Efficacy Of Training Research Articles

Efficacy of typodont and simulation training in orthodontic education: a systematic review

This systematic review evaluates the efficacy of analogue and virtual typodont/simulation training in orthodontic education, focusing on its impact on clinical skills, motivation, and communication (PROSPERO ID# CRD42024560497). A comprehensive search across PubMed, the Cochrane Library, ERIC, and CINAHL identified 11 studies that examined various simulation-based learning approaches. The findings indicate that simulation-based training enhances short-term diagnostic accuracy, treatment planning, and practical skills in orthodontics, particularly in tasks such as bracket bonding and clinical reasoning. Simulations also fostered student engagement and motivation, bridging the gap between theoretical learning and practical application. However, challenges such as variability in simulation technologies, technical issues with advanced tools like VR, and the perception of increased workload were identified. While most students reported high satisfaction, the heterogeneity among study designs and training objectives limits generalizability. This review highlights the potential of simulation-based learning to enhance clinical preparedness in orthodontics and recommends further research to explore strategies for standardizing simulation tools, sustaining motivation, and evaluating long-term clinical outcomes.

Effect of inspiratory and expiratory muscle training on respiratory function and decannulation outcome in patients with tracheostomy after stroke: a randomized controlled trial

ABSTRACT Background This study aimed to evaluate the clinical efficacy and safety of inspiratory and expiratory muscle training (IEMT) for patients who underwent tracheostomy after stroke. Methods The study was an investigator-initiated, single-center, two-arm, evaluator-blinded, randomized clinical trial conducted at West China Hospital of Sichuan University, China, from January 2022 to June 2022. The patients were randomly divided into the intervention group and control group. Patients in both groups received conventional clinical, rehabilitation treatment and usual care. The intervention group also received IEMT for three weeks. There were three primary outcomes including maximal inspiratory pressure (MIP, cmH2O), maximal expiratory pressure (MEP, cmH2O) and decannulation outcome after intervention (n, %). The secondary outcomes were other respiratory function outcomes, motor function, activities of daily living (ADL), quality of life (QoL) and the new pulmonary infection rate after the intervention at three weeks. Results A total of 50 participants were enrolled [25 in each group; 39 (78%) were men; mean (SD) age, 55.94 (11.97) years]. At three weeks, significant differences were found in the MIP [control vs IEMT: 39.04 (6.21) vs 56.28 (10.41), p < 0.001]; MEP [43.48 (5.36) vs 62.16 (10.18), p < 0.001], and tracheal tube extraction success rate [2 (8%) vs 9 (36%), p = 0.019] between the two groups. In addition, the new pulmonary infection rate in the intervention groups were significantly different (all p < 0.05) from those in the control group. Conclusions IEMT can improve respiratory function, decannulation outcome, among patients with tracheostomy after stroke.

Evaluation of the effect of biofeedback system driven by optoelectronic conjugate materials in VR exercise rehabilitation

Presently, Exercise Rehabilitation is crucial to restoring and improving a person’s well-being in physical fitness. The present research investigates the significance of Optoelectronic Conjugate Materials (OCM) in Biofeedback Systems to enhance the effectiveness of Virtual Reality (VR) Exercise Rehabilitation. This work presents the Design of Biofeedback System-driven Optoelectronic Conjugate Materials (DBS-OCM), which offers a novel methodology to enhance VR Exercise Rehabilitation. The DBS-OCM framework amalgamates synthesis and material characterization techniques for tissue healing during VR Exercise Rehabilitation. The present research aims to elucidate a resistance trainer’s design and preparation methodology during the Evaluation of the Effect of a Biofeedback System Driven by OCM. This approach seeks to optimize the flexibility of the system to cater to individual requirements during VR Exercise Rehabilitation. The experimental findings demonstrate notable advancements, including a 93.9% augmentation in tissue regeneration, a 95.5% enhancement in the efficacy of resistance training, and a 95.5% boost in involvement during virtual reality exercise. The results highlight the DBS-OCM’s capacity to enhance the Biofeedback System’s efficiency Driven by Optoelectronic Conjugate Materials in VR Exercise Rehabilitation. These findings provide essential insights that have the potential to shape future research endeavors and facilitate the development of practical applications in the domain of VR Exercise Rehabilitation.

Cost effective clinical methods to evaluate the efficacy of gait training devices: A systematic review of randomized controlled trials

Abstract Aim: This systematic review was aimed at identifying cost-effective outcome assessment metrics to perform clinical trials for assessing the efficacy of novel, low-cost gait training devices. Materials and methods: The search was conducted by the investigators through electronic databases, namely SCOPUS (91), Web of Science (93), PubMed (141) and Cochrane Library (164), from origination to 31st March 2024. The study design was a PRISMA style systematic review of randomized controlled trials (RCTs) of robotic gait training devices (RGTDs) that treated stroke patients. Results: Based on our inclusion and exclusion criteria, 17 randomized controlled trials were studied to identify suitable outcome assessment measures. This involved 705 patients at different stages of stroke, who were treated with different intervention durations, devices, randomization and blinding methods. It was observed from the extensive clinical trials with the RGTDs that it was tested with a variety of assessment methods. Cost-effective outcome assessment measures that require commonly available materials are chosen and discussed in this review. It is identified that the most extensively used measures possess concurrent validity, sufficient inter-rater, intra-rater and test-retest reliability. Conclusion: Clinical trials with a sophisticated setup cannot be afforded by clinics in low-income countries. It is vital to identify assessment methods that require commonly available materials that do not incur huge material costs. The methods discussed in this review can be administered without special training. This can facilitate quantifying and comparing the efficacy of these devices through clinical trials and multi-centric investigations.

Insights into Building VR Solutions in the Police Domain

Advanced technologies such as Virtual Reality (VR) hold immense significance for society, particularly in the law enforcement domain. VR offers a unique platform for immersive, experiential learning, allowing Police Officers (POs) to engage with dynamic scenarios that mirror real-world situations, incidents, and challenges. In the Police domain, the integration of VR solutions provides valuable insights into enhancing POs’ training, situational awareness, and community relations. Learning objectives in VR-based Police training encompass a spectrum of skills, including decision-making under pressure, effective communication and collaboration, and de-escalation techniques. Furthermore, evaluation perspectives span both quantitative and qualitative measures, assessing training efficacy, officer performance, and societal impact. This research adopts a systematic literature review approach where it becomes evident that the future of VR-enabled policing lies in interdisciplinary collaboration, technological innovation, and ethical considerations. Future directions entail refining VR solutions to reflect evolving societal dynamics, integrating Artificial Intelligence (AI) for adaptive scenario generation and analytics, and addressing concerns related to algorithmic transparency, fairness, security, and privacy. By leveraging VR technologies, law enforcement agencies can enhance operational effectiveness and foster trust, accountability, and responsibility within the communities they serve.

A mean curvature flow arising in adversarial training

We connect adversarial training for binary classification to a geometric evolution equation for the decision boundary. Relying on a perspective that recasts adversarial training as a regularization problem, we introduce a modified training scheme that constitutes a minimizing movements scheme for a nonlocal perimeter functional. We prove that the scheme is monotone and consistent as the adversarial budget vanishes and the perimeter localizes, and as a consequence we rigorously show that the scheme approximates a weighted mean curvature flow. This highlights that the efficacy of adversarial training may be due to locally minimizing the length of the decision boundary. In our analysis, we introduce a variety of tools for working with the subdifferential of a supremal-type nonlocal total variation and its regularity properties.

The impact of concurrent training in team sports and soccer.

Team sports players are frequently required to integrate multiple physical components, including strength and endurance capacity, to maximize their performance during both training and competitions. The combination of strength and endurance within a periodized program is known as concurrent training. Recently, concurrent training has emerged as an innovative method to enhance both muscular strength and aerobic performance, tailored to the specific requirements and diverse nature of each sport. However, concurrent training may induce excessive fatigue and compromise performance potentiation, depending on training prescription parameters. This distinction between the interference effect and performance enhancement presents a complex challenge for any team sports system. Consequently, the objective of this review was to scrutinize the efficacy, practical application, and methodological aspects of concurrent training. Additionally, it aims to elucidate strategies to mitigate the complexities associated with the interference effect, thereby optimizing the benefits of concurrent training modality for team sports, emphasizing soccer. The major findings indicate that concurrent training can improve strength and endurance qualities in athletes, but under certain conditions, it can also interfere with adaptations. Appropriate programming strategies, such as proper sequencing, scheduling, volume, intensity, and recovery, can help mitigate these negative effects. Additionally, whereas more experienced athletes display greater proficiency in executing concurrent training programs, younger players, particularly those under 14, tend to experience minimal interference effects from this training approach, making it well-suited for their development. Overall, concurrent training has been demonstrated as an effective and efficient method for improving strength and endurance performance in team sports players.

Nomogram Based on HRV for Predicting the Therapeutic Effects of Orthostatic Training in Children with Vasovagal Syncope

Background: This study intended to find out whether the parameters of heart rate variability (HRV) can predict the treatment efficacy of orthostatic training among pediatric cases of vasovagal syncope (VVS). Methods: Patients with VVS who underwent orthostatic training were retrospectively enrolled. Lasso and logistic regression were used to sift through variables and build the model, which is visualized using a nomogram. The model’s performance was evaluated through calibration plots, a receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) for both datasets. Results: In total, 119 participants were included in the analysis, and 73 and 46 were assigned to the training and validation datasets, respectively. Five factors with nonzero coefficients were chosen based on lasso regression: age, the root means square of successive differences between normal sinus beats (rMSSD), standard deviation of the averages normal-to-normal intervals in all 5-min segments, minimum heart rate, and high frequency. Drawing from the logistic regression analysis results, the visual predictive model incorporated two variables, namely age and rMSSD. For the training dataset, the sensitivity was 0.686 and the specificity was 0.868 with an area under the curve (AUC) of 0.81 (95% CI, 0.71–0.91) for the ROC curve. For the validation dataset, the AUC of the ROC was 0.80 (95% CI, 0.66–0.93), while sensitivity and specificity were recorded at 0.625 and 0.909, respectively. In the calibration plots for both datasets, the predicted probabilities correlated well with the actual probabilities. According to the DCA, the visual predictive model gained a significant net benefit across a wide threshold range. Conclusions: Pediatric patients with VVS can benefit from orthostatic training using a visual predictive model comprising age and rMSSD.

Gaming-Based Tele-Exercise Program to Improve Physical Function in Frail Older Adults: Feasibility Randomized Controlled Trial.

Frailty leads to reduced physical activity can cause increased fall risk. This contributes to accelerated aging processes, leading to adverse health outcomes and reduced quality of life. We have developed and piloted the design, usability, safety, and feasibility of a gaming-based cognitive-motor (CogXergaming) tele-exercise protocol in prefrail older adults. This pilot randomized control trial tested preliminary feasibility and effectiveness of the CogXergaming telehealth protocol for improving physical function. Community-dwelling, prefrail older adults were randomly assigned to CogXergaming (n=13) or a control group (n=14). The CogXergaming group received supervised tele-exercises in a gaming format for 6 weeks (3 sessions per week) comprising 18 sessions lasting 90 minutes each. Control group participants participated in a Matter of Balance (MOB), an 8-week, once-a-week structured 90-minute tele-session that has been shown to reduce the fear of falling and increase physical activity. Feasibility of training was obtained by computing the median duration of training sessions for the CogXergaming group. Effectiveness was assessed using dynamic balance control (Four Square Step Test), subjective self-efficacy (Activities-Specific Balance Confidence scale), gait function (Tinetti Performance Oriented Mobility Assessment), muscle strength (30-second chair stand test), and endurance (2-minute step in-place test). Of the 45 participants enrolled in the study, 4 participants from CogXergaming group and 5 from MOB group lost contact after signing the consent form and did not receive their respective intervention. Eighteen participants were randomized to each group. In the CogXergaming group, 15 (83%) completed the intervention, with 3 (16%) dropping out in the first week. In the MOB group, 16 (88%) completed the program, with 2 (11%) withdrawing during the first week. In addition, there was a significant time group interaction for Four Square Step Test (F1,21=5.55, P=.03), Tinetti Performance Oriented Mobility Assessment (F1,25=4.16, P=.05), and 30-second chair stand test (F1,21=5.06, P=.03), with a significant improvement in these measures for the CogXergaming group post training, compared with no change observed in the MOB group. These pilot findings indicate that CogXergaming is feasible and applicable in prefrail older adults. Such game-based protocols can be beneficial in improving physical function among community-dwelling, prefrail older adults, however, the efficacy of such training requires further investigation. ClinicalTrials.gov NCT04534686; https://clinicaltrials.gov/study/NCT04534686.

Efficacy and safety of Neurocognitive Adaptive Training for Depression combined with SSRIs for treating cognitive impairment among patients with late-life depression: a 12-week, randomized controlled study

BackgroundThis randomized, open-label study examined the therapeutic effects of Neurocognitive Adaptive Training for Depression (NCAT-D) combined with selective serotonin reuptake inhibitors (SSRIs) on cognitive impairment among patients with late-life depression (LLD).MethodStudy data were collected from May 5, 2021, to April 21, 2023. Outpatients who met the diagnostic criteria for major depressive disorder according to the fifth revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (i.e., a total score on the 17-item Hamilton Depression Rating Scale (HAMD-17) ≥ 18 and a total score on the Montreal Cognitive Assessment scale (MOCA) < 26) were recruited at Beijing Anding Hospital. These participants were randomly assigned to receive up to 12 weeks of NCAT-D and SSRIs treatment (n = 57) or SSRIs with a control treatment (n = 61). Primary outcomes included changes in Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) scores from baseline to week 12 between the two groups. Assessments were conducted at baseline, after 2 weeks, 4 weeks, 8 weeks, and at 12 weeks. Mixed model repeated measures (MMRM) analysis was performed on modified intention-to-treat (mITT) and completer populations.ResultsThe full analysis set (FAS) included 118 patients (NCAT-D and SSRIs group, n = 57; SSRIs and Control group, n = 61). During the 12-week study period, MMRM analysis revealed a significantly greater reduction in cognitive function (as indicated by ADAS-cog total scores) from baseline to post-treatment in the NCAT-D and SSRIs group compared to the SSRIs and Control groups [(F (1,115) = 13.65, least-squares mean difference [95% CI]: −2.77 [− 3.73, − 1.81], p < 0.001)]. The intervention group showed a significantly greater reduction in HAMD-17 scores compared to the control group [MMRM, estimated mean difference (SE) between groups: −3.59 [− 5.02, − 2.15], p < 0.001]. There was no significant difference in the incidence of adverse events between the two groups.ConclusionsNCAT-D combined with SSRIs was efficacious and well tolerated in LLD patients with cognitive impairment.Trial registrationRegistered on October 18, 2022, at ClinicalTrials.gov Identifier: (#NCT05588102).

The efficacy of short-term in-service teacher training in implementing a behavioural intervention

ABSTRACT Teachers feel inadequate to face pupils’ behavioural problems in schools, yet teachers are expected to play significant roles in providing and supporting pupils’ appropriate social behaviour. Unfortunately, teacher education in Finland and globally seems to offer little content for behavioural support, and in-service training sessions are filling this educational gap. The key context in this study is the in-service training that was directed at primary school teachers and teaching staff. The aim of this study was to explore the efficacy of short-term in-service training and discuss the support that in-service training can provide for behavioural problems that are experienced in school. From 2017 to 2021, over 200 teachers or teaching staff members participated in check-in, check-out (CICO), in-service training sessions that were funded by the Finnish National Agency of Education and organised by the Niilo Mäki Institute. Afterwards, the participants were asked to answer a questionnaire concerning their use of the CICO model in their own schools. The post-training questionnaire reached over 80 participants. The results indicated that CICO support was introduced well to schools after the training; however, for some teachers, the short in-service training did not sufficiently support this implementation. Also, factors that complicated and contributed the implementation were examined. According to the results of the study, implementation can be supported with active learning, e.g. opportunities to practise new skills and the use of models.

Machine learning-assisted literature screening for a medication-use process-related systematic review.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This article summarizes a novel methodology of applying machine learning (ML) algorithms trained with external training data to assist with article screening for 2 annual review series related to the medication-use process (MUP) generally and the MUP in ambulatory care settings (ACMUP) specifically. As the literature review for these 2 series grew over time, it became essential for the authors to develop methods to be efficient while still capturing most of the relevant literature. The ML model can be used to predict whether search results are likely to be relevant or not relevant. Results least likely to be relevant can then be excluded without manual screening, allowing research teams to save time that would otherwise be spent reviewing a portion of the search results for inclusion. ML models require a large training dataset typically derived from the unclassified corpus. In this study, the authors demonstrate the efficacy of training the ML model using external training data, which is possible in scenarios such as a systematic review update or ongoing review series such as those for the MUP and ACMUP. The authors ran 3 simulations using screening decisions from previous publications and in-process manuscripts for the MUP and ACMUP review series to test the efficacy of the approach. The simulations were compared to actual manual screening decisions made by the research teams to include or exclude articles using title and abstract text. For each simulation, the authors developed a training dataset using a sample of screening decisions from previous years to predict article relevance in an "unclassified" corpus. In this case, the screening decisions for the unclassified corpus were actually known, allowing us to calculate recall (percent of relevant articles captured) and time saved using the number of articles that would be excluded without manual review. Combined, the ML approach correctly labeled 187 of 192 relevant studies. The 3 simulations included 17,227 unique studies, and using ML the authors demonstrated that 13,201 studies could have been excluded without manual screening while still maintaining recall of relevant articles of 95% or greater. This novel approach is applicable to systematic reviews and ongoing review series, including those for the MUP and ACMUP. Pharmacists have a duty to review and incorporate best practices into their organizations to improve the efficiency and cost of care, optimally utilize technology, and reduce the potential for medication errors. This methodology will allow evidence syntheses for the MUP and other disciplines in pharmacy practice to be published more expeditiously by saving significant time during the article screening step.

Effectiveness of resistance training in preventing sarcopenia among breast cancer patients undergoing chemotherapy: A systematic review and meta-analysis.

Breast cancer patients undergoing chemotherapy experience body composition changes impacting treatment outcomes. The role of resistance training in mitigating chemotherapy-induced sarcopenia in breast cancer patients is not well defined. This study aims to assess the efficacy of resistance training in preventing sarcopenia among breast cancer patients undergoing chemotherapy. A systematic search was conducted across PubMed, EMBASE, Medline, the Cochrane Library, and CINAHL until May 5, 2023. Selected literature focused on the effects of resistance training on body fat, muscle mass, muscle strength, and physical performance in breast cancer patients undergoing chemotherapy. Cochrane Risk of Bias tool version 2.0 was employed for quality assessment, and data were analyzed using Comprehensive Meta-Analysis version 2.0. Eleven randomized controlled trials (RCTs) showed that resistance training had a significant positive impact on reducing body fat (SMD = -0.250, 95% CI [-0.450, -0.050]), increasing lean body mass (SMD = 0.374, 95% CI [0.178, 0.571]), and enhancing handgrip strength at both the affected site (SMD = 0.326, 95% CI [0.108, 0.543]) and the nonaffected site (SMD = 0.276, 95% CI [0.059, 0.492]). Additionally, significant improvements were observed in leg press strength (SMD = 0.598, 95% CI [0.401, 0.796]) and overall physical performance (SMD = 0.671, 95% CI [0.419, 0.923]). Resistance training is a recommended intervention for reducing body fat, increasing muscle mass, muscle strength, and enhancing physical performance in breast cancer patients undergoing chemotherapy. Ideal low-intensity resistance training programs span 8-24 weeks, with 20-to-90-min sessions 2-4 times weekly. Regimens generally entail 8-12 repetitions at 40%-90% of one-repetition maximum test, with free-weight resistance training targeting major muscle groups yielding substantial benefits. Further research should explore outcomes across different chemotherapy phases and investigate long-term resistance training effects for a comprehensive view.

Evaluation of virtual training delivery for health information systems implementation in Canada: A qualitative study.

As health information systems (HIS) become a critical part of patient care, it is crucial to build an effective education strategy that facilitates the adoption and sustained use of these systems. The COVID-19 pandemic (2019-2023) has contributed to the rapid shift in virtual education and training for healthcare staff. We sought to evaluate the efficacy and long-term sustainability of virtual training for using a HIS by examining opportunities and challenges. An exploratory, multimethods study was conducted with staff who had taken part in a virtual HIS training program as part of the clinical transformation journey at a large academic health science center in Canada. The study was guided by the Accelerating the Learning Cycle framework. Data were collected through pre- and post-training surveys, as well as semi-structured interviews. An iterative, inductive, constant comparative analysis approach, outlined by Braun and Clarke, was taken to thematically analyse the data. Of the 33 participants in this study, 13 were educational champions, and 20 were end-users. The pre- and post-training surveys yielded a total of 1479 responses in both groups. Three prominent themes emerged from this study: (1) fostering dynamic facilitation techniques to cultivate an inclusive culture and adapt to diverse learning needs; (2) integrating practical learning activities that contribute to knowledge retention; and (3) ensuring training resources are accessible and consistent for an optimal training experience. As HIS continue to be part of the transformation of the healthcare ecosystem, education is vital in preparing healthcare providers to perform their clinical tasks and effectively use these technologies. Findings from this study can be used to inform the development of virtual training that is inclusive and addresses the needs of care providers.

A Data-Loader Tunable Knob to Shorten GPU Idleness for Distributed Deep Learning

Deep Neural Networks (DNNs) have been applied as an effective machine learning algorithm to tackle problems in different domains. However, the endeavor to train sophisticated DNN models can stretch from days into weeks, presenting substantial obstacles in the realm of research focused on large-scale DNN architectures. Distributed Deep Learning (DDL) contributes to accelerating DNN training by distributing training workloads across multiple computation accelerators, for example, graphics processing units (GPUs). Despite the considerable amount of research directed toward enhancing DDL training, the influence of data loading on GPU utilization and overall training efficacy remains relatively overlooked. It is non-trivial to optimize data-loading in DDL applications that need intensive central processing unit (CPU) and input/output (I/O) resources to process enormous training data. When multiple DDL applications are deployed on a system (e.g., Cloud and High-Performance Computing (HPC) system), the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. Therefore, our work first focuses on investigating the impact of data-loading on the global training throughput. We then propose a throughput prediction model to predict the maximum throughput for an individual DDL training application. By leveraging the predicted results, A-Dloader is designed to dynamically allocate CPU and I/O resources to concurrently running DDL applications and use the data-loader allocation as a knob to reduce GPU idle intervals and thus improve the overall training throughput. We implement and evaluate A-Dloader in a DDL framework for a series of DDL applications arriving and completing across the runtime. Our experimental results show that A-Dloader can achieve a 28.9% throughput improvement and a 10% makespan improvement compared with allocating resources evenly across applications.

Altered Dynamic Brain Functional Network Connectivity Related to Visual Network in Spinal Cord Injury.

Visual feedback training (VFT) plays an important role in the motor rehabilitation of patients with spinal cord injury (SCI). However, the neural mechanisms are unclear. We aimed to investigate the changes in dynamic functional network connectivity (FNC) related to visual networks (VN) in patients with SCI and to reveal the neural mechanism of VFT promoting motor function rehabilitation. Dynamic FNC and the sliding window method were performed in 18 complete SCI (CSCI), 16 patients with incomplete SCI (ISCI), and 42 healthy controls (HCs). Then, k-mean clustering was implemented to identify discrete FNC states, and temporal properties were computed. The correlations between these dynamic features and neurological parameters in all patients with SCI were calculated. The majority of aberrant FNC was manifested between VN and executive control network (ECN). In addition, compared with HCs, temporal metrics derived from state transition vectors were decreased in patients with CSCI including the mean dwell time and the fraction of time spent in state 3. Furthermore, the disrupted FNC between salience network and ECN in state 2 and the number of transitions were all positively correlated with neurological scores in patients with SCI. Our findings indicated that SCI could result in VN-related FNC alterations, revealing the possible mechanism for VFT in rehabilitation of patients with SCI and increasing the training efficacy and promoting rehabilitation for SCI.

Development and validation of a cost-effective DIY simulation model for McDonald cerclage training.

The prevention of preterm birth is a challenging task for obstetricians. Cervical cerclage, used as both a primary and secondary prevention method for spontaneous preterm birth, is a crucial surgical intervention. It is essential that obstetricians can learn this procedure in a simulated environment before performing the stitches on high-risk patients. This study aimed to develop a simulator based on 3D printing and evaluate its validity for clinical training. The objectives of this study were (1) to design and construct a cost-effective simulator for McDonald cerclage with two different cervix models-a closed cervix and a cervix with bulging membranes-using common material from a DIY store and 3D printing technology and (2) to validate its effectiveness through feedback from learners and experts in cervical cerclage. The self-made simulator was evaluated by obstetricians using a questionnaire with Likert scale. Obstetricians and gynecologists assessed the simulator and found it useful for learning and practicing cervical cerclage. The simulator was deemed valuable for skill training. Cervical cerclage is a complex procedure that should be mastered through simulation rather than initial practice on real patients. Our simulator is a cost-effective model suitable for various clinical settings. It has been validated by obstetricians for both preventive and therapeutic cerclage, demonstrating its efficacy for training in cerclage techniques. Future research should focus on less skilled obstetricians and gynecologists and investigate how repeated use of the simulator can enhance their performance in cerclage stitching.

FPGA-based component-wise LSTM training accelerator for neural granger causality analysis

Component-wise LSTM (cLSTM) constitutes multiple LSTM cells of distinct parameters, which has particular benefits of functional Magnetic Resonance Imaging (fMRI)-based neural Granger causality (NGC) analysis for the human brain. Back-propagation through time training on CPU and GPU suffers from low utilization due to inherent data dependencies within the LSTM cell. Moreover, batch 1 cLSTM training and few weight reuses across input feature maps worsen such a utilization problem. To this end, this study provides an FPGA-based training solution for cLSTM-based NGC analysis. The proposed cLSTM training accelerator identifies different data dependencies in forward and backward paths, and features two key components: (1) a fine-grained pipeline within the LSTM cell that achieves the lowest initial interval, and (2) a coarse-grained pipeline that trains input feature sequences across different LSTM cells in parallel. Experiments on the DAN sub-brain network from the COBRE dataset demonstrate the efficacy of FPGA-based cLSTM training, which achieves microseconds iteration latency compared with milliseconds on general-purpose platforms, e.g., 465× and 216× faster than Intel Core 13900K CPU and Nvidia RTX 2080Ti respectively. To the best of our knowledge, this work is the first to demonstrate LSTM training on FPGA, significantly accelerating the analysis and modeling of complex brain networks, and offering valuable advancements for neuroscience research at the edge.

An approach to reduce inhaler errors using Donabedian's triad.

Inhaler errors inversely affect the outcome of respiratory diseases. Inhaler devices, such as the metered-dose inhalers (MDI) and dry powder inhalers (DPI), are commonly used in treating respiratory diseases like asthma and chronic obstructive pulmonary disease (COPD), and incorrect use of these devices can result in suboptimal treatment outcomes, increased probabilities of hospitalizations or admissions, and poorer quality of life. Patient related factors to inhaler errors include age, cognitive and physical abilities, education, language barriers, and preferences. Device-related factors such as inhaler design and operational complexity can also lead to errors. Finally, factors related to healthcare professionals (HCP) such as competency, level of knowledge in disease and inhaler device and availability to educate patients, can play a role in inhaler error. Quality management is a potential solution to this problem. Quality improvement strategies towards addressing inhaler misuse can increase patient satisfaction and improve patient outcomes. Donabedian's triad, which includes structure, process, and outcome can be utilized in developing a framework for reducing inhaler errors. Institutional solutions are more towards the structural and process changes in the triad, such as HCP training, checklists on training efficacy, provision of action plans, and availability of staff to educate and train patients. Patient-centered solutions focus more on process and outcome domains, such as improvement in lung functions, patient education, re-assessment and re-education of inhaler techniques, and adherence to treatment regimen. By focusing on structural and process domains, the quality of care can be enhanced, resulting in improved outcomes.

Efficacy of mindfulness-based stress reduction and cognitive rehabilitation training on executive functioning of stroke patients

Efficacy of mindfulness-based stress reduction and cognitive rehabilitation training on executive functioning of stroke patients