Biomimetic Electrospun Self-Assembling Peptide Scaffolds for Neural Stem Cell Transplantation in Neural Tissue Engineering

Spinal cord regeneration using stem cell transplantation is a promising strategy for regenerative therapy. Stem cells transplanted onto scaffolds that can mimic natural extracellular matrix (ECM) have the potential to significantly improve outcomes. In this study, we strived to develop a cell carrier by culturing neural stem cells (NSCs) onto electrospun 2D and 3D constructs made up of specific crosslinked functionalized self-assembling peptides (SAPs) featuring enhanced biomimetic and biomechanical properties. Morphology, architecture, and secondary structures of electrospun scaffolds in the solid-state and electrospinning solution were studied step by step. Morphological studies showed the benefit of mixed peptides and surfactants as additives to form thinner, uniform, and defect-free fibers. It has been observed that β-sheet conformation as evidence of self-assembling has been predominant throughout the process except for the electrospinning solution. In vitro NSCs seeded on electrospun SAP scaffolds in 2D and 3D conditions displayed desirable proliferation, viability, and differentiation in comparison to the gold standard. In vivo biocompatibility assay confirmed the permissibility of implanted fibrous channels by foreign body reaction. The results of this study demonstrated that fibrous 2D/3D electrospun SAP scaffolds, when shaped as micro-channels, can be suitable to support NSC transplantation for regeneration following spinal cord injury

UWB device for breast microwave imaging: phantom and clinical validations

Microwave imaging has received increasing attention in the last decades, motivated by its application in diagnostic imaging. Such effort has been encouraged by the fact that, at microwave frequencies, it is possible to distinguish between tissues with different dielectric properties. In such framework, a novel microwave device is presented here. The apparatus, consisting of two antennas operating in air, is completely safe and non-invasive since it does not emit any ionizing radiation and it can be used for breast lesion detection without requiring any breast crushing. We use Huygens Principle to provide a novel understanding into microwave imaging; specifically, the algorithm based on this principle provides images which represent homogeneity maps of the dielectric properties (dielectric constant and/or conductivity). The experimental results on phantoms having inclusions with different dielectric constants are presented here. In addition, the capability of the device to detect breast lesions has been verified through clinical examinations on 51 breasts. We introduce a metric to measure the non-homogeneous behaviour of the image, establishing a modality to detect the presence of inclusions inside phantoms and, similarly, the presence of a lesion inside a breast

Arsenic movement and fractionation in agricultural soils which received wastewater from an adjacent industrial site for 50 years

Arsenic (As) is an element with important environmental and human health implications due to its toxic properties. It is naturally occurring since it is contained in minerals, but it can also be enriched and distributed in the environment by anthropogenic activities. This paper reports on the historic As contamination of agricultural soils in one of the most important national relevance site for contamination in Italy, the so-called SIN Brescia-Caffaro, in the city of Brescia, northern Italy. These agricultural areas received As through the use of irrigation waters from wastewater coming from a factory of As-based pesticides (lead and calcium arsenates, sodium arsenite). Pesticide production started in 1920 and ended in the '70. Concentrations in the areas are generally beyond the legal threshold values for different soil uses and are up to >200 mg/kg. Arsenic contamination was studied to assess the long-time trend and the dynamics related to the vertical movement of As down to 1 m depth and its horizontal diffusion with surface irrigation in the entire field. Arsenic fractionation analysis (solid phase speciation by sequential extraction procedure) was also performed on samples collected from these areas and employed in greenhouse experiments with several plant species to evaluate the long-term contamination and the role of plant species in modifying As availability in soil. The results of this work can help in the evaluation of the conditions controlling the vertical transfer of As towards surface aquifers, the bioaccumulation likelihood in the agricultural food chain and the selection of sustainable remediation techniques such as phytoextraction

Novel microwave apparatus for breast lesions detection: Preliminary clinical results

This paper presents preliminary results of an innovative microwave imaging apparatus for breast lesions detection. Specially, a Huygens Principle based method is employed to process the microwave signals and to build the respective microwave images. The apparatus has been first tested on phantoms. Next, its performance has been verified through clinical examinations on 22 healthy breasts and on 29 breast having lesions, using as gold standard the output of the radiologist study review obtained using conventional techniques. Specifically, we introduce a metric, which is the ratio between maximum and average of the image intensity (MAX/AVG). We found that MAX/AVG of microwave images can be used for classifying breasts containing lesions. In addition, using MAX/AVG as classification parameter, receiver operating characteristic curves have been empirically determined. Furthermore, for one randomly selected breast having lesion, we have demonstrated that the localisation of the inclusion acquired through microwave imaging is compatible with mammography images

A Multicentric, Single Arm, Prospective, Stratified Clinical Investigation to Confirm MammoWave’s Ability in Breast Lesions Detection

Novel techniques, such as microwave imaging, have been implemented in different prototypes and are under clinical validation, especially for breast cancer detection, due to their harmless technology and possible clinical advantages over conventional imaging techniques. In the prospective study presented in this work, we aim to investigate through a multicentric European clinical trial (ClinicalTrials.gov Identifier NCT05300464) the effectiveness of the MammoWave microwave imaging device, which uses a Huygens-principle-based radar algorithm for image reconstruction and comprises dedicated image analysis software. A detailed clinical protocol has been prepared outlining all aspects of this study, which will involve adult females having a radiologist study output obtained using conventional exams (mammography and/or ultrasound and/or magnetic resonance imaging) within the previous month. A maximum number of 600 volunteers will be recruited at three centres in Italy and Spain, where they will be asked to sign an informed consent form prior to the MammoWave scan. Conductivity weighted microwave images, representing the homogeneity of the tissues’ dielectric properties, will be created for each breast, using a conductivity = 0.3 S/m. Subsequently, several microwave image parameters (features) will be used to quantify the images’ non-homogenous behaviour. A selection of these features is expected to allow for distinction between breasts with lesions (either benign or malignant) and those without radiological findings. For all the selected features, we will use Welch’s t-test to verify the statistical significance, using the gold standard output of the radiological study review

MammoWave Breast Imaging Device: Prospective Clinical Trial Results and AI Enhancement

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data

Towards large scale automated cage monitoring - Diurnal rhythm and impact of interventions on in-cage activity of C57BL/6J mice recorded 24/7 with a non-disrupting capacitive-based technique.

BACKGROUND AND AIMS: Automated recording of laboratory animal\u27s home cage behavior is receiving increasing attention since such non-intruding surveillance will aid in the unbiased understanding of animal cage behavior potentially improving animal experimental reproducibility. MATERIAL AND METHODS: Here we investigate activity of group held female C57BL/6J mice (mus musculus) housed in standard Individually Ventilated Cages across three test-sites: Consiglio Nazionale delle Ricerche (CNR, Rome, Italy), The Jackson Laboratory (JAX, Bar Harbor, USA) and Karolinska Insititutet (KI, Stockholm, Sweden). Additionally, comparison of female and male C57BL/6J mice was done at KI. Activity was recorded using a capacitive-based sensor placed non-intrusively on the cage rack under the home cage collecting activity data every 250 msec, 24/7. The data collection was analyzed using non-parametric analysis of variance for longitudinal data comparing sites, weekdays and sex. RESULTS: The system detected an increase in activity preceding and peaking around lights-on followed by a decrease to a rest pattern. At lights off, activity increased substantially displaying a distinct temporal variation across this period. We also documented impact on mouse activity that standard animal handling procedures have, e.g. cage-changes, and show that such procedures are stressors impacting in-cage activity. These key observations replicated across the three test-sites, however, it is also clear that, apparently minor local environmental differences generate significant behavioral variances between the sites and within sites across weeks. Comparison of gender revealed differences in activity in the response to cage-change lasting for days in male but not female mice; and apparently also impacting the response to other events such as lights-on in males. Females but not males showed a larger tendency for week-to-week variance in activity possibly reflecting estrous cycling. CONCLUSIONS: These data demonstrate that home cage monitoring is scalable and run in real time, providing complementary information for animal welfare measures, experimental design and phenotype characterization

Geotechnical characterization of the upper Pleistocene-Holocene alluvial deposits of Roma (Italy) by means of multivariate geostatistics: Cross-validation results

We are presenting an attempt to evaluate the spatial variability of geotechnical parameters in the upper Pleistocene–Holocene alluvial deposits of Roma (Italy) by means of multivariate geostatistics. The upper Pleistocene–Holocene alluvial deposits of Roma are sensitive to high levels of geohazard. They occupy a sizable and significant part of the city, being the foundation for many monuments, historical neighborhoods, and archaeological areas, and the main host of the present and future subway lines. We have stored information from more than 2000 geotechnical boreholes crossing the alluvial deposits into a relational database. For the present study, only the boreholes with lithologic/textural interpretation and geotechnical information were selected. The set includes 283 boreholes and 719 samples, which have a set of geotechnical information comprising physical properties and mechanical parameters. Techniques of multivariate statistics and geostatistics were combined and compared to evaluate the estimation methods of the mechanical parameters, with special reference to the drained friction angle from direct shear test (φ′). Principal Component Analysis was applied to the dataset to highlight the relationships between the geotechnical parameters. Through cross-validation analysis, multiple linear regression, kriging, and cokriging were tested as estimators of φ′. Cross-validation demonstrates that the cokriging with granulometries as auxiliary variables is the most suitable method to estimate φ′. In addition to proving that cokriging is a good estimator of φ′, cross-validation demonstrates that input data are coherent and this allows us to use them for estimation of geotechnical parameters, although they come from different laboratories and different vintages. Nevertheless, to get the same good results of cross-validation in estimation, it is necessary for granulometries to be available at grid points. Since this information being not available at all grid points, it is expected that, in the future, textural information can be derived in an indirect way, i.e., from lithologic/textural spatial reconstructions.Published251-2682.3. TTC - Laboratori di chimica e fisica delle rocceJCR Journalope

Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images' parameters.

MammoWave is a microwave imaging device for breast lesions detection, which operates using two (azimuthally rotating) antennas without any matching liquid. Images, subsequently obtained by resorting to Huygens Principle, are intensity maps, representing the homogeneity of tissues' dielectric properties. In this paper, we propose to generate, for each breast, a set of conductivity weighted microwave images by using different values of conductivity in the Huygens Principle imaging algorithm. Next, microwave images' parameters, i.e. features, are introduced to quantify the non-homogenous behaviour of the image. We empirically verify on 103 breasts that a selection of these features may allow distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e. with lesions which may be benign or malignant. Statistical significance was set at p<0.05. We obtained single features Area Under the receiver operating characteristic Curves (AUCs) spanning from 0.65 to 0.69. In addition, an empirical rule-of-thumb allowing breast assessment is introduced using a binary score S operating on an appropriate combination of features. Performances of such rule-of-thumb are evaluated empirically, obtaining a sensitivity of 74%, which increases to 82% when considering dense breasts only

Geotechnical characterization of the upper Pleistocene-Holocene alluvial deposits of Roma (Italy) by means of multivariate geostatistics: Cross-validation results

We are presenting an attempt to evaluate the spatial variability of geotechnical parameters in the upper Pleistocene–Holocene alluvial deposits of Roma (Italy) by means of multivariate geostatistics. The upper Pleistocene–Holocene alluvial deposits of Roma are sensitive to high levels of geohazard. They occupy a sizable and significant part of the city, being the foundation for many monuments, historical neighborhoods, and archaeological areas, and the main host of the present and future subway lines. We have stored information from more than 2000 geotechnical boreholes crossing the alluvial deposits into a relational database. For the present study, only the boreholes with lithologic/textural interpretation and geotechnical information were selected. The set includes 283 boreholes and 719 samples, which have a set of geotechnical information comprising physical properties and mechanical parameters. Techniques of multivariate statistics and geostatistics were combined and compared to evaluate the estimation methods of the mechanical parameters, with special reference to the drained friction angle from direct shear test (φ′). Principal Component Analysis was applied to the dataset to highlight the relationships between the geotechnical parameters. Through cross-validation analysis, multiple linear regression, kriging, and cokriging were tested as estimators of φ′. Cross-validation demonstrates that the cokriging with granulometries as auxiliary variables is the most suitable method to estimate φ′. In addition to proving that cokriging is a good estimator of φ′, cross-validation demonstrates that input data are coherent and this allows us to use them for estimation of geotechnical parameters, although they come from different laboratories and different vintages. Nevertheless, to get the same good results of cross-validation in estimation, it is necessary for granulometries to be available at grid points. Since this information being not available at all grid points, it is expected that, in the future, textural information can be derived in an indirect way, i.e., from lithologic/textural spatial reconstructions