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Enablers and barriers to community pharmacists’ readiness to implement deprescribing of inappropriate medications for older adults in Qatar

There is paucity of studies focused on the enablers and barriers to community pharmacists’ readiness to deprescribe inappropriate medications for older adults in developing settings. The current study assessed the enablers and barriers to community pharmacists’ readiness to implement deprescribing of inappropriate medications for older adults. A cross-sectional survey of 252 community pharmacists was conducted in Qatar with a pre-tested 24-item questionnaire developed with the theory of domain framework. Information about perceived enablers and barriers were elicited with a 5-point Likert-type scale. The response rate was 79.4% (200/252). The majority of the community pharmacists were females (54.5%), within the age range of 20–40 years (88.0%), had BSc / BPharm as the highest educational qualification (70.5%), were full-time employee (97.0%). The top-ranked enablers of community pharmacists’ readiness to implement deprescribing were exposure to CPD on the use of deprescribing toolkits and algorithm (66%), interprofessional collaboration with physicians (60.5%) and shared electronic patient record (59.5%), and improved remuneration / re-imbursement 58%). The top-ranked barriers were lack of access to patient records (70.5%), ineffective collaboration with physicians (66.5%), lack of time due to heavy workload (65%), regulatory framework that limit expansion of clinical roles (51%) and intense focus on sales target (49%). The top-ranked enablers of community pharmacists’ readiness to implement deprescribing were exposure to CPD on the use of deprescribing toolkits and algorithm, interprofessional collaboration with physicians and shared electronic patient record. These findings bode well for the implementation of community pharmacists-led deprescribing of inappropriate medications for older adults in Qatar. However, a number of critical barriers were identified, and these will require institutional, regulatory and organizational interventions to improve readiness.

RNA molecule rejuvenates ageing mice by restoring old cells

Computational analysis of the effect of a binding protein (RbpA) on the dynamics of Mycobacterium tuberculosis RNA polymerase assembly

Background RNA polymerase-binding protein A (RbpA) is an actinomycetes-specific protein crucial for the growth and survival of the pathogen Mycobacterium tuberculosis. Its role is essential and influences the transcription and antibiotic responses. However, the regulatory mechanisms underlying RbpA-mediated transcription remain unknown. In this study, we employed various computational techniques to investigate the role of RbpA in the formation and dynamics of the RNA polymerase complex. Results Our analysis reveals significant structural rearrangements in RNA polymerase happen upon interaction with RbpA. Hotspot residues, crucial amino acids in the RbpA-mediated transcriptional regulation, were identified through our examination. The study elucidates the dynamic behavior within the complex, providing insights into the flexibility and functional dynamics of the RbpA-RNA polymerase interaction. Notably, potential allosteric mechanisms, involving the interface of subunits α1 and α2 were uncovered, shedding light on how RbpA modulates transcriptional activity. Conclusions Finally, potential ligands meant for the α1–α2 binding site were identified through virtual screening. The outcomes of our computational study serve as a foundation for experimental investigations into inhibitors targeting the RbpA-regulated dynamics in RNA polymerase. Overall, this research contributes valuable information for understanding the intricate regulatory networks of RbpA in the context of transcription and suggests potential avenues for the development of RbpA-targeted therapeutics.

Trump’s science advisers: how they could influence his second presidency

An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection

Detecting low birth weight is crucial for early identification of at-risk pregnancies which are associated with significant neonatal and maternal morbidity and mortality risks. This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios. This method is particularly valuable in contexts where labeled data are scarce or labels for the anomaly class are not available, allowing for preliminary insights and detection that can inform further data labeling and more focused supervised learning efforts. We employed fourteen different anomaly detection algorithms and evaluated their performance using Area Under the Receiver Operating Characteristics (AUCROC) and Area Under the Precision-Recall Curve (AUCPR) metrics. Our experiments demonstrated that One Class Support Vector Machine (OCSVM) and Empirical-Cumulative-distribution-based Outlier Detection (ECOD) effectively identified anomalies across different birth weight categories. The OCSVM attained an AUCROC of 0.72 and an AUCPR of 0.0253 for extreme LBW detection, while the ECOD model showed competitive performance with an AUCPR of 0.045 for very low LBW cases. Additionally, a novel feature perturbation technique was introduced to enhance the interpretability of the anomaly detection models by providing insights into the relative importance of various prenatal features. The proposed interpretation methodology is validated by the clinician experts and reveals promise for early intervention strategies and improved neonatal care.

The effect of empathy on remote social connections via paired robots

A range of devices and technologies are available to mediate social connections between geographically distant people. Some of these methods exploit awareness information to enhance the connectedness of distant users. However, the effect of user traits on the experience of interpersonal communication through awareness systems remains unclear. In this study, we explored the relationship between user empathy and the experience of interpersonal communication mediated by a paired robot able to convey user awareness information. We conducted an experiment to facilitate communication among distant family members using the paired robot over the course of 1 week. Participants’ empathy level and their experience of the communication were assessed using questionnaires. The findings indicated that individuals with higher empathy can better interpret their partner’s situation and feelings from the awareness information.

Comprehensive analysis of ceRNA Networks in UCEC: Prognostic and therapeutic implications

Endometrial cancer (UCEC) is the most prevalent gynecological malignancy in high-income countries, and its incidence is rising globally. Although early-stage UCEC can be treated with surgery, advanced cases have a poor prognosis, highlighting the need for effective molecular biomarkers to improve diagnosis and prognosis. In this study, we analyzed mRNA and miRNA sequencing data from UCEC tissues and adjacent non-cancerous tissues from the TCGA database. Differential expression analysis was conducted using the DESeq2 package, identifying differentially expressed lncRNAs, miRNAs, and mRNAs (DElncRNAs, DEmiRNAs, and DEmRNAs). Key molecules were screened using LASSO regression, and a ceRNA network was constructed by predicting lncRNA-miRNA and miRNA-mRNA interaction, which were visualized with Cytoscape. Functional enrichment analysis elucidated the roles and mechanisms of the network. The prognostic potential of the identified RNAs was assessed through survival and Cox regression analyses, while methylation and immune infiltration analyses explored regulatory mechanisms and immune interactions. We identified a prognostic lncRNA-miRNA-mRNA ceRNA network in UCEC, centered on the CDKN2B-AS1-hsa-miR-497-5p-IGF2BP3 axis. Survival analyses confirmed the prognostic significance of this network, with univariate Cox regression demonstrating a strong association between its aberrant expression and overall prognosis in UCEC. However, multivariate Cox regression suggested that other clinical factors may modulate this relationship. Methylation analysis revealed low methylation levels of IGF2BP3, possibly contributing to its overexpression. Furthermore, immune infiltration studies highlighted significant correlations between CDKN2B-AS1, IGF2BP3, and multiple immune cell types, suggesting that this axis regulates the tumor immune microenvironment. These findings suggest that the CDKN2B-AS1-hsa-miR-497-5p-IGF2BP3 axis is a key regulatory element in UCEC and a potential therapeutic target.

Correction: Price tag of glaucoma care is minor compared with the total direct and indirect costs of glaucoma: Results from nationwide survey and register data

Prognostic value of immune biomarkers in melanoma loco-regional metastases

The prognosis for patients with melanoma loco-regional metastases is very heterogenous. Adjuvant PD-L1-inhibitors have improved clinical outcome for this patient group, but the prognostic impact of tumour PD-L1 expression and number of tumour infiltrating lymphocytes (TILs) is still largely unknown. Here, we investigated the impact on survival for CD3, CD8, FOXP3 and PD-L1 TIL counts and tumour PD-L1 expression in melanoma loco-regional metastases. In a patient series of loco-regional metastases from nodular melanomas (n = 78; n = 26 skin metastases, n = 52 lymph node metastases), expression of PD-L1 in tumour cells and the number of CD3, CD8, FOXP3 and PD-L1 positive TILs were determined by immunohistochemistry on tissue microarray (TMA) slides. Due to limited tumour tissue in the paraffin blocks, 67 of the 78 cases were included for tissue microarrays. Low FOXP3 TIL count and negative tumour PD-L1 expression (cut off 1%) were both significantly associated with reduced survival in lymph node metastases. Low FOXP3 TIL count was significantly associated with low CD8, CD3 and PD-L1 TIL counts. Negative tumour PD-L1 expression was significantly associated with low CD8 and PD-L1 TIL count, large lymph node metastasis tumour size and presence of necrosis in lymph node metastases. Our findings demonstrate for the first time the negative prognostic value of low FOXP3 TIL count and confirm a negative prognostic value of negative tumour PD-L1 expression in melanoma lymph node metastases.

Advancing ex vivo functional whole-organ prostate gland model for regeneration and drug screening

Estimating the trend of COVID-19 in Norway by combining multiple surveillance indicators

Estimating the trend of new infections was crucial for monitoring risk and for evaluating strategies and interventions during the COVID-19 pandemic. The pandemic revealed the utility of new data sources and highlighted challenges in interpreting surveillance indicators when changes in disease severity, testing practices or reporting occur. Our study aims to estimate the underlying trend in new COVID-19 infections by combining estimates of growth rates from all available surveillance indicators in Norway. We estimated growth rates by using a negative binomial regression method and aligned the growth rates in time to hospital admissions by maximising correlations. Using a meta-analysis framework, we calculated overall growth rates and reproduction numbers including assessments of the heterogeneity between indicators. We find that the estimated growth rates reached a maximum of 25% per day in March 2020, but afterwards they were between -10% and 10% per day. The correlations between the growth rates estimated from different indicators were between 0.5 and 1.0. Growth rates from indicators based on wastewater, panel and cohort data can give up to 14 days earlier signals of trends compared to hospital admissions, while indicators based on positive lab tests can give signals up to 7 days earlier. Combining estimates of growth rates from multiple surveillance indicators provides a useful description of the COVID-19 pandemic in Norway. This is a powerful technique for a holistic understanding of the trends of new COVID-19 infections and the technique can easily be adapted to new data sources and situations.

Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus

Abstract Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi’s biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy. The study identified the Teak biodiesel blend (TB20) with a 5 ml Elaeocarpus Ganitrus additive (TB20 + R5) as the optimal formulation, achieving the highest Brake Thermal Efficiency and reduced Brake-Specific Fuel Consumption. Desirability analysis further confirmed the blend’s superior performance and emissions characteristics, with a desirability rating of 0.9282. This work highlights the potential of hybrid optimization approaches for improving biodiesel performance and emissions without engine modifications, contributing to the advancement of sustainable energy practices in internal combustion engines.

Correction: Kidney damage on fertility and pregnancy: A Mendelian randomization

Robot-assisted PFNA surgery improves clinical outcomes in the treatment of unstable femoral intertrochanteric fractures in elderly patients compared with traditional PFNA surgery

Author Correction: Evolution of immune genes is associated with the Black Death

Semi-field studies on biochemical markers of honey bee workers (Apis mellifera) after exposure to pesticides and their mixtures

Due to the fact that many different pesticides are used in crop production and their residues can accumulate in the environment, bees are in contact with various pesticides at the same time. Most studies on their influence on honey bees focus on single substances in concentrations higher than those found in the environment. Our study assessed the chronic effects of commonly used pesticides and their mixtures on selected biochemical markers in worker bee hemolymph. Workers developed in the hive and were provisioned with to pesticides in concentrations corresponding to residues detected in pollen, honey, and/or nectar. Colonies were exposed daily to 0.5L for 7 days by feeding a sugar syrup containing a formulation of acetamiprid (250 ppb) (insecticide), glyphosate (7200 ppb) (herbicide), and tebuconazole (147 ppb) (fungicide) administered alone, in a binary or ternary mixture. Administered alone, acetamiprid significantly decreased the level of urea in the hemolymph of worker honey bees. Glyphosate did not affect significantly the level/activity of any of the biochemical markers. Tebuconazole caused changes in the levels of most of the studied biochemical markers. We found that tebuconazole, which as a fungicide is generally considered safe for bees, may be harmful and more research is required. The impact of fungicides is a crucial element of the assessment of threats to honey bees.

Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model

Developing and validating a HEalthCare NAvigation Competency (HECNAC) Scale for refugees in the United States

The complex healthcare system in the United States (US) poses significant challenges for people, particularly minorities such as refugees. Refugees often encounter additional layers of challenges to healthcare navigation due to unfamiliarity with the system, limited health literacy, and language barriers. Despite their challenges, it is difficult to identify the gaps as few tools exist to measure navigation competency among this population and many conventional tools assume English proficiency, making them inadequate for refugees and other immigrants. To address this gap, this study developed and validated a HEalthCare NAvigation Competency (HECNAC) Scale tailored to refugees’ needs. The scale development process followed three phases: domain identification through a literature review and stakeholder interviews (n = 15), content validation through the Delphi method (2 rounds, n = 12), and face validity assessment via cognitive interviews (2 rounds, n = 4). Based on a literature review and stakeholder interviews, the initial version of the scale was developed, including ten domains and 47 items. An introductory email concerning the scale and the Delphi process was subsequently sent to 21 eligible experts, including staff from refugee resettlement agencies, health care providers serving refugee communities, and refugees. Twelve experts completed the two rounds of the Delphi, resulting in a consensus on 39 items. After conducting cognitive interviews with 4 Afghan refugees, the scale was finalized with ten domains and 35 items. The finalized scale captures multifaceted aspects of healthcare navigation crucial for refugees, organized into domains such as health system knowledge, insurance, making an appointment, transportation, preparing for a visit, in the clinic, interpretation, medicine, medical bills, and preventive care. Overall, the HECNAC Scale represents a significant step towards understanding and assessing refugees’ competencies in navigating the US healthcare system. It has the potential to guide tailored interventions and standardized training curricula and ultimately mitigate persistent barriers faced by refugees in accessing healthcare services.

Author Correction: Binding affinity screening of polyphenolic compounds in Stachys affinis extract (SAE) for their potential antioxidant and anti-inflammatory effects

The assessment of routine health information system performance towards improvement of quality of reproductive, maternal, newborn, child and adolescent health services in Ondo and Ekiti States, Nigeria

Background Nigeria’s reproductive, maternal, newborn, child, and adolescent health indicators have remained unsatisfactory in the face of poor-quality healthcare services. Nigeria initiated the reproductive, maternal, newborn, child, and adolescent, elderly + nutrition (RMNCAEH+N) quality of care (QoC) agenda to address the challenge. The health management information system (HMIS) is integral to the agenda but there is sparse evidence on its performance so far. This study assessed the performance of routine HMIS for RMNCAEH+N QoC in Ondo and Ekiti States. Methods This paper described the review of health facility records and health facility survey components of a multi-component study which employed a mixed-method research design. Using the routine health information system performance diagnostic tool, service data captured for over one year were critically reviewed in randomly selected sample of 169 public health facilities (Ondo:117; Ekiti:52) and information was obtained from facility heads or designates. Performance of routine HMIS for RMNCAEH+N QoC in terms of data collection, data quality, and data use were analysed using univariate and bivariate statistics. Results Results show that 67.3% of health facilities in Ekiti and 88.9% of facilities in Ondo had all required HMIS tools for selected RMNCAEH+N services (p<0.001). Data accuracy was 70.1% for Ondo and 40.4% for Ekiti (p <0.001); 82.9% of facilities in Ondo and 44.2% in Ekiti had complete data (p <0.001); almost all facilities (Ondo: 99.1%; Ekiti: 96.2%, p = 0.224) demonstrated data consistency; and, 82.9% of facilities in Ondo and 94.2% of facilities in Ekiti demonstrated timeliness in data submission (p = 0.048). Also, 70.1% (Ondo) and 78% (Ekiti) of facilities had quality improvement (QI) teams (p = 0.338); 53.5% (Ondo) and 77.1% (Ekiti) of QI teams regularly extracted data, calculated, and visualised prioritized indicators (p = 0.007); while 72.1% (Ondo) and 79.2% (Ekiti) regularly reviewed data and used it to make QI decisions (p = 0.367). Conclusion Routine RMNCAEH+N QoC data management system in Ondo and Ekiti States vary in terms of the status of reporting forms, data quality, and data use for decision-making, and there were specific performance gaps. The routine RMNCAEH+N QoC data management system in Ondo and Ekiti States needs improvement and findings from this study can serve as the basis for evidence-based advocacy for the required efforts and investment toward improved performance.