Validation of a laser visual feedback and core stability exercise programme to improve lateropulsion reduction and pushing intensity in stroke patients with pusher syndrome: A Delphi study.

BACKGROUND: The patient with pusher syndrome (PS) is characterized by showing postural control alterations due to a lack of perception of his own body in the space. It appears when the patient actively pushes with his unaffected limbs towards the injured side, reacting with resistance to passive straightening towards the midline. Between 10% and 50% of strokes present PS. Nowadays, there is no clearly defined treatment for PS. OBJECTIVE: To design and validate an exercise program using visual feedback and specific core stability exercises (FeViCoS) for the treatment of patients with PS. METHODS: Validation was conducted by expert consensus using the Delphi method. Thirteen neurorehabilitation experts participated in the process. An online questionnaire with 18 Likert-type questions was used to evaluate the designed program. Consensus was considered reached if there was convergence between the quartile 1 and 3 values (RIQ = Q1-Q3) or if the relative interquartile range (RIR) was less than 20%. The degree of agreement between experts was measured by calculating the Fleiss' kappa coefficient. RESULTS: A total of 2 rounds were required to achieve 97.44% consensus with 100% participation. The RIR was less than or equal to 20% for all questions. The Fleiss' kappa index (0.831) showed that the degree of agreement between experts was excellent. CONCLUSION: Neurorehabilitation experts considered FeViCoS valid for the therapeutic approach to patients with PS. Expert consensus suggests a novel strategy in physical therapy clinical practice to improve balance and postural orientation in patients with subacute stroke and PS.

Deep Learning-Assisted interactive contouring of lung Cancer: Impact on contouring time and consistency.

BACKGROUND AND PURPOSE: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring. MATERIALS AND METHODS: Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared. RESULTS: Use of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p < 0.01). The mean observation time for all clinicians and cases made up nearly 60 % of interaction time for both contouring approaches. On average the time spent contouring per case was reduced from 22 min to 19 min when using the DL-assisted tool. Additionally, the DL-assisted tool reduced contour variability in the parts of tumour where clinicians tended to disagree the most, while the consensus contour was similar whichever of the two contouring approaches was used. CONCLUSIONS: A DL-assisted interactive contouring approach decreased active contouring time and local inter-observer variability when used to delineate lung cancer GTVs compared to a standard manual method. Integration of this tool into the clinical workflow could assist clinicians in contouring tasks and improve contouring efficiency.

Using Interactive Television Instruction (ITV) to Reduce the Propensity to Engage in Substance Abuse and Drug Trafficking Among Vulnerable Adolescents in Nigeria.

This study examined the impact of ITV intervention on reduction in the propensity to abuse substances and engage in drug trafficking. The researcher conducted this study using an experiment of 517 vulnerable adolescents aged 10-19 years. The participants were randomly assigned to control (n = 258) and treatment (n = 259) groups. The researchers found a significant main effect of treatment conditions on reduction in the propensity to engage in substance abuse and drug trafficking among vulnerable adolescents. That is, before the intervention, there was no significant statistical difference between the control and treatment groups on the propensity to engage in substance abuse and drug trafficking. However, vulnerable children who received the intervention reported a significant reduction in propensity after the intervention. The results highlight the usefulness of ITV as a behaviour change strategy for vulnerable children.

Non-invasive neurally adjusted ventilatory assist (NIV-NAVA) in the neonatal intensive care unit (NICU): an Australian NICU experience.

BACKGROUND: Preterm infants often require non-invasive breathing support while their lungs and control of respiration are still developing. Non-invasive neurally adjusted ventilatory assist (NIV-NAVA) is an emerging technology that allows infants to breathe spontaneously while receiving support breaths proportional to their effort. This study describes the first Australian Neonatal Intensive Care Unit (NICU) experience of NIV-NAVA. METHODS: Retrospective cohort study of infants admitted to a major tertiary NICU between October 2017 and April 2021 supported with NIV-NAVA. Infants were divided into three groups based on the indication to initiate NIV-NAVA (post-extubation; apnoea; escalation). Successful application of NIV-NAVA was based on the need for re-intubation within 48 h of application. RESULTS: There were 169 NIV-NAVA episodes in 122 infants (82 post-extubation; 21 apnoea; 66 escalation). The median (range) gestational age at birth was 25 + 5 weeks (23 + 1 to 43 + 3 weeks) and median (range) birthweight was 963 g (365-4320 g). At NIV-NAVA application, mean (SD) age was 17 days (18.2), and median (range) weight was 850 g (501-4310 g). Infants did not require intubation within 48 h in 145/169 (85.2%) episodes [72/82 (87.8%) extubation; 21/21 (100%) apnoea; 52/66 (78.8%) escalation). CONCLUSION: NIV-NAVA was successfully integrated for the three main indications (escalation; post-extubation; apnoea). Prospective clinical trials are still required to establish its effectiveness versus other modes of non-invasive support.

3D dynamic landscape simulation of artificial intelligence in environmental landscape design.

Three-dimensional (3D) simulations and precise landscape visualizations are crucial for various applications, like landscape management and planning, computer and connection of the landscape, evaluation, and tracking of land use. The consequences of several plans and a large scene cannot be communicated using older methods of comprehensive environmental planning and development in a timely, rational, and coordinated manner. Architects have trouble incorporating ideas into other comprehensive planning implementation processes. Architects did not thoroughly investigate the neighbourhood's demographics and matching behavioural needs and lacked critical thinking. The 3D dynamic landscape simulation is a detailed computerized three-dimensional simulation of the environment that can be dynamically presented. With the aid of Artificial Intelligence (AI) technology, the system possesses a strong sense of reality, a user-friendly interface, and interactive features that can be tailored to the requirements of the contemporary urban environmental landscape. Regarding exterior publicity, domestic assistance, environmental land use planning, and information systems. The novelty of the proposed Interactive Design System based on AI (IDS-AI) is to create a 3D dynamic landscape model based on a real-life environmental scene, utilizing a Geographic Information System (GIS) to optimize landscape vision. Secondly, 3D environmental landscape design simulation was implemented using GIS spatial analysis in conjunction with the Fuzzy Analytical Hierarchical Process (FAHP) to reduce the data overlap rate and help make an accurate decision. Finally, the design incorporates the development of the interactive interface system application of landscape design and environmental resources for viewing the landscape, the factors that affect them, and the area coverage ratio of various land cover types. The experimental outcomes show that the suggested IDS model increases the gradient sensitivity level of 98.3 % and area coverage ratio of 93.4 % compared to other existing models.

Unsupervised and interpretable discrimination of lithium-bearing minerals with Raman spectroscopy imaging.

As lithium-bearing minerals become critical raw materials for the field of energy storage and advanced technologies, the development of tools to accurately identify and differentiate these minerals is becoming essential for efficient resource exploration, mining, and processing. Conventional methods for identifying ore minerals often depend on the subjective observation skills of experts, which can lead to errors, or on expensive and time-consuming techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Optical Emission Spectroscopy (ICP-OES). More recently, Raman Spectroscopy (RS) has emerged as a powerful tool for characterizing and identifying minerals due to its ability to provide detailed molecular information. This technique excels in scenarios where minerals have similar elemental content, such as petalite and spodumene, by offering distinct vibrational information that allows for clear differentiation between such minerals. Considering this case study and its particular relevance to the lithium-mining industry, this manuscript reports the development of an unsupervised methodology for lithium-mineral identification based on Raman Imaging. The deployed machine-learning solution provides accurate and interpretable results using the specific bands expected for each mineral. Furthermore, its robustness is tested with additional blind samples, providing insights into the unique spectral signatures and analytical features that enable reliable mineral identification.

Designing visual hierarchies for the communication of health data.

OBJECTIVES: Visual hierarchy underlies all visual design decisions related to information presentation. This manuscript describes the experience of a multidisciplinary health data visualization and software design team in using visual hierarchy to redesign a hereditary colorectal cancer lab report. MATERIALS AND METHODS: A series of interviews with representative users were conducted to identify target user groups and determine information hierarchy for each user type. Visual elements (eg, size, color, contrast, etc.) were then assigned to mirror the information hierarchy and workflow for each user type. RESULTS: User research identified 2 distinct user groups as consumers of the redesigned lab report. An interactive design employing a 2-level page hierarchy was developed, which stratified the content to support the needs of each user type. CONCLUSIONS: The challenges related to displaying the complex nature of digital and personal health data can be addressed by applying foundational design methods such as visual hierarchy. DISCUSSION: Visual hierarchy, a foundational design principle, can be used by visualization teams to clearly and efficiently present complex datasets associated with healthcare.

Spinal maps of motoneuron activity during human locomotion: neuromechanical considerations.

The spatial segmental location of motoneurons in the human spinal cord is influenced by both evolutionary and functional principles tending to optimize motor control, reflex integration, and adaptation to the demands of movement. Bearing in mind the biomechanics of limb muscles, it is logical to examine how motoneuron activity clusters functionally during typical daily activities like walking. This article provides a summary of advancements in the study of spinal maps of motoneuron activation during human locomotion by reviewing data gathered over ∼20 years. The effects of child development, aging, and neurological disorders show the salient characteristics of spinal segmental activity during different human locomotor tasks and conditions. By exploiting the neuromechanics of the spinal motor circuits, that is, the link between motoneuron activity and gait mechanics, neuroprosthetics and other focused treatments may better help individuals with locomotor impairments.

PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.

OBJECTIVES: To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies. MATERIALS AND METHODS: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies. RESULTS: The PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME_supplementary_materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME. DISCUSSION: PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records. CONCLUSION: PheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization.

The impact of anthropogenic pressures on microbial diversity and river multifunctionality relationships on a global scale.

Conserving biodiversity is crucial for maintaining essential ecosystem functions, as indicated by the positive relationships between biodiversity and ecosystem functioning. However, the impacts of declining biodiversity on ecosystem functions in response to mounting human pressures remain uncertain. This uncertainty arises from the complexity of trade-offs among human activities, climate change, river properties, and biodiversity, which have not been comprehensively addressed collectively. Here, we provide evidence that river biodiversity was significantly and positively associated with multifunctionality and contributed to key ecosystem functions such as microbially driven water purification, leaf litter decomposition and pathogen control. However, human pressure led to abrupt changes in microbial diversity and river multifunctionality relationships at a human pressure value of 0.5. In approximately 30 % (N = 58) of countries globally, the ratio of area above this threshold exceeded the global average (∼11 %), especially in Europe. Results show that human pressure affected ecosystem functions through direct effects and interactive effects. We provide more direct evidence that the nonadditive effects triggered by prevailing human pressure impact the multifunctionality of rivers globally. Under high levels of human stress, the beneficial effects of biodiversity on nutrient cycling, carbon storage, gross primary productivity, leaf litter decomposition, and pathogen control tend to diminish. Our findings highlight that considering interactions between human pressure and local abiotic and biotic factors is key for understanding the fate of river ecosystems under climate change and increasing human pressure.

TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data.

BACKGROUND: As single-cell sequencing technologies continue to advance, the growing volume and complexity of the ensuing data present new analytical challenges. Large cellular populations from single-cell atlases are more difficult to visualize and require extensive processing to identify biologically relevant subpopulations. Managing these workflows is also laborious for technical users and unintuitive for nontechnical users. RESULTS: We present TooManyCellsInteractive (TMCI), a browser-based JavaScript application for interactive exploration of cell populations. TMCI provides an intuitive interface to visualize and manipulate a radial tree representation of hierarchical cell subpopulations and allows users to easily overlay, filter, and compare biological features at multiple resolutions. Here we describe the software architecture and demonstrate how we used TMCI in a pan-cancer analysis to identify unique survival pathways among drug-tolerant persister cells. CONCLUSIONS: TMCI will facilitate exploration and visualization of large-scale sequencing data in a user-friendly way. TMCI is freely available at https://github.com/schwartzlab-methods/too-many-cells-interactive. An example tree from data within this article is available at https://tmci.schwartzlab.ca/.

New interactive machine learning tool for marine image analysis.

Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua, was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate M. lingua models were created using RootPainter, with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.

Transmogrifying Anatomy Learning by Kaizen Strategies and Game-Based Education.

INTRODUCTION: The study explores the significance of continuous improvement through Kaizen in the evolving landscape of anatomy education. In this study, our objectives were twofold: 1) to assess the effectiveness of incorporating games in the first-year medical curriculum for reinforcing anatomy knowledge, and 2) to explore whether game-based sessions elicit improved student responses in the learning of anatomy. METHODOLOGY: A total of 100 first-year Bachelor of Medicine and Bachelor of Surgery (MBBS) students at All India Institute of Medical Sciences (AIIMS), Bibinagar, Hyderabad, Telangana, India, were exposed to game-based learning which involved six rounds: acronym mnemonics (Redolent), jigsaw puzzle solving (Dumbfound), Filling gaps in concept maps (Blogging), Connecting images (Kinship), case scenario creation (Penman), and rapid-fire round (Rattling). RESULTS: At the end of the intervention, a test was taken and feedback was obtained from all the participants using a prevalidated questionnaire prepared based on a 5-point Likert scale. Questionnaire responses were subjected to descriptive analysis, and reliability analysis (Cronbach's α) was performed to evaluate the internal consistencies of items. A paired t-test indicated that there was a significantly large difference between before (mean (M) = 17.2, standard deviation (SD) = 9.1) and after (M = 25.9, SD = 8), t(99) = 18.4, p < .001, signifying that the performance of the students was far better with game-based learning approaches than conventional learning. CONCLUSION: Combining game-based education with Kaizen principles in anatomy education not only prepares students for success in their academic pursuits but also empowers them to navigate the complexities of the ever-evolving healthcare landscape with confidence and proficiency.

The impact of response type on affordance and spatial compatibility effects in human and object interactions.

Everyday social interactions or goal-directed actions towards objects activate action plans appropriate to their affordances. The spatial compatibility of a stimulus and a response might interfere with the activation of these action plans. In the present study, we examined how framing of interactions affects the interplay between affordance and spatial compatibility effects towards humans and objects in two separate experiments. In a motor priming task designed to simultaneously assess these two effects, participants were presented with interactive hand gestures and objects with a single handle. Participants responded either with their left or right hand according to the colour mask of the stimulus, regardless of the spatial position or the affordance-related orientation of the stimulus. In Experiment 1, when responses were given by keypresses, we found independent affordance and spatial compatibility effects towards objects. Surprisingly, interactive hand gestures induced a reversed affordance effect, that is, imitative action tendencies. Changing the responses from keypresses to the performance of grasping actions in Experiment 2 drastically altered these findings, resulting in the enhancement of affordance and the elimination of spatial compatibility effects for both human and object interactions. These findings highlight the importance of contextual influences on the emergence of automatic action tendencies.

Effective dynamic energy management algorithm for grid-interactive microgrid with hybrid energy storage system.

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink.

Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning.

Background: The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions. Objective: We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient's individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations. Methods: The prediction of unplanned visits was achieved by employing a white-box ML algorithm (i.e., rule learner), which learned rules from patient data (i.e., ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms). Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plausibility check and for extending them with additional conditions. This hybrid approach comprised the application of a sensitive ML algorithm producing several potentially unreliable, but fully human-interpretable and -modifiable rules, which could then be adjusted by human experts. Results: From a cohort of 214 patients and more than 16'000 available data entries, the machine-learned rule set achieved a recall of 19% on the entire dataset and a precision of 5%. We compared this performance to a set of conditions that a human expert had defined to predict adverse events. This "human baseline" did not discover any of the adverse events recorded in our dataset, i.e., it came with a recall and precision of 0%. Despite more plentiful results were expected by our machine learning approach, the involved medical experts a) had understood and were able to make sense of the rules and b) felt capable to suggest modification to the rules, some of which could potentially increase their precision. Suggested modifications of rules included e.g., adding or tightening certain conditions to make them less sensitive or changing the rule consequences: sometimes further monitoring the situation, applying certain test (such as a CRP test) or applying some simple pain-relieving measures was deemed sufficient, making a costly consultation with the physician unnecessary. We can thus conclude that it is possible to apply machine learning as an inspirational tool that can help human experts to formulate rules for an EWS. While humans seem to lack the ability to define such rules without such support, they are capable of modifying the rules to increase their precision and generalizability. Conclusions: Learning rules from dynamic ePRO datasets may be used to assist human experts in establishing an early warning system for cancer patients in outpatient settings.

Interactive Cascaded Network for Prostate Cancer Segmentation from Multimodality MRI with Automated Quality Assessment.

The accurate segmentation of prostate cancer (PCa) from multiparametric MRI is crucial in clinical practice for guiding biopsy and treatment planning. Existing automated methods often lack the necessary accuracy and robustness in localizing PCa, whereas interactive segmentation methods, although more accurate, require user intervention on each input image, thereby limiting the cost-effectiveness of the segmentation workflow. Our innovative framework addresses the limitations of current methods by combining a coarse segmentation network, a rejection network, and an interactive deep network known as Segment Anything Model (SAM). The coarse segmentation network automatically generates initial segmentation results, which are evaluated by the rejection network to estimate their quality. Low-quality results are flagged for user interaction, with the user providing a region of interest (ROI) enclosing the lesions, whereas for high-quality results, ROIs were cropped from the automatic segmentation. Both manually and automatically defined ROIs are fed into SAM to produce the final fine segmentation. This approach significantly reduces the annotation burden and achieves substantial improvements by flagging approximately 20% of the images with the lowest quality scores for manual annotation. With only half of the images manually annotated, the final segmentation accuracy is statistically indistinguishable from that achieved using full manual annotation. Although this paper focuses on prostate lesion segmentation from multimodality MRI, the framework can be adapted to other medical image segmentation applications to improve segmentation efficiency while maintaining high accuracy standards.

Perinatal Predictors and Mediators of Attachment Patterns in Preschool Children: Exploration of Children's Contributions in Interactions with Mothers.

Insecure and disorganized attachment patterns in children are linked to poor health outcomes over the lifespan. Attachment patterns may be predicted by variables that influence the quality of children's interactions with their primary caregivers/parents (usually mothers) such as prenatal and postnatal exposures and the children's own behaviours in interactions. The purposes of this exploratory study were to examine: (1) prenatal predictors of children's attachment patterns, and (2) postnatal mediators and moderators of associations between prenatal predictors and children's attachment patterns, with adjustment for relevant covariates. Mother-child dyads (n = 214) from the longitudinal Alberta Pregnancy Outcomes and Nutrition (APrON) cohort were studied using valid and reliable measures. Hayes' mediation analysis was employed to determine direct and indirect effects. Mothers' prenatal cortisol levels directly predicted disorganized (versus organized) child attachment in unadjusted models. Children's passivity (in adjusted models) and compulsivity (in unadjusted and adjusted models) in parent-child interactions mediated the pathway between mothers' prenatal cortisol levels and children's disorganized attachment patterns. Serial mediation analyses revealed that mothers' cortisol levels predicted their children's cortisol levels, which predicted children's compulsivity, and, ultimately, disorganized attachment in both unadjusted and adjusted models. No predictors were correlated with children's insecure (versus secure) attachment. This exploratory research suggests that prenatal exposure to mothers' cortisol levels and children's behavioural contributions to parent-child interaction quality should be considered in the genesis of children's attachment patterns, especially disorganization. Interventions focused on parent-child interactions could also focus on addressing children's behavioral contributions.

The Association of Conventional Therapy Associated with Somatosensory Interactive Game Enhances the Effects of Early Pulmonary Rehabilitation for Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Randomized Controlled Trial.

Objective: This study aimed to evaluate the effect of somatosensory interactive games in combination with pulmonary rehabilitation programs (PRPs) on exercise tolerance, balance function, pulmonary function, inflammatory markers, and healthcare utilization in individuals with acute exacerbation of chronic obstructive pulmonary disease over 12 months. Design: In a randomized controlled trial, 80 patients were divided into two groups. The control group participated in a lasted 30 minutes daily program composed of postural training for 10 minutes, limb movement for 10 minutes, and breathing exercises for 10 minutes based on regular oxygen therapy and medication. The experimental group received a once-daily, 20-minute somatosensory interactive game session based on the control group. Patients began treatment within 48 hours after admission and lasted for 6 weeks. Results: The time × group interactions on 6-minute walk distance (6MWD) and Brief Balance Evaluation Systems Test (Brief-BESTest) between the two groups were significant (P < 0.001). At the postintervention and each time point of follow-up, the 6-minute walk distance (6MWD) and Brief-BESTest of the intervention group were significantly higher than those of the control group (P < 0.05). The effects of time factor on forced expiratory volume in one second and forced vital capacity were statistically significant (P < 0.05). The 6MWD and Brief-BESTest of the intervention group peaked 3 months after the intervention and were higher than the control group within 12 months. C-reactive protein and procalcitonin were similar between the groups before and after intervention (P > 0.05). The readmission rates and mean length of time spent in the hospital were comparable between the groups at 12 months (P > 0.05). Conclusions: The addition of somatosensory interactive games based on a PRP was safe and feasible, and this benefit persisted for 12 months, peaked at 3 months after the intervention, and then gradually decreased.

Testing combined effects of environmental trace metals/arsenic and marine trophic status on the bioaccumulation in Pacific oysters: Insights from 22-site field samplings.

Pacific oysters were sampled from 22 human-impacted sites in northeastern Japan to measure Cr, Cu, Zn, Pb, Cd, and As. The hazard quotient was slightly >1 for Cu and/or As at two sites, but <1 for all metal species and As at the other sites, indicating low human health risks. Oysters' Cu, Zn, and Pb contents were positively related to their concentrations in the sediment, while Cr and As were not. Oysters' Cu and Zn were negatively related to the inorganic nitrogen in seawater, while oysters' Pb and As showed positive relationships with the particulate organic carbon. These findings suggest that marine trophic status affects oysters' metal uptake differently among the metal species. Furthermore, oysters' Cr, Cu, Zn, and Pb contents were negatively related to their eicosapentaenoic acid content and condition index. Therefore, the nutritional conditions of oysters may influence their elimination or accumulation of these metals.