Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates.

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.

Transcriptome innovations in primates revealed by single-molecule long-read sequencing.

Transcriptomic diversity greatly contributes to the fundamentals of disease, lineage-specific biology, and environmental adaptation. However, much of the actual isoform repertoire contributing to shaping primate evolution remains unknown. Here, we combined deep long- and short-read sequencing complemented with mass spectrometry proteomics in a panel of lymphoblastoid cell lines (LCLs) from human, three other great apes, and rhesus macaque, producing the largest full-length isoform catalog in primates to date. Around half of the captured isoforms are not annotated in their reference genomes, significantly expanding the gene models in primates. Furthermore, our comparative analyses unveil hundreds of transcriptomic innovations and isoform usage changes related to immune function and immunological disorders. The confluence of these evolutionary innovations with signals of positive selection and their limited impact in the proteome points to changes in alternative splicing in genes involved in immune response as an important target of recent regulatory divergence in primates.

Improving In Vitro-In Vivo Extrapolation of Clearance Using Rat Liver Microsomes for Highly Plasma Protein-Bound Molecules.

It is common practice in drug discovery and development to predict in vivo hepatic clearance from in vitro incubations with liver microsomes or hepatocytes using the well-stirred model (WSM). When applying the WSM to a set of approximately 3000 Novartis research compounds, 73% of neutral and basic compounds (extended clearance classification system [ECCS] class 2) were well-predicted within 3-fold. In contrast, only 44% (ECCS class 1A) or 34% (ECCS class 1B) of acids were predicted within 3-fold. To explore the hypothesis whether the higher degree of plasma protein binding for acids contributes to the in vitro-in vivo correlation (IVIVC) disconnect, 68 proprietary compounds were incubated with rat liver microsomes in the presence and absence of 5% plasma. A minor impact of plasma on clearance IVIVC was found for moderately bound compounds (fraction unbound in plasma [fup] ≥1%). However, addition of plasma significantly improved the IVIVC for highly bound compounds (fup <1%) as indicated by an increase of the average fold error from 0.10 to 0.36. Correlating fup with the scaled unbound intrinsic clearance ratio in the presence or absence of plasma allowed the establishment of an empirical, nonlinear correction equation that depends on fup Taken together, estimation of the metabolic clearance of highly bound compounds was enhanced by the addition of plasma to microsomal incubations. For standard incubations in buffer only, application of an empirical correction provided improved clearance predictions. SIGNIFICANCE STATEMENT: Application of the well-stirred liver model for clearance in vitro-in vivo extrapolation (IVIVE) in rat generally underpredicts the clearance of acids and the strong protein binding of acids is suspected to be one responsible factor. Unbound intrinsic in vitro clearance (CLint,u) determinations using rat liver microsomes supplemented with 5% plasma resulted in an improved IVIVE. An empirical equation was derived that can be applied to correct CLint,u-values in dependance of fraction unbound in plasma (fup) and measured CLint in buffer.

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs < 0.01 min-1), weak positive (0.01 ≤ kobs ≤ 0.025 min-1), or positive (kobs > 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information.

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an "out-of-the-box" solution to assist in drug design, synthesis prioritization, and experiment selection. However, drug discovery projects typically focus on confined parts of the chemical space (e.g., chemical series), where global models might not be applicable. Local ML models are sometimes generated to focus on specific projects or series. Herein, ML-based global models, local models, and hybrid global-local strategies were benchmarked. Analyses were done for more than 300 drug discovery projects at Novartis and ten absorption, distribution, metabolism, and excretion (ADME) assays. In this work, hybrid global-local strategies based on transfer learning approaches were proposed to leverage both historical ADME data (global) and project-specific data (local) to adapt model predictions. Fine-tuning a pretrained global ML model (used for weights' initialization, WI) was the top-performing method. Average improvements of mean absolute errors across all assays were 16% and 27% compared with global and local models, respectively. Interestingly, when the effect of training set size was analyzed, WI fine-tuning was found to be successful even in low-data scenarios (e.g., ∼10 molecules per project). Taken together, this work highlights the potential of domain adaptation in the field of molecular property predictions to refine existing pretrained models on a new compound data distribution.

Efficacy and safety of galcanezumab as chronic cluster headache preventive treatment under real world conditions: Observational prospective study.

BACKGROUND: Calcitonin gene-related peptide has shown to play a central role in cluster headache (CH) pathophysiology. A clinical trial with galcanezumab was carried out in chronic cluster headache (CCH) but did not meet its primay endpoint. However, its off-label use in patients with CCH refractory to other therapies could be considered. We aimed to asses the efficacy and safety of galcanezumab as CCH preventive treatment in a real-life setting. METHODS: An observational study was conducted. Patients with CCH who received at least one dose of 240 mg of galcanezumab. RESULTS: Twenty-one patients who tried a mean of 6.3 ± 1.9 preventive therapies, including onabotulinumtoxinA in 90.5%. At baseline, the median of frequency was 60 (37.5-105) monthly attacks with 10 (8.3-10) points in pain intensity (Numerical Rating Scale). After one month, the frequency decreased to 31 (10.5-45) (p = 0.003) with 8.5 (8-9.5) intensity (p = 0.007); 10 (47.6%) patients were 50% responders of whom four (19%) were 75% responders. Of the 15 patients with 3 months of follow-up, seven (46.6%) reduced their frequency by 50% and four (26.6%) by 75%, with 40 (10-60) monthly attacks (p = 0.07) and pain intensity of 8 (5-10) (p = 0.026). Some 52% patients experienced adverse events, mostly mild. CONCLUSIONS: In our cohort of refractory CCH, galcanezumab was effective in almost 50% of patients. This finding supports individual off-label treatment attempts.

Beneficial effects of physical exercise on the osteo-renal Klotho-FGF-23 axis in Chronic Kidney Disease: A systematic review with meta-analysis.

The aim of this study was to investigate the efficacy of physical exercise in chronic kidney disease, describing its impact on the Klotho-FGF23 axis. PubMed, Web of Science and Scopus databases, updated to January 2023, were searched. The present study employed mean difference and a 95% confidence interval (CI) to examine the efficacy of the intervention. Heterogeneity was assessed through inconsistency statistics (I2). Out of the 299 studies identified, a total of 4 randomized controlled trials (RCTs), comprising 272 participants, met the eligibility criteria. Compared with the control group, physical exercise significantly decreased the concentrations of FGF23 (MD: -102.07 Pg/mL, 95% CI: -176.23.47, -27.91 I2= 97%, p = 0.001), and a significantly increased the concentrations of Klotho protein: (MD: 158.82 Pg/mL, 95% CI: 123.33, -194.31, I2 = 0%, p = 0.001). The results of our study indicated that the exercise has a direct relationship with Klotho-FGF23 axis. We can conclude that physical exercise in patients with CKD produces beneficial effects on the pathophysiological components related to this disease, including cardiorespiratory fitness and vascular functions. As observed, both endurance and aerobic physical exercise increase Klotho production and decrease FGF23 levels. Evidence indicates that exercise attenuates the progression of CKD, improves uremic parameters and down-regulates inflammation-related markers.

High-risk pregnancy and its relationship with the neurodevelopment and behavior of 2-year-old children.

High-risk pregnancies elevate maternal stress, impacting offspring neurodevelopment and behavior. This study, involving 112 participants, aimed to compare perceived stress, neurodevelopment, and behavior in high-risk and low-risk pregnancies. Two groups, high-risk and low-risk, were assessed during pregnancy for stress using hair cortisol and psychological analysis. At 24 months post-birth, their children's neurodevelopment and behavior were evaluated. Results revealed higher perceived stress and pregnancy-related concerns in high-risk pregnancies, contrasting with low-risk pregnancies. Offspring from high-risk pregnancies displayed elevated internalizing behavior scores, while low-risk pregnancies showed higher externalizing behavior scores. Additionally, women in low-risk pregnancies exhibited increased cortisol concentrations 24 months post-delivery. These findings underscore the necessity for early stress detection and prevention programs during pregnancy, particularly in high-risk cases, to enhance maternal and infant health.

Identification of reversible OATP1B1 and time-dependent CYP3A4 inhibition as the major risk factors for drug-induced cholestasis (DIC).

Hepatic bile acid regulation is a multifaceted process modulated by several hepatic transporters and enzymes. Drug-induced cholestasis (DIC), a main type of drug-induced liver injury (DILI), denotes any drug-mediated condition in which hepatic bile flow is impaired. Our ability in translating preclinical toxicological findings to human DIC risk is currently very limited, mainly due to important interspecies differences. Accordingly, the anticipation of clinical DIC with available in vitro or in silico models is also challenging, due to the complexity of the bile acid homeostasis. Herein, we assessed the in vitro inhibition potential of 47 marketed drugs with various degrees of reported DILI severity towards all metabolic and transport mechanisms currently known to be involved in the hepatic regulation of bile acids. The reported DILI concern and/or cholestatic annotation correlated with the number of investigated processes being inhibited. Furthermore, we employed univariate and multivariate statistical methods to determine the important processes for DILI discrimination. We identified time-dependent inhibition (TDI) of cytochrome P450 (CYP) 3A4 and reversible inhibition of the organic anion transporting polypeptide (OATP) 1B1 as the major risk factors for DIC among the tested mechanisms related to bile acid transport and metabolism. These results were consistent across multiple statistical methods and DILI classification systems applied in our dataset. We anticipate that our assessment of the two most important processes in the development of cholestasis will enable a risk assessment for DIC to be efficiently integrated into the preclinical development process.

Iatrogenic rectal perforation due to application of topical treatment in a patient with ulcerative colitis: a rare complication.

Rectal perforations due to topical treatments (enemas or foams) are unusual complications and they have been mostly reported in the use of barium enemas or in elderly patients with constipation. Very little has been reported about perforations secondary to topical treatment in patients with ulcerative colitis. We present the case of a patient with ulcerative colitis who suffered a rectal perforation complicated with a superinfected collection after the application of topical mesalazine foam.

A comparison of gene expression and DNA methylation patterns across tissues and species.

Previously published comparative functional genomic data sets from primates using frozen tissue samples, including many data sets from our own group, were often collected and analyzed using nonoptimal study designs and analysis approaches. In addition, when samples from multiple tissues were studied in a comparative framework, individuals and tissues were confounded. We designed a multitissue comparative study of gene expression and DNA methylation in primates that minimizes confounding effects by using a balanced design with respect to species, tissues, and individuals. We also developed a comparative analysis pipeline that minimizes biases attributable to sequence divergence. Thus, we present the most comprehensive catalog of similarities and differences in gene expression and DNA methylation levels between livers, kidneys, hearts, and lungs, in humans, chimpanzees, and rhesus macaques. We estimate that overall, interspecies and inter-tissue differences in gene expression levels can only modestly be accounted for by corresponding differences in promoter DNA methylation. However, the expression pattern of genes with conserved inter-tissue expression differences can be explained by corresponding interspecies methylation changes more often. Finally, we show that genes whose tissue-specific regulatory patterns are consistent with the action of natural selection are highly connected in both gene regulatory and protein-protein interaction networks.

Human Wharton's jelly-derived mesenchymal stromal cells promote bone formation in immunodeficient mice when administered into a bone microenvironment.

BACKGROUND: Wharton's Jelly (WJ) Mesenchymal Stromal Cells (MSC) have emerged as an attractive allogeneic therapy for a number of indications, except for bone-related conditions requiring new tissue formation. This may be explained by the apparent recalcitrance of MSC,WJ to differentiate into the osteogenic lineage in vitro, as opposed to permissive bone marrow (BM)-derived MSCs (MSC,BM) that readily commit to bone cells. Consequently, the actual osteogenic in vivo capacity of MSC,WJ is under discussion. METHODS: We investigated how physiological bone environments affect the osteogenic commitment of recalcitrant MSCs in vitro and in vivo. To this end, MSC of BM and WJ origin were co-cultured and induced for synchronous osteogenic differentiation in vitro using transwells. For in vivo experiments, immunodeficient mice were injected intratibially with a single dose of human MSC and bone formation was evaluated after six weeks. RESULTS: Co-culture of MSC,BM and MSC,WJ resulted in efficient osteogenesis in both cell types after three weeks. However, MSC,WJ failed to commit to bone cells in the absence of MSC,BM's osteogenic stimuli. In vivo studies showed successful bone formation within the medullar cavity of tibias in 62.5% of mice treated with MSC, WJ. By contrast, new formed trabeculae were only observed in 25% of MSC,BM-treated mice. Immunohistochemical staining of human COXIV revealed the persistence of the infused cells at the site of injection. Additionally, cells of human origin were also identified in the brain, heart, spleen, kidney and gonads in some animals treated with engineered MSC,WJ (eMSC,WJ). Importantly, no macroscopic histopathological alterations, ectopic bone formation or any other adverse events were detected in MSC-treated mice. CONCLUSIONS: Our findings demonstrate that in physiological bone microenvironment, osteogenic commitment of MSC,WJ is comparable to that of MSC,BM, and support the use of off-the-shelf allogeneic MSC,WJ products in bone repair and bone regeneration applications.

How did the COVID-19 pandemic affect access to condoms, chlamydia and HIV testing, and cervical cancer screening at a population level in Britain? (Natsal-COVID).

OBJECTIVES: To investigate how differential access to key interventions to reduce STIs, HIV and their sequelae changed during the COVID-19 pandemic. METHODS: British participants (18-59 years) completed a cross-sectional web survey 1 year (March-April 2021) after the initial lockdown in Britain. Quota-based sampling and weighting resulted in a quasi-representative population sample. We compared Natsal-COVID data with Natsal-3, a household-based probability sample cross-sectional survey (16-74 years) conducted in 2010-2012. Reported unmet need for condoms because of the pandemic and uptake of chlamydia testing/HIV testing/cervical cancer screening were analysed among sexually experienced participants (18-44 years) (n=3869, Natsal-COVID; n=8551, Natsal-3). ORs adjusted for age and other potential confounders describe associations with demographic and behavioural factors. RESULTS: In 2021, 6.9% of women and 16.2% of men reported unmet need for condoms because of the pandemic. This was more likely among participants: aged 18-24 years, of black or black British ethnicity, and reporting same-sex sex (past 5 years) or one or more new relationships (past year). Chlamydia and HIV testing were more commonly reported by younger participants, those reporting condomless sex with new sexual partners and men reporting same-sex partners; a very similar distribution to 10 years previously (Natsal-3). However, there were differences during the pandemic, including stronger associations with chlamydia testing for men reporting same-sex partners; with HIV testing for women reporting new sexual partners and with cervical screening among smokers. CONCLUSIONS: Our study suggests differential access to key primary and secondary STI/HIV prevention interventions continued during the first year of the COVID-19 pandemic. However, there was not strong evidence that differential access has changed during the pandemic when compared with 2010-2012. While the pandemic might not have exacerbated inequalities in access to primary and secondary prevention, it is clear that large inequalities persisted, typically among those at greatest STI/HIV risk.

Sexual and reproductive health in Britain during the first year of the COVID-19 pandemic: cross-sectional population survey (Natsal-COVID-Wave 2) and national surveillance data.

OBJECTIVES: To assess sexual behaviour, and sexual and reproductive health (SRH) outcomes, after 1 year of the COVID-19 pandemic in Britain. METHODS: 6658 participants aged 18-59 and resident in Britain completed a cross-sectional web-panel survey (Natsal-COVID-Wave 2, March-April 2021), 1 year after the first lockdown. Natsal-COVID-2 follows the Natsal-COVID-Wave 1 survey (July-August 2020) which captured impacts in the initial months. Quota-based sampling and weighting resulted in a quasi-representative population sample. Data were contextualised with reference to the most recent probability sample population data (Natsal-3; collected 2010-12; 15 162 participants aged 16-74) and national surveillance data on recorded sexually transmitted infection (STI) testing, conceptions, and abortions in England/Wales (2010-2020). The main outcomes were: sexual behaviour; SRH service use; pregnancy, abortion and fertility management; sexual dissatisfaction, distress and difficulties. RESULTS: In the year from the first lockdown, over two-thirds of participants reported one or more sexual partners (women 71.8%; men 69.9%), while fewer than 20.0% reported a new partner (women 10.4%; men 16.8%). Median occasions of sex per month was two. Compared with 2010-12 (Natsal-3), we found less sexual risk behaviour (lower reporting of multiple partners, new partners, and new condomless partners), including among younger participants and those reporting same-sex behaviour. One in 10 women reported a pregnancy; pregnancies were fewer than in 2010-12 and less likely to be scored as unplanned. 19.3% of women and 22.8% of men were distressed or worried about their sex life, significantly more than in 2010-12. Compared with surveillance trends from 2010 to 2019, we found lower than expected use of STI-related services and HIV testing, lower levels of chlamydia testing, and fewer conceptions and abortions. CONCLUSIONS: Our findings are consistent with significant changes in sexual behaviour, SRH, and service uptake in the year following the first lockdown in Britain. These data are foundational to SRH recovery and policy planning.

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to develop quantitative structure-property relationship (QSPR) models. Traditional QSPR models were trained on compound sets of limited size. With the advent of more complex ML algorithms and data availability, training sets have become larger and more diverse. Most common training approaches consist in either training a model with a small set of similar compounds, namely, compounds designed for the same drug discovery project or chemical series (local model approach) or with a larger set of diverse compounds (global model approach). Global models are built with all experimental data available for an assay, combining compound data from different projects and disease areas. Despite the ML progress made so far, the choice of the appropriate data composition for building ML models is still unclear. Herein, a systematic evaluation of local and global ML models was performed for 10 different experimental assays and 112 drug discovery projects. Results show a consistent superior performance of global models for ADME property predictions. Diagnostic analyses were also carried out to investigate the influence of training set size, structural diversity, and data shift in the relative performance of local and global ML models. Training set and structural diversity did not have an impact in the relative performance on the methods. Instead, data shift helped to identify the projects with larger performance differences between local and global models. Results presented in this work can be leveraged to improve ML-based ADME properties predictions and thus decision-making in drug discovery projects.

Multispecies Machine Learning Predictions of In Vitro Intrinsic Clearance with Uncertainty Quantification Analyses.

In pharmaceutical research, compounds are optimized for metabolic stability to avoid a too fast elimination of the drug. Intrinsic clearance (CLint) measured in liver microsomes or hepatocytes is an important parameter during lead optimization. In this work, machine learning models were developed to relate the compound structure to microsomal metabolic stability and predict CLint for new compounds. A multitask (MT) learning architecture was introduced to model the CLint of six species simultaneously, giving as a result a multispecies machine learning model. MT graph neural network (MT-GNN) regression was identified as the top-performing method, and an ensemble of 10 MT-GNN models was evaluated prospectively. Geometric mean fold errors were consistently smaller than 2-fold. Moreover, high precision values were obtained in the prediction of "high" (>300 µL/min/mg) and "low" (<100 µL/min/mg) CLint compounds. Precision values ranged from 80 to 94% for low CLint predictions and from 75 to 97% for high CLint predictions, depending on the species. Uncertainty on experimental values and model predictions was systematically quantified. Experimental variability (aleatoric uncertainty) of all historical Novartis in vitro clearance experiments was analyzed. Interestingly, MT-GNN models' performance approached assays' experimental variability. Moreover, uncertainty estimation in predictions (epistemic uncertainty) enabled identifying predictions associated with lower and higher error. Taken together, our manuscript combines a multispecies deep learning model and large-scale uncertainty analyses to improve CLint predictions and facilitate early informed decisions for compound prioritization.

Balancing risk, intimacy and (non)compliance: a qualitative study of sex across household during COVID-19 social restrictions.

Government controls over intimate relationships, imposed to limit the spread of Sars-CoV-2, were unprecedented in modern times. This study draws on data from qualitative interviews with 18 participants in Natsal-COVID, a quasi-representative web-panel survey of the British population (n = 6,654 people), reporting that they had sex with someone from outside their household in the preceding four weeks; a period in which contact between households was restricted in the UK. Whilst only 10% of people reported sexual contact outside their household, among single people and those in non-cohabiting relationships, rates were much higher (Natsal-COVID). Our findings show that individuals did not take decisions to meet up with sexual partners lightly. Participants were motivated by needs-for connection, security, intimacy and a sense of normality. People balanced risks-of catching COVID-19, social judgement and punishment for rule-breaking-against other perceived risks, including to their mental health or relationships. We used situated rationality and social action theories of risk to demonstrate that people weighed up risk in socially situated ways and exhibited complex decision-making when deciding not to comply with restrictions. Understanding motivations for non-compliance is crucial to informing future public health messaging which accounts for the needs and circumstances of all population members.

Sexting among British adults: a qualitative analysis of sexting as emotion work governed by 'feeling rules'.

Sexting has generated considerable public and professional interest with concerns centring on young people, and potential harms to mental and sexual health. Little research thus far has explored the practice among adults and none has focused on the cultural norms relating to the emotional experience of sexting across different ages and genders. We conducted 40 semi-structured interviews with a diverse sample of adults aged 18-59 years in Britain on the role of digital technologies in participants' sexual lives. In this paper, we draw on the accounts of 34 people with experience of sexting. We identified three main themes in participants' accounts related to the emotional aspects of sexting: (1) trust, (2) desire/intimacy and (3) shame. Under each theme, we identified motivations, 'feeling rules', and examples of 'emotion work' relating to the self, the other and the dyad. We conclude that there are shared cultural norms that constitute what appropriate sexting should feel like. Interventions aiming to minimise harms arising from sexting need to build on commonly held cultural conventions regarding the 'rules of the game' concerning feelings as well as behaviours.

A systematic review on the use of action research methods in mental health nursing care.

AIMS: To identify and synthesize evidence on the use of action research methods in mental health nursing care. DESIGN: Systematic review. DATA SOURCES: CINAHL, Web of Science, PubMed and Scopus databases were searched in January 2021. REVIEW METHODS: Data were selected using the updated Preferred Reporting Items for Systematic Reviews and Meta-Analysis framework. Two reviewers independently conducted the study selection, and quality appraisal using Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research, data extraction and data analysis procedures. RESULTS: Sixteen studies, half of which used participatory action research, were included in this review. Nurses, along with other stakeholders, were an active part of the action research process. The main topics of interest addressed were categorized as improving the adoption of a person-centred approach to care and improving decision-making procedures. The use of action research helped the participants to identify the meaning they attached to the topic of interest to be improved. Moreover, this method helped to identify needs and strategies for improving care. The studies concurred that the use of action research enabled participants to gain awareness, improve attitudes and acquire knowledge. In addition, it enabled participants to gain confidence and security in the group context, as key aspects of their empowerment. CONCLUSION: This review shows the usefulness of action research in any mental health nursing context, contributing to the improvement of care at both the individual and collective levels. IMPACT: This paper demonstrates the use of the action research method in the field of mental health nursing. Its use has improved the clinical practice of nurses as well as that of teams in both community and hospital settings, addressing issues of the person-centred approach to care and decision-making procedures.

Unraveling the Potential of miRNAs from CSCs as an Emerging Clinical Tool for Breast Cancer Diagnosis and Prognosis.

Breast cancer (BC) is the most diagnosed cancer in women and the second most common cancer globally. Significant advances in BC research have led to improved early detection and effective therapies. One of the key challenges in BC is the presence of BC stem cells (BCSCs). This small subpopulation within the tumor possesses unique characteristics, including tumor-initiating capabilities, contributes to treatment resistance, and plays a role in cancer recurrence and metastasis. In recent years, microRNAs (miRNAs) have emerged as potential regulators of BCSCs, which can modulate gene expression and influence cellular processes like BCSCs' self-renewal, differentiation, and tumor-promoting pathways. Understanding the miRNA signatures of BCSCs holds great promise for improving BC diagnosis and prognosis. By targeting BCSCs and their associated miRNAs, researchers aim to develop more effective and personalized treatment strategies that may offer better outcomes for BC patients, minimizing tumor recurrence and metastasis. In conclusion, the investigation of miRNAs as regulators of BCSCs opens new directions for advancing BC research through the use of bioinformatics and the development of innovative therapeutic approaches. This review summarizes the most recent and innovative studies and clinical trials on the role of BCSCs miRNAs as potential tools for early diagnosis, prognosis, and resistance.