Anorectal Malformations: The Pivotal Role of the Good Clinical Practice.

Anorectal malformations (ARM) without a fistula are a rare congenital condition. Although may seem more simple to repair compared with ARM with fistulas, surgery has proved to be challenging. We report the case of a newborn who presented a well-formed anus and normal genitalia; a blind-ending anal canal was detected after the insertion of a rectal probe, thus allowing the diagnosis of ARM. Anal probing straight after birth avoids the possible complications related to intestinal obstruction due to a missed diagnosis of ARM. Examination of the perineal region is an important step in the evaluation of the newborn and represents the tool for a prompt identification of ARM. Adding anal probing to accurate inspection perineum is a good clinical practice and should always be performed even in presence of a normal-looking perineum.

Metric Learning in Histopathological Image Classification: Opening the Black Box.

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task of classifying images, can provide additional information able to support the decision of the classification system. In particular, triplet networks have been employed to create a representation in the embedding space that gathers together images of the same class while tending to separate images with different labels. The obtained representation shows an evident separation of the classes with the possibility of evaluating the similarity and the dissimilarity among input images according to distance criteria. The model has been tested on the BreakHis dataset, a reference and largely used dataset that collects breast cancer images with eight pathology labels and four magnification levels. Our proposed classification model achieves relevant performance on the patient level, with the advantage of providing interpretable information for the obtained results, which represent a specific feature missed by the all the recent methodologies proposed for the same purpose.

pH-sensing hybrid hydrogels for non-invasive metabolism monitoring in tumor spheroids.

The constant increase in cancer incidence and mortality pushes biomedical research towards the development of in vitro 3D systems able to faithfully reproduce and effectively probe the tumor microenvironment. Cancer cells interact with this complex and dynamic architecture, leading to peculiar tumor-associated phenomena, such as acidic pH conditions, rigid extracellular matrix, altered vasculature, hypoxic condition. Acidification of extracellular pH, in particular, is a well-known feature of solid tumors, correlated to cancer initiation, progression, and resistance to therapies. Monitoring local pH variations, non-invasively, during cancer growth and in response to drug treatment becomes extremely important for understanding cancer mechanisms. Here, we describe a simple and reliable pH-sensing hybrid system, based on a thermoresponsive hydrogel embedding optical pH sensors, that we specifically apply for non-invasive and accurate metabolism monitoring in colorectal cancer (CRC) spheroids. First, the physico-chemical properties of the hybrid sensing platform, in terms of stability, rheological and mechanical properties, morphology and pH sensitivity, were fully characterized. Then, the proton gradient distribution in the spheroids proximity, in the presence or absence of drug treatment, was quantified over time by time lapse confocal light scanning microscopy and automated segmentation pipeline, highlighting the effects of the drug treatment in the extracellular pH. In particular, in the treated CRC spheroids the acidification of the microenvironment resulted faster and more pronounced over time. Moreover, a pH gradient distribution was detected in the untreated spheroids, with more acidic values in proximity of the spheroids, resembling the cell metabolic features observed in vivo in the tumor microenvironment. These findings promise to shed light on mechanisms of regulation of proton exchanges by cellular metabolism being essential for the study of solid tumors in 3D in vitro models and the development of personalized medicine approaches.

Quantifying heterogeneity to drug response in cancer-stroma kinetics.

A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.

A pH-sensor scaffold for mapping spatiotemporal gradients in three-dimensional in vitro tumour models.

The detection of extracellular pH at single cell resolution is challenging and requires advanced sensibility. Sensing pH at high spatial and temporal resolution might provide crucial information in understanding the role of pH and its fluctuations in a wide range of physio-pathological cellular processes, including cancer. Here, a method to embed silica-based fluorescent pH sensors into alginate-based three-dimensional (3D) microgels tumour models, coupled with a computational method for fine data analysis, is presented. By means of confocal laser scanning microscopy, live-cell time-lapse imaging of 3D alginate microgels was performed and the extracellular pH metabolic variations were monitored in both in vitro 3D mono- and 3D co-cultures of tumour and stromal pancreatic cells. The results show that the extracellular pH is cell line-specific and time-dependent. Moreover, differences in pH were also detected between 3D monocultures versus 3D co-cultures, thus suggesting the existence of a metabolic crosstalk between tumour and stromal cells. In conclusion, the system has the potential to image multiple live cell types in a 3D environment and to decipher in real-time their pH metabolic interplay under controlled experimental conditions, thus being also a suitable platform for drug screening and personalized medicine.

Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors.

pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to measure intracellular pH; however, an accurate methodology to precisely monitor acidification kinetics of organelles in living cells has not been established, limiting the scope of this class of sensors. Here, silica-based fluorescent microparticles were utilized to probe the pH of intracellular organelles in MDA-MB-231 and MCF-7 breast cancer cells. In addition to the robust, ratiometric, trackable, and bioinert pH sensors, we developed a novel dimensionality reduction algorithm to automatically track and screen massive internalization events of pH sensors. We found that the mean acidification time is comparable among the two cell lines (ΔTMCF-7 = 16.3 min; ΔTMDA-MB-231 = 19.5 min); however, MCF-7 cells showed a much broader heterogeneity in comparison to MDA-MB-231 cells. The use of pH sensors and ratiometric imaging of living cells in combination with a novel computational approach allow analysis of thousands of events in a computationally inexpensive and faster way than the standard routes. The reported methodology can potentially be used to monitor pH as well as several other parameters associated with endocytosis.

Golgi maturation-dependent glycoenzyme recycling controls glycosphingolipid biosynthesis and cell growth via GOLPH3.

Glycosphingolipids are important components of the plasma membrane where they modulate the activities of membrane proteins including signalling receptors. Glycosphingolipid synthesis relies on competing reactions catalysed by Golgi-resident enzymes during the passage of substrates through the Golgi cisternae. The glycosphingolipid metabolic output is determined by the position and levels of the enzymes within the Golgi stack, but the mechanisms that coordinate the intra-Golgi localisation of the enzymes are poorly understood. Here, we show that a group of sequentially-acting enzymes operating at the branchpoint among glycosphingolipid synthetic pathways binds the Golgi-localised oncoprotein GOLPH3. GOLPH3 sorts these enzymes into vesicles for intra-Golgi retro-transport, acting as a component of the cisternal maturation mechanism. Through these effects, GOLPH3 controls the sub-Golgi localisation and the lysosomal degradation rate of specific enzymes. Increased GOLPH3 levels, as those observed in tumours, alter glycosphingolipid synthesis and plasma membrane composition thereby promoting mitogenic signalling and cell proliferation. These data have medical implications as they outline a novel oncogenic mechanism of action for GOLPH3 based on glycosphingolipid metabolism.

The Revolutionary Roads to Study Cell-Cell Interactions in 3D In Vitro Pancreatic Cancer Models.

Pancreatic cancer, the fourth most common cancer worldwide, shows a highly unsuccessful therapeutic response. In the last 10 years, neither important advancements nor new therapeutic strategies have significantly impacted patient survival, highlighting the need to pursue new avenues for drug development discovery and design. Advanced cellular models, resembling as much as possible the original in vivo tumor environment, may be more successful in predicting the efficacy of future anti-cancer candidates in clinical trials. In this review, we discuss novel bioengineered platforms for anticancer drug discovery in pancreatic cancer, from traditional two-dimensional models to innovative three-dimensional ones.

miRTissue ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions.

BACKGROUND: Non-coding RNAs include different classes of molecules with regulatory functions. The most studied are microRNAs (miRNAs) that act directly inhibiting mRNA expression or protein translation through the interaction with a miRNAs-response element. Other RNA molecules participate in the complex network of gene regulation. They behave as competitive endogenous RNA (ceRNA), acting as natural miRNA sponges to inhibit miRNA functions and modulate the expression of RNA messenger (mRNA). It became evident that understanding the ceRNA-miRNA-mRNA crosstalk would increase the functional information across the transcriptome, contributing to identify new potential biomarkers for translational medicine. RESULTS: We present miRTissue ce, an improvement of our original miRTissue web service. By introducing a novel computational pipeline, miRTissue ce provides an easy way to search for ceRNA interactions in several cancer tissue types. Moreover it extends the functionalities of previous miRTissue release about miRNA-target interaction in order to provide a complete insight about miRNA mediated regulation processes. miRTissue ce is freely available at http://tblab.pa.icar.cnr.it/mirtissue.html . CONCLUSIONS: The study of ceRNA networks and its dynamics in cancer tissue could be applied in many fields of translational biology, as the investigation of new cancer biomarker, both diagnostic and prognostic, and also in the investigation of new therapeutic strategies of intervention. In this scenario, miRTissue ce can offer a powerful instrument for the analysis and characterization of ceRNA-ceRNA interactions in different tissue types, representing a fundamental step in order to understand more complex regulation mechanisms.

Morphogenesis Guided by 3D Patterning of Growth Factors in Biological Matrices.

Three-dimensional (3D) control over the placement of bioactive cues is fundamental to understand cell guidance and develop engineered tissues. Two-photon patterning (2PP) provides such placement at micro- to millimeter scale, but nonspecific interactions between proteins and functionalized extracellular matrices (ECMs) restrict its use. Here, a 2PP system based on nonfouling hydrophilic photocages and Sortase A (SA)-based enzymatic coupling is presented, which offers unprecedented orthogonality and signal-to-noise ratio in both inert hydrogels and complex mammalian matrices. Improved photocaged peptide synthesis and protein functionalization protocols with broad applicability are introduced. Importantly, the method enables 2PP in a single step in the presence of fragile biomolecules and cells, and is compatible with time-controlled growth factor presentation. As a corollary, the guidance of axons through 3D-patterned nerve growth factor (NGF) within brain-mimetic ECMs is demonstrated. The approach allows for the interrogation of the role of complex signaling molecules in 3D matrices, thus helping to better understand biological guidance in tissue development and regeneration.

MiRNA therapeutics based on logic circuits of biological pathways.

BACKGROUND: In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics. RESULTS: In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data. CONCLUSIONS: The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.

Constitutive alterations in vesicular trafficking increase the sensitivity of cells from celiac disease patients to gliadin.

Celiac Disease (CD) is an autoimmune disease characterized by inflammation of the intestinal mucosa due to an immune response to wheat gliadins. Some gliadin peptides (e.g., A-gliadin P57-68) induce an adaptive Th1 pro-inflammatory response. Other gliadin peptides (e.g., A-gliadin P31-43) induce a stress/innate immune response involving interleukin 15 (IL15) and interferon α (IFN-α). In the present study, we describe a stressed/inflamed celiac cellular phenotype in enterocytes and fibroblasts probably due to an alteration in the early-recycling endosomal system. Celiac cells are more sensitive to the gliadin peptide P31-43 and IL15 than controls. This phenotype is reproduced in control cells by inducing a delay in early vesicular trafficking. This constitutive lesion might mediate the stress/innate immune response to gliadin, which can be one of the triggers of the gliadin-specific T-cell response.

The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes.

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was "Methods, tools and platforms for Personalised Medicine in the Big Data Era", but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

KDEL receptor regulates secretion by lysosome relocation- and autophagy-dependent modulation of lipid-droplet turnover.

Inter-organelle signalling has essential roles in cell physiology encompassing cell metabolism, aging and temporal adaptation to external and internal perturbations. How such signalling coordinates different organelle functions within adaptive responses remains unknown. Membrane traffic is a fundamental process in which membrane fluxes need to be sensed for the adjustment of cellular requirements and homeostasis. Studying endoplasmic reticulum-to-Golgi trafficking, we found that Golgi-based, KDEL receptor-dependent signalling promotes lysosome repositioning to the perinuclear area, involving a complex process intertwined to autophagy, lipid-droplet turnover and Golgi-mediated secretion that engages the microtubule motor protein dynein-LRB1 and the autophagy cargo receptor p62/SQSTM1. This process, here named 'traffic-induced degradation response for secretion' (TIDeRS) discloses a cellular mechanism by which nutrient and membrane sensing machineries cooperate to sustain Golgi-dependent protein secretion.

Robot-assisted resection of gastric duplication cysts in a child: a detailed case report.

Gastric duplication cysts (GDCs) represent 4-9% of alimentary tract duplications. Early diagnosis and surgical excision are essential to avoid morbidity or neoplastic degeneration. Roboticassisted excision of GDCs has never been described in childhood. We report an asymptomatic male patient with 2 gastric cystic masses at ultrasonography (US)-study (diameter 25mm and 8mm), increasing in size at follow-up. At 20 months of age, magnetic- resonance-imaging-scan confirmed 2 round gastric masses (44×35mm and 16×12mm, respectively). Two months later, an elective robotic-assisted excision of GDCs was completed without complications. The patient was discharged at day 6 after procedure. Histology confirmed the diagnosis of GDCs. At a 2-year follow- up, US-study did not evidence any issue. In this first reported case of robotic-assisted cystectomy for CGD in childhood, the procedure seems safe, effective, and feasible. This approach improves the movements of the surgical instruments with better 3- D visualization in comparison with the laparoscopic approach.

Bloch surface wave enhanced biosensor for the direct detection of Angiopoietin-2 tumor biomarker in human plasma.

Quantitative detection of angiogenic biomarkers provides a powerful tool to diagnose cancers in early stages and to follow its progression during therapy. Conventional tests require trained personnel, dedicated laboratory equipment and are generally time-consuming. Herein, we propose our developed biosensing platform as a useful tool for a rapid determination of Angiopoietin-2 biomarker directly from patient plasma within 30 minutes, without any sample preparation or dilution. Bloch surface waves supported by one dimensional photonic crystal are exploited to enhance and redirect the fluorescence arising from a sandwich immunoassay that involves Angiopoietin-2. The sensing units consist of disposable and low-cost plastic biochips coated with the photonic crystal. The biosensing platform is demonstrated to detect Angiopoietin-2 in plasma samples at the clinically relevant concentration of 6 ng/mL, with an estimated limit of detection of approximately 1 ng/mL. This is the first Bloch surface wave based assay capable of detecting relevant concentrations of an angiogenic factor in plasma samples. The results obtained by the developed biosensing platform are in close agreement with enzyme-linked immunosorbent assays, demonstrating a good accuracy, and their repeatability showed acceptable relative variations.

The distinct clinical features of giant cell tumor of bone in pagetic and non-pagetic patients are associated with genetic, biochemical and histological differences.

Giant Cell Tumor of Bone (GCT) is a tumor characterized by neoplastic mesenchymal stromal cells and a high number of osteoclast-like multinucleated giant cells. Rarely, GCT could arise in bones affected by Paget's disease of bone (GCT/PDB). Although it is already known that GCT/PDB and GCT show a different clinical profile regarding the age-onset and skeletal localization, our deep clinical comparison between the two GCT/PDB and GCT cohorts, permitted us to identify additional differences (e.g. focality, ALP serum levels, the 5-year survival rate and the familial recurrence), strongly suggesting a different molecular basis. Accordingly, driver somatic mutations in H3F3A and IDH2 were described in GCT patients, while we recently identified a germline mutation in ZNF687 as the genetic defect of GCT/PDB patients. Here, we detected H3F3A mutations in our GCT cohort, confirming its molecular screening as the elected diagnostic tool, and then we excluded the two-hit in H3F3A and IDH2 as the trigger event for the GCT/PDB development. Importantly, we also identified an alternative biochemical profile with GCT/PDB not exhibiting the up-regulation of the GCT marker FGFR2IIIc. Finally, our histological analysis also showed a different appearance of the two forms of the tumor, with GCT/PDB showing a higher number of osteoclast-like giant cells (twice), with an abnormal number of nuclei per cell, corroborating its different behaviour in terms of neoplastic properties. We demonstrated that the distinct clinical features of pagetic and conventional GCT are associated with different genetic background, resulting in a specific biochemical and histological behaviour of the tumour.

GOLPH3 and oncogenesis: What is the molecular link?

The Golgi phosphoprotein 3 (GOLPH3) is encoded by a gene that is located in a region of the human genome that is often amplified in different solid tumours. GOLPH3, an evolutionary conserved phosphatidylinositol 4-phosphate (PI4P) binding protein, is mainly localised at trans Golgi network (TGN). It regulates several cellular functions like Golgi vesicular trafficking, Golgi glycosylation and mitochondrial cardiolipin production. Recently, GOLPH3 was discovered to be part of the DNA damage response signalling pathway, with a role in cell survival following DNA damage. In this review, we will explore the cellular functions regulated by GOLPH3 and discuss if and how they contribute to the oncogenic activity of this intriguing Golgi localized oncoprotein.

MiRNATIP: a SOM-based miRNA-target interactions predictor.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments. In this paper we propose the miRNA Target Interaction Predictor (miRNATIP) algorithm, a Self-Organizing Map (SOM) based method for the miRNA target prediction. SOM is trained with the seed region of the miRNA sequences and then the mRNA sequences are projected into the SOM lattice in order to find putative interactions with miRNAs. These interactions will be filtered considering the remaining part of the miRNA sequences and estimating the free-energy necessary for duplex stability. RESULTS: We tested the proposed method by predicting the miRNA target interactions of both the Homo sapiens and the Caenorhbditis elegans species; then, taking into account validated target (positive) and non-target (negative) interactions, we compared our results with other target predictors, namely miRanda, PITA, PicTar, mirSOM, TargetScan and DIANA-microT, in terms of the most used statistical measures. We demonstrate that our method produces the greatest number of predictions with respect to the other ones, exhibiting good results for both species, reaching the for example the highest percentage of sensitivity of 31 and 30.5 %, respectively for Homo sapiens and for C. elegans. All the predicted interaction are freely available at the following url: http://tblab.pa.icar.cnr.it/public/miRNATIP/ . CONCLUSIONS: Results state miRNATIP outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

OBJECTIVES: In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. METHODS: In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". RESULTS: The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. CONCLUSIONS: Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments.