Language Learning for People Living with Dementia and Their Caregivers: Feasibility and the Quality of Experience.

BACKGROUND: A body of research from around the world has reported positive effects of bilingualism on cognitive ageing and dementia. However, little is known about whether foreign language learning could be applied as an intervention for people already living with dementia. Yet, before it is possible to determine the efficacy of language courses as an intervention for people living with dementia (PLWD), it is necessary to establish whether such an intervention is feasible. Our study explored this possibility. METHODS: We conducted an exploratory study to examine the feasibility and tolerability of 2-week Italian beginner courses for PLWD in early stages and their family carers in two Scottish Dementia Resource Centres (DRCs). The courses were delivered by trained tutors from Lingo Flamingo, a social enterprise specialising in language teaching for older learners and learners with dementia. Twelve PLWD and seven carers participated in the study. Focus groups preceded and followed the courses. Additional post-course open interviews with the DRC managers were conducted, with a follow-up via telephone approximately one year later. RESULTS: Qualitative content analysis resulted in 12 themes, 5 reflected in the interview schedule and 7 arising from the focus groups and interviews. Overall, the courses were perceived positively by PLWD, carers, and DRC managers, although a few logistically and linguistically challenging aspects were also mentioned. The courses were found to positively impact both the individual by increasing self-esteem and producing a sense of accomplishment as well as the group by creating a sense of community. Notably, no adverse effects (in particular no confusion or frustration) were reported. CONCLUSION: The positive outcomes of our study open a novel avenue for future research to explore foreign language training in dementia as an intervention and its implications.

Cardiovascular disrupting effects of bisphenols, phthalates, and parabens related to endothelial dysfunction: Review of toxicological and pharmacological mechanisms.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. CVDs are promoted by the accumulation of lipids and immune cells in the endothelial space resulting in endothelial dysfunction. Endothelial cells are important components of the vascular endothelium, that regulate the vascular flow. The imbalance in the production of vasoactive substances results in the loss of vascular homeostasis, leading the endothelial dysfunction. Thus, endothelial dysfunction plays an essential role in the development of atherosclerosis and can be triggered by different cardiovascular risk factors. On the other hand, the 17ß-estradiol (E2) hormone has been related to the regulation of vascular tone through different mechanisms. Several compounds can elicit estrogenic actions similar to those of E2. For these reasons, they have been called endocrine-disrupting compounds (EDCs). This review aims to provide up-to-date information about how different EDCs affect endothelial function and their mechanistic roles in the context of CVDs.

Graph neural networks for clinical risk prediction based on electronic health records: A survey.

OBJECTIVE: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. METHODS: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. RESULTS: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. CONCLUSION: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Broken silence: 22,841 predicted deleterious synonymous variants identified in the human exome through computational analysis.

Synonymous single nucleotide variants (sSNVs) do not alter the primary structure of a protein, thus it was previously accepted that they were neutral. Recently, several studies demonstrated their significance to a range of diseases. Still, variant prioritization strategies lack focus on sSNVs. Here, we identified 22,841 deleterious synonymous variants in 125,748 human exomes using two in silico predictors (SilVA and CADD). While 98.2% of synonymous variants are classified as neutral, 1.8% are predicted to be deleterious, yielding an average of 9.82 neutral and 0.18 deleterious sSNVs per exome. Further investigation of prediction features via Heterogeneous Ensemble Feature Selection revealed that impact on amino acid sequence and conservation carry the most weight for a deleterious prediction. Thirty nine detrimental sSNVs are not rare and are located on disease associated genes. Ten distinct putatively non-deleterious sSNVs are likely to be under positive selection in the North-Western European and East Asian populations. Taken together our analysis gives voice to the so-called silent mutations as we propose a robust framework for evaluating the deleteriousness of sSNVs in variant prioritization studies.

Brazilian women in Bioinformatics: Challenges and opportunities.

Bioinformatics is a growing research field that received great notoriety in the years of the COVID-19 pandemic. It is a very integrative area, comprising professionals from science, technology, engineering, and mathematics (STEM). In agreement with the other STEM areas, several women have greatly contributed to bioinformatics ascension; however, they had to surpass prejudice and stereotypes to achieve recognition and leadership positions, a path that studies have demonstrated to be more comfortable to their male colleagues. In this review, we discuss the several difficulties that women in STEM, including bioinformatics, surpass during their careers. First, we present a historical context on bioinformatics and the main applications for this area. Then, we discuss gender disparity in STEM and present the challenges that still contribute to women's inequality in STEM compared to their male colleagues. We also present the opportunities and the transformation that we can start, acting in academia, inside the family and school environments, and as a society, hence contributing to gender equality in STEM. Finally, we discuss specific challenges in the bioinformatics field and how we can act to overcome them, especially in low and middle-income countries, such as Brazil.

Bioinformatics Methods for Transcriptome Analysis on Teratogenesis Testing.

Teratogenesis testing can be challenging due to the limitations of both in vitro and in vivo models. Test-systems, based especially on human embryonic cells, have been helping to overcome the difficulties when allied to omics strategies, such as transcriptomics. In these test-systems, cells exposed to different compounds are then analyzed in microarray or RNA-seq platforms regarding the impacts of the potential teratogens in the gene expression. Nevertheless, microarray and RNA-seq dataset processing requires computational resources and bioinformatics knowledge. Here, a pipeline for microarray and RNA-seq processing is presented, aiming to help researchers from any field to interpret the main transcriptome results, such as differential gene expression, enrichment analysis, and statistical interpretation. This chapter also discusses the main difficulties that can be encountered in a transcriptome analysis and the better alternatives to overcome these issues, describing both programming codes and user-friendly tools. Finally, specific issues in the teratogenesis field, such as time-course analysis, are also described, demonstrating how the pipeline can be applied in these studies.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection.

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Effects of calcitriol upon TGF-ßs and their receptors in trophoblast cells.

Calcitriol levels increase during pregnancy, contributing to the hormonal and immunological balance, but its deficiency has been associated with problems during this period. Meanwhile, transforming growth factors-ß (TGF-ßs) play an important role in the maintenance of fetal-maternal immune tolerance; however, exacerbated concentrations of this growth factor are associated with complicated pregnancies. Therefore, we studied the effects of calcitriol on TGF-ßs and their receptors in trophoblast cells. Term placentas from uncomplicated pregnancies after cesarean sections were used for cell cultures. Basal gene expression and the effect of calcitriol upon TGF-ß1, TGF-ß2, TGF-ß3, and their receptors TGF-ßR1 and TGF-ßR2 were assessed using real-time PCR from trophoblast cells. The presence of TGF-ß1, 2, 3, and TGF-ßR1 were evaluated by immunofluorescence, and the protein abundance and secretion of TGF-ß1 were assessed by Western blot and ELISA, respectively. Basal gene expression of TGF-ß1 in trophoblast from term placentas was higher than TGF-ß2 and TGF-ß3, while TGF-ßR2 was higher than TGF-ßR1. The presence and cellular localization of TGF-ß1, 2, 3, and TGF-ßR1 were detected in the cytoplasm of syncytiotrophoblast, with TGF-ß1 showing the highest intensity. Calcitriol significantly inhibited gene expression of TGF-ß1, TGF-ß2, and TGF-ßR1. Likewise, calcitriol decreased the secretion and abundance of TGF-ß1. In conclusion, results indicate that calcitriol is a regulator of TGF-ßs in cultured trophoblast cells from term placentas and therefore may be an important player in the development of healthy pregnancies.

Resultados maternos y fetales en mujeres embarazadas con enfermedad renal crónica basada en la tasa de filtración glomerular / Maternal And Fetal Outcomes In Pregnant Women With Chronic Kidney Disease Based On Glomerular Filtration Rate

Introducción: el embarazo causa adaptaciones en el riñón, tanto en anatomía como en función, para mantener el entorno extracelular, hemodinámico y hormonal. Sin embargo, estos pueden no llevarse a cabo de manera completamente óptima en presencia de enfermedad renal. El objetivo era estudiar la relación entre la enfermedad renal y los resultados maternos de fetal durante el embarazo, asociado con un rechazo por paciente y/o en relación con el tratamiento especializado. Material y métodos: estudio observacional y retrospectivo en una serie de casos, revisando 134 archivos de pacientes embarazadas con cierto grado de enfermedad renal antes del embarazo. Los resultados maternos registrados fueron: enfermedad hipertensiva durante el embarazo, deterioro renal agudo, necesidad de terapia de sustitución renal y en productos: prematuridad, restricción del crecimiento intrauterino, muerte fetal y aborto espontáneo. Resultados: Resultados maternos: tasa media de filtración glomerular (GFR) de 58.23 ml/min, aumento de peso de 7 kg; La preeclampsia fue diagnosticada en 92 mujeres (55 severas). 46 pacientes mostraron lesión renal aguda, 40 se resolvieron conservativamente; 1 requirió diálisis peritoneal y 15 hemodiálisis (con una decisión retrasada un promedio de un mes por rechazo por paciente y/o pariente). La resolución del embarazo fue por cesárea en 111 pacientes; Nacieron 116 productos antes de las 37 semanas de gestación, con un peso promedio de 1910 g, 94 mostraron restricción del crecimiento intrauterino. Conclusión: la enfermedad renal influyó directamente en el mayor número de resultados adversos maternos y fetales cuando se rechazó la atención médica especializada. Existe una correlación entre el ligero estado de Davison con los estados I, II y IIIA de Kdigo en el análisis de correspondencia

Introduction: Pregnancy causes adaptations in the kidney, both in anatomy and function, to maintain the extracellular, hemodynamic and hormonal environment. However, these may not be carried out completely optimally in the presence of kidney disease. The objective was to study the relation between kidney disease and maternal-fetal outcomes during pregnancy, associated with a rejection by patient and/or relative to specialized treatment. Material and Methods: Observational, retrospective study in a series of cases, reviewing 134 files of pregnant patients with some degree of kidney disease prior to pregnancy. Maternal outcomes recorded were: hypertensive disease during pregnancy, acute renal deterioration, need for renal substitution therapy, and in products: prematurity, restriction of intrauterine growth, fetal death and miscarriage. Results: Maternal outcomes: mean glomerular filtration rate (GFR) of 58.23ml/min, weight gain of 7 kg; preeclampsia was diagnosed in 92 women (55 severe). 46 patients showed acute renal lesion, 40 were conservatively resolved; 1 required peritoneal dialysis and 15 hemodialysis (with decision delayed an average of one month by rejection by patient and/or relative). Resolution of pregnancy was by cesarean in 111 patients; 116 products were born before 37 weeks of gestation, with average weight of 1910 g, 94 showed restriction of intrauterine growth. Conclusion: Kidney disease directly influenced the greater number of adverse maternal and fetal outcomes when specialized medical care was rejected. There is a correlation between slight Davison state with states I, II and IIIa of KDIGO in correspondence analysis.

Reproductive benefits and reduced investment in parental care behavior associated with reproductive groups of males in Abudefduf troschelii.

Fishes of the family Pomacentridae present a wide diversity of mating systems, ranging from polygyny to promiscuity and from individual territorial defense to the establishment of reproductive colonies of males. The damselfish species Abudefduf troschelii has a reproductive colony mating system, in which males temporarily aggregate in reproductive areas to court and attract females. Males defend an individual territory where they receive eggs and perform paternal care behaviors for their offspring. The present study evaluated the advantages of the colonial mating system in A. troschelii. During an entire reproductive period, in a breeding colony within a rocky reef, we located, marked, geo-referenced, and measured the distances between the territories of all males. We quantified the variance among males in their patterns of paternal care investment, eggs acquired, hatching success, reproductive success, body size, and changes in body coloration. We found that males spatially distributed their nests in groups or independently (i.e., solitary nests). Nesting groups are formed by larger males that show intense nuptial coloration during the entire receptivity period. They are located centrally to the colony and consist of three to six males whose territories overlap. In contrast, small solitary males that fail to acquire or maintain nuptial coloration during the receptivity period establish their nests peripherally to the colony, away from the territories of other males. Our results highlight that the reproductive benefits of colonial nesting are unequal for males, as the spatial distribution of nests within the colony determines the reproductive success of males. Group nesting confers the highest reproductive benefits to males regarding eggs obtained, hatching success, and relative fitness and also enables males to reduce their parental investment in brood care behaviors. The preference of females for oviposition could be associated with greater intrasexual competitiveness, defense ability, body condition, or experience of group-nesting males located at the center of the colony or because their progeny will have a lower probability of predation than they would in solitary nests males.

Downregulation of Microcephaly-Causing Genes as a Mechanism for ZIKV Teratogenesis: A Meta-analysis of RNA-Seq Studies.

Zika virus (ZIKV) is a neurotropic teratogen that causes congenital Zika syndrome (CZS), characterized by brain and eye anomalies. Impaired gene expression in neural cells after ZIKV infection has been demonstrated; however, there is a gap in the literature of studies comparing whether the differentially expressed genes in such cells are similar and how it can cause CZS. Therefore, the aim of this study was to compare the differential gene expression (DGE) after ZIKV infection in neural cells through a meta-analysis approach. Through the GEO database, studies that evaluated DGE in cells exposed to the Asian lineage of ZIKV versus cells, of the same type, not exposed were searched. From the 119 studies found, five meet our inclusion criteria. Raw data of them were retrieved, pre-processed, and evaluated. The meta-analysis was carried out by comparing seven datasets, from these five studies. We found 125 upregulated genes in neural cells, mainly interferon-stimulated genes, such as IFI6, ISG15, and OAS2, involved in the antiviral response. Furthermore, 167 downregulated, involved with cellular division. Among these downregulated genes, classic microcephaly-causing genes stood out, such as CENPJ, ASPM, CENPE, and CEP152, demonstrating a possible mechanism by which ZIKV impairs brain development and causes CZS.

Classification of Thyroid Tumors Based on DNA Methylation Patterns.

Background: Alterations in DNA methylation are stable epigenetic events that can serve as clinical biomarkers. The aim of this study was to analyze methylation patterns among various follicular cell-derived thyroid neoplasms to identify disease subtypes and help understand and classify thyroid tumors. Methods: We employed an unsupervised machine learning method for class discovery to search for distinct methylation patterns among various thyroid neoplasms. Our algorithm was not provided with any clinical or pathological information, relying exclusively on DNA methylation data to classify samples. We analyzed 810 thyroid samples (n = 256 for discovery and n = 554 for validation), including benign and malignant tumors, as well as normal thyroid tissue. Results: Our unsupervised algorithm identified that samples could be classified into three subtypes based solely on their methylation profile. These methylation subtypes were strongly associated with histological diagnosis (p < 0.001) and were therefore named normal-like, follicular-like, and papillary thyroid carcinoma (PTC)-like. Follicular adenomas, follicular carcinomas, oncocytic adenomas, and oncocytic carcinomas clustered together forming the follicular-like methylation subtype. Conversely, classic papillary thyroid carcinomas (cPTC) and tall cell PTC clustered together forming the PTC-like subtype. These methylation subtypes were also strongly associated with genomic drivers: 98.7% BRAFV600E-driven cancers were PTC like, whereas 96.0% RAS-driven cancers had a follicular-like methylation pattern. Interestingly, unlike other diagnoses, follicular variant PTC (FVPTC) samples were split into two methylation clusters (follicular like and PTC like), indicating a heterogeneous group likely to be formed by two distinct diseases. FVPTC samples with a follicular-like methylation pattern were enriched for RAS mutations (36.4% vs. 8.0%; p < 0.001), whereas FVPTC- with PTC-like methylation patterns were enriched for BRAFV600E mutations (52.0% vs. 0%, Fisher exact p = 0.004) and RET fusions (16.0% vs. 0%, Fisher exact p = 0.003). Conclusions: Our data provide novel insights into the epigenetic alterations of thyroid tumors. Since our classification method relies on a fully unsupervised machine learning approach for subtype discovery, our results offer a robust background to support the classification of thyroid neoplasms based on methylation patterns.

A New Strategy for the Old Challenge of Thalidomide: Systems Biology Prioritization of Potential Immunomodulatory Drug (IMiD)-Targeted Transcription Factors.

Several molecular mechanisms of thalidomide embryopathy (TE) have been investigated, from anti-angiogenesis to oxidative stress to cereblon binding. Recently, it was discovered that thalidomide and its analogs, named immunomodulatory drugs (IMiDs), induced the degradation of C2H2 transcription factors (TFs). This mechanism might impact the strict transcriptional regulation of the developing embryo. Hence, this study aims to evaluate the TFs altered by IMiDs, prioritizing the ones associated with embryogenesis through transcriptome and systems biology-allied analyses. This study comprises only the experimental data accessed through bioinformatics databases. First, proteins and genes reported in the literature as altered/affected by the IMiDs were annotated. A protein systems biology network was evaluated. TFs beta-catenin (CTNNB1) and SP1 play more central roles: beta-catenin is an essential protein in the network, while SP1 is a putative C2H2 candidate for IMiD-induced degradation. Separately, the differential expressions of the annotated genes were analyzed through 23 publicly available transcriptomes, presenting 8624 differentially expressed genes (2947 in two or more datasets). Seventeen C2H2 TFs were identified as related to embryonic development but not studied for IMiD exposure; these TFs are potential IMiDs degradation neosubstrates. This is the first study to suggest an integration of IMiD molecular mechanisms through C2H2 TF degradation.

Suicide risk classification with machine learning techniques in a large Brazilian community sample.

Even though suicide is a relatively preventable poor outcome, its prediction remains an elusive task. The main goal of this study was to develop machine learning classifiers to identify increased suicide risk in Brazilians with common mental disorders. With the use of clinical and sociodemographic baseline data (n = 4039 adult participants) from a large Brazilian community sample, we developed several models (Elastic Net, Random Forests, Naïve Bayes, and ensemble) for the classification of increased suicide risk among individuals with common mental disorders. 1120 participants (27.7%) presented increased suicide risk. The Random Forests model achieved the best AUC ROC (0.814), followed by Naive Bayes (0.798) and Elastic Net (0.773). Sensitivity varied from 0.922 (Naive Bayes) to 0.630 (Random Forests), while specificity varied from 0.792 (Random Forests) to 0.473 (Naive Bayes). The ensemble model presented an AUC ROC of 0.811, sensitivity of 0.899, and specificity of 0.510. Features representing depression symptoms were the most relevant for the classification of increased suicide risk. Some of our models presented good performance metrics in the classification of increased suicide risk in the investigated sample, which can provide the means to early preventive interventions.

Economic Cost of the Waste of Anti-inflammatory and Analgesic Drugs in Mexico City.

Anti-inflammatory and analgesic medications (AAMs) are widely used in Mexico and the rest of the world. Their excessive acquisition can lead to waste, representing an unnecessary expense for families and the public health system. The aim of this study was to estimate the economic cost of the waste of unused AAMs collected by the National System for the Collection of Residues of Containers and Medications (SINGREM, the acronym in Spanish) in Mexico City during 2019. Data from SINGREM on discarded AAMs in Mexico City were classified by the type and quantity of drug, pharmaceutical dosage form, origin, dose, and the complete or incomplete condition of the package. The unitary cost for each medication was based on public tenders of the Mexican Social Security Institute (IMSS) for the public sector and the prices in large drug store franchises for the private sector. A decision-making model was constructed to appraise the total cost of discarded AAMs. The economic cost of the 48924 units of discarded AAMs in SINGREM containers in Mexico City during 2019 was approx. USD$143500, of which over USD$127000 corresponded to the private health sector. The current findings evidence an enormous accumulation of unneeded or expired AAMs in Mexico City. According to the present data, the cost of such waste is substantial. The estimated cost was 8-fold higher for discarded medications originating from the private versus the public healthcare sector. It is important to implement measures to prevent this waste and increase awareness of the consequences of inadequate drug disposal.

Computerized cognitive interventions for adults with ADHD: A systematic review and meta-analysis.

OBJECTIVE: Treatments for adults with attention-deficit hyperactivity disorder (ADHD) are understudied, compared to children and adolescents with the same condition. In this systematic review and random-effects meta-analysis, we aim to evaluate the outcomes of computerized cognitive training (CCT) interventions in randomized controlled trials (RCTs) including adults with ADHD. METHOD: Cognitive outcomes and ADHD symptom severity were analyzed separately. In addition, the Cattell-Horn-Carroll (CHC) theory of cognitive abilities was used to categorize outcome variables into subdomains, which were analyzed separately in a subsequent analysis. RESULTS: The results revealed a small positive change in overall cognitive functioning, a measure of all cognitive outcomes in each study, for individuals who took part in CCT compared to controls (k = 9, Hedge's g = 0.235, 95% CI [0.002, 0.467], p = 0.048, τ² = 0.000, I² = 0.000). However, neither symptom severity nor specific cognitive outcomes (executive functioning, cognitive speed, or working memory) showed a significant improvement. CONCLUSIONS: We analyzed the risk of bias in the chosen studies and discuss the findings in terms of effect size. It is concluded that CCT has a small positive effect in adults with ADHD. Due to the lack of heterogeneity in intervention designs across the included studies, increased heterogeneity in future studies could help inform clinicians about the aspects of CCT, such as training type and length, that are most beneficial for this group. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Phytochemical α-Mangostin Inhibits Cervical Cancer Cell Proliferation and Tumor Growth by Downregulating E6/E7-HPV Oncogenes and KCNH1 Gene Expression.

Cervical cancer is the fourth most common cancer among women worldwide. The main factor associated with the onset and progression of this neoplasia is the human papillomavirus (HPV) infection. The HPV-oncogenes E6 and E7 are critical drivers of cellular transformation, promoting the expression of oncogenes such as KCNH1. The phytochemical α-mangostin (AM) is a potent antineoplastic and antiviral compound. However, its effects on HPV oncogenes and KCNH1 gene expression remain unknown. This study evaluated the effects of AM on cell proliferation, cell cycle distribution and gene expression, including its effects on tumor growth in xenografted mice. AM inhibited cell proliferation in a concentration-dependent manner, being the most sensitive cell lines those with the highest number of HPV16 copies. In addition, AM promoted G1-cell cycle arrest in CaSki cells, while led to cell death in SiHa and HeLa cells. Of interest was the finding of an AM-dependent decreased gene expression of E6, E7 and KCNH1 both in vitro and in vivo, as well as the modulation of cytokine expression, Ki-67, and tumor growth inhibition. On these bases, we suggest that AM represents a good option as an adjuvant for the treatment and prevention of cervical cancer.

The Interplay of GPER1 with 17ß-Aminoestrogens in the Regulation of the Proliferation of Cervical and Breast Cancer Cells: A Pharmacological Approach.

The G-protein-coupled receptor for estrogen (GPER1) is a transmembrane receptor involved in the progression and development of various neoplasms whose ligand is estradiol (E2). 17ß-aminoestrogens (17ß-AEs) compounds, analogs to E2, are possible candidates for use in hormone replacement therapy (HRT), but our knowledge of their pharmacological profile is limited. Thus, we explored the molecular recognition of GPER1 with different synthetic 17ß-AEs: prolame, butolame, and pentolame. We compared the structure and ligand recognition sites previously reported for a specific agonist (G1), antagonists (G15 and G36), and the natural ligand (E2). Then, the biological effects of 17ß-AEs were analyzed through cell viability and cell-cycle assays in two types of female cancer. In addition, the effect of 17ß-AEs on the phosphorylation of the oncoprotein c-fos was evaluated, because this molecule is modulated by GPER1. Molecular docking analysis showed that 17ß-AEs interacted with GPER1, suggesting that prolame joins GPER1 in a hydrophobic cavity, similarly to G1, G15, and E2. Prolame induced cell proliferation in breast (MCF-7) and cervical cancer (SIHA) cells; meanwhile, butolame and pentolame did not affect cell proliferation. Neither 17ß-AEs nor E2 changed the activation of c-fos in MCF-7 cells. Meanwhile, in SIHA cells, E2 and 17ß-AEs reduced c-fos phosphorylation. Thus, our data suggest that butolame and pentolame, but not prolame, could be used for HRT without presenting a potential risk of inducing breast- or cervical-cancer-cell proliferation. The novelty of this work lies in its study of compound analogs to E2 that may represent important therapeutic strategies for women in menopause, with non-significant effects on the cell viability of cancer cells. The research focused on the interactions of GPER1, a molecule recently associated with promoting and maintaining various neoplasms.