Data Analysis Protocols Research Articles

The impact of garbage codes on the quality of death coding in Belgrade in 2010, 2015, and 2020

Abstract Background Accurate data on the causes of death (CoDs) is the foundation for analyzing the health situation and developing health policy in various countries. The percentage of garbage codes (GCs) indicates the quality of the CoDs data. Within the Western Balkan Strategic Partnership Project (Work Package 4: Use of Population Health Metrics for Improved Surveillance), we evaluated the impact of the four levels of GCs on the quality of mortality data of Belgrade. Methods In a retrospective study, we examined the underlying CoDs of Belgrade residents by age and sex and the change in GCs’ share in the CoDs in 2010, 2015, and 2020. We used anonymous individual mortality data and population age and sex annual data from the electronic databases of the City Institute of Public Health of Belgrade, as well as the list of GS levels 1-4 from the 2019 Global Burden of Disease Study. Results Over the observed years, the percentage of all GCs in underlying CoDs was approximately 20%. The GCs with profound policy implications level 1 accounted for 52% of all GCs in 2020, higher than those recorded in previous years - 46.3% in 2010 and 45.1% in 2015. The total number of GCs per 100,000 inhabitants in Belgrade was the highest in 2020, with a rate of 325.2/100,000 (males: 374.4, females: 301.3). This rate has increased by about one-third since 2010 (both sexes: 245.4, males: 269.2, females: 221.1) and by about two-fifths since 2015 (both sexes: 235.8, males: 262.7, females: 211.8). Conclusions This research has revealed a significant decline in the quality of data related to the causes of death in Belgrade. One-fifth of the recorded causes of death are inaccurate or unreliable. The study highlights the importance of maintaining strict protocols for data collection, analysis, and management to ensure the accuracy and reliability of CoDs data in developing effective public health policies. Key messages • The rate of total garbage codes per 100,000 inhabitants of Belgrade has increased by about two-fifths since 2015. • Failure in quality, accuracy, and reliability of causes of death can lead to suboptimal health policymaking.

Convolution Analysis in Electrodeposition

Convolution is a well established method for data analysis for electrochemical processes where both product and reactants are soluble. Advantages of convolution analysis include (a) facile evaluation of kinetic and system parameters, (b) ready corrections for nonfaradaic uncompensated resistance and double layer charging, (c) track of surface concentrations with time, independent of the voltage perturbation sequence, and (d) ease of convolution calculation from digitized current data. Here, equations are derived for convolution of electrodeposition data and a data analysis protocol is presented. Advantages are found for convolution analysis in electrodeposition. Parameters that can be determined from convolution analysis in electrodeposition are standard heterogeneous rate constant k 0 and exchange current density j 0, transfer coefficients α and β, formal potential E0′ , resistance to charge transfer, and diffusion coefficient of the solution soluble reactant D M . Convolution in electrodeposition is illustrated for silver deposited from an ionic liquid onto a glassy carbon electrode, where experimental data are tracked cyclic voltammetrically.

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis.

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Towards a Standardized Operating Procedure for eDNA‐Based Biomonitoring in Coastal Marine Salmon Aquaculture

ABSTRACTThe marine aquaculture industry and regulators are in the process of implementing environmental DNA (eDNA) metabarcoding of microbial communities for compliance monitoring. This requires standardization of sampling, laboratory, and data analysis protocols. Towards this goal, we in this study completed two further milestones using samples collected from two Scottish salmon farms: (i) We tested the effect of using two different PCR protocols (i.e., different DNA polymerases, master mixes, and annealing temperatures), which are frequently being used in eDNA biomonitoring of aquaculture installations, for the amplification of the taxonomic marker gene (V3‐V4 hypervariable region of the bacterial 16S rRNA gene). (ii) We quantified sampling background noise obtained from eDNA samples and statistically compared results with the sampling bias observed in macrofaunal samples from the same source sediments. We detected differences in bacterial community structures resulting from the performance of different PCR protocols, profoundly influencing the interpretation of biomonitoring results. Furthermore, we found that sampling‐induced errors for eDNA samples were similar to errors for macrofaunal samples collected according to compliance monitoring protocol (~25% variability in both cases). Finally, we showed that within‐grab variances of microbial community structures were in the same order of magnitude (less than 10× difference in all cases) as the one obtained from replicate grabs collected from the same locale (impact category). Based on our findings, we suggest using a consistent PCR protocol for biomonitoring efforts to improve the comparability of results, especially when different service providers are conducting the biomonitoring. We propose a sampling scheme to be considered in eDNA biomonitoring that includes taking three replicate grabs at each locale, with one replicate sample from each grab. This minimizes sampling‐induced errors and makes upcoming eDNA‐based monitoring results comparable with previous compliance monitoring results obtained from macrofaunal data.

Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection.

The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a data analysis protocol leading to a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC. 539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC. The signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9, the only biomarker currently accepted by the FDA and the National Comprehensive Cancer Network guidelines for pancreatic cancer, and other individual biomarkers and combinations in both discovery and held-out validation sets. An AUC of 0.98 (95% CI 0.98-0.99) and sensitivity of 0.99 (95% CI 0.98-1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66-0.91) and sensitivity 0.67 (95% CI 0.50-0.83), also at 90% specificity, for CA19-9, in the discovery set (p = 0.0016 and p = 0.00050, respectively). During ensemble signature validation in the held-out set, an AUC of 0.95 (95% CI 0.91-0.99), sensitivity 0.86 (95% CI 0.68-1), was attained compared to an AUC of 0.80 (95% CI 0.66-0.93), sensitivity 0.65 (95% CI 0.48-0.56) at 90% specificity for CA19-9 alone (p = 0.0082 and p = 0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92-0.99), sensitivity 0.82 (95% CI 0.64-0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64-0.93) and sensitivity of 0.18 (95% CI 0.03-0.69) (p = 0.013 and p = 0.0055, respectively). Our ensemble modelling technique outperformed CA19-9, individual biomarkers and indices developed with prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk.

Transcriptomics and epigenetic data integration learning module on Google Cloud.

Abstract Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. Highlights

Repertory grid technique and Honey’s content analysis: a methodological application to advance qualitative research in OSCM

PurposeThis paper aims to explain and demonstrate how the Repertory Grid Technique (RGT) and Honey’s Content Analysis (HCA) can make new contributions to the field of Operations and Supply Chain Management (OSCM). The proposition involves integrating these complementary analyses to fortify the rigor of qualitative research and establish robust data analysis protocols to identify the main attributes of interviewees regarding a phenomenon while understanding in their perspective how these attributes impact the desired analysis outcome.Design/methodology/approachThis study uses examples with rich empirical data from 40 interviewees across two organizations. The examples use a protocol that allows the grouping of meanings from different knowledgeable individuals and capturing relevant constructs related to an outcome.FindingsThe combination of RGT and HCA permits researchers to effectively identify and analyze the constructs individuals and groups utilize to comprehend the subject matter under investigation. Consequently, these techniques present a structured means to conduct grounded theory investigations and interpretive research, thereby enabling the iterative development of the preliminary conceptual models necessary for OSCM field advancement.Originality/valueWe present two examples in which the protocol is applied to the field of OSCM. These examples illustrate that the techniques provide valuable opportunities for OSCM research, particularly for addressing the limitations related to sample size. Ultimately, RGT and HCA complement quantitative methodologies by uncovering nuanced variations and micro-foundations within firm- and network-level phenomena, offering insights essential for advancing our understanding of OSCM dynamics in specific contexts.

Simultaneous Optical Imaging of Action Potentials and Calcium Transients in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Cardiovascular diseases have emerged as one of the leading causes of human mortality, but the discovery of new drugs has been hindered by the absence of suitable in vitro platforms. In recent decades, continuously refined protocols for differentiating human induced pluripotent stem cells (hiPSCs) into hiPSC-derived cardiomyocytes (hiPSC-CMs) have significantly advanced disease modeling and drug screening; however, this has led to an increasing need to monitor the function of hiPSC-CMs. The precise regulation of action potentials (APs) and intracellular calcium (Ca2+) transients is critical for proper excitation-contraction coupling and cardiomyocyte function. These important parameters are usually adversely affected in cardiovascular diseases or under cardiotoxic conditions and can be measured using optical imaging-based techniques. However, this procedure is complex and technologically challenging. We have adapted the IonOptix system to simultaneously measure APs and Ca2+ transients in hiPSC-CMs loaded with the fluorescent dyes FluoVolt and Rhod 2, respectively. This system serves as a powerful high-throughput platform to facilitate the discovery of new compounds to treat cardiovascular diseases with the cellular phenotypes of abnormal APsand Ca2+ handling. Here, we present a comprehensive protocol for hiPSC-CM preparation, device setup, optical imaging, and data analysis. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Maintenance and seeding of hiPSC-CMs Basic Protocol 2: Simultaneous detection of action potentials and Ca2+ transients in hiPSC-CMs.

A site-wise reliability analysis of the ABCD diffusion fractional anisotropy and cortical thickness: impact of scanner platforms.

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce non-biological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61±0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76±0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared to those using GE/Philips scanners for both FA (AICC=0.71±0.12 vs 0.46±0.17, p<0.001) and CT (AICC=0.80±0.10 vs 0.69±0.11, p<0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

Research protocol for an observational health data analysis to assess the applicability of randomized controlled trials focusing on newly diagnosed metastatic prostate cancer using real-world data: PIONEER IMI’s “big data for better outcomes” program

Background: Metastatic prostate cancer (PCa) constitutes ~5% of all new PCa diagnoses in Western countries. For most cases, primary consideration should be given to systemic therapies as the first-line approach based on evidence from randomized controlled trials (RCTs). Despite the importance of RCTs as the pinnacle of evidence in modern medicine, concerns have been raised about their applicability to real-life scenarios. These trials often feature participants who are younger with better performance statuses and prognoses compared to their real-world counterparts. The PIONEER project falls under the Innovative Medicine Initiative’s (IMI) “Big Data for Better Outcomes” initiative, aimed at revolutionizing PCa care in Europe. The central focus lies in improving cancer-related outcomes, enhancing health system efficiency, and elevating the quality of health and social care. This study endeavours to evaluate the generalizability of RCT findings concerning newly diagnosed metastatic PCa. Methods: A systematic review of the literature will be conducted to compile patient characteristics from RCTs addressing this subject within the past decade. To create a real-world benchmark, patients with recently diagnosed metastatic PCa from a network of population-based databases will serve as a comparison group. The objective is to assess the applicability of RCT results in two ways. First, a comparison will be made between the characteristics of patients with newly diagnosed metastatic PCa enroled in RCTs and those with the same condition included in our databases which might represent the real-world setting. Second, an evaluation will be undertaken to determine the proportion of real-world patients with newly diagnosed metastatic PCa who meet the criteria for RCT enrolment. This study will rely on extensive observational data, primarily sourced from population-based registries, electronic health records, and insurance claims data. The study cohort is established upon routinely gathered healthcare data, meticulously mapped to the Observational Medical Outcomes Partnership Common Data Model.

Evaluating Source, Scale Risk, and Corrosion Risk of the Produced Water Samples from the Appalachian Basin Based on a Geochemical Database

Summary Statistically, oil and gas production can generate up to 20 times the oil equivalent of produced water. The composition of produced water samples reflects its source, its interactions with reservoir rocks, and downhole (DH) facilities, which are critical for basin evolution, water source determination, and the monitoring, management, and optimization of oil and gas production. For example, scale and corrosion, two of the most severe flow assurance issues accompanied by produced water, can lead to billions of dollars lost every year. However, few studies have developed a standard protocol to extract such valuable information from produced water compositions due to a lack of data and professional models. Using produced water geochemical data from the Appalachian Basin, one of the largest natural gas producers in the US, from the United States Geological Survey (USGS) Produced Waters Geochemical Database (PWGD), we developed a standard protocol to investigate the produced water source, evolution history, and scale and corrosion risks under both DH and surface conditions by means of incorporating the professional models for water-rock interaction and corrosion. The results show that the produced water from the Appalachian Basin possibly evolves from seawater evaporation following a typical evolution pattern of ion concentration and water isotopes, while a group of time-elapsed samples indicates that such an evolution pattern can also be due to the mixture of the injected water and reservoir water. In addition, most produced water samples show obvious risks of mineral scaling (e.g., calcite, barite, and siderite) and CO2 corrosion with corresponding mitigation strategies recommended. This study not only developed a reliable data processing and analysis protocol but also showed the valuable information a systematic analysis of produced water samples can provide for actual oil and gas production.

Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review.

Periodontitis is the main cause of tooth loss and is related to many systemic diseases. Artificial intelligence (AI) in periodontics has the potential to improve the accuracy of risk assessment and provide personalized treatment planning for patients with periodontitis. This systematic review aims to examine the actual evidence on the accuracy of various AI models in predicting periodontitis. Using a mix of MeSH keywords and free text words pooled by Boolean operators ('AND', 'OR'), a search strategy without a time frame setting was conducted on the following databases: Web of Science, ProQuest, PubMed, Scopus, and IEEE Explore. The QUADAS-2 risk of bias assessment was then performed. From a total of 961 identified records screened, 8 articles were included for qualitative analysis: 4 studies showed an overall low risk of bias, 2 studies an unclear risk, and the remaining 2 studies a high risk. The most employed algorithms for periodontitis prediction were artificial neural networks, followed by support vector machines, decision trees, logistic regression, and random forest. The models showed good predictive performance for periodontitis according to different evaluation metrics, but the presented methods were heterogeneous. AI algorithms may improve in the future the accuracy and reliability of periodontitis prediction. However, to date, most of the studies had a retrospective design and did not consider the most modern deep learning networks. Although the available evidence is limited by a lack of standardized data collection and protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area. The use of AI in periodontics can lead to more accurate diagnosis and treatment planning, as well as improved patient education and engagement. Despite the current challenges and limitations of the available evidence, particularly the lack of standardized data collection and analysis protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.

Wireless Coal Mine Monitoring System based on ESP-NOW Protocol for Real-Time Data Acquisition and Analysis

Wireless Coal Mine Monitoring System based on ESP-NOW Protocol for Real-Time Data Acquisition and Analysis

Modeling and correction of image drift in dynamic shadowgraphy experiments

The study of phoretic transport phenomena under non-stationary conditions presents several challenges, mostly related to the stability of the experimental apparatus. This is particularly true when investigating with optical means the subtle temperature and concentration fluctuations that arise during diffusion processes, superimposed to the macroscopic state of the system. Under these conditions, the tenuous signal from fluctuations is easily altered by the presence of artifacts. Here, we address an experimental issue frequently reported in the investigation by means of dynamic shadowgraphy of the non-equilibrium fluctuations arising in liquid mixtures under non-stationary conditions, such as those arising after the imposition or removal of a thermal stress, where experiments show systematically the presence of a spurious contribution in the reconstructed structure function of the fluctuations, which depends quadratically from the time delay. We clarify the mechanisms responsible for this artifact, showing that it is caused by the imperfect alignment of the sample cell with respect to gravity, which couples the temporal evolution of the concentration profile within the sample with the optical signal collected by the shadowgraph diagnostics. We propose a data analysis protocol that enables disentangling the spurious contributions and the genuine dynamics of the fluctuations, which can be thus reliably reconstructed.Graphic The imposition of a thermal gradient across a liquid mixture results in a time-dependent refractive index distribution. In the presence of a misalignment of the confining cell with respect to gravity, this leads to a deflection of the optical probe beam used to monitor concentration fluctuations within the sample in quantitative shadowgraphy experiments. If not properly accounted for, this effect can introduce a significant bias in the optical signal.

Evaluation of the Gonadotoxicity of Cancer Therapies to Improve Counseling of Patients About Fertility and Fertility Preservation Measures: Protocol for a Retrospective Systematic Data Analysis and a Prospective Cohort Study.

Cytotoxic treatments such as chemo- and radiotherapy and immune therapies are required in cancer diseases. These therapies have the potential to cure patients but may also have an impact on gonadal function and, therefore, on fertility. Consequently, fertility preservation treatments such as freezing of gametes and gonadal tissue might be required. However, as detailed data about the necessity to perform fertility preservation treatment are very limited, this study was designed to fill this data gap. Primary objective of this study is to analyze the impact of cancer therapies and chemotherapies on the ovarian reserve and sperm quality. Secondary objectives are to analyze the (1) impact of cancer therapies and chemotherapies on other fertility parameters and (2) probability of undergoing fertility preservation treatments in relation to specific cancer diseases and treatment protocols and the probability to use the frozen gametes and gonadal tissue to achieve pregnancies. First, previously published studies on the gonadotoxicity of chemo- and radiotherapies among patients with cancer will be systematically analyzed. Second, a prospective cohort study set up by approximately 70 centers in Germany, Switzerland, and Austria will collect the following data: ovarian function by analyzing anti-Müllerian hormone (AMH) concentrations and testicular function by analyzing sperm parameters and total testosterone immediately before and around 1 year after gonadotoxic therapies (short-term fertility). A follow-up of these fertility parameters, including history of conceptions, will be performed 5 and 10 years after gonadotoxic therapies (long-term fertility). Additionally, the proportion of patients undergoing fertility-preserving procedures, their satisfaction with these procedures, and the amount of gametes and gonadal tissue and the children achieved by using the frozen material will be analyzed. Third, the data will be merged to create the internet-based data platform FertiTOX. The platform will be structured in accordance with the ICD (International Classification of Diseases) classification of cancer diseases and will be easily be accessible using a specific App. Several funding bodies have funded this study. Ten systematic reviews are in progress and the first one has been accepted for publication. All Swiss and many German and Austrian ethics committees have provided their approval for the prospective cohort study. The study registry has been set up, and a study website has been created. In total, 50 infertility centers have already been prepared for data collection, which started on December 1, 2023. The study can be expected to bridge the data gap regarding the gonadotoxicity of cancer therapies to better counsel patients about their infertility risk and their need to undergo fertility preservation procedures. Initial data are expected to be uploaded on the FertiTOX platform in 2026. ClinicalTrials.gov NCT05885048; https://clinicaltrials.gov/study/NCT05885048. DERR1-10.2196/51145.

Awareness of Health Professionals about Candidacy of Cochlear Implant

Background: Cochlear implants serve as a transformative solution for individuals with sensorineural hearing loss, particularly benefiting children aged 12-24 months with bilateral profound deafness who find little to no benefit from traditional hearing aids. This advanced auditory technology converts acoustic signals into electrical stimuli, directly stimulating the auditory nerve and thereby providing an essential hearing function. The determination of candidacy for cochlear implants is a collaborative process involving ENT specialists, audiologists, and speech and language pathologists. These devices significantly impact the lives of profoundly deaf individuals by offering a means to access sound and develop communication skills, crucial for their integration into a hearing world.&#x0D; Objective: To assess the awareness of health professionals about candidacy of cochlear implant.&#x0D; Methods: This cross-sectional study was meticulously designed, adhering to standardized protocols for data collection and analysis to enhance the reliability and validity of findings. The research unfolded in various government and private hospitals across Lahore, focusing on a specific cohort of health professionals—ENT specialists and speech and language pathologists. A total of 55 participants, both male and female, aged between 29 to 52 years, were systematically selected through non-probability convenience sampling. The assessment of awareness among these professionals regarding the candidacy for cochlear implants was conducted using a carefully structured pediatric checklist. This tool encompassed multiple items directly relating to the core candidacy criteria, ensuring a comprehensive evaluation of professional knowledge and awareness. The analysis was performed using statistical methods appropriate for cross-sectional data, aiming to draw meaningful insights from the responses collected.&#x0D; Results: The study's participant pool consisted of 24 ENT specialists (43.6%) and 31 speech and language pathologists (56.4%), highlighting a diverse representation of the field. Among these professionals, a significant discrepancy in awareness was observed. Those who had practical experience working in cochlear implant centers demonstrated a higher level of awareness, with nuanced understanding of both audiometric and speech-based candidacy criteria. Conversely, participants lacking this practical exposure showed considerable gaps in their knowledge, particularly in applying these criteria to pediatric cases. The numerical data revealed that only a fraction of the respondents could accurately identify all key candidacy indicators, underscoring the need for targeted educational interventions.&#x0D; Conclusion: The findings of this study underline a palpable deficiency in the awareness and understanding of cochlear implant candidacy criteria among ENT specialists and speech and language pathologists. Despite the critical role these professionals play in the identification and management of potential implant candidates, a notable portion exhibited limited knowledge, especially in aspects beyond their immediate field of expertise. This gap was more pronounced among those without direct experience in cochlear implant centers, suggesting that hands-on involvement significantly contributes to a deeper comprehension of candidacy nuances. Therefore, there is an urgent need for comprehensive training programs and continued professional development opportunities to bridge these knowledge gaps, ensuring that all children who could benefit from cochlear implants are accurately identified and referred.

Clinical experiences of RN to BScN nursing students in Kenyan universities

Introduction and objective: Clinical learning environments play a great role in nursing training as they allow nursing students to develop their clinical skills by combining cognitive, psychomotor and affective skills. Consequently, clinical learning environments enable nursing students to bridge the theory-practice gap. Fewer studies have examined the clinical experiences of RN to BScN students in Kenya. This paper is part of analytical memo of a larger PhD study that sought to explore and describe the support needs of RN to BScN students in Kenyan universities. The paper focuses on clinical experiences of RN to BScN students.Methods: Using a qualitative phenomenological approach, ten focus group discussions were conducted with 100 RN to BScN students, purposively sampled from four universities in Kenya. Data were analyzed using Tesch’s data analysis protocol. The article has adhered to Consolidated criteria for reporting qualitative studies.Results: The data on RN to BScN students’ clinical experience revealed two themes: curriculum challenges and practice environment and six sub-themes: redundant learning outcomes, redundant clinical assessments, not acknowledging prior learning, lack of clinical supervision, lack of learning resources and “an extra pair of hands”.Conclusions: The study findings highlight the need for review of clinical learning outcomes for the RN to BScN students in Kenya. The findings emphasize the need for collaborative partnerships between universities, clinical learning environments, nurse educators, and policy makers, to design of clinical learning outcomes relevant to RN to BScN students in Kenya.

A primer on terrestrial passive acoustic monitoring data management: Best practices and guidelines

Recent advances in recording technology and data storage capabilities have revolutionized how we monitor vocal animals and their habitats. Recently, there has been an explosion of interest in the use of terrestrial passive acoustic monitoring (PAM) which relies on autonomous acoustic recording units (ARU), but terrestrial PAM studies are limited by a lack of standardized protocols for data collection, analysis, and archiving. Here, we draw on collective experiences of recording, analyzing, and archiving, hundreds of years of PAM data to provide guidelines and recommendations for PAM practitioners. First, we highlight some of the major pitfalls in field data collection, including inappropriate recording settings for the research question and incomplete metadata. We then provide recommendations for data management, including suggestions for backing up, compressing, and archiving the data. Our goal is to provide the community with standardized working guidelines that will help facilitate comparative, large-scale terrestrial studies using PAM.

A data-driven customer complaint management model for residential building companies

ABSTRACT Many companies in charge of the development and construction of residential building projects do not appropriately manage customer complaint records, especially regarding providing feedback about quality. Data collection and procedures for data analysis are often ineffective, which limits the generation of knowledge. Previous studies on customer complaints have been mostly focused on the analysis of large databases but do not propose overall solutions for managing this type of information, including data collection and analysis. This investigation aims to devise a data-driven customer complaint management model for providing feedback to the design and production phases of residential building projects. This model has a set of protocols for: (i) data collection that can be used for developing digital applications; and (ii) analyzing defect cause–effect relationships, considering the experts’ perspective, by using Directed Acyclic Graph and Bayesian Network. This investigation was conducted in collaboration with a Brazilian residential building company, using Design Science Research as methodological approach. The main outcome of this investigation is a set of data collection and analysis protocols that support the migration from the traditional approach of simply providing repair services during the defect liability period to a data-driven customer complaint management approach that provides feedback to quality management systems. HIGHLIGHTS A customer complaint management model must consider several perspectives; Digital applications must be used to support the implementation of data collection on defects; A complaint management model must deal with the complexity of defect formation. Abbreviations: BN: Bayesian Network; CRM: Customer Relationship Management; DAG: Direct Acyclic Graph; DLP: Defect Liability Period; DSR: Design Science Research; MEP: Mechanical, Electrical, and Plumbing Systems

Predictors associated with successful weaning of veno-venous extracorporeal membrane oxygenation and mortality in adult patients with severe acute lung failure: Protocol of a pooled data analysis of cohort studies.

Severe acute lung failure (ALF) often necessitates veno-venous extracorporeal membrane oxygenation (VV-ECMO), where identifying predictors of weaning success and mortality remains crucial yet challenging. The study aims to identify predictors of weaning success and mortality in adults undergoing VV-ECMO for severe ALF, a gap in current clinical knowledge. PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials will be searched for cohort studies examining the predictive factors of successful weaning and mortality in adult patients on VV-ECMO due to severe ALF. Risk of bias assessment will be conducted using the Newcastle-Ottawa scale for each included study. The primary outcomes will be successful weaning from VV-ECMO and all-cause mortality. Between-study heterogeneity will be evaluated using the I2 statistic. Sensitivity, subgroup, and meta-regression analyses will be performed to ascertain potential sources of heterogeneity and assess the robustness of our results. We will use the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool to recommend the level of evidence. This study seeks to provide clinically significant insights into predictors for weaning and mortality during VV-ECMO treatment for ALF, aiming to support clinical decisions and potentially influence health policy, thereby improving patient outcomes. Given the absence of direct engagement with human subjects or access to personal medical records, ethical approval for this study is deemed unnecessary. The study findings will be shared at a scientific conference either at the global or national level. Alternatively, the results will be presented for publication in a rigorously peer-reviewed journal regarding critical care medicine.