Innovative, ecofriendly biosorbent-biodegrading biofilms for bioremediation of oil- contaminated water

Immobilization of microorganisms capable of degrading specific contaminants significantly promotes bioremediation processes. In this study, innovative and ecofriendly biosorbent-biodegrading biofilms have been developed in order to remediate oil-contaminated water. This was achieved by immobilizing hydrocarbon-degrading gammaproteobacteria and actinobacteria on biodegradable oil-adsorbing carriers, based on polylactic acid and polycaprolactone electrospun membranes. High capacities for adhesion and proliferation of bacterial cells were observed by scanning electron microscopy. The bioremediation efficiency of the systems, tested on crude oil and quantified by gas chromatography, showed that immobilization increased hydrocarbon biodegradation by up to 23 % compared with free living bacteria. The resulting biosorbent biodegrading biofilms simultaneously adsorbed 100 % of spilled oil and biodegraded more than 66 % over 10 days, with limited environmental dispersion of cells. Biofilm-mediated bioremediation, using eco-friendly supports, is a low-cost, low-impact, versatile tool for bioremediation of aquatic systems

Insulin secretory granules labelled with phogrin-fluorescent proteins show alterations in size, mobility and responsiveness to glucose stimulation in living β-cells

The intracellular life of insulin secretory granules (ISGs) from biogenesis to secretion depends on their structural (e.g. size) and dynamic (e.g. diffusivity, mode of motion) properties. Thus, it would be useful to have rapid and robust measurements of such parameters in living β-cells. To provide such measurements, we have developed a fast spatiotemporal fluctuation spectroscopy. We calculate an imaging-derived Mean Squared Displacement (iMSD), which simultaneously provides the size, average diffusivity, and anomalous coefficient of ISGs, without the need to extract individual trajectories. Clustering of structural and dynamic quantities in a multidimensional parametric space defines the ISGs’ properties for different conditions. First, we create a reference using INS-1E cells expressing proinsulin fused to a fluorescent protein (FP) under basal culture conditions and validate our analysis by testing well-established stimuli, such as glucose intake, cytoskeleton disruption, or cholesterol overload. After, we investigate the effect of FP-tagged ISG protein markers on the structural and dynamic properties of the granule. While iMSD analysis produces similar results for most of the lumenal markers, the transmembrane marker phogrin-FP shows a clearly altered result. Phogrin overexpression induces a substantial granule enlargement and higher mobility, together with a partial de-polymerization of the actin cytoskeleton, and reduced cell responsiveness to glucose stimulation. Our data suggest a more careful interpretation of many previous ISG-based reports in living β-cells. The presented data pave the way to high-throughput cell-based screening of ISG structure and dynamics under various physiological and pathological conditions

Chiral ionic liquid assisted synthesis of some metal oxides

A chiral ionic liquid with a natural alcohol based chain was used as a tailoring agent for the synthesis of simple and cost effective materials such as ZnO, CuO, CuO-ZnO with peculiar morphology. The morphology and chemical composition of the microstructures were investigated by bright-field and scanning transmission microscopy (BF-TEM and STEM), energy dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), and UV-VIS spectroscopy. Furthermore, the photocatalytic activity of ZnO, CuO and ZnO-CuO nanostructures was quantified for methylene blue (MB) dye. CuO needles had the lowest photocatalytic activity (23.8% in 40 min). Due to their peculiar forms, ZnO (flower like shape) and ZnO/CuO (leaf like shape where ZnO nanoparticles were deposited) had the highest photocatalytic activity in 40 min (93.6% for ZnO nanoparticles and 95% for ZnO-CuO nanostructures)

Presence of oxytocin, vasopressin and atrial natriuretic peptide and their modification in rat hypothalamic paraventricular nucleus during resistance training.

Many studies have demonstrated the physiological effects of oxytocin (OT), atrial natriuretic peptide (ANP) and vasopressin (VP) in the homoeostasis of body fluids during physical exercise. However, a little information is available about the related immunohistochemical changes in hypothalamic magnocellular neurosecretory system during and after the training. The aim of the present work was to study the immunohistochemical changes in OT, ANP and VP levels in the hypothalamic paraventricular nucleus during and after resistance exercise protocol. Three groups of Wistar rats were trained by a rung ladder protocol for 15, 30 and 45 days, respectively; a fourth group was left to rest for 15 days after the training. Finally, four sedentary groups were used as controls. The results show that resistance training induces a significant reduction in the percentage of OT-positive neurons, compared with sedentary controls. In contrast, this protocol did not induce any change in VP levels, and ANP levels did not change significantly. However, VP increased after the resting period of 15 days. Our work shows that neurons of the paraventricular nucleus are involved in body fluid homoeostasis during and after resistance exercise. The functional significance of these changes in OT and VP levels, during and after the protocol, needs to be further investigated

An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images

Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance— to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis

Default and Control Networks Connectivity Dynamics Track the Stream of Affect at Multiple Timescales

In everyday life, the stream of affect results from the interaction between past experiences, expectations and the unfolding of events. How the brain represents the relationship between time and affect has been hardly explored, as it requires modeling the complexity of everyday life in the laboratory setting. Movies condense into hours a multitude of emotional responses, synchronized across subjects and characterized by temporal dynamics alike real-world experiences. Here, we use time-varying intersubject brain synchronization and real-time behavioral reports to test whether connectivity dynamics track changes in affect during movie watching. The results show that polarity and intensity of experiences relate to the connectivity of the default mode and control networks and converge in the right temporoparietal cortex. We validate these results in two experiments including four independent samples, two movies and alternative analysis workflows. Finally, we reveal chronotopic connectivity maps within the temporoparietal and prefrontal cortex, where adjacent areas preferentially encode affect at specific timescales

Human Microglia Extracellular Vesicles Derived from Different Microglia Cell Lines: Similarities and Differences

ABSTRACT: Microglial cells are a component of the innate immune system in the brain that support cell-to-cell communication via secreted molecules and extracellular vesicles (EVs). EVs can be divided into two major populations: large (LEVs) and small (SEVs) EVs, carrying different mediators, such as proteins, lipids, and miRNAs. The microglia EVs cargo crucially reflects the status of parental cells and can lead to both beneficial and detrimental effects in many physiopathological states. Herein, a workflow for the extraction and characterization of SEVs and LEVs from human C20 and HMC3 microglia cell lines derived, respectively, from adult and embryonic microglia is reported. EVs were gathered from the culture media of the two cell lines by sequential ultracentrifugation steps and their biochemical and biophysical properties were analyzed by Western blot, transmission electron microscopy, and dynamic light scattering. Although the C20and HMC3-derived EVs shared several common features, C20-derived EVs were slightly lower in number and more polydispersed. Interestingly, C20- but not HMC3-SEVs were able to interfere with the proliferation of U87 glioblastoma cells. This correlated with the different relative levels of eight miRNAs involved in neuroinflammation and tumor progression in the C20- and HMC3-derived EVs, which in turn reflected a different basal activation state of the two cell types. Our data fill a gap in the community of microglia EVs, in which the preparations from human cells have been poorly characterized so far. Furthermore, these results shed light on both the differences and similarities of EVs extracted from different human microglia cell models, underlining the need to better characterize the features and biological effects of EVs for therein useful and correct application

Quantum computing algorithms: getting closer to critical problems in computational biology

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation