Availability Of Tools Research Articles

Creating anatomically-derived, standardized, customizable, and three-dimensional printable head caps for functional neuroimaging.

Consistent and accurate probe placement is a crucial step towards enhancing the reproducibility of longitudinal and group-based functional neuroimaging studies. While the selection of headgear is central to these efforts, there does not currently exist a standardized design that can accommodate diverse probe configurations and experimental procedures. We aim to provide the community with an open-source software pipeline for conveniently creating low-cost, 3-D printable neuroimaging head caps with anatomically significant landmarks integrated into the structure of the cap. We utilize our advanced 3-D head mesh generation toolbox and 10-20 head landmark calculations to quickly convert a subject's anatomical scan or an atlas into a 3-D printable head cap model. The 3-D modeling environment of the open-source Blender platform permits advanced mesh processing features to customize the cap. The design process is streamlined into a Blender add-on named "NeuroCaptain". Using the intuitive user interface, we create various head cap models using brain atlases, and share those with the community. The resulting mesh-based head cap designs are readily 3-D printable using off-the-shelf printers and filaments while accurately preserving the head topology and landmarks. The methods developed in this work result in a widely accessible tool for community members to design, customize and fabricate caps that incorporate anatomically derived landmarks. This not only permits personalized head cap designs to achieve improved accuracy, but also offers an open platform for the community to propose standardizable head caps to facilitate multi-centered data collection and sharing.

A Simple MRI Score Predicts Pathological General Movements in Very Preterm Infants with Brain Injury-Retrospective Cohort Study.

Very preterm infants are at increased risk of brain injury and impaired brain development. The Total Abnormality Score and biometric parameters, such as biparietal width, interhemispheric distance and transcerebellar diameter, are simple measures to evaluate brain injury, development and growth using cerebral magnetic resonance imaging data at term-equivalent age. The aim of this study was to evaluate the association between the Total Abnormality Score and biometric parameters with general movements in very preterm infants with brain injury. This single-center retrospective cohort study included 70 very preterm infants (≤32 weeks' gestation and/or <1500 g birth weight) born between January 2017 and June 2021 in a level-three neonatal intensive care unit with brain injury-identified using cerebral magnetic resonance imaging data at term-equivalent age. General movements analysis was carried out at corrected age of 8-16 weeks. Binary logistic regression and Spearman correlation were used to examine the associations between the Total Abnormality Score and biometric parameters with general movements. There was a significant association between the Total Abnormality Score and the absence of fidgety movements [OR: 1.19, 95% CI = 1.38-1.03] as well as a significant association between the transcerebellar diameter and fidgety movements (Spearman ρ = -0.269, p < 0.05). Among very preterm infants with brain injury, the Total Abnormality Score can be used to predict the absence of fidgety movements and may be an easily accessible tool for identifying high-risk very preterm infants and planning early interventions accordingly.

MACSFeD-a database of mosquito acoustic communication and swarming features.

Acoustic communication plays an important role during the courtship of many mosquito species. Male mosquitoes show strong attraction to female wing beat frequencies, mediated via spectral matching between female wing beat frequency and male ear mechanical tuning frequency. Such acoustic communication typically occurs within swarms, male-dominated aggregations with species-specific properties. Despite hundreds of relevant publications being available, the lack of a central platform hosting all associated data hinders research efforts and limits cross-species comparisons. Here, we introduce MACSFeD (Mosquito Acoustic Communication and Swarming Features Database), an interactive platform for the exploration of our comprehensive database containing 251 unique reports focusing on different aspects of mosquito acoustic communication, including hearing function, wing beat frequency and phonotaxis, as well as male swarming parameters. MACSFeD serves as an easily accessible, efficient, and robust data visualization tool for mosquito acoustic communication research. We envision that further in-depth studies could arise following the use of this new platform. Database URL: https://minmatt.shinyapps.io/MACSFeD/.

Comparative proteomics analysis of the mouse mini-gut organoid: insights into markers of gluten challenge from celiac disease intestinal biopsies.

Organoid models enable three-dimensional representation of cellular systems, providing flexible and accessible research tools, and can highlight key biomolecules. Such models of the intestinal epithelium can provide significant knowledge for the study of celiac disease and provide an additional context for the nature of markers observed from patient biopsy data. Using LC-MS/MS, the proteomes of the crypt and enterocyte-like states of a mouse mini-gut organoid model were measured. The data were further compared with published biopsy data by comparing the changes induced by gluten challenge after a gluten-free diet. These analyses identified 4,850 protein groups and revealed how 400 putative biomarkers of dietary challenge were differentially expressed in the organoid model. In addition to the extensive changes within the differentiated cells, the data reiterated the disruption of the crypt-villus axis after gluten challenge. The mass spectrometry data are available via ProteomeXchange with the identifier PXD025690.

Frailty detection with routine blood tests using data from the english longitudinal study of ageing (ELSA).

Frailty is a rising global health issue in ageing society. Easily accessible and sensitive tools are needed for frailty monitoring while routine blood factors can be potential candidates. Data from 1907 participants (aged 60years or above) were collected from the 4th to 9th wave of the English longitudinal study of ageing. 14 blood factors obtained from blood tests were included in the analysis. A 52-item frailty index (FI) was calculated for frailty evaluation. Logistic regression and Cox proportional hazards analysis were used to explore the relationships between baseline blood factors and the incidence of frailty over time respectively. All analyses were controlled for age and sex. The mean age of participants was 67.3years and 47.2% of them were male. Our study identified that 8 blood factors (mean corpuscular haemoglobin, HDL, triglyceride, ferritin, hsCRP, dehydroepiandrosterone, haemoglobin, and WBC) involved in inflammatory, nutritional and metabolic processes were associated with frailty. The combined model with these 8 blood factors had an AUC of 0.758 at cross-sectional level. In the Cox proportional hazards analysis, higher triglyceride (HR: 1.30, 95%CI: 1.07 ~ 1.59), WBC (HR: 1.16, 95%CI: 1.05 ~ 1.28), and lower HDL (HR: 0.58, 95%CI: 0.38 ~ 0.90) at baseline were linked to greater risk of developing frailty within 10years. Compared to adults without abnormal blood factors at baseline, the hazard ratios of participants with two or more abnormal blood factors were almost twofold higher in developing frailty over time. Routine blood factors, particularly triglyceride, HDL and WBC, could be used for frailty screening in clinical practice and estimate the development of frailty over time.

Linguistic and cultural adaptation of the National Quality of Life Survey in Colombia for the deaf population

Abstract This article reports on the linguistic and cultural adaptation of Items of the National Quality of Life Survey (NQLS 2018), which are included in a National Survey that explores the availability of reasonable accommodation for Deaf users in a health care setting as well as health care access. We document the cultural and linguistic adaptation step-by-step, guided by a literature review, participatory group discussions, and qualitative testing. This research addresses the dearth of accessible research instruments for D‍/‍deaf persons in low and middle-income countries. This lack of accessible tools leads to double layers of exclusion, and data gaps often lead to erroneous generalizations that do not accurately represent D‍/‍deaf populations. Evidence gaps may result in poor service planning and inadequate policymaking. This study contributes to good practices in language interpreting in research settings, disability research, and global health.

Development of a Best Practice Guidance on Online Peer Support for People with Young-Onset Dementia.

This work aimed to develop a Best Practice Guidance on online peer support for people with young-onset dementia (YOD). The Best Practice Guidance was developed through a systematic literature review, focus groups, an online survey, and interviews and consultations with people with YOD and professionals. The Best Practice Guidance consists of two parts. Part 1 contains information for people with YOD about what online peer support entails, what to expect from it, and how to get involved. Part 2 is aimed at those who facilitate or moderate online peer support (professionals or people with lived experience) and includes guidelines on how to optimize online peer support for people with YOD. The Best Practice Guidance on online peer support provides (1) people with YOD with evidence-based, relevant, and accessible information about what online peer support entails and how it could help them, (2) providers and facilitators with guidelines on how to optimize online peer support for people with YOD, and (3) healthcare professionals with a concise and accessible tool for signposting. Future research is needed to implement and disseminate the Best Practice Guidance among dementia organizations and healthcare practices and should include rigorous studies on the implementation and sustainability of online peer support for people with YOD.

The feasibility of ultrasound-guided latex labeling of the anterolateral ligament in anatomical dissection – A cadaveric study

BackgroundThe present study aims to investigate the feasibility of labeling ligaments using ultrasound-guided injections. On formalin-fixed cadavers, the anterolateral ligament was selected and targeted for demonstration. The development of portable ultrasound machines and the ability to connect them to tablets via Bluetooth or WLAN makes it an accessible tool to implement into the anatomical dissection courses in order to associate medical imaging (MRI and ultrasound), anatomical structures and their subsequent dissection. Methods8 formalin fixed human cadavers were used for the ultrasound-guided injections of 1 mL of blue latex into the anterolateral ligament. 8 cadavers were not injected with latex for comparative purposes. The injections were performed by an experienced ultra-sonographer. After approximately 10 months, five dissections were carried out by students during the dissection course and three specimens were dissected by anatomists. ResultsThe anterolateral ligament was successfully marked and demonstrated in 7 out of 8 cases. In 4 out of 5 cases, the dissection was primarily conducted by students, while in 3 out of 3 cases, it was performed by anatomists. The accuracy was 80 % and 100 %, respectively. ConclusionThe present study demonstrated that labeling obscure ligaments, such as the anterolateral ligament, using ultrasound guidance is feasible on formalin-fixed cadavers. It also showed that students can successfully perform the dissections as the structure is highlighted and that the time between injection and dissection (approximately 10 months) has little impact on the outcome. The use of ultrasound in dissection courses should be further encouraged.

Opportunities overcome challenges in adopting immersive virtual reality in online learning

The use of immersive Virtual Reality (IVR) has witnessed an increasing demand in online learning, though challenges are slowing its adoption. This paper presents a scoping review on the deployment of IVR in online learning: its utilisation, challenges, and opportunities. The study reveals that IVR is mainly used to provide remote access to learning experiences, improve cognitive, affective and skills outcomes, and simulate experiences that are often available in traditional classrooms. Hardware-related issues are among the greatest challenges for the ubiquitous use of IVRs in online learning, with internet access being cited as the primary concern. Cost of hardware, physical side-effects, software design and amount of educator training required have also been raised as common challenges. The analysis of the reported opportunities revealed that the increasing availability of VR technologies shows the potential to overcome these challenges associated with hardware, where smartphone devices are still considered the most accessible and scalable tool. In addition, the increasing availability of modelling software creates an opportunity for faster and cheaper development of VR applications. Furthermore, the use of IVR in online learning presents solutions to common online learning issues associated with reduced student engagement and learning, access to work-based activities and lack of connectedness.

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks

Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool’s ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.

Fungal Identifier (FId): An Updated Polymerase Chain Reaction-Restriction Fragment Length Polymorphism Approach to Ease Ascomycetous Yeast Isolates' Identification in Ecological Studies.

Culturomics has been temporarily exceeded by the advent of omics approaches such as metabarcoding and metagenomics. However, despite improving our knowledge of microbial population composition, both metabarcoding and metagenomics are not suitable for investigating and experimental testing inferences about microbial ecological roles and evolution. This leads to a recent revival of culturomics approaches, which should be supported by improvements in the available tools for high-throughput microbial identification. This study aimed to update the classical PCR-RFLP approach in light of the currently available knowledge on yeast genomics. We generated and analyzed a database including more than 1400 ascomycetous yeast species, each characterized by PCR-RFLP profiles obtained with 143 different endonucleases. The results allowed for the in silico evaluation of the performance of the tested endonucleases in the yeast species' identification and the generation of FId (Fungal Identifier), an online freely accessible tool for the identification of yeast species according to experimentally obtained PCR-RFLP profiles.

Visualizing Nursing Narratives: An Evaluation of Latent Dirichlet Allocation Topic Modeling for Care Reports.

The increasing volume of unstructured textual data in healthcare, particularly in nursing care reports, presents both challenges and opportunities for enhancing patient care and operational efficiency. This study explores the application of Latent Dirichlet Allocation (LDA) topic modeling to analyze free-text nursing narratives from inpatient stays in three different clinics, aiming to uncover the latent thematic structures within. Utilizing the R programming environment and the visualization tool LDAvis, we identified three main themes: "Patient Well-being," "Patient Mobility and Care Activities," and "Treatment and Pain Management," the latter combining two closely related but initially distinct topics due to their overlapping content. Our findings demonstrate the potential of LDA topic modeling in extracting meaningful insights from nursing narratives, which could inform patient care strategies and healthcare practices. However, the study also highlights significant challenges associated with the method, including the sensitivity to parameter settings, the lack of updates for key software packages, and concerns about reproducibility. These issues highlight the need for meticulous parameter validation and the exploration of alternative text analysis methodologies for future research. By addressing these methodological challenges and emphasizing the importance of comparative method analysis, this study contributes to the advancement of text analytics in healthcare. It opens avenues for further research aimed at developing more robust, efficient, and accessible tools for analyzing free-text data, thereby enhancing the ability of healthcare professionals to use unstructured data to improve decision making and patient outcomes.

Serum glial fibrillary acidic protein as a marker of brain MRI metrics in multiple sclerosis: A scoping review.

Magnetic resonance imaging (MRI) is heavily relied upon for the diagnosis and monitoring of multiple sclerosis (MS), a chronic, demyelinating disease of the central nervous system. Serum biomarkers may serve as an accessible tool for increasing sensitivity, improving accessibility, corroborating symptoms, and providing additional data to guide clinical management. This scoping review investigates the current understanding of how the serum biomarker glial fibrillary acidic protein (sGFAP) relates to brain MRI metrics. We adhered to the Joanna Briggs Institute methodology for scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. The databases Medline (Ovid), Embase (Ovid), CINAHL (Ebsco), and Web of Science (University of British Columbia institutional access) were searched on August 24, 2023 using a combination of medical subject headings and keyword terms for the topic of serum biomarkers in MS. A total of 9880 articles were retrieved in total of which 6271 unique titles and abstracts were screened. Twelve of the 259 resultant papers contained sGFAP data and proceeded to extraction. It was found that lesion MRI metrics generally had a positive relationship with sGFAP, while gray matter and white matter metrics, including normal-appearing white matter, were related negatively or not at all. These results highlight that while sGFAP may not be specific for MS, it may have utility for increasing sensitivity in postdiagnosis monitoring of MS progression.

How does pedestrian accessibility vary for different people? Development of a Perceived user-specific Accessibility measure for Walking (PAW)

Current accessibility measures assume that all people are the same, whereas in reality there are many different user groups with different needs and perceptions. Furthermore, the concepts of walking accessibility and walkability are often analysed independently from each other. This leads to a mismatch between calculated accessibility and perceived accessibility. This paper seeks to propose a new methodological approach that considers user-specific perceptions and walkability needs when calculating pedestrian accessibility. A Perceived user-specific Accessibility measure for Walking (PAW) is developed for four sample user groups: seniors, children, women, and wheelchair users. This is done by adjusting the Geo Open Accessibility Tool (GOAT) and imputing the perceptions. Per user group, the most important walkability attributes are therefore included in the accessibility formula and weighted according to their relevance based on the literature review using the Analytic Hierarchy Process (AHP) method. Results for a district of Munich, Germany are visualised. When juxtaposed with conventional time-based accessibility measures, our results unveil a more nuanced understanding of pedestrian infrastructure and its variabilities across different user demographics. This approach can help to provide a more realistic portrayal of pedestrian accessibility and to uncover critical gaps in current infrastructure, tailored to the needs of diverse population groups. The method can assist urban and transport planners in designing more inclusive, equitable urban environments. This contributes to a shift towards cities that are not only walkable but also attuned to the diverse needs and perceptions of their residents, ultimately enhancing quality of life and promoting equitable access to urban amenities.

Improved Accuracy of the Addenbrooke's Cognitive Examination-Revised in the Diagnosis of Mild Cognitive Impairment, Mild Dementia Due to Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Mokken Scale Analysis.

The Addenbrooke's Cognitive Examination-Revised (ACE-R) is an accessible cognitive tool that supports the early detection of mild cognitive impairment (MCI), Alzheimer's disease (AD), and behavioral variant frontotemporal dementia (bvFTD). To investigate the diagnostic efficacy of the ACE-R in MCI, AD, and bvFTD through the identification of novel coefficients for differentiation between these diseases. We assessed 387 individuals: 102 mild AD, 37 mild bvFTD, 87 with amnestic MCI patients, and 161 cognitively unimpaired controls. The Mokken scaling technique facilitated the extraction out of the 26 ACE-R items that exhibited a common latent trait, thereby generating the Mokken scales for the AD group and the MCI group. Subsequently, we performed logistic regression, integrating each Mokken scales with sociodemographic factors, to differentiate between AD and bvFTD, as well as between AD or MCI and control groups. Ultimately, the Receiver Operating Characteristic curve analysis was employed to assess the efficacy of the coefficient's discrimination. The AD-specific Mokken scale (AD-MokACE-R) versus bvFTD exhibited an Area Under the Curve (AUC) of 0.922 (88% sensitivity and specificity). The AD-MokACE-R versus controls achieved an AUC of 0.968 (93% sensitivity, 94% specificity). The MCI-specific scale (MCI-MokACE-R) versus controls demonstrated an AUC of 0.859 (78% sensitivity, 79% specificity). The ACE-R's capacity is enhanced through statistical methods and demographic integration, allowing for accurate differentiation between AD and bvFTD, as well as between MCI and controls. This new method not only reinforces its clinical value in early diagnosis but also surpasses traditional approaches noted in prior studies.

Estimating optimal decision trees for treatment assignment: The case of K > 2 treatment alternatives.

For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis.

This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the data using least-squares regression and evaluating the model fit using the coefficient of determination (R-squared). The results highlight the strong performance of the quadratic polynomial fit, as evidenced by high R-squared values, indicating the model's ability to explain a substantial proportion of the data variability. Comparisons with linear and cubic models further underscore the quadratic model's balance between simplicity and precision for many practical applications. The study also acknowledges the limitations of quadratic polynomials and proposes future research directions to enhance their accuracy and efficiency for diverse data analysis tasks. This research bridges the gap between theoretical concepts and practical implementation, providing an accessible Python-based tool for leveraging quadratic polynomials in data analysis.

SILP: Enhancing skin lesion classification with spatial interaction and local perception

Because of the harmful effects of ultraviolet rays and global environmental factors, the number of patients with skin lesions is increasing. If left untreated, skin lesions may lead to skin cancer. However, limited access to specialized medical care remains a challenge in certain regions. Therefore, there is an urgent need for an efficient, accurate, and accessible tool to identify suspicious lesions. Although there are many classification models for skin lesions, there is still room for improvement in terms of accuracy. To enhance the accuracy of skin lesion classification, a novel system named SILP is proposed in this study. There are two modules in SILP: the LPM and the SIM. Additionally, we have changed the activation function from GELU to SiLU to improve both training time and accuracy. SILP, along with several other models, has been tested on two public skin lesion datasets. The results demonstrate that our proposed system outperforms the state-of-the-art skin lesion classification model, not only in terms of accuracy but also in various other evaluation metrics.

The Effects of the Virtual Background on French as a Second Foreign Language Vocabulary Learning

The purpose of this research was to investigate how the use of green screen technology affects vocabulary acquisition in LOTE (languages other than English) learners, particularly Chinese students learning French as a second foreign language. Two groups (a control group and an experimental group) with a total of 58 students participated in the study. Data were collected through in-class and delayed vocabulary tests, learning questionnaire, and semi-structured interviews. The study results demonstrate significant benefits in terms of providing an authentic learning experience, improving understanding, increasing motivation, and facilitating interaction. The results also confirm the technology’s effectiveness in enhancing both immediate and long-term vocabulary retention. The effectiveness is particularly evident for location-related words, as the technology provides visual context that facilitates recall of these words, which may not have easily retrievable mental images compared to more concrete words with tangible references. Further research can also be undertaken to compare the impact of virtual background on vocabulary retention with other immersive technologies. Nevertheless, virtual backgrounds are likely to be a cost-effective and accessible tool that can already offer promising improvements in learning outcomes and can be easily implemented with existing software like Zoom (5.2.0 or higher) and Tencent Meeting (1.3.0 or higher).

Relevance of ultrasound detection and assessment of vascular calcifications in chronic kidney disease

Introduction: Chronic kidney disease (CKD) affects approximately 10% of the Spanish population and constitutes an independent cardiovascular risk factor. Vascular calcifications, especially in the abdominal aorta, are significantly associated with increased cardiovascular mortality in patients with CKD. This systematic review evaluates the evidence on the usefulness of ultrasound to detect and assess vascular calcifications in patients with CKD, and its relevance as a cardiovascular risk factor.Methods: A systematic search of multiple electronic databases was conducted until July 2024. Original studies published between 2000 and 2024 evaluating the use of ultrasound to detect vascular calcifications in adult patients with CKD at any stage were included. Reviews, letters, editorials, animal studies and those not published in English or Spanish were excluded. Methodological quality was assessed using the Newcastle-Ottawa scale for observational studies and the Cochrane tool for clinical trials.Results: Twenty-eight studies were included with a total of 50 to 3,000 participants per study. Most were observational studies, with 3 randomised clinical trials. Ultrasound showed good correlation with computed tomography (r=0.65-0.82) to quantify aortic and femoral calcifications. The presence of aortic calcifications was associated with increased risk of cardiovascular mortality (HR 1.8-3.2) in patients with advanced CKD and on haemodialysis. Iliac/femoral calcifications were associated with increased risk of cardiovascular events (RR 1.6-2.4) in patients with stage 3-5 CKD. Incorporation of ultrasound assessment of calcifications into CKD-specific risk algorithms significantly improved their predictive performance.Discussion: Ultrasonography emerges as a valuable tool for the detection of vascular calcifications due to its non-invasive nature, low cost and ability to perform repeated assessments. However, limitations such as heterogeneity in assessment methods, variable sample sizes and lack of long-term follow-up in some studies were identified. Further research is needed to standardise protocols and assess the long-term impact of interventions based on ultrasound detection of calcifications.Conclusions: Ultrasonography is an effective and accessible tool for detecting and assessing vascular calcifications in patients with CKD, providing relevant prognostic information for cardiovascular risk stratification. Its consideration as a first-line tool in nephrological practice is recommended. However, large-scale prospective studies are needed to definitively validate its prognostic value in different stages of CKD and to evaluate the efficacy of early interventions based on these findings.