Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis.

One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager , is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities.

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

LOX-1 and cancer: an indissoluble liaison.

Recently, a strong correlation between metabolic disorders, tumor onset, and progression has been demonstrated, directing new therapeutic strategies on metabolic targets. OLR1 gene encodes the LOX-1 receptor protein, responsible for the recognition, binding, and internalization of ox-LDL. In the past, several studied, aimed to clarify the role of LOX-1 receptor in atherosclerosis, shed light on its role in the stimulation of the expression of adhesion molecules, pro-inflammatory signaling pathways, and pro-angiogenic proteins, including NF-kB and VEGF, in vascular endothelial cells and macrophages. In recent years, LOX-1 upregulation in different tumors evidenced its involvement in cancer onset, progression and metastasis. In this review, we outline the role of LOX-1 in tumor spreading and metastasis, evidencing its function in VEGF induction, HIF-1alpha activation, and MMP-9/MMP-2 expression, pushing up the neoangiogenic and the epithelial-mesenchymal transition process in glioblastoma, osteosarcoma prostate, colon, breast, lung, and pancreatic tumors. Moreover, our studies contributed to evidence its role in interacting with WNT/APC/ß-catenin axis, highlighting new pathways in sporadic colon cancer onset. The application of volatilome analysis in high expressing LOX-1 tumor-bearing mice correlates with the tumor evolution, suggesting a closed link between LOX-1 upregulation and metabolic changes in individual volatile compounds and thus providing a viable method for a simple, non-invasive alternative monitoring of tumor progression. These findings underline the role of LOX-1 as regulator of tumor progression, migration, invasion, metastasis formation, and tumor-related neo-angiogenesis, proposing this receptor as a promising therapeutic target and thus enhancing current antineoplastic strategies.

Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial-temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of "social forces" from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. The originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning Convolutional Neural Network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. For each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called "motility style") which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories.

Exploring the inquiry-based learning structure to promote scientific culture in the classrooms of higher education sciences.

This article examines student engagement in an inquiry-based learning activity, planned to provide students with elements in the social, epistemic, and conceptual dimensions related to the scientific practice in immunology. The activity was applied to 39 groups of students enrolled in immunology or biochemistry courses in a public university in Brazil. Students performed data-collection through the execution of an in vitro assay. We analyzed how students represent data and use them to support their claims in their written constructs. To clarify which are the productive epistemologies in students' reports, we developed a framework for epistemic practice analysis. Our findings point to a pattern of several epistemic practice categories in their written text, mostly related to the particular contingences of data analysis, rather than to theoretical concepts. In addition, we observed that students performed literary inscriptions to represent their data; however, they tended not to cite all data obtained in their written texts. These results suggest that immunology education strategies should provide students with approaches that explore the role of data representation in the scientific text rhetoric. © 2019 International Union of Biochemistry and Molecular Biology, 47(6):672-680, 2019.

Focal adhesion clustering drives endothelial cell morphology on patterned surfaces.

In many cell types, shape and function are intertwined. In vivo, vascular endothelial cells (ECs) are typically elongated and aligned in the direction of blood flow; however, near branches and bifurcations where atherosclerosis develops, ECs are often cuboidal and have no preferred orientation. Thus, understanding the factors that regulate EC shape and alignment is important. In vitro, EC morphology and orientation are exquisitely sensitive to the composition and topography of the substrate on which the cells are cultured; however, the underlying mechanisms remain poorly understood. Different strategies of substrate patterning for regulating EC shape and orientation have been reported including adhesive motifs on planar surfaces and micro- or nano-scale gratings that provide substrate topography. Here, we explore how ECs perceive planar bio-adhesive versus microgrooved topographic surfaces having identical feature dimensions. We show that while the two types of patterned surfaces are equally effective in guiding and directing EC orientation, the cells are considerably more elongated on the planar patterned surfaces than on the microgrooved surfaces. We also demonstrate that the key factor that regulates cellular morphology is focal adhesion clustering which subsequently drives cytoskeletal organization. The present results promise to inform design strategies of novel surfaces for the improved performance of implantable cardiovascular devices.

The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications.

Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors' discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

Management specificities for abdominal, pelvic and vascular penetrating trauma.

Management of patients with penetrating trauma of the abdomen, pelvis and their surrounding compartments as well as vascular injuries depends on the patient's hemodynamic status. Multiple associated lesions are the rule. Their severity is directly correlated with initial bleeding, the risk of secondary sepsis, and lastly to sequelae. In patients who are hemodynamically unstable, the goal of management is to rapidly obtain hemostasis. This mandates initial laparotomy for abdominal wounds, extra-peritoneal packing (EPP) and resuscitative endovascular balloon occlusion of the aorta (REBOA) in the emergency room for pelvic wounds, insertion of temporary vascular shunts (TVS) for proximal limb injuries, ligation for distal vascular injuries, and control of exteriorized extremity bleeding with a tourniquet, compressive or hemostatic dressings for bleeding at the junction or borderline between two compartments, as appropriate. Once hemodynamic stability is achieved, preoperative imaging allow more precise diagnosis, particularly for retroperitoneal or thoraco-abdominal injuries that are difficult to explore surgically. The surgical incisions need to be large, in principle, and enlarged as needed, allowing application of damage control principles.

Specific elements of thoracic wound management.

Damage control for thoracic trauma combines definitive and temporary surgical gestures specifically adapted to the lesions present. A systematic assessment of all injuries to prioritize the specific lesions and their treatments constitutes the first operative stage. Packing and temporary closure have a place in the care of chest injuries.

Structural investigation of nucleophosmin interaction with the tumor suppressor Fbw7γ.

Nucleophosmin (NPM1) is a multifunctional nucleolar protein implicated in ribogenesis, centrosome duplication, cell cycle control, regulation of DNA repair and apoptotic response to stress stimuli. The majority of these functions are played through the interactions with a variety of protein partners. NPM1 is frequently overexpressed in solid tumors of different histological origin. Furthermore NPM1 is the most frequently mutated protein in acute myeloid leukemia (AML) patients. Mutations map to the C-terminal domain and lead to the aberrant and stable localization of the protein in the cytoplasm of leukemic blasts. Among NPM1 protein partners, a pivotal role is played by the tumor suppressor Fbw7γ, an E3-ubiquitin ligase that degrades oncoproteins like c-MYC, cyclin E, Notch and c-jun. In AML with NPM1 mutations, Fbw7γ is degraded following its abnormal cytosolic delocalization by mutated NPM1. This mechanism also applies to other tumor suppressors and it has been suggested that it may play a key role in leukemogenesis. Here we analyse the interaction between NPM1 and Fbw7γ, by identifying the protein surfaces implicated in recognition and key aminoacids involved. Based on the results of computational methods, we propose a structural model for the interaction, which is substantiated by experimental findings on several site-directed mutants. We also extend the analysis to two other NPM1 partners (HIV Tat and CENP-W) and conclude that NPM1 uses the same molecular surface as a platform for recognizing different protein partners. We suggest that this region of NPM1 may be targeted for cancer treatment.

Systematic approach in Mg2+ ions analysis with a combination of tailored fluorophore design.

A systematic study of a series of diaza-18-crown-6 8-hydroxyquinoline (DCHQ) chemosensors, devoted to Mg(II) ion detection, was performed. Functionalization of DCHQ by peripheral substituents allowed the development of novel all-solid-state optodes via inclusion inside PVC-based polymeric films. The influence on the DCHQ-based optode response of the lipophilic sites functionalization and of the nature of the plasticizer, was investigated. Fluorimetric studies on optodes sensitivity towards a number of different metal cations (Ca2+, Na+, K+, Li+, Co2+, Cd2+, Pb2+, Cu2+, Hg2+, Zn2+) and NH4+ were carried out. The results demonstrated the suitability of the DCHQ optodes to perform fast monitoring (<10s) of magnesium (II) ions. Emission light signal was sufficiently brilliant to be captured by a low-cost computer webcam. The phenyl-substituted DCHQ-Ph derivative showed the best performance with a wide range for Mg(II) ion determination between 2.7 × 10-7 and 2.2 × 10-2 mol/L. It was possible, therefore, to determine the concentrations of Mg(II) in commercial fertilizer samples by DCHQ-Ph-based optodes with acceptable results: recoveries of 96.2-104.9% and relative standard deviation (RSD, n = 6) less than 5%. Moreover, in comparison to single sensors, the use of an array composed of five optodes (the ones showing the best performances in the preliminary tests) has allowed to reduce the RSD of magnesium determination in real samples (down to 3.7% with respect to 5.5% for single optodes) and to achieve a detection limit (estimated by s/n = 3 method) as low as 4.6 × 10-7 mol/L.

Towards localization of malignant sites of asymmetry across bilateral mammograms.

BACKGROUND AND OBJECTIVES: The analysis of patterns of asymmetry between the left and right mammograms of a patient can provide meaningful insights into the presence of an underlying tumor in its early stage. However, the identification of breast cancer by investigating bilateral asymmetry is difficult to perform due to the indistinct and borderline nature of the asymmetric signs as they appear on mammograms. METHODS: In this study, to increase the positive-predictive value of asymmetry in mammographic screening, a novel computerized approach for the automatic localization of malignant sites of asymmetry in mammograms is proposed. The sites of anatomical correspondence between the right and left regions of each radiographic projection were extracted by means of two bilateral masking procedures, inspired by radiologists' criteria in interpreting mammograms and based on the use of detected landmarking structures. Relative variations of spatial patterns of intensity values and of orientations of directional components within each site were quantified by combining multidirectional Gabor filters and indices of structural similarity. The localization of the sites of malignant asymmetry was performed by coupling two quadratic discriminant analysis classifiers, one for each masking procedure, that assigned the likelihood of malignancy to each site of correspondence. RESULTS: The performance of the proposed method was assessed on 94 mammographic images from two publicly available databases and containing at least one asymmetric site. Sensitivity, specificity and balanced accuracy levels of 0.83 (0.09), 0.75 (0.06), and 0.79 (0.04), respectively were obtained in the classification of malignant asymmetric sites vs benign/normal sites using cross-validation. In addition, a further blind test on a dataset of Full Field Digital Mammograms achieved levels of sensitivity, specificity, and balanced accuracy of 0.86, 0.65, and 0.75, respectively. CONCLUSIONS: The achieved performance indicates that the proposed system is effective in localizing sites of malignant asymmetry and it is expected to improve computer-aided diagnosis of breast cancer.

Vortexes tune the chirality of graphene oxide and its non-covalent hosts.

Graphene oxide (GO) is one of the most appealing bidimensional materials able to interact non-covalently with achiral molecules and to act as chiral inducers. Vortexes can tune chirality and, consequently transfer a specific handedness to non-covalent host molecules, either when dispersed in water or when deposited on a solid surface.

Electronic tongue for microcystin screening in waters.

The potentiometric E-tongue system was employed for water toxicity estimation in terms of cyanobacterial microcystin toxins (MCs) detection. The data obtained from E-tongue were correlated to the MCs content detected by the standard chromatographic technique UHPLC-DAD (Ultra High Performance Liquid Chromatography with Diode Array Detector), as far as by the colorimetric enzymatic approach. The prediction of MCs released by toxic Microcystis aeruginosa strains was possible with Root Mean Squared Error of Validation (RMSEV) lower or very close to 1µg/L, the provisional guideline value of WHO for MCs content in potable waters. The application of E-tongue system opens up a new perspective offset for fast and inexpensive analysis in the field of environmental monitoring, offering also the possibility to distinguish toxin producing and non-toxic M. aeruginosa strains present in potable water.

[Catamenial pneumothorax: easy to see, difficult to manage]. / Pneumothorax cataménial : facile à reconnaître, difficile à traiter.

INTRODUCTION: Catamenial pneumothorax (PNO) is a real clinical occurrence. Several cases are reported in the literature as a spontaneous PNO occurring during the catamenial period among women in their thirties. There is no consensus about management and the recurrence rate is very high whatever the initial treatment. PATIENTS AND METHODS: Among 310 cases of spontaneous PNO operated in our institution in 10 years, we identified five cases of catamenial PNO. A retrospective study of these cases was used to study the initial operating data, including the existence of intrathoracic lesions and the choice of technique of pleurodesis. Patient follow-up was clinically and radiologically. Adjuvant hormonal therapies, recurrence of PNO and treatment modalities have been studied. RESULTS: These five patients of average age 37.6 years (37,38) who had 2.6 (2.3) episodes of right catamenial PNO before hospitalization in surgery department. No patient was smoker. Two of them had a known thoracic or pelvic endometriosis. The initial surgery was video assisted thoracic surgery with a parietal pleurectomy and twice a mesh upon the diaphragm. There were no immediate postoperative complications, and the average length of stay was 6.6 days (5.9). Two patients had adjuvant hormonal therapy. All patients had at least one recurrence and three of them had redo surgery. CONCLUSION: The diagnosis of catamenial PNO must be mentioned in any woman who has a spontaneous pneumothorax right in catamenial period. Endometriosis should be systematically sought. A standardized therapeutic approach to establish the role of surgery and the most appropriate technique as well as the appropriateness and duration of peroperative hormonal therapy remains to be defined.

Disseminated, large-sized neonatal pneumatoceles: the wait-and-see strategy.

Pulmonary pneumatoceles are thin-walled, air-filled cysts that develop within the lung parenchyma. Most often, they occur as a sequel of acute pneumonia, commonly caused by Staphylococcus aureus in children. Limited data are available about infective pulmonary cysts in newborns. We report a case of a newborn, who developed multiple pneumatoceles after Escherichia coli pneumonia.

The anaesthetic and recovery profile of two concentrations (0.25% and 0.50%), of intrathecal isobaric levobupivacaine for combined spinal-epidural (CSE) anaesthesia in patients undergoing modified Stark method caesarean delivery: a double blinded randomized trial.

BACKGROUND: In spinal anaesthesia for a Caesarean delivery, it is important to limit anaesthesia only at the surgical area, and to resolve fast motor block. We compared the intraoperative effectiveness, hemodynamic effects, anaesthetic recovery times and patients satisfaction after isobaric levobupivacaine (L) 0.25% versus L0.50% spinal anaesthesia during elective Caesarean deliveries performed with the Stark technique. PATIENTS AND METHODS: In this double-blinded prospective study, seventy women undergoing elective caesarean delivery were randomized to receive either intrathecal 7.5 mg Levobupivacaine 0.25% plus sufentanil 2.5 µg (Group L0.25), or intrathecal 7.5 mg L 0.50% plus sufentanil 2.5 µg (GroupControl). The onset time, duration of anaesthesia, analgesia and sensory and motor block and hemodynamic parameters were measured from the beginning of spinal anaesthesia until four hours after spinal anaesthesia (T240). RESULTS: Onset time, duration of anaesthesia and haemodynamic variations were similar in the two groups. No patients required general anesthesia to complete surgery. Motor block vanished faster in Group L0.25 as compared with GroupControl (p < .01). The cephalad spread of the 0.50% solution was higher than that of the 0.25% solution: no patient in Group L0.25 experienced paresthesia of the upper limbs vs 14% in GroupControl (p < .05). In GroupControl anaesthesia reached the dermatome T1 in 15% of cases. Maternal and surgeon satisfaction was good in every patient. CONCLUSIONS: Levobupivacaine 7.5 milligrams at 0.25% may be used as a suitable alternative to L 0.50% for spinal anaesthesia for caesarean deliveris with the Stark technique with good maternal satisfaction. In Group L0.25 a lower appearance of nausea and hypotension were observed and motor and sensitive block developed and diminished faster while no clinically significant differences in hemodynamic behavior was observed between groups.

From peptides to small molecules: an intriguing but intricated way to new drugs.

A variety of peptides active in biological pathways have been identified e.g. receptor antagonists or inhibitors of protein-protein interactions and several peptide or peptide-derived compounds are on the drug market or in clinical trials. Through the rational design or the combinatorial preparation and High-throughput screening of arrays of compounds, peptides play a pivotal role for the rapid identification of ligands, but, despite these favorable properties, they often present poorer bioavailability and lower metabolic stability respect to traditional drugs. The process of conversion of a peptide in a small molecule provides the reduction of the peptide to the minimum active sequence (MAS) testing truncated peptides from the C- and N- termini alternatively. Then the influence of individual amino acid on the biological activity is determined by systematically replacing each residue in the peptide with specific amino acids. After structure-activity relationship (SAR) of each amino acid in the sequence has been assessed, the bioactive conformational flexibility is reduced by introducing constraints at various positions. These features are used for the design of a pharmacophore model in which functional groups crucial for activity are pre-positioned. Here we propose a panoramic review of the common principles for the conversion of peptides into small organic molecules and the most interesting findings in peptide-based leads of the last decades.