Genomics: Think Global, Act Local

Long a slogan for environmentalists, “think global, act local” could be a new rallying cry for biologists. As genome-wide techniques advance and their costs drop, scientists are expanding into larger and larger territories—metagenomics, global proteomic approaches, and analyses of thousands of genomes. These massive data sets are opening up new possibilities for understanding some of the smallest details of the genome. Here, we look at four such cases—investigating the evolutionary role of insertions and deletions in the genome, connecting an orphan enzyme with its gene, mapping the fine details of chromatin structure, and characterizing global interactions between proteins and RNA—all of which depend on a combination of global thinking and local action

Dermoscopic hemorrhagic dots: an early predictor of response of psoriasis to biologic agents

17siBACKGROUND: Biologic agents are routinely used in the treatment of severe psoriasis. The evaluation of treatment response is mainly based on the physician's global clinical assessment. OBJECTIVE: To investigate whether dermoscopy might enhance the assessment of response of psoriasis to treatment with biologic agents. METHODS: Patients with severe psoriasis scheduled to receive a biologic agent were enrolled in the study. A target lesion from each patient was clinically and dermoscopically documented at baseline and after one, two and six months. The clinical response was evaluated by the recruiting clinicians at all visits, while dermoscopic images were evaluated by two independent investigators, blinded to the clinical information. Chi Square test was used for cross-tabulation comparisons, while odds ratios, 95% confidence intervals and p values were calculated using univariate logistic regression. RESULTS: Overall, there was a significant correlation between clinical response and vessel distribution at all time points: a regular vessel distribution correlated with no response, a clustered distribution with partial response, and the dermoscopic absence of vessels with complete response. The presence of dermoscopic hemorrhagic dots was a potent predictor of favorable clinical response at the subsequent visit at all time points. Among lesions initially clinically responding and later recurring, 87.5% displayed dermoscopic dotted vessels despite the macroscopic remission. CONCLUSION: Dermoscopy might be a useful additional tool for evaluating the response of psoriatic patients to biologic agents. Hemorrhagic dots represent an early predictor of clinical response, while the persistence or reappearance of dotted vessels might predict clinical persistence or recurrence, respectively.openopenLallas, Aimilios; Argenziano, Giuseppe; Zalaudek, Iris; Apalla, Zoe; Ardigo, Marco; Chellini, Patricia; Cordeiro, Natalia; Guimaraes, Mariana; Kyrgidis, Athanassios; Lazaridou, Elizabeth; Longo, Caterina; Moscarella, Elvira; Papadimitriou, Ilias; Pellacani, Giovanni; Sotiriou, Elena; Vakirlis, Efstratios; Ioannides, DimitriosLallas, Aimilios; Argenziano, Giuseppe; Zalaudek, Iris; Apalla, Zoe; Ardigo, Marco; Chellini, Patricia; Cordeiro, Natalia; Guimaraes, Mariana; Kyrgidis, Athanassios; Lazaridou, Elizabeth; Longo, Caterina; Moscarella, Elvira; Papadimitriou, Ilias; Pellacani, Giovanni; Sotiriou, Elena; Vakirlis, Efstratios; Ioannides, Dimitrio

Segmentation of skin strata in reflectance confocal microscopy depth stacks

Reflectance confocal microscopy is an emerging tool for imaging human skin, but currently requires expert human assessment. To overcome the need for human experts it is necessary to develop automated tools for automatically assessing reflectance confocal microscopy imagery

Towards data-driven quantification of skin ageing using reflectance confocal microscopy

Introduction: Evaluation of skin ageing is a non-standardized, subjective process, with typical measures relying coarse, qualitatively defined features. Reflectance confocal microscopy depth stacks contain indicators of both chrono-ageing and photo-ageing. We hypothesize that an ageing scale could be constructed using machine learning and image analysis, creating a data-driven quantification of skin ageing without human assessment. Methods: En-face sections of reflectance confocal microscopy depth stacks from the dorsal and volar forearm of 74 participants (36/18/20 training/testing/validation) were represented using a histogram of visual features learned using unsupervised clustering of small image patches. A logistic regression classifier was trained on these histograms to differentiate between stacks from 20- to 30-year-old and 50- to 70-year-old volunteers. The probabilistic output of the logistic regression was used as the fine-grained ageing score for that stack in the testing set ranging from 0 to 1. Evaluation was performed in two ways: on the test set, the AUC was collected for the binary classification problem as well as by statistical comparison of the scores for age and body site groups. Final validation was performed by assessing the accuracy of the ageing score measurement on 20 depth stacks not used for training or evaluating the classifier. Results: The classifier effectively differentiated stacks from age groups with a test set AUC of 0.908. Mean scores were significantly different when comparing age groups (mean 0.70 vs. 0.44; t = −6.62, p = 0.0000) and also when comparing stacks from dorsal and volar body sites (mean 0.64 vs. 0.53; t = 3.12, p = 0.0062). On the final validation set, 17 out of 20 depth stacks were correctly labelled. Discussion: Despite being limited to only coarse training information in the form of example stacks from two age groups, the trained classifier was still able to effectively discriminate between younger skin and older skin. Curiously, despite being only trained with chronological age, there was still evidence for measurable differences in age scores due to sun exposure—with marked differences in scores on sun-exposed dorsal sites of some volunteers compared with less sun-exposed volar sites. These results suggest that fine-grained data-driven quantification of skin ageing is achievable.</p

Enabling heterogeneous data integration and biomedical event prediction through ICT: The test case of cancer reoccurrence

Early prediction of cancer reoccurrence constitutes a challenge for oncologists and surgeons. This paper describes one ongoing experience, the EU-Project NeoMark, where scientists from different medical and biology research fields joined efforts with Information Technology experts to identify methods and algorithms able to early predict the reoccurrence risk for one of the most devastating tumors, the Oral cavity Squamous Cell Carcinoma (OSCC). The challenge of NeoMark is to develop algorithms able to identify a "signature" or bio-profile of the disease, by integrating multiscale and multivariate data from medical images, genomic profile from tissue and circulating cells RNA and other medical parameters collected from patients before and after treatment. A limited number of relevant biomarkers will be identified and used in a real-time PCR device, for early detection of disease reoccurrence