Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Structural changes in the innercortex cells of soybean root nodules are induced by short-term exposure to high salt or oxygen concentrations

After a 2 h exposure of intact soybean nodules to high concentrations of NaCl (100mol m_?3) or oxygen (8OkPa O₂), morphometric computations carried out using an image analysis technique on semi-thin sections showed that both treatments induced a decrease in the area of the inner-cortex cells, which were then characterized by a tangential elongation. In contrast, no significant change in area occurred in the middle-cortex cells although their elongation decreased. Electron microscopic observations showed that in the inner-cortex cells changes included the presence of wall infoldings, an enlarged periplasmic space and a lobate nucleus whose chromatin distribution differed from that of the control. Structural changes also occurred in the endoplasmic reticulum, microbodies, mitochondria and plastids. From several of these changes, which are similar to those noted in osmocontractil cells in response to external stimuli, it can be hypothesized that the inner cortex may provide a potential mechanism for the control of oxygen diffusion through the nodules.     

Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis

Computer-aided detection (CAD) technology has been developed and demonstrated its potential to assist radiologists in detecting pulmonary nodules especially at an early stage. In this paper, we present a novel scheme for automatic detection of pulmonary nodules in CT images based on a 3D tensor filtering algorithm and local image feature analysis. We first apply a series of preprocessing steps to segment the lung volume and generate the isotropic volumetric CT data. Next, a unique 3D tensor filtering approach and local image feature analysis are used to detect nodule candidates. A 3D level set segmentation method is used to correct and refine the boundaries of nodule candidates subsequently. Then, we extract the features of the detected candidates and select the optimal features by using a CFS (Correlation Feature Selection) subset evaluator attribute selection method. Finally, a random forest classifier is trained to classify the detected candidates. The performance of this CAD scheme is validated using two datasets namely, the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database. By applying a 10-fold cross-validation method, the CAD scheme yielded a sensitivity of 79.3% at an average of 4 false positive detections per scan (FP/Scan) for the former dataset, and a sensitivity of 84.62% and 2.8 FP/Scan for the latter dataset, respectively. Our detection results show that the use of 3D tensor filtering algorithm combined with local image feature analysis constitutes an effective approach to detect pulmonary nodules.     

Anti-biofilm and anti-adherence properties of novel cyclic dipeptides against oral pathogens

Microorganisms embedded in a biofilm are significantly more resistant to antimicrobial agents and the defences of the human immune system, than their planktonic counterpart. Consequently, compounds that can inhibit biofilm formation are of great interest for novel therapeutics. In this study, a screening approach was used to identify novel cyclic dipeptides that have anti-biofilm activity against oral pathogens. Five new active compounds were identified that prevent biofilm formation by the cariogenic bacterium Streptococcus mutans and the pathogenic fungus Candida albicans. These compounds also inhibit the adherence of microorganisms to a hydroxylapatite surface. Further investigations were conducted on these compounds to establish the structure–activity relationship, and it was deduced that the common cleft pattern is required for these molecules to act effectively against biofilms.     

Quorum-sensing- and type VI secretion-mediated spatiotemporal cell death drives genetic diversity in Vibrio cholerae

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Validation of a deformable image registration produced by a commercial treatment planning system in head and neck

In recent years one of the areas of interest in radiotherapy has been adaptive radiation therapy (ART), with the most efficient way of performing ART being the use of deformable image registration (DIR). In this paper we use the distances between points of interest (POIs) in the computed tomography (CT) and the cone beam computed tomography (CBCT) acquisition images and the inverse consistence (IC) property to validate the RayStation treatment planning system (TPS) DIR algorithm. This study was divided into two parts: Firstly the distance-accuracy of the TPS DIR algorithm was ascertained by placing POIs on anatomical features in the CT and CBCT images from five head and neck cancer patients. Secondly, a method was developed for studying the implication of these distances on the dose by using the IC. This method compared the dose received by the structures in the CT, and the structures that were quadruply-deformed. The accuracy of the TPS was 1.7 ± 0.8 mm, and the distance obtained with the quadruply-deformed IC method was 1.7 ± 0.9 mm, i.e. the difference between the IC method multiplied by two, and that of the TPS validation method, was negligible. Moreover, the IC method shows very little variation in the dose-volume histograms when comparing the original and quadruply-deformed structures. This indicates that this algorithm is useful for planning adaptive radiation treatments using CBCT in head and neck cancer patients, although these variations must be taken into account when making a clinical decision to adapt a treatment plan.     

A multiscale analysis of ecosystem services supply in the NW Iberian Peninsula from a functional perspective

In recent years, the assessment of ecosystem services (ES) supply has been based on the use of Land Use/Land Cover (LULC) data as proxies for spatial representation of ecosystems. Nevertheless, some shortcomings of this method, such as uncertainties derived from generalization of the ecosystem types and assumptions of invariance across spatial scales, indicate the need for new approaches. Such approaches could be aimed at improving knowledge of the relationships between ecosystem services and landscape structure and the spatial characteristics of ES patterns. In this study, we propose an integrative approach that involves the generation and analysis of continuous maps representing the supply of five ES potentially related to the amount of biomass. Five remote sensing images of the Northwestern Iberian Peninsula, obtained with Landsat-5 TM, were used to generate a proxy for net primary production by combining the normalized difference vegetation index (NDVI) of each image to calculate a ΣNDVI index that could act as a potential indicator of some ecosystem services. This information was combined with three variables – terrain slope, population density and occurrence of protected areas – to produce spatial models for the five ES and eventually a series of five supply maps. Food, materials and energy provision services showed a clustered pattern, with high supply values in flat zones and areas with high population densities. In contrast, mass flow and climate regulation services were more widely distributed throughout the study area. The five ecosystem service patterns were analyzed at different scales by two methods: lacunarity and four term local quadrat variance (4TLQV) analysis. These methods revealed differences in the spatial pattern: lacunarity analysis was useful for detection of scale thresholds at the local level, whereas 4TLQV was more sensitive to scale thresholds at larger spatial levels. Thus, the variance analysis yielded higher values for larger windows sizes, particularly for provisioning services. The results demonstrated the suitability of the proposed approach for the spatially explicit modeling of ecosystem services, avoiding the uncertainty of other assessments such as those based on LULC data, and for the exploratory analysis of ES supply from a spatial point of view.     

Mechano- and pH-sensing convergence on Ca2+-mobilising proteins – A recipe for cancer?

Alterations in the (bio)chemical and physical microenvironment of cells accompany and often promote disease formation and progression. This is particularly well established for solid cancers, which are typically stiffer than the healthy tissue in which they arise, and often display profound acidification of their interstitial fluid. Cell surface receptors can sense changes in the mechanical and (bio)chemical properties of the surrounding extracellular matrix and fluid, and signalling through these receptors is thought to play a key role in disease development and advancement. This review will look at ion channels and G protein coupled receptors that are activated by mechanical cues and extracellular acidosis, and stimulation of which results in increases in intracellular Ca²⁺ concentrations. Cellular Ca²⁺ levels are dysregulated in cancer as well as cancer-associated cells, and mechano- and proton-sensing proteins likely contribute to these aberrant intracellular Ca²⁺ signals, making them attractive targets for therapeutic intervention.