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Background: Carpal tunnel syndrome (CTS) compromises fine sensorimotor function during activities of daily living and affects a large number of individuals with high burden costs for society. The purpose of this study was to quantitatively characterize fine movement skills in CTS patients preoperatively and at 1 month postoperatively by means of a sensor-engineered glove, in order to provide new insights for evaluative and finally therapeutic purposes. Methods: Forty-one CTS patients and 41 age- and gender-matched healthy controls (HC) were analyzed by adopting the engineered glove Hand Test System (HTS), which previously demonstrated its reliability and sensitivity to detect hands dysfunction in several neurological diseases. A sub-group of 11 CTS subjects was re-tested 1 month after surgery. Three parameters—touch duration (TD), inter-tapping interval (ITI), and movement rate (MR)—were considered to characterize hand function. Results: The affected hand of CTS patients generally showed worst finger opposition performances than HC. Comparing the dominant hand, all parameters were able to significantly discriminate CTS patients from HC. Considering the nondominant hand, the best performing parameter in discriminating CTS from HC was TD. The follow-up assessment at 1 month after surgery showed that considered parameters were able to monitor patients’ recovery. In particular, the TD parameter recorded at the 3 different assigned task modalities resulted significantly enhanced. Conclusions: Results of this pilot study proved the validity of the parameters obtained through the sensor-engineered glove to assess objectively hand functional status and surgical outcomes in CTS.

The advent of Whole Genome Sequencing (WGS) broadened the genetic variation detection range, revealing the presence of variants even in non-coding regions of the genome, which would have been missed using targeted approaches. One of the most challenging issues in WGS analysis regards the interpretation of annotated variants. This review focuses on tools suitable for the functional annotation of variants falling into non-coding regions. It couples the description of non-coding genomic areas with the results and performance of existing tools for a functional interpretation of the effect of variants in these regions. Tools were tested in a controlled genomic scenario, representing the ground-truth and allowing us to determine software performance.

[EN] Mesenchymal stem cells represent an important resource, for bone regenerative medicine and therapeutic applications. This review focuses on new advancements and biophysical tools which exploit different physical and chemical markers of mesenchymal stem cell populations, to finely characterize phenotype changes along their osteogenic differentiation process. Special attention is paid to recently developed label-free methods, which allow monitoring cell populations with minimal invasiveness. Among them, quantitative phase imaging, suitable for single-cell morphometric analysis, and nanoindentation, functional to cellular biomechanics investigation. Moreover, the pool of ion channels expressed in cells during differentiation is discussed, with particular interest for calcium homoeostasis.Altogether, a biophysical perspective of osteogenesis is proposed, offering a valuable tool for the assessment of the cell stage, but also suggesting potential physiological links between apparently independent phenomena.

The spatial and temporal changes of morphological and mechanical properties of living cells reflect complex functionally-associated processes. Monitoring these modifications could provide a direct information on the cellular functional state. Here we present an integrated biophysical approach to the quantification of the morphological and mechanical phenotype of single cells along a maturation pathway. Specifically, quantitative phase microscopy and single cell biomechanical testing were applied to the characterization of the maturation of human foetal osteoblasts, demonstrating the ability to identify effective label-free biomarkers along this fundamental biological process.

An innovative platform for the study of the molecular mechanisms at the basis of mechanotransduction has been implemented, developing an experimental approach capable of providing controlled dynamic compression stimuli and retrieving the biomolecular response with single-cell sensitivity. The system provides the ability to perform compression-release cycles on single cells with controlled forces in the nN range and a user-defined repetition rate. Experimental procedures to perform qPCR from a small set of single cells were finely tuned. The experimental platform was tested in the context of bone (cell line hFOB 1.19), a physiological environment highly subjected to mechanical stimuli. Target genes were identified in the literature, based on their involvement in the osteogenesis process or in the bone response to mechanical stimuli. qPCR analysis shows an increase in expression of the chosen targets, and confirms the effectiveness of the presented approach for studying living single cells response to dynamic compression.

Human body is subject to many and variegated mechanical stimuli, actuated in different ranges of force, frequency, and duration. The process through which cells "feel" forces and convert them into biochemical cascades is called mechanotransduction. In this review, the effects of fluid shear stress on bone cells will be presented. After an introduction to present the major players in bone system, we describe the mechanoreceptors in bone tissue that can feel and process fluid flow. In the second part of the review, we present an overview of the biological processes and biochemical cascades initiated by fluid shear stress in bone cells.

Over the last decade, toxic events along the Mediterranean coast associated with exceptional harmful blooms of the dinoflagellate Ostreopsis cf. ovata have increased in frequency and distribution, causing not only the death of marine organisms and human health problems, but also economic loss on the tourism and aquaculture industries. In order to reduce the burden of routine algal counting, an innovative automated, low-cost, opto-electronic system called OvMeter was developed. It is able to speed up the monitoring process and therefore it enables early warning of incipient harmful algal blooms. An ad-hoc software tool provides automated cell recognition, counting and real-time calculation of the final algal concentration. The core of dinoflagellate recognition relies on a localization step which takes advantage of the synergistic exploitation of 2D bright-field and quantitative phase microscopy images, and a classification phase performed by a machine learning algorithm based on Boosted Trees approach. The architectural design of the OvMeter device is presented here, together with a performance evaluation on sea samples.

25 p.-5 fig.-1 tab. + 6 fig. supl.+ 1 tab. supl., αβ-Tubulin is a validated target for anticancer drug discovery, and molecules binding to this protein are used to treat several types of tumors. Here, we report on a combined X-ray crystallography and molecular dynamics approach to study drug binding within the colchicine site of αβ-tubulin, focusing on plinabulin, an agent currently in phase 3 clinical testing for the treatment of cancer and chemotherapy-induced neutropenia. We found that plinabulin is more persistently bound to the colchicine site of βII- compared to βIII-tubulin, allowing for a prediction of isotype-expression-dependent drug sensitivity. Additionally, computational residence time and exit paths from the βII-tubulin were compared between plinabulin and two other compounds, colchicine and combretastatin-A4. The former displayed the highest residence time, followed by plinabulin and then distantly by combretastatin-A4. Our combined experimental and computational protocol could help to investigate anti-tubulin drugs, improving our understanding of their mechanism of action, residence time, and tubulin isotype selectivity., This work was financially supported by BeyondSpring Pharmaceuticals Inc. (to M.O.S. and A.C.) and by grants from the Ministerio de Ciencia Innovacion y Universidades (BFU2016-75319-R; to J.F.D.), from the Swiss National Science Foundation (31003A_166608, to M.O.S.) and from the Regione Lombardia (Accordo per la Ricerca e l’Innovazione).

The ability to sense mechanical stimuli and elaborate a response to them is a fundamental process in all organisms, driving crucial mechanisms ranging from cell volume regulation up to organ development or regeneration. Nevertheless, only in few cases the underlying molecular players are known. In particular, mammals possess a large variety of mechanoreceptors, providing highly specialized functions in sensory cells, but also several housekeeping molecular systems are involved in the complex mechanism of mechanotransduction. Recently, a new class of almost ubiquitous membrane channels has been identified in mammalians, namely piezo1 and piezo2, that is thought to play a crucial role in the mechanobiology of mammals. This review focuses on recent findings on these novel channels, and highlights open biophysical questions that largely remain to be addressed.

The stroma of human bone marrow contains a population of skeletal stem cells (hBM-MSC) which are common ancestors, among the others, of osteoblasts and adipocytes. It has been proposed that the imbalance between hBM-MSC osteogenesis and adipogenesis, which naturally accompanies bone marrow senescence, may contribute to the development of bone-associated diseases, like osteoporosis. The possibility to reproduce this mechanism in vitro has been demonstrated, providing a good model to disclose the details of the complex bone-fat generation homeostasis. Nevertheless, the lack of a simple approach to quantitatively assess the actual stage of a cellular population hindered the adoption of this in vitro model. In this work, the direct differentiation of hBM-MSCs towards a single (osteo or adipo) lineage was characterized using quantitative biophysical and biological approaches, together with the parallel process of trans-differentiation from one lineage to the other. The results confirm that the original plasticity of hBM-MSCs is maintained along the initial stages of the differentiation, showing that in vitro conversion of pre-osteoblasts into adipocytes and, vice versa, of pre-adipocytes into osteoblasts is extremely efficient, comparable with the direct differentiation. Moreover, a method based on digital holography is proposed, providing a quantitative indication of the phenotype stage along differentiation.

Summary The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker. An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

Meta-analysis of microarray data is increasingly important, considering both the availability of multiple platforms using disparate technologies and the accumulation in public repositories of data sets from different laboratories. We addressed the issue of comparing Gene expression profiles from two microarray platforms by devising a standardized investigative strategy. We tested this procedure by studying MDA-MB-231 cells, which undergo apoptosis on treatment with resveratrol. Gene expression profiles were obtained using high-density. short-oligonucleotide, single-color microarray platforms: GeneChip (Affymetrix) and CodeLink (Amersham). Interplatform analyses were carried out on 8414 common transcripts represented on both platforms, as identified by LocusLink ID, representing 70.8% and 88.6% of annotated GeneChip and CodeLink features, respectively. We identified 105 differentially expressed genes (DEGs) on CodeLink and 42 DEGs on GeneChip. Among them, only 9 DEGs were commonly identified by both platforms. Multiple analyses (BLAST alignment of probes with target sequences. gene ontology, literature mining, and quantitative real-time PCR) permitted LIS to investigate the factors contributing to the generation of platform-dependent results in single-color microarray experiments. Ail effective approach to cross-platform comparison involves microarrays of similar technologies, samples prepared by identical methods, and a standardized battery of bioinformatic and statistical analyses. (c) 2006 Elsevier Inc. All rights reserved.

Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously, allowing faster analysis and considerably reducing costs incurred in staining. The presented work supports the \emph{pre-array} phase of this technique, i. e. the automatic discrimination between normal and pathological regions within the analyzed tissues, and it works in the specific context of tubular breast cancer. The diagnosis is performed by automatically analyzing specific morphological features of the breast samples, in order to define if tissues present a normal behavior either they show pathological characteristics, in particular the absence of a double layer of cells around the lumen or the decay of a regular glands-and-lobules structure. Tissue structure is investigated through a parallel image processing algorithm, which performs the extraction of morphological parameters from the acquired images and compares them to experimentally validated threshold values. The input image is divided into a number of independent sub-images, to be singularly analyzed and labeled according to the result of the computed diagnosis. The time spent to actually analyze each sub-image generally varies depending on the pathological characteristics of each part of the tissue. In order to properly manage and exploit this feature of the algorithm, the analysis of the sub-images is dynamically dispatched among parallel processes. Experimental results, carried on by exploiting the parallel paradigm, certify the actual improvement in the execution time, leading to almost linear speed-up values on the actual tissue elaboration.

The merging between molecular biology and computer science is becoming a key point in knowledge discovery, in particular when huge amount of data coming from different laboratories need to be integrated and analyzed For example Tissue MicroArray (TMA) is a high throughput technology that allows researchers to analyze several tissue samples in a parallel way. TMA is a good validation method of researcher's hypothesis about pathological tissue morphology and biomarkers evaluation. In this context, the Grid technology is a good opportunity to share data and solve intensive storage and computing problems. However, the exploitation of the Grid is not a trivial pursuit for many users. Nowadays, an important issue is to provide a simplified use of Grid resources, enabling the use of tools that scientists usually employ. In this paper, we present TMA inspect, a Web based system for Tissue Microarray experiment analysis that provides to not expert users a graphical interface to search for TMA images and to efficiently process them in EGEE. For the image elaboration we use PIMA(GE)(2) Lib, the Parallel IMAGE processing GEnoa Library.

The grid represents a great opportunity in the scientific community to solve data/compute intensive problems and to share data. Using the grid, different application domains could achieve great advantages; however, the exploitation of the grid is not a trivial pursuit for many users. The key issue is to enable a simplified use of grid resources and tools that scientists usually employ. In this paper, we investigate these topics in bioinformatics community to allow the efficient elaboration on the grid of images obtained through the tissue microarray technique. We present a grid framework for tissue microarray analysis, GF4TMA, that allows the selection of TMA images and their efficient analysis by the exploitation of the grid architecture. In particular, we exploited PIMA(GE)2 Lib, the Parallel IMAGE processing GEnoa Library. A critical point was to enable parallel computations on the grid without compromising the efficiency and the user-friendliness of the native library. We tackled this issue by encapsulating the library in a grid service.

The Tissue MicroArray (TMA) technique is assuming even more importance. Digital images acquisition becomes fundamental to provide an automatic system for subsequent analysis. The accuracy of the results depends on the image resolution, which has to be very high in order to provide as many details as possible. Lossless formats are more suitable to bring information, but data file size become a critical factor researchers have to deal with. This affects not only storage methods but also computing times and performances. Pathologists and researchers who work with biological tissues, in particular with the TMA technique, need to consider a large number of case studies to formulate and validate their hypotheses. It is clear the importance of image sharing between different institutes worldwide to increase the amount of interesting data to work with. In this context, preserving the security of sensitive data is a fundamental issue. In most of the cases copying patient data in places different from the original database is forbidden by the owner institutes. Storage, computing and security are key problems of TMA methodology. In our system we tackle all these aspects using the EGEE (Enabling Grids for E-sciencE) Grid infrastructure. The Grid platform provides good storage, performance in image processing and safety of sensitive patient information: this architecture offers hundreds of Storage and Computing Elements and enables users to handle images without copying them to physical disks other than where they have been archived by the owner, giving back to end-users only the processed anonymous images. The efficiency of the TMA analysis process is obtained implementing algorithms based on functions provided by the Parallel IMAge processing Genoa Library (PIMA(GE)2 Lib). The acquisition of remotely distributed TMA images is made using specialized I/O functions based on the Grid File Access Library (GFAL) API. In our opinion this approach may represent important contribution to tele-pathology development.

Several systems have been presented in the last years in order to manage the complexity of large microarray experiments. Although good results have been achieved, most systems tend to lack in one or more fields. A Grid based approach may provide a shared, standardized and reliable solution for storage and analysis of biological data, in order to maximize the results of experimental efforts. A Grid framework has been therefore adopted due to the necessity of remotely accessing large amounts of distributed data as well as to scale computational performances for terabyte datasets. Two different biological studies have been planned in order to highlight the benefits that can emerge from our Grid based platform. The described environment relies on storage services and computational services provided by the gLite Grid middleware. The Grid environment is also able to exploit the added value of metadata in order to let users better classify and search experiments. A state-of-art Grid portal has been implemented in order to hide the complexity of framework from end users and to make them able to easily access available services and data. The functional architecture of the portal is described. As a first test of the system performances, a gene expression analysis has been performed on a dataset of Affymetrix GeneChip® Rat Expression Array RAE230A, from the ArrayExpress database. The sequence of analysis includes three steps: (i) group opening and image set uploading, (ii) normalization, and (iii) model based gene expression (based on PM/MM difference model). Two different Linux versions (sequential and parallel) of the dChip software have been developed to implement the analysis and have been tested on a cluster. From results, it emerges that the parallelization of the analysis process and the execution of parallel jobs on distributed computational resources actually improve the performances. Moreover, the Grid environment have been tested both against the possibility of uploading and accessing distributed datasets through the Grid middleware and against its ability in managing the execution of jobs on distributed computational resources. Results from the Grid test will be discussed in a further paper.

Single energy X-ray imaging, due to its low cost and flexibility, is one of the most used and common technique to assess bone state and bone remodeling over time. Standardized X-ray images are needed to compare sets of radiographs for semi-quantitative analyses of tissue remodeling. However, useful mathematical modeling for the analysis of high level radiographic images are not easily available. In order to propose a useful evaluation tool to a wide clinical scenario, we present an innovative calibration algorithm for a semi-quantitative analysis of non-standardized digitized X-ray images. For calibration on a unique standardization scale, three time invariant regions (ROI) of radiographs were selected and analyzed. The accuracy of the normalization method for X-ray films was successfully validated by using an aluminum step wedge for routine X-ray exposures as tool to standardize serial radiographs (Pearson correlation test: R(2) = 0.96). This method was applied to investigate the progression of the new bone deposition within ceramic scaffolds used as osteoconductive substitute in large bone defects taking advantage of a large animal model. This innovative image-processing algorithm allowed the identification and semi-quantification of the bone matrix deposited within the implant. The osteo-integration at the bone-implant interface was also investigated. A progressively increasing bone tissue deposition within the porous bioceramic implant and a progressive osteo-integration was observed during the 12 months of the trial.

Motivations Current researches on osteochondral tissue focus on understanding the biomolecular mechanisms of bone/cartilage development [1,2], which helps in understanding the onset of the related genetics diseases [3] and prompt the development of new approaches for tissue engineering [4]. Nowadays, regulation of tissue formation and remodelling is not completely understood, especially when dealing with the process of differentiation into bone and cartilage or with tissues affected by complex pathologies. Concerning the biomolecular mechanisms of bone formation, to authors’ knowledge the only data collection existing is the Skeletal Gene Database [5], a list of genes involved in the bone metabolism accompanied by PubMed [6] references to scientific papers. This database consists of a pdf file, which describes and annotates many genes involved in osteochondrial development in a simple, but static way. Other data are sparse in literature, although a rationalization of the available information about bone pathologies has been done by OMIM [7]. In order to overcome this limitation, authors designed a database, the OsteoChondroDB, by employing a vertical data integration strategy to connect tissue specific information referred to diverse biomolecular levels. In particular, the database stores information about bone development in physiological conditions together with data about osteochondral pathologies, which helps in highlighting pathways of differentiation and tissue maintaining. The resource aims at collecting and organizing data, to facilitate mining of active components in bone tissue cells. The resource is intended to be a reference knowledge base for research studies about the genetics of bone and cartilage pathologies, with the aim of improving the knowledge about physiological pathways involved in the development of this tissue. Moreover it represents a support for tissue engineering, to identify always better methods to grow cells on biomaterial scaffolds, and for new therapies identification, proposing molecules as possible targets for drug treatments of bone diseases.