Previous Knowledge Research Articles

Saving lives: assessing knowledge and attitudes toward basic life support among teachers and parents in Tabuk City

BackgroundAssessing the current awareness levels of teachers and parents about basic life support is essential for identifying training gaps, obstacles to acquiring basic life support skills, and areas that require improvement.AimThis study aimed to evaluate the knowledge, skills, and attitudes concerning basic life support among kindergarten and primary school teachers and parents in Tabuk City, Saudi Arabia.MethodsThis cross-sectional survey was conducted in Tabuk City, Saudi Arabia, involving teachers and parents of children enrolled in kindergartens or elementary schools, aged 18 years or older. Data were collected using a previously validated, self-administered Arabic questionnaire, distributed through an online link via social media Apps. The total knowledge and skills score was then calculated.ResultsMost participants (70.3%) were parents, with ages ranging from 18 to 70 years and an average age of 35.1 ± 10.91 years. Female participants (59.7%) outnumbered males. Over half (53.3%) had no prior cardiopulmonary resuscitation (CPR) training, and 55.6% had never witnessed CPR being performed. Approximately half (50.3%) demonstrated a fair level of knowledge, while 44.6% had poor levels, and only 5.1% achieved a good level. A significant association was found between previous CPR training and knowledge and skill levels (p < 0.001). Additionally, the majority (81.0%) expressed interest in receiving further CPR training.ConclusionThis study identified substantial gaps in basic life support awareness and training among kindergarten and primary school teachers and parents in Tabuk City, Saudi Arabia. Most participants exhibited either poor or average levels of knowledge, with critical deficiencies in CPR skills. Prior exposure to CPR training was significantly associated with higher knowledge and skill scores, underscoring the importance of accessible and structured training programs. The main barrier to training was a lack of awareness of available CPR courses. Nevertheless, the high level of expressed interest in future training highlights a strong potential for improving BLS competency through targeted educational initiatives.

Comparison between two Canadian Provinces on technology use for social interaction by older adults: comparative cross-sectional survey study

BackgroundThe COVID-19 pandemic prompted most people to embrace different approaches to their daily activities. Due to various measures to slow transmission in many jurisdictions, older adults were particularly impacted by measures restricting interactions. Our previous cross-sectional study identified barriers and facilitators for technology use for social interaction among older adults in British Columbia (BC), Canada. To investigate whether regional differences exist, the same survey from the previous study was conducted in Saskatchewan (SK), Canada during the same time. We also explored whether education and income levels were associated with older adults’ social technology usage.MethodsThe cross-sectional survey was conducted through random-digit dialing to older adults who were 65 or older in BC and SK. Data were analyzed through the Statistical Package for the Social Sciences (SPSS) software (IBM corporation) and Microsoft Excel. Thematic analysis was performed on the survey's responses to open-ended questions.ResultsThere were 806 participants, 400 from BC and 406 from SK. Education levels were associated with awareness of using technology for social interaction for both BC and SK while only SK had an association between new technology use and education levels. Similarly, income levels were also associated with awareness of technology use for social interaction for both provinces while only BC had an association between income levels to uptake of new technology. From the previous and current study, the barriers identified for technology use for social interactions in BC and SK were lack of interest, access (including financial issues) and physical limitations. SK participants identified perceived low self-efficacy as an additional barrier. For facilitators, BC and SK participants identified current and previous technology knowledge, help from others and motivation to keep social connections. Access to technology was unique to BC while better technology was unique to SK.ConclusionsOur study suggests that when older adults have access to resources to support their technology use, they will use them more, possibly enhancing their capacity for technology use. Future studies with more diverse populations around Canada may identify varying factors for older adults’ technology use and regional variations in how technology is used for social interaction.

Different CMV-specific effector T cell subtypes are associated with age, CMV serostatus, and increased systolic blood pressure

BackgroundCytomegalovirus (CMV) infection is one of the most common infections in humans, and CMV antigens are the major drivers of repetitive T-cell stimulation as a part of a well-adapted immune response in immunocompetent individuals. With higher age, the recurrent clonal expansion of CMV-specific T cells results in high frequencies of CMV-specific effector T cells. Further on, CMV seropositivity has been linked to an increased risk of developing cardiovascular diseases (CVD). Here we investigated the frequency and phenotype of CMV-specific T cells in the circulation of a population cohort of 650 individuals focusing on the age group over 60 years. Circulating immune cells of individuals carrying the HLA-A*02 allele were investigated (n = 302) applying MHC class I tetramers.ResultsWe add to previous knowledge by showing that the frequency of CMVpp65-specific CD8+ T cells is associated with the total percentage and absolute counts of CD8+ and CD4+CD8+ double-positive T cells within leukocytes, and further with systolic blood pressure (SBP) and history of CVD. An investigation into the differentiation status of CMV-specific T cells revealed an association of higher age and increased frequencies of both TEM and CD27-expressing TEMRA cells. In contrast, higher CMV-IgG titers were found to be associated with TEM and CD27− TEMRA cell frequencies. SBP significantly correlated with CMV-specific effector CD8+ T cells, which was mostly reflected by CD27− TEMRA cells.ConclusionsWithin the circulating CMV-specific T cell population, different effector T-cell subtypes were associated with age, serostatus and SBP. This suggests that it is not age or infection per se that render CMV-positive individuals susceptible to CVD, but rather the cellular immune response to CMV. Detailed immunophenotyping may identify individuals whose immune systems are strongly influenced by the response to CMV, leading to health consequences and impairing healthy aging.

Design of Clofazimine-Loaded Lipid Nanoparticles Using Smart Pharmaceutical Technology Approaches

Background/Objectives: Clofazimine (CFZ) is a versatile antimicrobial active against several bacterial species, although its reduced aqueous solubility and the occurrence of side effects limit its use. Nanostructured lipid carriers (NLCs) constitute an interesting approach to increase drug bioavailability and safety. However, the development of nanoparticle-based formulations is challenging. In the present work, a combination of smart pharmaceutical technology approaches was proposed to develop CFZ-loaded NLCs, taking advantage of previous knowledge on NLCs screening. Methods: A design space previously established using Artificial Intelligence (AI) tools was applied to develop CFZ-loaded NLC formulations. After formulation characterization, Neurofuzzy Logic (NFL) and in silico docking simulations were employed to enhance the understanding of lipid nanocarriers. Then, the performance of formulations designed following NFL guidelines was characterized in terms of biocompatibility, using murine fibroblasts, and antimicrobial activity against several strains of Staphylococcus aureus. Results: The followed approach enabled CFZ-loaded NLC formulations with optimal properties, including small size and high antimicrobial payload. NFL was useful to investigate the existing interactions between NLC components and homogenization conditions, that influence CFZ-loaded NLCs’ final properties. Also, in silico docking simulations were successfully applied to examine interactions and affinity between the drug and the lipid matrix components. Finally, the designed CFZ-loaded formulations demonstrated suitable biocompatibility, together with antimicrobial activity. Conclusions: The implementation of smart strategies during nanoparticle-based therapeutics development, such as those described in this manuscript, would enable the more efficient design of new systems for suitable antimicrobial delivery.

The Therapeutic Potential of TGF-β as a Target for Multiple Chronic Diseases: A Comprehensive Review of Pharmacologically Approved Drugs and Investigational Agents

Background: Noncommunicable chronic diseases account for the highest number of mortalities across the globe and are responsible as the greatest contributor to medical healthcare expenses. To create new medicines to fight these diseases, we need to fully understand the pathological mechanisms behind them in addition to the usual therapeutic targets. TGF-β has become a promising target for therapy to help a number of fatal diseases, and it may be possible to challenge it therapeutically by either increasing its activity or decreasing it. Objective: The present review aims to highlight the therapeutic importance of TGF-β as a potent target to cure multiple chronic diseases, such as cardiovascular disorders and malignant tumors. It also mentions pharmacologically approved drugs as well as drugs that are currently under investigation. Methods: Our approach entailed a comprehensive literature review employing keywords such as “TGF-β signaling pathway,” “myocardial fibrosis,” and “neurological disorders.” We sourced pertinent information from reputable databases, including PubMed, Scopus, and Elsevier. The TGF-β signaling pathway is what makes cancer grow and spread, as well as fibrotic proliferation in many organs, including the lungs, heart, kidneys, and liver. In such diseases, enhanced signaling is implicated in the progression. However, in the case of nervous disorders such as Parkinson’s disease and Alzheimer’s disease, TGF-β signaling is found to be hampered. Both augmentation and inhibition of TGF-β are proven to be useful as therapeutic targets to counter these diseases. Conclusion: This review aims to provide an in-depth analysis of the historical development of research on TGF-β and the molecular mechanisms that underlie its biosynthesis, activation, and signaling transmission. Our objective is to offer a comprehensive and systematic understanding of TGF-β signaling, building on previous knowledge and recent updates, and to encourage further research in this area.

Immunology of the Bovine Mammary Gland: Advances in Recent Years.

Immunology of the Bovine Mammary Gland: Advances in Recent Years.

Fostering mathematical thinking through a computer algebra system in a differential equation course

This study explores the effects of a computer algebra system on students’ mathematical thinking. Mathematical thinking is identified with mathematical thinking powers and structures. We define mathematical thinking as students’ capacity to specialize and generalize their previous knowledge to solve new mathematical problems. The study was conducted in three phases: a preliminary study, a teaching experiment (main study), and task-based interviews (follow-up study). In the first phase, we intended to get to know students’ levels of mathematical thinking; the second phase sought to promote students’ mathematical thinking; and the final phase was designed to help us identify the enhancement of students’ usage of their mathematical thinking powers. A test was conducted at the preliminary study, a teaching experiment was run at the main study, and task-based interviews, like those in the main study, were conducted in the follow up phase. The main study’s participants were part of an undergraduate differential equations class in Malaysia. The worksheets used in the main and follow up studies were designed by the researchers, based on the instrumental genesis, prompts, and questions to be used in the teaching experiment sessions. Qualitative data analyses showed that using a computer algebra system for learning differential equations had a positive impact on the development, identification, and usage of students’ mathematical thinking. Moreover, it was revealed that the students applied specializing powers, imagining, expressing, changing, varying, comparing, sorting, organizing, and checking the calculation in general to make sense of mathematical structures. The findings could be incorporated not only in the mathematics curriculum at the tertiary level but could also be extended to k-12 schools.

Enhancing chronic wound assessment through agreement analysis and tissue segmentation

Accurate monitoring of chronic wound progression is crucial for assessing healing dynamics. However, the current manual process of tissue segmentation and quantification, which is an indicator of the healing progress, is time-consuming and subject to variability, so automated methods that can effectively monitor wound healing are required. In this work, inter-rater agreement analyses were conducted to evaluate the consistency of manual annotations performed by multiple experts and an automated methodology for tissue segmentation leveraging advanced deep learning techniques is proposed. For this, the convolutional neural network DeepLabV3-R50 and a transformer-based approach (SegFormer-B0) were explored. Furthermore, the potential of transferring knowledge from open wound segmentation models trained on different available datasets and fine-tuning them for this specific task was investigated. The tissue segmentation model is integrated into a framework that combines a wound and reference marker detection model with a wound segmentation model to refine the predicted tissue masks. The results show the benefits of employing previous knowledge gained from simpler tasks within the same domain, as well as the efficacy of post-processing operations. The top-performing tissue segmentation model, DeepLabV3-R50, achieved an overall mean Intersection over Union of 62.95% and a mean Dice score of 76.82% for the three analysed tissues on the Wounds dataset, when assessed independently. Considering the complete framework, the same model returns a mean Intersection over Union of 59.67% and a mean Dice of 74.38%, resulting in mean absolute errors of 14.33%, 14.31% and 8.84% for granulation, slough and eschar proportion estimation, respectively. Moreover, the obtained inter-rater agreement scores still emphasize the inherent complexity of the task, as even experienced healthcare professionals may differ in delineating tissue boundaries. Given the proven intricacy of tissue characterisation and the promising results that were achieved, the proposed pipeline contributes to streamline the tissue segmentation and quantification task, leading to the automation of the wound bed characterisation process and enhancing consistency and efficiency in wound healing.

ANALYSIS OF WOMEN’S ADOPTION AND UTILIZATION OF SELECTED NCAM AGRO-PROCESSING EQUIPMENT

The study examined the level of awareness, perception and utilization of NCAM agro-processing equipment by women agro-processors. A cross-section of 76 women agro-processors drown from across the six geo-political zones of the country was used for the study. A well-structured questionnaire was used to collect data for the study and analysed using both descriptive and inferential statistics respectively. Result from the analysis indicated that there was generally a very low awareness and level of exposure to agro-processing equipment among the respondents with only 20% exposure rate and just 18% awareness of NCAM technologies among the women agro-processors. The result of the correlation analysis showed that, there exist a significant and positive relationship between the practice of mechanized agriculture, previous operational knowledge of mechanized processing machines and interaction with NCAM Equipment, highlighting the significant effect of exposure and hands-on practical experience in promoting the adoption of machines. There was a very high level of participant’s engagement with cassava and oil palm processing equipment, the most engaged includes cassava peelers, mechanical graters, and garri fryers being the most used and rated the easiest to operate among all the machines. In contrast, there was a lower level of use with the melon and oil palm processing equipment demonstrating the need for strategic awareness campaigns among women agro-processors. Further-more, the study underscore the significance of gender-responsive technology designs and the need for continuous capacity building in the promotion of agro-processing equipment, in order to drive high rate of adoption among the agro-processors thereby, enhancing productivity and, decreasing drudgery as well as increasing rural lively-hood suppor

Characterizing the trophic ecology of herbivorous coral reef fishes using stable isotope and fatty acid biomarkers

Understanding the trophic ecology of herbivorous and detritivorous fishes is essential for evaluating their ecological roles in coral reef ecosystems. In this study, we combined bulk stable isotope (δ15N and δ13C) and fatty acid analyses to investigate trophic partitioning and dietary resource use among herbivorous and detritivorous fishes from the Great Barrier Reef, Australia. Isotopic niches and fatty acid profiles confirmed significant trophic partitioning among algivores, detritivorous surgeonfishes, and parrotfishes. We also applied mixing models based on these ecological tracers to quantify the contributions of basal dietary sources to the fish. Our findings further support previous dietary knowledge for several species, including algivorous acanthurids, kyphosid chubs, and the rabbitfish Siganus doliatus. However, they also highlight trophic niche specializations within these groups, particularly in Naso unicornis, which assimilates substantial dietary protein from epiphytic cyanobacteria despite a macroalgal diet, and in the detritivorous Ctenochaetus striatus, which exhibited isotopic similarities to parrotfishes but differed in fatty acid composition, likely due to a higher intake of diatoms. Additionally, our analyses reinforce the distinctive dietary composition of parrotfishes, emphasizing the complexity of their feeding biology, in which microscopic photoautotrophs such as cyanobacteria and dinoflagellates play a key dietary role that has often been overlooked in previous studies on their nutritional ecology. Furthermore, these findings underscore the usefulness of multi-tracer approaches in refining our understanding of coral reef fish trophic ecology.

Progress in microbial photoelectrotrophic denitrification

Microbial denitrification is a major pathway for nitrogen removal from water bodies. However, denitrification is often difficult to continue when there is a lack of microbially available organic matter in the water body to serve as electron donors. In recent years, studies have shown that some denitrifying bacteria can directly utilize photoelectrons generated by sunlight-excited semiconductor minerals or natural organic matter for denitrification without the need for bioavailable organic matter as electron donors. This process is defined as microbial photoelectrotrophic denitrification. The discovery of microbial photoelectrotrophic denitrification phenomenon reshapes the previous knowledge about the chemoheterotrophic mode of denitrifying bacteria and broadens the pathway of nitrogen removal by the new photoelectrotrophic metabolism, which is of great significance to our understanding and exploration of sunlight-driven nitrogen cycling process. In this paper, we comprehensively sort out the existing research reports in the field of microbial photoelectrotrophic denitrification, systematically summarize the principle and the current research progress of microbial photoelectrotrophic denitrification, deeply analyze the problems and challenges faced by this technology, and make an outlook on the future research directions and application prospects of this technology, providing a reference for the further research and application of this technology.

A DNA-based checklist of the stipitate hydnoids (Bankeraceae s.l.) of Czechia.

Based on a long tradition of field research aimed at this group, stipitate hydnoid fungi belong to the best explored macromycetes in Czechia. However, following groundbreaking taxonomic changes emerging from recent molecular studies, previous knowledge of particular taxa and their features has been questioned. To resolve this issue, selected specimens of Czech hydnoids were sequenced to confirm their identity. Generally, the presence of 39 taxa of Boletopsis, Hydnellum, Phellodon, and Sarcodon in the Czech Republic was confirmed, including the recently described species Boletopsis mediterraneensis, Hydnellum bomiense, H. fagiscabrosum, H. rubidofuscum, Phellodon aquiloniniger, P. castaneoleucus, and two so far undescribed taxa of Boletopsis and Hydnellum, as well as the recently reported Hydnellum gracilipes, H. illudens, Phellodon secretus and Sarcodon quercinofibulatus. Ecological requirements of newly delimited species are specified based on collection data from Czech localities, and GlobalFungi data are presented to supplement the current knowledge of the global distribution of particular species. Taxonomic issues, identification limits in species complexes, ecology, and threats to the species are discussed in relevant cases. Several more species, documented from or probably present in Czechia but not sequenced yet, are commented as well.

The cascading effect of fishing down marine top predators in the northeastern Mediterranean Sea

Both overexploitation and sea warming have been linked with smaller observed somatic sizes of fish and invertebrate species over time. Overfishing large predators has also been shown to cause cascading effects across food webs. Here, we focused on the Aegean Sea, a major fishing ground of the eastern Mediterranean and a warming hotspot, to investigate changes in the mean size of commercial marine fishes and invertebrates (as approximated by the mean trophic level of the catch), as well as any related top-down cascading effects on the food web or sea warming effects. Our analyses revealed a steady decline of the overall mean weighted trophic level of the Aegean catch from 1982 to 2020, indicating the overexploitation of large, high trophic level, predatory organisms that led to increases in intermediate trophic levels, and subsequent declines in low trophic level species. Any individual or additive effects of overexploitation and climate change on marine populations may affect the structure of ecosystems in unpredictable ways, jeopardizing the health of valuable marine resources and the sustainability of fishing operations. Analyzing historical local patterns may inform future management advice that should aim at using previous knowledge to mitigate the negative effects of anthropogenic pressures on the natural environment.

Robots reading recipes: large language models as translators between humans and machines

Abstract Large Language Models (LLMs) are a type of machine learning model trained on vast amounts of natural language that have demonstrated novel capabilities in tasks such as text prediction and generation. These tasks allow LLMs to be remarkably suited for understanding the semantics of natural language, which in turn enables applications such as planning real world tasks, writing code for computers, and translating between human languages. Even though LLMs could provide more flexibility in interpreting user requests and have shown to possess some commonsense knowledge, their capabilities for translating natural language instructions into code to control robot actions is only starting to be explored. More specifically, in this paper we are interested in the control of robots tasked with preparing cocktails. Within this context, it is assumed that the LLM has access to a repository of well-formatted recipes. This means that each recipe is written according to the following layout: a list of ingredients, then a subsequent description of how to prepare and mix the various items. Moreover, a set of low-level modules responsible for robot manipulation and vision-related tasks is also provided to the LLM in the shape of an application programming interface (API). Consequently, the main focus of the LLM is on generating a sequence of calls to the API, along with the right parameters, to produce the cocktail requested by users in natural language. Here, we show that it is feasible for LLMs to perform this type of translation on a small number of custom modules, and that certain techniques provide a measurable benefit to the accuracy and consistency of this task without fine-tuning. We found in particular that the use of an ensemble-voting strategy, where multiple trials are repeated and the most common answer is selected, increases accuracy to a certain extent. In addition, there is moderate support for the use of natural language parsing to adjust the prompt of the LLM prior to translation. Lastly, building on previous knowledge we also provide a set of guidelines to help design prompts to improve the accuracy of the resulting sequence of actions. In general, these results suggest that while LLMs can be used as translators of robot instructions, they are best applied in conjunction with these other strategies. The impact of these findings could influence future robotics development, as it provides directions for implementing LLMs more effectively and broadening the accessibility of robotic control to users without an extensive software background.

Strategy for Visual Measurement of Power Quality Based on Higher-Order Statistics and Exploratory Big Data Analysis

This article proposes a strategy for the visual characterization of power quality in big data analysis contexts, culminating in the development of a visualization tool based on higher-order statistics, which exhibits an efficiency between 83.33% and 100% in detecting 50 Hz synthetic and real-life simple and hybrid events, showing its significant potential for real-world applications marked by non-linear loads and non-Gaussian behaviors and surpassing the detection of traditional tools such as boxplot by up to 50%. Efficient energy management is closely accompanied by an optimum energy data management (EDM). It implies the acquisition, analysis, and interpretation of data to make decisions regarding the best energy usage with subsequent cost reductions. Through a study of indicators, including higher-order statistics, crest factor, SNR and THD, the article establishes nominal values and behavioral patterns, expanding the previous knowledge of these parameters. The indicators are presented as vertices in a radar-type charting tool, providing a multidimensional spatial visualization from individual indices that allows the behavioral pattern associated with each type of disturbance to be characterized combined with a decision tree. In addition, boxplots reflecting data processing are included, which facilitates the comparison and discussion of both visualization instruments: radar chart and boxplot.

Probing dynamic oxygen exchange for hydrogen production with operando neutron diffraction

Abstract A chemical looping process exploiting the variable oxygen content of ABO3−δ perovskite materials can achieve super-equilibrium conversions of societally important reactions such as the water–gas shift reaction (CO + H2O ⇋ CO2 + H2). The approach relies on an evolving oxygen chemical potential gradient within a reactor bed. Here we show that the oxygen-sensitivity of operando neutron powder diffraction experiments can reveal how the reactor functions with high spatial- (≲1 cm) and time- (≲30 s) resolution. We show how this operando method enables rapid testing of new high-capacity bed materials without previous knowledge of their thermodynamic properties, and gives direct information on their long-term stability. We introduce how this memory reactor concept can also be applied to the steam methane reforming reaction (CH4 + H2O ⇋ CO + 3H2), the key preprocess to the water–gas shift reaction in H2 production.

I KNOW AND I LEARN! PRE-SERVICE TEACHERS REFLECT ON KWL IN TAIWAN

Effective reading strategies for pre-service teachers are of critical importance in this day and age, amidst the changes taking place in English as a Foreign Language (EFL) classes. This study highlights the KWL (Know, Want to Know, Learned) strategy as a powerful approach to boost comprehension skills of prospective teachers. This qualitative case study conducted at National Ping Tung University (NPTU) in Taiwan draws from semi-structured interview data with master’s students who encountered KWL in their reading courses. Thematic analysis was employed not only to evaluate the effectiveness of KWL in the case but also to explore reading instruction in its more general aspects. The study's findings suggest KWL promotes enhanced engagement, reflective thought, and the integration of previous knowledge with new information. Still, participants noted some difficulties—particularly around setting clearly defined learning objectives and synthesizing essential information. Within these constraints, KWL proves to be a useful metacognitive framework in teacher education. The research argued for the application of structured approaches such as KWL to develop sophisticated reading strategies and to stimulate active learning. Subsequent research could explore the use of digital tools to broaden the application of KWL in diverse cultural and linguistic classroom settings.

How to Monitor Growth and Identify Developmental Stages of Maize (Zea mays).

Properly characterizing the stages of corn growth is critical to conducting successful experiments in maize genetics and breeding. Specifically, accurately identifying stages of growth is required to perform developmentally dependent sampling or data collection, to predict time to flowering and seed maturation, and to allow for comparisons between different lines and populations based on developmental time. In this protocol, we summarize previous knowledge about maize development and describe how to monitor these stages in the reference inbred line B73, a yellow dent corn.

MnOxGeneTool: A Comprehensive Tool for Identifying and Quantifying Mn(II)-Oxidizing Genes, Revealing Phylogenetic Diversity and Environmental Drivers of Mn(II)-Oxidizers.

Manganese (Mn) oxides are crucial for degrading organic pollutants and driving biogeochemical cycles. Microorganisms drive Mn(II) oxidation, but traditional cultivation-dependent identification methods are inefficient and error-prone. To overcome these limitations, we developed MnOxGeneTool, a bioinformatics tool for identifying and quantifying Mn(II)-oxidizing genes from genomic and metagenomic data. MnOxGeneTool consists of three main components: (1) a curated database of known Mn(II)-oxidizing proteins and their homologues, (2) a hidden Markov models (HMMs) database derived from this protein data set, and (3) a computational pipeline that integrates bioinformatics tools (e.g., HMMER and BLASTX) to identify and quantify Mn(II)-oxidizing genes. We assessed the accuracy and sensitivity of these HMMs through cross-validation, demonstrating their effectiveness in identifying Mn(II)-oxidizing genes in bacterial genomes. Using MnOxGeneTool, we explored the phylogenetic diversity of Mn(II)-oxidizers and identified 824 bacterial genera containing Mn(II)-oxidizing genes, significantly expanding previous knowledge in this field. Additionally, we analyzed metagenomic data from various environments to explore environmental drivers of Mn(II)-oxidizing genes, identifying two potential drivers: oligotrophic conditions and alkaline environments. These findings enable targeted discovery of novel Mn(II)-oxidizers and genetic determinants through identification of their ecological niches and expression optima, thereby expanding MnOxGeneTool's predictive coverage of uncatalogued Mn(II)-oxidizing proteins. By providing an innovative bioinformatics tool that enables efficient identification and quantification of Mn(II)-oxidizing genes from both genomic and metagenomic data, this study offers significant advancements in the research of biogenic Mn(II) oxidation. The tool is available at https://github.com/wyh19990121/MnOxGeneTool.

Video-based genetic counseling to reduce physician workload and enhance consulter understanding: A prospective randomized clinical trial.

1502 Background: Genetic counseling is an essential part of germline genetic testing, which is reccomended for use of PARP inhibitors in breast cancer treatment. Despite its importance, only about 40-60% of breast cancer patients receive genetic counseling. A video tool has been developed to provide genetic counseling, aiming to reduce the physician's workload and improve the patient's understanding. Methods: Advice seekers at increased risk for hereditary breast and ovarian cancer as well as breast and ovarian cancer patients were included in the trial. They were randomly assigned 1:1 to either standard of care (physician only, PO) or video based followed by physician genetic counseling (VPO). A 15-minute video tool was created for VPO participants, who watched it on an iPad and answered 6 comprehension questions online. The physician then clarified any misunderstood topics. In both groups, counseling time was measured from conversation start to blood donation. Afterwards, participants completed a questionnaire with 9 comprehension questions (16 points total). Data analysis included Bernard’s and Pearson’s tests for categorical data, Kendall’s test for correlations, and ordinal logistic regression for multivariable analysis. Results: A total of 110 participants with a median age of 47 years were randomized into two groups: PO counseling (55 participants, 50%) and VPO counseling (55 participants, 50%). Among them, 29% (32 participants) received therapeutic counseling for breast/ovarian cancer, while 71% (78 participants) received predictive counseling with no cancer diagnosis. Participant characteristics were well balanced between groups, with no significant differences in age, indication for counseling, sex (90% female), level of education, or German-speaking proficiency. Participants reported their sources of genetics knowledge as previous knowledge (30%) and physician counseling (70%) in the PO group, and as previous knowledge (20%), physician counseling (30%), and the video tool (45%) in the VPO group. The video significantly improved comprehension scores from 62.5% (10 points, PO group) to 81.3% (13 points,VPO group) (p < 0.0001). Additionally, the video tool significantly reduced the time physicians spent on counseling from 6.6 minutes to 2.4 minutes (p < 0.0001). According to the logistic regression model, both the level of education (estimate -1.34, p = 0.002) and German language comprehension level (estimate 1.62, p = 0.001) significantly influenced the genetic counseling comprehension score. Conclusions: In this prospective randomized trial, video genetic counseling improved comprehension and reduced physician counseling time. The genetic video tool, which can be translated into various languages, facilitates genetic counseling by decreasing the workload for physicians, which may increase the genetic counseling rate in the clinic.