Inhibiting Metastatic Breast Cancer Cell Migration via the Synergy of Targeted, pH-triggered siRNA Delivery and Chemokine Axis Blockade

Because breast cancer patient survival inversely correlates with metastasis, we engineered vehicles to inhibit both the C-X-C chemokine receptor type 4 (CXCR4) and lipocalin-2 (Lcn2) mediated migratory pathways. pH-responsive liposomes were designed to protect and trigger the release of Lcn2 siRNA. Liposomes were modified with anti-CXCR4 antibodies to target metastatic breast cancer (MBC) cells and block migration along the CXCR4-CXCL12 axis. This synergistic approach—coupling the CXCR4 axis blockade with Lcn2 silencing—significantly reduced migration in triple-negative human breast cancer cells (88% for MDA-MB-436 and 92% for MDA-MB-231). The results suggested that drug delivery vehicles engineered to attack multiple migratory pathways may effectively slow progression of MBC

CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- I. Constraining physical processes in local star-forming galaxies

We compare the structural properties and dark matter content of star-forming galaxies taken from the CAMELS cosmological simulations to the observed trends derived from the SPARC sample in the stellar mass range $[10^{9}, 10^{11}]\,\textrm{M}_{\odot}$, to provide constraints on the value of cosmological and astrophysical (SN- and AGN-related) parameters. We consider the size-, internal DM fraction-, internal DM mass- and total-stellar mass relations for all the 1065 simulations from the IllustrisTNG, SIMBA and ASTRID suites of CAMELS, and search for the parameters that minimize the $\chi^{2}$ with respect to the observations. For the IllustrisTNG suite, we find the following constraints for the cosmological parameters: $\Omega_{\textrm{m}} = 0.27_{-0.05}^{+0.01}$, $\sigma_{8} = 0.83_{-0.11}^{+0.08}$ and $S_{8} = 0.78_{-0.09}^{+0.03}$, which are consistent within $1\sigma$ with the results from the nine-year WMAP observations. SN feedback-related astrophysical parameters, which describe the departure of outflow wind energy per unit star formation rate and wind velocity from the reference IllustrisTNG simulations, assume the following values: $A_{\textrm{SN1}} = 0.48_{-0.16}^{+0.25}$ and $A_{\textrm{SN2}} = 1.21_{-0.34}^{+0.03}$, respectively. Therefore, simulations with a lower value of outflow wind energy per unit star formation rate with respect to the reference illustrisTNG simulation better reproduce the observations. Simulations based on SIMBA and ASTRID suites predict central dark matter masses substantially larger than those observed in real galaxies, which can be reconciled with observations only by requiring values of $\Omega_{\textrm{m}}$ inconsistent with cosmological constraints for SIMBA, or simulations characterized by unrealistic galaxy mass distributions for ASTRID.Comment: 24 pages, 10 figures, 9 tables. Accepted by MNRAS for publication; Added a reference to sec. 4.

Automation of finding strong gravitational lenses in the Kilo Degree Survey with U-DenseLens (DenseLens + Segmentation)

In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold, to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes P$_{\rm mean}$ and IC$_{\rm mean}$ parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores, filtering based on Information Content, and segmentation score. Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data

AMICO galaxy clusters in KiDS-DR3: Constraints on ΛCDM from extreme value statistics

We constrain the ΛCDM cosmological parameter s(8) by applying the extreme value statistics for galaxy cluster mass on the AMICO KiDS-DR3 catalogue. We sample the posterior distribution of the parameters by considering the likelihood of observing the largest cluster mass value in a sample of N-obs = 3644 clusters with intrinsic richness λ(*) > 20 in the redshift range z ∈ [0.10, 0.60]. We obtain s(8) = 0 . 90( + 0 .20) (-0.18), consistent within 1s with the measurements obtained by the Planck collaboration and with previous results from cluster cosmology exploiting AMICO KiDS-DR3. The constraints could improve by applying this method to forthcoming missions, such as Euclid and LSST, which are expected to deliver thousands of distant and massive clusters

Automation of finding strong gravitational lenses in the Kilo Degree Survey with U – DenseLens (DenseLens + Segmentation)

In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold (ns), to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes Pmean and ICmean parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores (Pmean), filtering based on Information Content (ICmean), and segmentation score (ns). Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data.</p

Euclid:Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE &lt; 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent

AMICO galaxy clusters in KiDS-DR3: Cosmological constraints from angular power spectrum and correlation function

We study the tomographic clustering properties of the photometric cluster catalogue derived from the Third Data Release of the Kilo Degree Survey, focusing on the angular correlation function and its spherical harmonic counterpart, the angular power spectrum. We measure the angular correlation function and power spectrum from a sample of 5162 clusters, with an intrinsic richness $\lambda^*\geq 15$, in the photometric redshift range $z\in [0.1, 0.6]$, comparing our measurements with theoretical models, in the framework of the $\Lambda$-Cold Dark Matter cosmology. We perform a Monte Carlo Markov Chain analysis to constrain the cosmological parameters $\Omega_{\mathrm{m}}$, $\sigma_8$ and the structure growth parameter $S_8\equiv\sigma_8 \sqrt{\Omega_{\mathrm{m}}/0.3}$. We adopt Gaussian priors on the parameters of the mass-richness relation, based on the posterior distributions derived from a previous joint analysis of cluster counts and weak lensing mass measurements carried out with the same catalogue. From the angular correlation function, we obtain $\Omega_{\mathrm{m}}=0.32^{+0.05}_{-0.04}$, $\sigma_8=0.77^{+0.13}_{-0.09}$ and $S_8=0.80^{+0.08}_{-0.06}$, in agreement, within $1\sigma$, with 3D clustering result based on the same cluster sample and with existing complementary studies on other datasets. For the angular power spectrum, we derive statistically consistent results, in particular $\Omega_{\mathrm{m}}=0.24^{+0.05}_{-0.04}$ and $S_8=0.93^{+0.11}_{-0.12}$, while the constraint on $\sigma_8$ alone is weaker with respect to the one provided by the angular correlation function, $\sigma_8=1.01^{+0.25}_{-0.17}$. Our results show that the 2D clustering from photometric cluster surveys can provide competitive cosmological constraints with respect to the full 3D clustering statistics, and can be successfully applied to ongoing and forthcoming spectro/photometric surveys.Comment: 14 pages, 9 figures. Submitted to Astronomy & Astrophysics (A&A

Ubiquitination of CXCR7 Controls Receptor Trafficking

The chemokine receptor CXCR7 binds CXCL11 and CXCL12 with high affinity, chemokines that were previously thought to bind exclusively to CXCR4 and CXCR3, respectively. Expression of CXCR7 has been associated with cardiac development as well as with tumor growth and progression. Despite having all the canonical features of G protein-coupled receptors (GPCRs), the signalling pathways following CXCR7 activation remain controversial, since unlike typical chemokine receptors, CXCR7 fails to activate Gαi-proteins. CXCR7 has recently been shown to interact with β-arrestins and such interaction has been suggested to be responsible for G protein-independent signals through ERK-1/2 phosphorylation. Signal transduction by CXCR7 is controlled at the membrane by the process of GPCR trafficking. In the present study we investigated the regulatory processes triggered by CXCR7 activation as well as the molecular interactions that participate in such processes. We show that, CXCR7 internalizes and recycles back to the cell surface after agonist exposure, and that internalization is not only β-arrestin-mediated but also dependent on the Serine/Threonine residues at the C-terminus of the receptor. Furthermore we describe, for the first time, the constitutive ubiquitination of CXCR7. Such ubiquitination is a key modification responsible for the correct trafficking of CXCR7 from and to the plasma membrane. Moreover, we found that CXCR7 is reversibly de-ubiquitinated upon treatment with CXCL12. Finally, we have also identified the Lysine residues at the C-terminus of CXCR7 to be essential for receptor cell surface delivery. Together these data demonstrate the differential regulation of CXCR7 compared to the related CXCR3 and CXCR4 receptors, and highlight the importance of understanding the molecular determinants responsible for this process

An Essential Role of the Cytoplasmic Tail of CXCR4 in G-Protein Signaling and Organogenesis

CXCR4 regulates cell proliferation, enhances cell survival and induces chemotaxis, yet molecular mechanisms underlying its signaling remain elusive. Like all other G-protein coupled receptors (GPCRs), CXCR4 delivers signals through G-protein-dependent and -independent pathways, the latter involving its serine-rich cytoplasmic tail. To evaluate the signaling and biological contribution of this G-protein-independent pathway, we generated mutant mice that express cytoplasmic tail-truncated CXCR4 (ΔT) by a gene knock-in approach. We found that ΔT mice exhibited multiple developmental defects, with not only G-protein-independent but also G-protein-dependent signaling events completely abolished, despite ΔT's ability to still associate with G-proteins. These results reveal an essential positive regulatory role of the cytoplasmic tail in CXCR4 signaling and suggest the tail is crucial for mediating G-protein activation and initiating crosstalk between G-protein-dependent and G-protein-independent pathways for correct GPCR signaling

Data monitoring roadmap. The experience of the Italian Multiple Sclerosis and Related Disorders Register

Introduction Over the years, disease registers have been increasingly considered a source of reliable and valuable population studies. However, the validity and reliability of data from registers may be limited by missing data, selection bias or data quality not adequately evaluated or checked.This study reports the analysis of the consistency and completeness of the data in the Italian Multiple Sclerosis and Related Disorders Register.MethodsThe Register collects, through a standardized Web-based Application, unique patients.Data are exported bimonthly and evaluated to assess the updating and completeness, and to check the quality and consistency. Eight clinical indicators are evaluated.ResultsThe Register counts 77,628 patients registered by 126 centres. The number of centres has increased over time, as their capacity to collect patients.The percentages of updated patients (with at least one visit in the last 24 months) have increased from 33% (enrolment period 2000-2015) to 60% (enrolment period 2016-2022). In the cohort of patients registered after 2016, there were &gt;= 75% updated patients in 30% of the small centres (33), in 9% of the medium centres (11), and in all the large centres (2).Clinical indicators show significant improvement for the active patients, expanded disability status scale every 6 months or once every 12 months, visits every 6 months, first visit within 1 year and MRI every 12 months.ConclusionsData from disease registers provide guidance for evidence-based health policies and research, so methods and strategies ensuring their quality and reliability are crucial and have several potential applications