Acceptability of COVID-19 vaccination among health care workers: a cross-sectional survey in Morocco

Introduction This study aimed to evaluate the acceptability of COVID-19 vaccination among health care workers prior to the start of the COVID-19 vaccination campaign in Morocco. Methods This cross-sectional study was conducted in the Cheikh Khalifa University Hospital of Casablanca-Morocco. Utilizing a web-based platform surveys were distributed over a 3-week period via professional mailing list and social media. Odds ratios and 95% confidence intervals were estimated by using logistic regression models. Results A total of 303 health care workers participated in this study. The results showed a relatively high rate of vaccination acceptability (62.0%) among health care workers included in this study. Participants were more likely to accept the COVID-19 vaccine if they were physician, nurse, or technician (OR 1.79; 95% CI: 1.09–2.95), had high score of confidence in the information circulating about COVID-19 (OR 1.91; 95% CI: 1.36–2.69), or had high score of perceived severity of COVID-19 (OR 1.55; 95% CI: 1.11–2.15). Reasons of hesitation/or refusal of COVID-19 vaccine were fear of the likely side effects of the vaccine for 74.8% of participants, and concern about the effectiveness of the vaccine for 47.8%. Conclusion This study showed a relatively high rate of the COVID-19 vaccination acceptance among health care workers in Morocco. Willingness to be vaccinated was significantly associated with job category, confidence in the information circulating about COVID-19, and perceived severity of COVID-19. These results could be useful in the development of educational interventions to increase the acceptance of COVID-19 vaccine among health care workers in Morocco and similar settings

Age-Specific Gene Expression Signatures for Breast Tumors and Cross-Species Conserved Potential Cancer Progression Markers in Young Women

<div><p>Breast cancer in young women is more aggressive with a poorer prognosis and overall survival compared to older women diagnosed with the disease. Despite recent research, the underlying biology and molecular alterations that drive the aggressive nature of breast tumors associated with breast cancer in young women have yet to be elucidated. In this study, we performed transcriptomic profile and network analyses of breast tumors arising in Middle Eastern women to identify age-specific gene signatures. Moreover, we studied molecular alterations associated with cancer progression in young women using cross-species comparative genomics approach coupled with copy number alterations (CNA) associated with breast cancers from independent studies. We identified 63 genes specific to tumors in young women that showed alterations distinct from two age cohorts of older women. The network analyses revealed potential critical regulatory roles for Myc, PI3K/Akt, NF-κB, and IL-1 in disease characteristics of breast tumors arising in young women. Cross-species comparative genomics analysis of progression from pre-invasive ductal carcinoma <i>in situ</i> (DCIS) to invasive ductal carcinoma (IDC) revealed 16 genes with concomitant genomic alterations, <i>CCNB2, UBE2C, TOP2A</i>, <i>CEP55</i>, TPX2, <i>BIRC5, KIAA0101</i>, <i>SHCBP1</i>, <i>UBE2T</i>, <i>PTTG1</i>, <i>NUSAP1</i>, <i>DEPDC1</i>, <i>HELLS</i>, <i>CCNB1</i>, KIF4A, and <i>RRM2,</i> that may be involved in tumorigenesis and in the processes of invasion and progression of disease. Array findings were validated using qRT-PCR, immunohistochemistry, and extensive <i>in silico</i> analyses of independently performed microarray datasets. To our knowledge, this study provides the first comprehensive genomic analysis of breast cancer in Middle Eastern women in age-specific cohorts and potential markers for cancer progression in young women. Our data demonstrate that cancer appearing in young women contain distinct biological characteristics and deregulated signaling pathways. Moreover, our integrative genomic and cross-species analysis may provide robust biomarkers for the detection of disease progression in young women, and lead to more effective treatment strategies.</p></div

List of 16 cross-species conserved DCIS to IDC potential progression gene signature.

<p>¹Genes with asterisk are also located in the chromosomal CNA region and ** Mutation found in patients in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063204#pone.0063204-1" target="_blank">[30]</a>.</p><p>^aDCIS indicates fold change between the mean values of expression observed in DCIS (ductal carcinoma <i>in situ</i>) and age-matched normal controls.</p><p>^bIDC indicates fold change between the mean values of expression observed in IDC (invasive ductal carcinoma) and age-matched normal controls.</p><p>^cHazard ration (HR) with 95% confidence intervals and ^dlogrank P-value for Recurrence free survival (RFS) using data from Gyorfffy et al <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063204#pone.0063204-Gyorffy1" target="_blank">[51]</a>.</p><p>^† and ^‡ logrank P-value for distant metastasis free survival using data from GSE7390 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063204#pone.0063204-Desmedt1" target="_blank">[52]</a> and GSE12093 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063204#pone.0063204-Zhang1" target="_blank">[53]</a>, respectively. NS. Not significant; na:Not available.</p

Progression from ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) in young women.

<p>(<b>A</b>) The Venn diagram illustrates that there are 1015 genes differentially expressed (up- or down-regulated) in DCIS compared to normal, whereas 4873 genes differentially expressed in IDC compared to normal controls. 143 genes differentially regulated between IDC and DCIS (green circle). (<b>B</b>) The functional analysis of 16 potential progression genes identified through cross-species comparative genomics analysis. Y-axis indicates the significance (-log P value) of the functional association that is dependent on the number of genes in a class as well as biologic relevance. The threshold line represents a P value of 0.05. (<b>C</b>) Gene interaction networks and pathways analyses of 16-gene progression signature. Green/red indicates decreased/increased mRNA expression in IDC compared to normal controls. The color intensity is correlated with fold change. Straight lines are for direct gene to gene interactions, dashed lines are for indirect ones. (<b>D</b>) Invasive breast tumor cases (from TCGA, Nature 2012 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063204#pone.0063204-1" target="_blank">[30]</a>) displayed altered amplification/homozygous deletion/up-or down-regulation (RNA) or mutation in our 16-progression gene signature. Cases are denoted in columns, and genes in rows (gene symbols are listed on the left).</p

Age-specific patients’ characteristics.

<p>Age-specific patients’ characteristics.</p

Protein expression of selected genes by immunohistochemical staining in breast cancer patients’ samples using antibodies directed against (B) TGFA, (C) IL1RN, and (D) PI3K.

<p>Representative images of positively stained tumors are shown (magnification, ×200).</p

Identification of genes specific to young women with breast cancer.

<p>(<b>A</b>)The unsupervised principal component analysis (PCA) separated samples according to their age group hence supporting the conclusion that there is a distinct gene expression changes associated with the tumor in different age groups. The red spheres refer to young patients (≤45; Young), green for 45–55 years (Pre), and blue for ≥55 years (Post). (<b>B</b>) Venn diagram characterizing differential gene expression between and specific to different age groups. The red circle (left) shows the 804 probes that are differentially expressed between Young and Post; 77 probes (corresponding to 63 genes) were found to be specific to tumor in young women only (circled in <i>light pink)</i>. <b>(C</b>) Unsupervised two-dimensional hierarchical clustering of all tumor samples based on their gene expression similarity using young-age-specific 77 probes was performed using Pearson’s correlation with average linkage clustering. The hierarchical clustering revealed clear pattern of genes deregulation defining two main transcriptome clusters, one was mainly composed primarily younger cases, and one was composed of primarily elderly women. Samples are denoted in columns and genes are denoted in rows (gene symbols listed on the right). The expression level of each gene across the samples is scaled to [−4, 4] interval. These mapped expression levels are depicted using a color scale as shown at the bottom of the figure, as such highly expressed genes are indicated in red, intermediate in black, and weakly expressed in green.</p

Functional and network analyses of genes specific to young women.

<p>(<b>A</b>) The gene ontology and functional analysis of young-age-tumor specific genes (up/down-regulated) were performed using the Ingenuity knowledge base. X-axis indicates the significance (-log P value) of the functional/pathway association that is dependent on the number of genes in a class as well as biologic relevance. The threshold line represents a P value of 0.05. (<b>B–C</b>) Gene interaction network analyses of genes specific to young women and very young women, respectively. Top scoring gene interaction networks with high relevancy scores (with highest relevance score) are shown. Green/red indicates decreased/increased mRNA expression in younger patients compared to older counterparts. The color intensity is correlated with fold change. Straight lines are for direct gene to gene interactions, dashed lines are for indirect ones (<b>D</b>) QRTPCR validation. Grey bars represent microarray hybridizations, and, and dark bars represent values from qRT-PCR. Ratio of expression for each gene in older group (>45) to very young group (≤35) is shown as fold change. A significant correlation existed between the microarray and realtime RT-PCR results.</p