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Bioengineering (Basel, Switzerland) Dec 2023The printing and manufacturing of anatomical 3D models has gained popularity in complex surgical cases for surgical planning, simulation and training, the evaluation of...
The printing and manufacturing of anatomical 3D models has gained popularity in complex surgical cases for surgical planning, simulation and training, the evaluation of anatomical relations, medical device testing and patient-professional communication. 3D models provide the haptic feedback that Virtual or Augmented Reality (VR/AR) cannot provide. However, there are many technologies and strategies for the production of 3D models. Therefore, the aim of the present study is to show and compare eight different strategies for the manufacture of surgical planning and training prototypes. The eight strategies for creating complex abdominal oncological anatomical models, based on eight common pediatric oncological cases, were developed using four common technologies (stereolithography (SLA), selectie laser sinterning (SLS), fused filament fabrication (FFF) and material jetting (MJ)) along with indirect and hybrid 3D printing methods. Nine materials were selected for their properties, with the final models assessed for application suitability, production time, viscoelastic mechanical properties (shore hardness and elastic modulus) and cost. The manufacturing and post-processing of each strategy is assessed, with times ranging from 12 h (FFF) to 61 h (hybridization of FFF and SLS), as labor times differ significantly. Cost per model variation is also significant, ranging from EUR 80 (FFF) to EUR 600 (MJ). The main limitation is the mimicry of physiological properties. Viscoelastic properties and the combination of materials, colors and textures are also substantially different according to the strategy and the intended use. It was concluded that MJ is the best overall option, although its use in hospitals is limited due to its cost. Consequently, indirect 3D printing could be a solid and cheaper alternative.
PubMed: 38247908
DOI: 10.3390/bioengineering11010031 -
PloS One 2023The limitations of the tractor virtual test system are evident in various aspects, including model reuse, system expansion, offsite interconnection, and virtual reality...
The limitations of the tractor virtual test system are evident in various aspects, including model reuse, system expansion, offsite interconnection, and virtual reality verification. To address these challenges, a distributed virtual test system for tractors based on the high-level architecture (HLA) is proposed. Involve analyzing the hardware structure and the tractor virtual test system, constructing the system federation and its members, and designing the federated object model (FOM) and simulation object model (SOM) tables. The system integrates multi-domain commercial software and enables real-time virtual testing through TCP/IP interconnection of multiple machines. To evaluate the system's performance, a virtual test of the tractor's reversing clutch engagement performance is conducted. The system's simulation performance and data transmission delay are thoroughly tested and analyzed. The results indicate that when the system's data volume reaches 5000KB, the data delay is 9.7ms, which satisfies the requirement of not exceeding 10ms for tractor virtual testing delay. The virtual test of the reversing clutch power reversal process demonstrates that it lasts 0.7s, with the vehicle speed changing from -3.5km/h to 3.5km/h, the forward gear piston oil pressure increasing from 0MPa to 5MPa, and the peak impact degree reaching 17m/s3. The slip work during the reversing process is measured to be 21kJ. Furthermore, the gray correlation method is employed to compare the virtual test results with the bench test results, confirming their consistency. The power reversal process exhibits relatively smooth speed changes overall. Therefore, the tractor power shift transmission (PST) reversing clutch virtual test model operates effectively within the HLA-based tractor virtual test system.
Topics: Accidents, Occupational; Agriculture; User-Computer Interface; Technology; Software
PubMed: 37871104
DOI: 10.1371/journal.pone.0293229 -
BMC Infectious Diseases Nov 2023The urgent need for new treatments for multidrug-resistant tuberculosis (MDR-TB) and pre-extensively drug-resistant tuberculosis (pre-XDR-TB) is evident. However, the...
Study protocol for safety and efficacy of all-oral shortened regimens for multidrug-resistant tuberculosis: a multicenter randomized withdrawal trial and a single-arm trial [SEAL-MDR].
INTRODUCTION
The urgent need for new treatments for multidrug-resistant tuberculosis (MDR-TB) and pre-extensively drug-resistant tuberculosis (pre-XDR-TB) is evident. However, the classic randomized controlled trial (RCT) approach faces ethical and practical constraints, making alternative research designs and treatment strategies necessary, such as single-arm trials and host-directed therapies (HDTs).
METHODS
Our study adopts a randomized withdrawal trial design for MDR-TB to maximize resource allocation and better mimic real-world conditions. Patients' treatment regimens are initially based on drug resistance profiles and patient's preference, and later, treatment-responsive cases are randomized to different treatment durations. Alongside, a single-arm trial is being conducted to evaluate the potential of sulfasalazine (SASP) as an HDT for pre-XDR-TB, as well as another short-course regimen without HDT for pre-XDR-TB. Both approaches account for the limitations in second-line anti-TB drug resistance testing in various regions.
DISCUSSION
Although our study designs may lack the internal validity commonly associated with RCTs, they offer advantages in external validity, feasibility, and ethical appropriateness. These designs align with real-world clinical settings and also open doors for exploring alternative treatments like SASP for tackling drug-resistant TB forms. Ultimately, our research aims to strike a balance between scientific rigor and practical utility, offering valuable insights into treating MDR-TB and pre-XDR-TB in a challenging global health landscape. In summary, our study employs innovative trial designs and treatment strategies to address the complexities of treating drug-resistant TB, fulfilling a critical gap between ideal clinical trials and the reality of constrained resources and ethical considerations.
TRAIL REGISTRATION
Chictr.org.cn, ChiCTR2100045930. Registered on April 29, 2021.
Topics: Humans; Antitubercular Agents; Extensively Drug-Resistant Tuberculosis; Mycobacterium tuberculosis; Tuberculosis, Multidrug-Resistant; Clinical Protocols; Randomized Controlled Trials as Topic; Multicenter Studies as Topic
PubMed: 38012543
DOI: 10.1186/s12879-023-08644-8 -
Database : the Journal of Biological... Feb 2024In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data...
In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.
Topics: Humans; Semantic Web; Databases, Factual; Language; Virtual Reality
PubMed: 38554132
DOI: 10.1093/database/baae008 -
Enfermeria Clinica (English Edition) Apr 2024Define the modes of procedure of the Deductive Care Methodology (DCM) in the generation of knowledge about person's health care.
OBJECTIVE
Define the modes of procedure of the Deductive Care Methodology (DCM) in the generation of knowledge about person's health care.
METHODOLOGY
Design and test of the DCM modes based on three phases: mapping of the DCM, generation of models from this methodology and testing of the models through studies in a clinical context.
RESULTS
The DCM presents five levels of abstraction with three modes broken down to 16 types. The modes are: Philosophical Mode to conceptualize and obtain generalities about reality, Mathematical Mode to operate with generalities, and Physical Mode to operationally verify, validating the results and the predictive capacity of the model. This MDC allows the creation of three models: Knowledge Model about Person Care, an ontology of care, Vulnerability Model about the person and Taxonomic Triangulation Model for knowledge management. All models generate products for computational knowledge management. In addition, the models are applied in teaching and generate research with more than a hundred participations in conferences and journals, of which five impact publications (from 2008 to 2022) classified in the categories of Nursing and Informatics are analysed.
CONCLUSIONS
The DCM collects prior knowledge to work with certainties, evidence and applying inferences that do not depend on the number of cases or inductive designs. This research presents a formal structure of the DCM with an interdisciplinary orientation between Health Sciences and Computer Sciences.
PubMed: 38614457
DOI: 10.1016/j.enfcle.2024.04.001 -
Cureus Nov 2023Across the world, there are few universal scenarios, but the pain of losing a loved one to heart disease is an exception and a reality shared by millions every year....
Across the world, there are few universal scenarios, but the pain of losing a loved one to heart disease is an exception and a reality shared by millions every year. Heart disease is the greatest killer in society today, and one prevalent root of this issue is untimely diagnosis, often caused by unsustainable costs and lack of accessible healthcare for underserved populations. Recognizing these disparities, the goal of this project was to create an easily available application and interface for all that accurately indicates one's risk of heart disease. To address this, a machine learning model, Predict2Protect, was built in Python. An open-source dataset compiled of 1025 patients of diverse backgrounds was scaled, adjusted to include inquiries answerable by patients, and split into 75% for training, 15% for validation, and 25% for testing. Four models were tested with the hypothesis that if the RandomForestClassifier was used, it would have the highest validity. This was not supported, as the DecisionTree model had a 100% accuracy for training data and 95% for test data. Through the application software Streamlit, this program was processed into a web application that is now found in browser extensions. The application reports the risk of one having heart disease with a 95% accuracy and describes the risk percentage of developing heart disease within the next year. With a simple interface and high accuracy, Predict2Protect aims to provide a view into one's health with the goals of accessible heart disease prediction and early treatment for patients around the world.
PubMed: 38024045
DOI: 10.7759/cureus.49305 -
Sports Medicine (Auckland, N.Z.) Oct 2023Decision making is vital in complex sporting tasks but is difficult to test and train. New technologies such as virtual and augmented reality offer novel opportunities... (Review)
Review
Decision making is vital in complex sporting tasks but is difficult to test and train. New technologies such as virtual and augmented reality offer novel opportunities for improving decision making, yet it remains unclear whether training gains using these new approaches will improve decision making on-field. To clarify the potential benefits, a clear conceptualization of decision making is required, particularly for invasive team sports such as football, basketball and field hockey, where decisions are complex with many possible options offered. Therefore, the aim of this position paper is to establish a framework for the design of virtual and augmented environments that help invasive team sport athletes to train their decision-making capacities. To achieve this, we propose a framework for conceptualising 'natural' decision making within the performance environment in invasive team sports that views decision making as a continuous cyclical process where the ball carrier interacts with teammates to create 'windows of opportunity', and where skilled decision makers often delay decisions to create time, and in turn new opportunities, rather than necessarily selecting the first option available to them. Within the framework, we make a distinction between decision making and anticipation, proposing that decision making requires a series of on-going anticipatory judgments. Based on the framework, we subsequently highlight the consequences for testing and training decision making using virtual and augmented reality environments, in particular outlining the technological challenges that need to be overcome for natural decision making to be represented within virtual and augmented environments.
Topics: Humans; Basketball; Team Sports; Football; Decision Making
PubMed: 37656407
DOI: 10.1007/s40279-023-01884-3 -
Journal of Clinical Medicine Dec 2023Virtual reality (VR) is a valuable tool for the treatment and prevention of psychiatric disorders and dysfunctional behaviors. Although VR software is mainly developed...
Protocol for a Randomized Controlled Trial Testing the Efficacy of a Transdiagnostic Virtual Reality-Based Intervention for the Reduction of Unhealthy Lifestyles and Behaviors in the General Population.
Virtual reality (VR) is a valuable tool for the treatment and prevention of psychiatric disorders and dysfunctional behaviors. Although VR software is mainly developed following a disorder-specific approach, this randomized controlled trial (RCT) will test the efficacy of a new transdiagnostic VR application (H.O.M.E. VR-based psychological intervention) in improving dysfunctional behaviors, three transdiagnostic factors concurrently (emotion regulation, experiential avoidance, and psychological flexibility), and stress. Three groups screened as at-risk for nicotine dependence, alcohol abuse, and eating disorders will be assigned to the H.O.M.E. VR intervention and compared to a waiting-list (WL) condition. Participants will be assessed before and after the H.O.M.E. intervention/WL and at the 3- and 6-month follow-ups in the levels of the displayed dysfunctional behavior, the three transdiagnostic factors, and stress. Changes in dysfunctional behaviors, transdiagnostic factors, and stress in each population VR group and differences in such improvements between each population of the VR and WL groups will be evaluated using mixed-model repeated measure analyses of variance. It is expected that, after the H.O.M.E. intervention and at follow-ups, participants will display improvements in physical and psychological health compared to controls. The H.O.M.E. protocol is expected to result in a cost-effective option to tackle cognitive-behavioral factors shared among several psychopathologies and dysfunctional behaviors.
PubMed: 38068522
DOI: 10.3390/jcm12237470 -
Microorganisms Nov 2023Leptospirosis represents a public health problem in Colombia. However, the underreporting of the disease is an unfortunate reality, with a clear trend towards a decrease... (Review)
Review
Leptospirosis represents a public health problem in Colombia. However, the underreporting of the disease is an unfortunate reality, with a clear trend towards a decrease in cases since 2019, when the guidelines for its confirmatory diagnosis changed with the requirement of two paired samples. The purpose of this review is to highlight the importance of leptospirosis. While the access to rapid diagnosis is available at practically all levels of care for dengue and malaria, leptospirosis-a doubly neglected disease-deserves recognition as a serious public health problem in Colombia. In this manner, it is proposed that molecular tests are a viable diagnostic alternative that can improve the targeted treatment of the patient and the timeliness of data and case reporting to SIVIGILA, and reduce the underreporting of the disease. Taking advantage of the strengthened technological infrastructure derived from the SARS-CoV-2 pandemic for molecular diagnosis in Colombia, with a network of 227 laboratories distributed throughout the national territory, with an installed capacity for PCR testing, it is proposed that molecular diagnosis can be used as an alternative for early diagnosis. This would allow case confirmation through the public health network in Colombia, and, together with the microagglutination (MAT) technique, the epidemiological surveillance of this disease in this country would be strengthened.
PubMed: 38004770
DOI: 10.3390/microorganisms11112759 -
NPJ Digital Medicine Dec 2023Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to...
Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to measure cognition needed for IADL in early Alzheimer's disease (AD) both in the clinic and in the home settings. The primary aim of this study was to distinguish and classify healthy controls (HC) from participants with AD pathology in an early AD stage using an AR app. The secondary aims were to test the association of the app with clinical cognitive and functional tests and investigate the feasibility of at-home testing using AR. We furthermore investigated the test-retest reliability and potential learning effects of the task. The digital score from the AR app could significantly distinguish HC from preclinical AD (preAD) and prodromal AD (proAD), and preAD from proAD, both with in-clinic and at-home tests. For the classification of the proAD group, the digital score (AUC = 0.84 [0.75-0.93], AUC = 0.77 [0.61-0.93]) was as good as the cognitive score (AUC = 0.85 [0.78-0.93]), while for classifying the preAD group, the digital score (AUC = 0.66 [0.53-0.78], AUC = 0.76 [0.61-0.91]) was superior to the cognitive score (AUC = 0.55 [0.42-0.68]). In-clinic and at-home tests moderately correlated (rho = 0.57, p < 0.001). The digital score was associated with the clinical cognitive score (rho = 0.56, p < 0.001). No learning effects were found. Here we report the AR app distinguishes HC from otherwise healthy Aβ-positive individuals, both in the outpatient setting and at home, which is currently not possible with standard cognitive tests.
PubMed: 38110486
DOI: 10.1038/s41746-023-00978-6