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Journal of Applied Biomechanics Jun 2024The purpose of this study was to investigate the hierarchical organization of digit force production and its effect on stability and performance during the simulated...
The purpose of this study was to investigate the hierarchical organization of digit force production and its effect on stability and performance during the simulated archery task. The simulated archery shooting task required the production of a prescribed level of force in virtual space with the left hand and an equivalent force with all 4 fingers of right hand. A single trial had 2 phases, including static force production as aiming in archery and quick force release to shoot the virtual arrow. The timing of the force release was determined by the participant's choice or response to the external cue. The coordination indices, that is, the synergy index, of force stabilization were quantified in 2 hierarchies by decomposing the variance components. The accuracy and precision of the hit position of the virtual arrow were calculated as performance-related indices. The results confirmed that the precision, that is, reproducibility, of the performance was greater when the force release time was determined by the self-selected time, suggesting the beneficial effect of the anticipatory mechanism. There was a distinct synergistic organization of digit forces for the stabilization of net forces in both bimanual and multifinger levels, which was especially correlated with the precision of performance.
PubMed: 38942418
DOI: 10.1123/jab.2022-0317 -
PLOS Digital Health Jun 2024Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these...
Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these limitations, this study investigates the feasibility and advantages of utilizing smartphone-based assessments to capture both cognitive functioning and digital phenotyping data and compare these results to gold standard measures. We conducted a secondary analysis of data from 76 individuals with schizophrenia, who were recruited across three sites (one in Boston, two in India) was conducted. The open-source mindLAMP smartphone app captured digital phenotyping data and Trails A/B assessments of attention / memory for up to 12 months. The smartphone-cognitive tasks exhibited potential for normal distribution and these scores showed small but significant correlations with the results from the Brief Assessment of Cognition in Schizophrenia, especially the digital span and symbol coding tasks (r2 = 0.21). A small but significant correlation (r2 = 0.29) between smartphone-derived cognitive scores and health-related behaviors such as sleep duration patterns was observed. Smartphone-based cognitive assessments show promise as cross-cultural tools that can capture relevant data on momentary states among individuals with schizophrenia. Cognitive results related to sleep suggest functional applications to digital phenotyping data, and the potential of this multimodal data approach in research.
PubMed: 38941349
DOI: 10.1371/journal.pdig.0000526 -
Journal of Cutaneous Pathology Jun 2024A wide spectrum of tumors may affect the periungual spaces of the digits. Superficial acral fibromyxoma (SAF) is a rare, benign soft tissue tumor with diverse clinical...
A wide spectrum of tumors may affect the periungual spaces of the digits. Superficial acral fibromyxoma (SAF) is a rare, benign soft tissue tumor with diverse clinical presentations. We present a case of a 55-year-old woman with a 2-year history of a solitary periungual tumor on the left thumb, subjected to multiple episodes of trauma. Initially suspected to be a periungual squamous cell carcinoma (SCC) based on clinical and dermoscopic features, the tumor was confirmed to be a CD34 SAF through histopathology and immunohistochemistry. Although CD34 immunoreactivity is common in SAF, one-third of these tumors, including this case, do not stain for this marker. Periungual SCC considered a "great mimicker of nail tumors," may resemble other benign nail tumors such as SAF. The patient underwent complete surgical excision with primary closure, resulting in no recurrence after 1 year. This case highlights SAF as an underrecognized benign entity that may manifest with features suspicious of malignancy, potentially leading to unnecessarily aggressive interventions. Recognizing SAF through accurate biopsy techniques and thorough histopathologic evaluation, even in the absence of CD34 reactivity, is crucial for appropriate treatment and preservation of hand function and appearance.
PubMed: 38940413
DOI: 10.1111/cup.14675 -
Mayo Clinic Proceedings. Digital Health Jun 2024This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future...
This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.
PubMed: 38938930
DOI: 10.1016/j.mcpdig.2024.03.007 -
JACS Au Jun 2024This report develops a point-of-use chemical trigger and applies it to a dual-functional chemical encryption chip that enables manual and digital identification with...
This report develops a point-of-use chemical trigger and applies it to a dual-functional chemical encryption chip that enables manual and digital identification with enhanced coding security levels suitable for on-site information verification. The concept relies on conducting continuous chemical synthesis and chromatographic separation of specified compounds on a paper device in a straightforward sketch. In addition to single-step chemical reactions, cascade syntheses and operations involving components of distinct mobilities are also demonstrated. The condensation of dione and hydrazine is first demonstrated on a linear paper reactor, where precursors can mix to react, followed by final product separation under optimized conditions. This linear paper reactor design can also support a multistep cascade Wittig reaction by controlling the relative mobility of reactants, intermediates, and final products. Furthermore, a three-dimensional paper reactor with appropriate mobile phases helps to initiate complex solvent system-driven azide-alkyne cycloaddition. By the use of a three-dimensional device design for spatially limited interdevice reactant transportation, reactants crossing designated boundaries trigger confined chemical reactions at specific positions. Accumulation of repetitive reactions leads to successful product gradient generation and mixing effects, representing a fully controllable intersubstrate chemical operation on the platform. Standing on initiating desired chemical reactions at particular interface regions, integration of appropriate selective reaction area, numerical digits overlay, color diversity, and mobile recognition realizes this dual-functional multicoding encryption process.
PubMed: 38938820
DOI: 10.1021/jacsau.4c00062 -
NPJ Digital Medicine Jun 2024Early-life exposure to stress results in significantly increased risk of neurodevelopmental impairments with potential long-term effects into childhood and even...
Early-life exposure to stress results in significantly increased risk of neurodevelopmental impairments with potential long-term effects into childhood and even adulthood. As a crucial step towards monitoring neonatal stress in neonatal intensive care units (NICUs), our study aims to quantify the duration, frequency, and physiological responses of care manipulation activities, based on bedside videos and physiological signals. Leveraging 289 h of video recordings and physiological data within 330 sessions collected from 27 neonates in 2 NICUs, we develop and evaluate a deep learning method to detect manipulation activities from the video, to estimate their duration and frequency, and to further integrate physiological signals for assessing their responses. With a 13.8% relative error tolerance for activity duration and frequency, our results were statistically equivalent to human annotations. Further, our method proved effective for estimating short-term physiological responses, for detecting activities with marked physiological deviations, and for quantifying the neonatal infant stressor scale scores.
PubMed: 38937643
DOI: 10.1038/s41746-024-01164-y -
A multi-center study on the adaptability of a shared foundation model for electronic health records.NPJ Digital Medicine Jun 2024Foundation models are transforming artificial intelligence (AI) in healthcare by providing modular components adaptable for various downstream tasks, making AI...
Foundation models are transforming artificial intelligence (AI) in healthcare by providing modular components adaptable for various downstream tasks, making AI development more scalable and cost-effective. Foundation models for structured electronic health records (EHR), trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness to distribution shifts. However, questions remain on the feasibility of sharing these models across hospitals and their performance in local tasks. This multi-center study examined the adaptability of a publicly accessible structured EHR foundation model (FM), trained on 2.57 M patient records from Stanford Medicine. Experiments used EHR data from The Hospital for Sick Children (SickKids) and Medical Information Mart for Intensive Care (MIMIC-IV). We assessed both adaptability via continued pretraining on local data, and task adaptability compared to baselines of locally training models from scratch, including a local foundation model. Evaluations on 8 clinical prediction tasks showed that adapting the off-the-shelf FM matched the performance of gradient boosting machines (GBM) locally trained on all data while providing a 13% improvement in settings with few task-specific training labels. Continued pretraining on local data showed FM required fewer than 1% of training examples to match the fully trained GBM's performance, and was 60 to 90% more sample-efficient than training local foundation models from scratch. Our findings demonstrate that adapting EHR foundation models across hospitals provides improved prediction performance at less cost, underscoring the utility of base foundation models as modular components to streamline the development of healthcare AI.
PubMed: 38937550
DOI: 10.1038/s41746-024-01166-w -
Experimental Gerontology Jun 2024The human vestibular system is adversely affected by the aging process. Recent evidence indicates that vestibular information and cognitive functions are related,...
The human vestibular system is adversely affected by the aging process. Recent evidence indicates that vestibular information and cognitive functions are related, suggesting that age-related vestibular loss may contribute to cognitive impairment. In this study, we aimed to investigate the effects of repetitive, home-based galvanic vestibular stimulation (GVS) on cognitive functions in healthy older adults. Twenty-one participants (age = 64.66 ± 2.97 years, 12 females) were randomly allocated to either a home-based GVS or an active control group. The GVS intervention lasted 20 min per session, five times a week, for two weeks (10 sessions). Cognitive functions were assessed before and after the intervention using the Stroop Test, Trail Making Test A&B, and Dual-Task (digit recall and paper-pencil tracking test). Our findings revealed a significant group-by-time interaction effect for the tracking accuracy (F(1,18) = 7.713, p = 0.012, η = 0.30), with only the home-based GVS group showing significant improvement (t = -2.544, p = 0.029). The proposed home-based GVS protocol offers a promising non-pharmacological avenue for enhancing visuospatial ability in healthy older adults. Further research is needed to investigate the effects of different GVS protocols on various cognitive functions, particularly in older individuals with different health conditions.
PubMed: 38936440
DOI: 10.1016/j.exger.2024.112504 -
PLOS Digital Health Jun 2024Analysing complex diseases such as chronic inflammatory joint diseases (CIJDs), where many factors influence the disease evolution over time, is a challenging task....
Analysing complex diseases such as chronic inflammatory joint diseases (CIJDs), where many factors influence the disease evolution over time, is a challenging task. CIJDs are rheumatic diseases that cause the immune system to attack healthy organs, mainly the joints. Different environmental, genetic and demographic factors affect disease development and progression. The Swiss Clinical Quality Management in Rheumatic Diseases (SCQM) Foundation maintains a national database of CIJDs documenting the disease management over time for 19'267 patients. We propose the Disease Activity Score Network (DAS-Net), an explainable multi-task learning model trained on patients' data with different arthritis subtypes, transforming longitudinal patient journeys into comparable representations and predicting multiple disease activity scores. First, we built a modular model composed of feed-forward neural networks, long short-term memory networks and attention layers to process the heterogeneous patient histories and predict future disease activity. Second, we investigated the utility of the model's computed patient representations (latent embeddings) to identify patients with similar disease progression. Third, we enhanced the explainability of our model by analysing the impact of different patient characteristics on disease progression and contrasted our model outcomes with medical expert knowledge. To this end, we explored multiple feature attribution methods including SHAP, attention attribution and feature weighting using case-based similarity. Our model outperforms temporal and non-temporal neural network, tree-based, and naive static baselines in predicting future disease activity scores. To identify similar patients, a k-nearest neighbours regression algorithm applied to the model's computed latent representations outperforms baseline strategies that use raw input features representation.
PubMed: 38935600
DOI: 10.1371/journal.pdig.0000422 -
PLOS Digital Health Jun 2024Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a...
Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study's outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.
PubMed: 38935590
DOI: 10.1371/journal.pdig.0000527