The awareness and acceptance of anti-COVID 19 vaccination in adolescence

Background: COVID-19 had devastating effects on children's and adolescents' life, including neuropsychological impairment, discontinuation of social life and education. Since June 2021, antiCOVID19 vaccination has become available to adolescents in Italy up to 12 years and since December 2021 to children aged more than 5 years. The pediatric population represents a challenging target for vaccination. Aim of the study is to perform a survey among adolescents to explore factors associated with COVID 19 immunization and their perceptions about COVID-19 vaccines. Methods: Italian students aged 10-17 years were invited to participate in an anonymous online survey regarding their immunization against COVID-19 and their opinion on the immunization practice through a web link to the questionnaire. The study period was March-June 2022. Statistical analysis was performed with SPSS v 21. Results: In the study period, 895 students entered the survey. A total of 87.3% of respondents were immunized against SARS-CoV2. The most important predictors of being immunized against SARS-CoV2 were having both parents immunized (p < 0, 001) and being aged over 12 years. In the unvaccinated group, the decision was mostly influenced by the family (65.8%). Regardless the immunization status, respondents were willing to receive information about COVID 19 vaccination mostly by their family doctor (51.8%) and at school (28.9%). Conclusions: Parents' decisions and attitudes strongly affected the immunization status of adolescents. Students' willing to receive COVID 19 vaccine information by family doctors and at school, underline the potential role of paediatricians and school educators in contributing to an increased vaccine coverage among the paediatric age

Comparative analysis of nuclear estrogen receptor alpha and beta interactomes in breast cancer cells.

Estrogen Receptor alpha and beta (ER-a and -b) are members of the nuclear receptor family of transcriptional regulators with distinct roles in mediating estrogen dependent breast cancer cell growth and differentiation. Following activation by the hormone, these proteins undergo conformation changes and accumulate in the nucleus, where they bind to chromatin at regulatory sites as homo- and/or heterodimers and assemble in large multiprotein complexes. Although the two ERs share a conserved structure, they exert specific and distinct functional roles in normal and transformed mammary epithelial cells and other cell types. To investigate the molecular bases of such differences, we performed a comparative computational analysis of the nuclear interactomes of the two ER subtypes, exploiting two datasets of receptor interacting proteins identified in breast cancer cell nuclei by Tandem Affinity Purification for their ability to associate in vivo with ligand- activated ER-a and/or ER-b. These datasets comprise 498 proteins, of which only 70 are common to both ERs, suggesting that differences in the nature of the two ER interactomes are likely to sustain the distinct roles of the two receptor subtypes. Functional characterization of the two interactomes and their topological analysis, considering node degree and closeness of the networks, confirmed this possibility. Indeed, clustering and network dissection highlighted the presence of distinct and ER subtype-specific subnetworks endowed with defined functions. Altogether, these data provide new insights on the protein–protein interaction networks controlled by ER-a and -b that mediate their ability to transduce estrogen signaling in breast cancer cells

AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo

Comparison of AlignNemo and NetAligner.

<p>The two algorithms are evaluated in terms of recovering known protein complexes in both (CYC2008) and (CORUM). Solutions matching known complexes are scored by means of precison, recall, and F₁ score.</p

The edges incident to a node are ranked according to the their score.

<p>A value plotted on the curve is the average over all nodes of the alignment graph of the scores of the edges of the same rank incident to the nodes. To have comparable distribution of values, we select all the nodes on the union graph with at least 100 edges. The black curve corresponds to the human-fly alignment graph with 1578 nodes and the red curve to the yeast-fly alignment graph with 9325 nodes. Independent of the aligned networks, scores decrease exponentially making the pruning step both essential and effective.</p

Comparison of AlignNemo, NetworkBLAST, and Mawish.

<p>The three algorithms are evaluated in terms of recovering known protein complexes in both (CYC2008) and (CORUM). Solutions matching known complexes are scored by means of precison, recall, and F₁ score. Obtained score distributions for each method are plotted in panel (A) for yeast-fly alignment, and panel (B) for human-fly alignment. Panels (C) and (D) show the average semantic similarity between proteins from different species mapped by each solution. Each solution is represented by a circle with the radius proportional to the size of the solution. The size of the solutions from each method varies significantly, thus small (<7 nodes) and big ( 7 nodes) solutions are shown separately. * Percentages refer to the set of complexes matched by at least one method.</p

Comparison of AlignNemo, Mawish, NetworkBLAST, and NetAligner.

<p><b>No. of S.</b>: Number of Solutions; <b>M.S.</b>: Matching Solutions; <b>S.C.R.</b>: Small Complex Recovered.</p><p>The number of solutions found by each algorithm (No. of S.) is listed in column 2 and 5 for the yeast-fly and the fly-human alignment, respectively. The number of solutions that match at least one known complex is reported in columns 3 and 6 (M.S. - Matching Solutions) for each alignment. The number of high-quality matches for complexes of size 4 is summarized in columns 4 and 7 (), while the number of small complexes (2-3 proteins) recovered is in columns 5 and 8 (S.C.R. - Small Complex Recovered).</p

A synopsis on network alignment tools.

*<p>All methods, as a last step, score and rank the solutions according to a similarity function.</p