, virtually all contemporary surveys measure sex as a binary “male” versus “female” categorization and fail to measure gender, ignoring important heterogeneity in gender identification that may exist within sex categories and any overlap that may occur across categories. Both gender scholars and survey researchers are potentially affected by this shortcoming of modern survey measurement. Gender scholars lose an important tool for assessing gender theories, especially on generalizable samples, risking conclusions that are specific to a small group of individuals rather than the population at large. Survey researchers risk producing theoretically obsolete data, limiting the utility of the data or potentially generating misleading conclusions. Survey data that fail to capture and reflect modern and complex understandings of our social realities also face increased risk of being replaced by “big data” such as administrative and social media data. Survey data that do reflect modern and complex understandings can bring value not available in administrative or other data and are therefore unlikely to be replaced. This paper is part of a growing chorus advocating for updates to how modern surveys measure sex and gender. We argue that the reliance on a single binary measure of sex (male or female) is out of step with current sociological understandings of sex and gender. In response, we propose and test a new theoretically-informed gradational measure of gender identification in a nationally representative mail survey. We evaluate whether respondents answer the gender measure and examine the reliability and predictive validity of the measure. In particular, we examine whether measuring gender gradationally adds explanatory value beyond sex on important social outcomes such as sexuality, childcare, grocery shopping, housework, working for pay, and military service. We also examine whether sex moderates the effect of gender identification in the ways that sociological theory would suggest on these outcomes

Proteomic Analysis of Colorectal Cancer: Prefractionation Strategies Using two-Dimensional Free-Flow Electrophoresis

This review deals with the application of a new prefractionation tool, free-flow electrophoresis (FFE), for proteomic analysis of colorectal cancer (CRC). CRC is a leading cause of cancer death in the Western world. Early detection is the single most important factor influencing outcome of CRC patients. If identified while the disease is still localized, CRC is treatable. To improve outcomes for CRC patients there is a pressing need to identify biomarkers for early detection (diagnostic markers), prognosis (prognostic indicators), tumour responses (predictive markers) and disease recurrence (monitoring markers). Despite recent advances in the use of genomic analysis for risk assessment, in the area of biomarker identification genomic methods alone have yet to produce reliable candidate markers for CRC. For this reason, attention is being directed towards proteomics as a complementary analytical tool for biomarker identification. Here we describe a proteomics separation tool, which uses a combination of continuous FFE, a liquid-based isoelectric focusing technique, in the first dimension, followed by rapid reversed-phase HPLC (1–6 min/analysis) in the second dimension. We have optimized imaging software to present the FFE/RP-HPLC data in a virtual 2D gel-like format. The advantage of this liquid based fractionation system over traditional gel-based fractionation systems is the ability to fractionate large quantity protein samples. Unlike 2D gels, the method is applicable to both high-Mr proteins and small peptides, which are difficult to separate, and in the case of peptides, are not retained in standard 2D gels