Augmenting forearm crutches with wireless sensors for lower limb rehabilitation

Forearm crutches are frequently used in the rehabilitation of an injury to the lower limb. The recovery rate is improved if the patient correctly applies a certain fraction of their body weight (specified by a clinician) through the axis of the crutch, referred to as partial weight bearing (PWB). Incorrect weight bearing has been shown to result in an extended recovery period or even cause further damage to the limb. There is currently no minimally invasive tool for long-term monitoring of a patient's PWB in a home environment. This paper describes the research and development of an instrumented forearm crutch that has been developed to wirelessly and autonomously monitor a patient's weight bearing over the full period of their recovery, including its potential use in a home environment. A pair of standard forearm crutches are augmented with low-cost off-the-shelf wireless sensor nodes and electronic components to provide indicative measurements of the applied weight, crutch tilt and hand position on the grip. Data are wirelessly transmitted between crutches and to a remote computer (where they are processed and visualized in LabVIEW), and the patient receives biofeedback by means of an audible signal when they put too much or too little weight through the crutch. The initial results obtained highlight the capability of the instrumented crutch to support physiotherapists and patients in monitoring usage

X-Ray Microanalysis of Dentin: A Review

The aim of this review was to present a condensed summary of the literature on X-ray microanalysis of dentin, including both energy-dispersive (EDS) and wavelength-dispersive (WDS) analysis. Estimations of concentrations by XMA of dentin should be regarded as semiquantitative values. The Ca level in rat odontoblasts was elevated in the secreting end of the cell body. In predentin Ca accumulated at a concentration of 2% that of mineralized dentin. In coronal dentin the peritubular areas were hypermineralized (Ca, P, Mg). Primary caries lesions showed a decrease of Ca, P, Mg and Cl, and usually an increase of S and Zn. The mineralized surface often present contained especially high concentrations of F and K. Considerable uptake of various ions in cavity walls exposed to filling materials was assessed: from silver amalgam, Zn and Sn, from silicate cement and glassionomer cement F, Al and Zn, and from zinc oxide-eugenol cement, Zn. The highest F concentrations following topical application were found with solutions of TiF4 and with the varnishes Duraphat® and FluorProtector®. Dentin wall lesions adjacent to amalgam fillings exhibited considerably reduced Ca and P values, but concomitantly considerable amounts of Zn and Sn, that explained the increased radiopacity seen in some microradiographs

Expression of cyclin D1a and D1b as predictive factors for treatment response in colorectal cancer.

BACKGROUND: The aim of this study was to investigate the value of the cyclin D1 isoforms D1a and D1b as prognostic factors and their relevance as predictors of response to adjuvant chemotherapy with 5-fluorouracil and levamisole (5-FU/LEV) in colorectal cancer (CRC). METHODS: Protein expression of nuclear cyclin D1a and D1b was assessed by immunohistochemistry in 335 CRC patients treated with surgery alone or with adjuvant therapy using 5-FU/LEV. The prognostic and predictive value of these two molecular markers and clinicopathological factors were evaluated statistically in univariate and multivariate survival analyses. RESULTS: Neither cyclin D1a nor D1b showed any prognostic value in CRC or colon cancer patients. However, high cyclin D1a predicted benefit from adjuvant therapy measured in 5-year relapse-free survival (RFS) and CRC-specific survival (CSS) compared to surgery alone in colon cancer (P=0.012 and P=0.038, respectively) and especially in colon cancer stage III patients (P=0.005 and P=0.019, respectively) in univariate analyses. An interaction between treatment group and cyclin D1a could be shown for RFS (P=0.004) and CSS (P=0.025) in multivariate analysis. CONCLUSION: Our study identifies high cyclin D1a protein expression as a positive predictive factor for the benefit of adjuvant 5-FU/LEV treatment in colon cancer, particularly in stage III colon cancer

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Thyroxine and triiodothyronine degradation in lines of pigs selected for rate of gain and thickness of backfat

International audienc

Nursing and midwifery students\u27 experiences and perception of their clinical learning environment in Malawi: A mixed-method study

© 2020 The Author(s). Background: The clinical learning environment is an important part of the nursing and midwifery training as it helps students to integrate theory into clinical practice. However, not all clinical learning environments foster positive learning. This study aimed to assess the student nurses and midwives\u27 experiences and perception of the clinical learning environment in Malawi. Methods: A concurrent triangulation mixed methods research design was used to collect data from nursing and midwifery students. Quantitative data were collected using a Clinical Learning Environment Inventory, while qualitative data were collected using focus group discussions. The Clinical Learning Environment Inventory has six subscales of satisfaction, involvement, individualisation, innovation, task orientation and personalisation. The focus group interview guide had questions about clinical learning, supervision, assessment, communication and resources. Quantitative data were analysed by independent t-test and multivariate linear regression and qualitative data were thematically analysed. Results: A total of 126 participants completed the questionnaire and 30 students participated in three focus group discussions. Satisfaction subscale had the highest mean score (M = 26.93, SD = 4.82) while individualisation had the lowest mean score (M = 18.01, SD =3.50). Multiple linear regression analysis showed a statistically significant association between satisfaction with clinical learning environment and personalization (β = 0.50, p = \u3c 0.001), and task orientation (β =0.16 p = \u3c 0.05). Teaching and learning resources, hostile environment, poor relationship with a qualified staff, absence of clinical supervisors, and lack of resources were some of the challenges faced by students in their clinical learning environment. Conclusion: Although satisfaction with clinical learning environment subscale had the highest mean score, nursing and midwifery students encountered multifaceted challenges such as lack of resources, poor relationship with staff and a lack of support from clinical teachers that negatively impacted on their clinical learning experiences. Training institutions and hospitals need to work together to find means of addressing the challenges by among others providing resources to students during clinical placement

Tundra microbial community taxa and traits predict decomposition parameters of stable, old soil organic carbon.

The susceptibility of soil organic carbon (SOC) in tundra to microbial decomposition under warmer climate scenarios potentially threatens a massive positive feedback to climate change, but the underlying mechanisms of stable SOC decomposition remain elusive. Herein, Alaskan tundra soils from three depths (a fibric O horizon with litter and course roots, an O horizon with decomposing litter and roots, and a mineral-organic mix, laying just above the permafrost) were incubated. Resulting respiration data were assimilated into a 3-pool model to derive decomposition kinetic parameters for fast, slow, and passive SOC pools. Bacterial, archaeal, and fungal taxa and microbial functional genes were profiled throughout the 3-year incubation. Correlation analyses and a Random Forest approach revealed associations between model parameters and microbial community profiles, taxa, and traits. There were more associations between the microbial community data and the SOC decomposition parameters of slow and passive SOC pools than those of the fast SOC pool. Also, microbial community profiles were better predictors of model parameters in deeper soils, which had higher mineral contents and relatively greater quantities of old SOC than in surface soils. Overall, our analyses revealed the functional potential of microbial communities to decompose tundra SOC through a suite of specialized genes and taxa. These results portray divergent strategies by which microbial communities access SOC pools across varying depths, lending mechanistic insights into the vulnerability of what is considered stable SOC in tundra regions

Generalizability of electroencephalographic interpretation using artificial intelligence : An external validation study

The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource-limited settings and reduce the workload at specialized centers. However, to facilitate broad clinical implementation, it is essential to establish generalizability across diverse patient populations and equipment. We assessed whether SCORE-AI demonstrates diagnostic accuracy comparable to that of experts when applied to a geographically different patient population, recorded with distinct EEG equipment and technical settings. We assessed the diagnostic accuracy of a "fixed-and-frozen" AI model, using an independent dataset and external gold standard, and benchmarked it against three experts blinded to all other data. The dataset comprised 50% normal and 50% abnormal routine EEGs, equally distributed among the four major classes of EEG abnormalities (focal epileptiform, generalized epileptiform, focal nonepileptiform, and diffuse nonepileptiform). To assess diagnostic accuracy, we computed sensitivity, specificity, and accuracy of the AI model and the experts against the external gold standard. We analyzed EEGs from 104 patients (64 females, median age = 38.6 [range = 16-91] years). SCORE-AI performed equally well compared to the experts, with an overall accuracy of 92% (95% confidence interval [CI] = 90%-94%) versus 94% (95% CI = 92%-96%). There was no significant difference between SCORE-AI and the experts for any metric or category. SCORE-AI performed well independently of the vigilance state (false classification during awake: 5/41 [12.2%], false classification during sleep: 2/11 [18.2%]; p =.63) and normal variants (false classification in presence of normal variants: 4/14 [28.6%], false classification in absence of normal variants: 3/38 [7.9%]; p =.07). SCORE-AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset