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Awareness, knowledge and belief regarding bitter leaf use: A cross-sectional study in Nigeria
Background Vernonia amygdalina, also known as bitter leaf, is a plant that is widespread in Nigeria. Bitter leaf plant has several medicinal properties, and the plant is also widely used due to its various gastronomic applications. This study aimed to assess awareness, knowledge, and beliefs regarding bitter leaf use. Methods A cross-sectional study was undertaken in Nigeria. Paper-based questionnaires were administered to participants, and the data were analysed using Statistical Package for Social Sciences. Results Of the 500 questionnaires that were administered, a total of 401 copies were completed and returned, resulting in a response rate of 80.2%. About two-thirds (65%) of the study participants were females, whilst 35% were males. Almost all the participants (98%) had heard about bitter leaf, the total mean score for knowledge of bitter leaf use amongst the respondents was 4.80 ± 2.14 (Range 0–9). Using the Bloom cut off, only about 11.7% of the population had good knowledge and 27.2% had moderate knowledge regarding bitter leaf. However, more than three-quarters of the sample (79.6%) linked bitter leaf to its glucose lowering properties, towards optimal maintenance of blood sugar levels. The respondents’ sources of information on bitter leaf use were mainly from relatives (88%) and social media (19.9%). Statistically significant findings revealed stronger belief amongst females regarding the employment of bitter leaf as a weight loss intervention (p = 0.042). Conclusion Although most participants were familiar with the bitter leaf plant, only a few of them had adequate knowledge of its properties. Given its widespread use, a comprehensive understanding is imperative to prevent misuse. Findings from this study indicate that most people rely on informal sources for information about the plant, potentially leading to misconceptions regarding proper use. Consequently, evidence-based public education is needed to promote safe consumption and fully harness the plant’s nutritional and medicinal benefits.
Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks
Correction for Bonetti et al., Quantum oscillations in the hole-doped cuprates and the confinement of spinons
Discovery of novel targets for important human and plant fungal pathogens via an automated computational pipeline HitList
Fungi are a major threat to human health and agricultural productivity, causing 1.7 million human deaths and billions of dollars in crop losses and spoilage annually. While various antifungal compounds have been developed to combat these fungi in medical and agricultural settings, there are concerns that effectiveness is waning due to the emergence of acquired drug resistance and novel pathogens. Effectiveness is further hampered due to the limited number of modes of action for available antifungal compounds. To develop new strategies for the control and mitigation of fungal disease and spoilage, new antifungals are needed with novel fungal-specific protein targets that can overcome resistance, prevent host toxicity, and can target fungi that have no effective control measures. The increasing availability of complete genomes of pathogenic and spoilage fungi has enabled identification of novel protein targets essential for viability and not found in host plants or humans. In this study, an automated bioinformatics pipeline utilizing BLAST, Clustal Ω , and subtractive genomics was created and used to identify potential new targets for any combination of hosts and pathogens with available genomic or proteomic data. This pipeline called HitList allows in silico screening of thousands of possible targets. HitList was then used to generate a list of potential antifungal targets for the World Health Organization fungal priority pathogens list and the top 10 agricultural fungal pathogens. Known antifungal targets were found, validating the approach, and an additional eight novel protein targets were discovered that could be used for the rational design of antifungal compounds.
Design synthesis, characterization, molecular docking and antimicrobial evaluation of novel heterocycles with acrylonitrile and anthracene moieties
Abstract The synthon 3-(anthracen-9-yl)-2-cyanoacryloyl chloride 4 was produced and exploited in the creation of a wide variety of highly reactive heterocyclic compounds, by its interaction with diverse nitrogen nucleophiles. Using spectral and elemental analysis, the structures of each synthesized heterocycles were fully investigated. Ten of the thirteen novel heterocycles showed encouraging efficacy against antibiotic-resistant bacteria (MRSA). Among these, compounds 6, 7, 10, 13b, and 14 demonstrated the highest antibacterial activity, showing inhibition zones near 4 cm. However, molecular docking studies revealed varied binding affinities for Penicillin-Binding Protein 2a (PBP2a), a crucial target in MRSA resistance. Some compounds, such as 7, 10, and 14, displayed higher binding affinities and interaction stability within the PBP2a active site compared to the co-crystallized quinazolinone ligand. In contrast, compounds 6 and 13b exhibited lower docking scores but still showed substantial antimicrobial activity, with 6 showing the lowest MIC (9.7 μg/100 μL) and MBC (78.125 μg/100 μL) values. The docking analysis revealed key interactions, including hydrogen bonding and π-stacking, particularly with residues like Lys 273, Lys 316, and Arg 298, which were identified as interacting with the co-crystallized ligand within the crystal structure of PBP2a. These residues are essential for the enzymatic activity of PBP2a. These findings suggest that the synthesized compounds could serve as promising anti-MRSA agents, highlighting the importance of integrating molecular docking with biological assays to identify effective therapeutic candidates.
Correction for Lai et al., <i>CIRCADIAN CLOCK-ASSOCIATED 1</i> regulates ROS homeostasis and oxidative stress responses
Gut microbiota and its influence on the Gut-Brain axis in comparison with chemotherapy patients and cancer-free control data in Breast cancer—A computational perspective
Breast cancer (BC) continues to be a major cause of cancer-related illness and death among women worldwide. Traditional treatments include surgery, radiation, hormone therapy, and chemotherapy, but these approaches often face challenges due to variability in patient response and adverse effects. This study investigated the relationship between gut microbiome diversity, community composition, and pathway analysis in women undergoing chemotherapy for BC (During Treatment-DT) compared to cancer-free controls (CFC). Using 16S rRNA amplicon sequencing, the study assessed alpha and beta diversity. Results showed differences in microbiome composition between DT and CFC samples, with Firmicutes being highly abundant in both groups. Core microbiome and correlation analysis at the phylum and genus levels identified significant microbiota. Specifically, the abundance of genera such as Pseudomonas and Akkermansia decreased, while Ruminococcus and Allistipes increased, as determined by statistical and machine learning approaches. Disease associations were examined based on KO abundance, identifying links to conditions such as autism spectrum disorder, Clostridium difficile infection, chronic kidney disease, and multiple sclerosis. Key KEGG pathways enriched in DT and CFC groups included the two-component system, tyrosine metabolism, and the pentose phosphate pathway. Conversely, dysbiosis or the presence of pathogenic bacteria (Ruminococcus) associated with the SOX8 gene could lead to chemoresistance, altered metabolic pathways, and increased toxicity. These findings underscore the potential implications for treatment outcomes and personalized medicine.
Placental whole transcriptome expression profile in patients with early-onset, late-onset preeclampsia and gestational diabetes mellitus
Influence of near-fault ground motions’ characteristics on the control performance of tuned viscous mass damper systems
Near-fault ground motions, characterized by pronounced pulse and forward-directivity effects, present significant challenges to dampers’ effective performance in controlling seismic activity. This study provides an in-depth analysis of the influence of near-fault pulse-type ground motions’ characteristics on the concentration of peak responses in multi-story steel frame structures, the distribution patterns of weak layer locations, and Tuned Viscous Mass Dampers’ (TVMDs) effective control. The results indicate that near-fault ground motions’ pulse effect, forward-directivity effect, and spectral coefficients affect the distribution of maximum inter-story drift ratios along the building height significantly. Notably, the forward-directivity effect amplifies structural responses and diminishes TVMDs control’s effectiveness. In addition, ground motions with smaller spectral coefficients lead to larger inter-story drift responses. The effectiveness of TVMDs’ “damping enhancement” effect is determined jointly by their mass ratio and the pulse period of the ground motion records. This study provides important theoretical foundation for the seismic design of multi-story steel frame structures and the earthquake-reduction design using TVMDs under near-fault ground motions.
Quantum neural networks with data re-uploading for urban traffic time series forecasting
Evaluating the Safe Steps for De-escalation: A protocol for a mixed concurrent control study in acute mental health units
There is a shared goal of organising reform efforts in mental health services to eliminate restrictive practices and improve therapeutic relationships. However, evidence on high-quality, culturally safe, co-produced, and strengths-based interventions and evaluations is limited, especially for complex interventions centred on therapeutic responding. In response, a multi-centre, mixed concurrent control study is underway to evaluate the Safe Steps for De-escalation, a multi-component intervention focused on a structured framework for mental health nurses’ therapeutic responses to emotional distress and interpersonal conflict in acute adult mental health inpatient units. The aims of this evaluation were: 1) What is the effectiveness of Safe Steps in reducing restrictive practice events and duration and physical injuries? 2) Does Safe Steps improve people’s service experience, perceived staff action towards violence prevention, and nurses’ professional quality of life and emotionally intelligent workplace behaviours? 3) What factors influence the successful implementation of Safe Steps? It is hypothesised that: a) intervention sites will demonstrate more significant decreases in restrictive practice events and duration and physical injuries, compared to within-group baseline and control group, and b) measures of people’s experiences and perceptions and nurses’ outcomes and behaviours will improve, compared to within-group baseline. Safe Steps has three components: i) a structured de-escalation framework, ii) an in-person and online training programme, and iii) a regular conduct of strengths-based, data-informed restrictive practice review meetings. The control group will be usual care. Other outcomes include nursing intervention clusters, their associations with various outcomes, and factors influencing intervention implementation and restrictive practice use. There is no randomisation, but inverse probability weighting will be applied. The sample sizes were determined through power analyses and supporting evidence on saturation in qualitative research. Various quantitative and qualitative data treatments and measures will be undertaken to minimise research biases.
Correction: Prefabricated building construction in materialization phase as catalysts for hotel low-carbon transitions via hybrid computational visualization algorithms
Defining and measuring acceptability of surgical interventions: A scoping review
Background Acceptability, in the context of healthcare interventions is a frequently used term, including in evaluations of surgical interventions. This reflects the importance of the concept to all stakeholders and significance to designing, implementing and evaluating interventions. Despite this, definitions and measurement of acceptability are not standardised, and acceptability is often poorly conceptualised. The aim of this scoping review was to identify how studies define, measure and report the acceptability of a surgical intervention. Methods A scoping review was conducted adhering to the Joanna Briggs Institute guidelines and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. A comprehensive search of MEDLINE; Embase:APA PsycInfo; EBHealth-KSR Evidence; Cochrane Central Register of Controlled Trials; International HTA database; ClinicalTrials.gov and WHO International Clinical Trials Registry Platform was conducted for the period January 2000 to November 2023. No language limits were applied. Results Sixty-seven studies from 25 countries were included. The majority of studies (n = 60; 90%) did not provide a definition of acceptability. Various methods were used to collect data on acceptability, most frequently a questionnaire (n = 36; 54%), followed by qualitative interviews (n = 16; 24%). Thirty-three studies (49%) reported acceptability of the surgical intervention received to patients, nine (13%) reported hypothetical acceptability of the surgical intervention to patients, four (6%) reported acceptability to both patients and surgeons, and four studies (6%) the acceptability to surgeons alone. Conclusion Studies assessing acceptability of a surgical intervention tended not to provide a definition of acceptability and demonstrated a lack of clarity in the use of acceptability in the context of surgical interventions. There was substantial variability in how and when acceptability was measured and from which perspective. Further research is required to explore the most appropriate approaches to address variability and promote a more consistent conceptualisation and accurate measurement of acceptability in evaluations of surgical interventions.
A multi-analytical approach to unveil Early Bronze Age population dynamics and metal exchange networks at the foot of Mount Vesuvius
Neuromechanisms and subjective experiences during human-dog interactions: Assessing motivation and mental state in a randomized, controlled trial
Animal Assisted Interventions (AAIs) have been shown to have several effects in humans but the underlying cerebral mechanisms are still widely unknown. This research explored the neurological aspects of human–animal interactions. Specifically, we focused on frontal alpha asymmetry (FAA), a feature indicating differences in alpha power between the left and right frontal cortex, which is recognized as a correlate of approach motivation and positive affect. Twenty-nine healthy adults participated in this study, in which we used electroencephalography to measure their brain activity. The study comprised five phases: baseline measurements, interaction with a real dog, interaction with a replica dog, interaction with a plant, and a neutral phase. Participants had both physical and visual contact with the real dog, the replica and the plant, and the procedure was repeated three times for each participant. We also assessed participants’ subjective experiences of mental states and intrinsic motivation through the Multidimensional Well-Being and the Intrinsic Motivation Inventory questionnaires. The objective measurements of motivation and positive affect through FAA did not show a significant difference between interactions with a real dog and control conditions, but the subjective assessments differed. Participants reported significantly higher motivation and a more positive state of mind after interacting with a real dog compared to the control conditions. These results could be considered in therapeutic settings when determining whether to incorporate an animal into a treatment plan. In summary, this study highlights the complexity of human–animal interactions (HAI) and shows an intricate interplay between objective and subjective measurements. Our findings emphasize the importance of considering both neural markers and subjective experiences for understanding the nuanced mechanisms involved in the meaningful connections humans have with animals.
Reversible image steganography based on residual structure and attention mechanism
Assessing generalizability of a dengue classifier across multiple datasets
Early diagnosis of dengue fever is important for individual treatment and monitoring disease prevalence in the population. To assist diagnosis, previous studies have proposed classification models to detect dengue from symptoms and clinical measurements. However, there has been little exploration of whether existing models can be used to make predictions for new populations. In this study, we assess the generalizability of dengue classification models to new datasets. We trained logistic regression models on five publicly available dengue datasets from previous studies, using three explanatory variables identified as important in prior work: age, white blood cell count, and platelet count. These five datasets were collected at different times in different locations, with a variety of disease rates and patient ages. A model was trained on each dataset, and predictive performance and model calibration was evaluated on both the original (training) dataset, and the other (test) datasets from different studies. By comparing the model’s performance when applied to data from a new location, we are able to assess the model’s generalizability to new populations. We further compared performance with larger models and other classification methods. In-sample area under the receiver operating characteristic curve (AUC) values for the logistic regression models ranged from 0.74 to 0.89, while out-of-sample AUCs ranged from 0.55 to 0.89. Matching age ranges in training/test datasets increased AUC values and balanced the sensitivity and specificity. Adjusting the predicted probabilities to account for differences in dengue prevalence improved calibration in 20/28 training-test pairs. Results were similar when other explanatory variables were included and when other classification methods (decision trees and support vector machines) were used. The in-sample performance of the logistic regression model was consistent with previous dengue classifiers, suggesting the chosen model is a good choice in a variety of settings and has decent overall performance. However, adjustments are required to make predictions on new datasets. Practitioners can use existing dengue classifiers in new settings but should be careful with different patient ages and disease rates.
Evaluation of automatic cell free DNA extraction metrics using different blood collection tubes
Abstract Liquid biopsies and cell-free DNA (cfDNA) analysis are used in numerous clinical applications. The amount of cfDNA is generally limited and many approaches require assessment of individual molecules. Optimized pre-analytical steps are therefore fundamental for accurate interpretation. Here, we established an automated extraction approach providing cfDNA of high yield and quality. We analyzed 649 blood plasma samples collected from 23 healthy individuals and assessed the performance of four different blood collection tubes, time between sampling and plasma isolation and number of centrifugation steps. CfDNA was quantified by fluorometric analysis and quantitative polymerase chain reaction, while contaminating cellular DNA was assessed by quantitative polymerase chain reaction and parallel capillary electrophoresis. Data showed that cfDNA yield depends on both choice of blood collection tube and time between sampling and plasma isolation. Plasma isolated directly after sampling in K2EDTA tubes and plasma isolated within one week from preservative Streck tubes provided high cfDNA yield. We demonstrate that contaminating cellular DNA may be challenging to detect and that quantitative polymerase chain reaction and parallel capillary electrophoresis provide complementary information. In summary, reliable cfDNA analysis requires optimized experimental workflows, where the effects of pre-analytical factors should be considered in study designs and in clinical implementations.
Research and analysis of an enhanced genetic algorithm identification method based on the LuGre model
Nonlinear friction in high-precision, ultra-low-speed servo systems severely degrades performance, causing low-speed crawling, static errors, and limit-cycle oscillations. This study introduces the LuGre friction model to describe these phenomena mathematically and proposes an improved genetic algorithm (GA) for precise parameter identification. Simulations demonstrate that LuGre-based feedforward compensation outperforms conventional proportional-integral-derivative (PID) control, effectively mitigating speed tracking errors and enhancing both speed and position accuracy. Experimental validation on a linear motor platform confirms the method’s efficacy, achieving a 25.1% improvement in tracking accuracy. The results highlight the practical relevance of this approach for precision servo systems. This work has achieved a practical identification framework for LuGre parameters, combining GA optimization with transient/steady-state data, feedforward compensation that directly injects estimated friction forces, bypassing feedback delays and experimental verification of the method’s industrial applicability.