Detecting affective states in virtual rehabilitation

Virtual rehabilitation supports motor training following stroke by means of tailored virtual environments. To optimize therapy outcome, virtual rehabilitation systems automatically adapt to the different patients' changing needs. Adaptation decisions should ideally be guided by both the observable performance and the hidden mind state of the user. We hypothesize that some affective aspects can be inferred from observable metrics. Here we present preliminary results of a classification exercise to decide on 4 states; tiredness, tension, pain and satisfaction. Descriptors of 3D hand movement and finger pressure were collected from 2 post-stroke participants while they practice on a virtual rehabilitation platform. Linear Support Vector Machine models were learnt to unfold a predictive relation between observation and the affective states considered. Initial results are promising (ROC Area under the curve (mean±std): 0.713 ± 0.137). Confirmation of these opens the door to incorporate surrogates of mind state into the algorithm deciding on therapy adaptation

Unobtrusive inference of affective states in virtual rehabilitation from upper limb motions: a feasibility study

Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength -relevant for rehabilitation-. Contributions are; (a) multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies

Characterization of a raspberry Pi as the core for a low-cost multimodal EEG-fNIRS platform.

Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts. A Raspberry Pi 4 is chosen as the CPU for the platform responsible for presenting the neurorehabilitation stimuli, acquiring, processing and storing concurrent neuroimaging records as well as the proper synchronization between the neuroimaging streams. We present here two experiments to assess the feasibility and characterization of the Raspberry Pi as the core for a multimodal EEG-fNIRS neuroimaging platform; one over controlled conditions using a combination of synthetic and real data, and another from a full test during resting state. CPU usage, RAM usage and operation temperature were measured during the tests with mean operational records below 40% for CPU cores, 13.6% for memory and 58.85 ° C for temperatures. Package loss was inexistent on synthetic data and negligible on experimental data. Current consumption can be satisfied with a 1000 mAh 5V battery. The Raspberry Pi 4 was able to cope with the required workload in conditions of operation similar to those needed to support a neurorehabilitation evaluation

Modulation of human macrophage responses to mycobacterium tuberculosis by silver nanoparticles of different size and surface modification

Exposure to silver nanoparticles (AgNP) used in consumer products carries potential health risks including increased susceptibility to infectious pathogens. Systematic assessments of antimicrobial macrophage immune responses in the context of AgNP exposure are important because uptake of AgNP by macrophages may lead to alterations of innate immune cell functions. In this study we examined the effects of exposure to AgNP with different particle sizes (20 and 110 nm diameters) and surface chemistry (citrate or polyvinlypyrrolidone capping) on cellular toxicity and innate immune responses against Mycobacterium tuberculosis (M.tb) by human monocyte-derived macrophages (MDM). Exposures of MDM to AgNP significantly reduced cellular viability, increased IL8 and decreased IL10 mRNA expression. Exposure of M.tb-infected MDM to AgNP suppressed M.tb-induced expression of IL1B, IL10, and TNFA mRNA. Furthermore, M.tb-induced IL-1β, a cytokine critical for host resistance to M.tb, was inhibited by AgNP but not by carbon black particles indicating that the observed immunosuppressive effects of AgNP are particle specific. Suppressive effects of AgNP on the M.tb-induced host immune responses were in part due to AgNP-mediated interferences with the TLR signaling pathways that culminate in the activation of the transcription factor NF-κB. AgNP exposure suppressed M.tb-induced expression of a subset of NF-κB mediated genes (CSF2, CSF3, IFNG, IL1A, IL1B, IL6, IL10, TNFA, NFKB1A). In addition, AgNP exposure increased the expression of HSPA1A mRNA and the corresponding stress-induced Hsp72 protein. Up-regulation of Hsp72 by AgNP can suppress M.tb-induced NF-κB activation and host immune responses. The observed ability of AgNP to modulate infectious pathogen-induced immune responses has important public health implications

Partial complementation of Sinorhizobium meliloti bacA mutant phenotypes by the Mycobacterium tuberculosis BacA protein

The Sinorhizobium meliloti BacA ABC transporter protein plays an important role in its nodulating symbiosis with the legume alfalfa (Medicago sativa). The Mycobacterium tuberculosis BacA homolog was found to be important for the maintenance of chronic murine infections, yet its in vivo function is unknown. In the legume plant as well as in the mammalian host, bacteria encounter host antimicrobial peptides (AMPs). We found that the M. tuberculosis BacA protein was able to partially complement the symbiotic defect of an S. meliloti BacA-deficient mutant on alfalfa plants and to protect this mutant in vitro from the antimicrobial activity of a synthetic legume peptide, NCR247, and a recombinant human \u3b2-defensin 2 (HBD2). This finding was also confirmed using an M. tuberculosis insertion mutant. Furthermore, M. tuberculosis BacA-mediated protection of the legume symbiont S. meliloti against legume defensins as well as HBD2 is dependent on its attached ATPase domain. In addition, we show that M. tuberculosis BacA mediates peptide uptake of the truncated bovine AMP, Bac71-16. This process required a functional ATPase domain. We therefore suggest that M. tuberculosis BacA is important for the transport of peptides across the cytoplasmic membrane and is part of a complete ABC transporter. Hence, BacA-mediated protection against host AMPs might be important for the maintenance of latent infections

Structural insights into RNA processing by the human RISC-loading complex.

Targeted gene silencing by RNA interference (RNAi) requires loading of a short guide RNA (small interfering RNA (siRNA) or microRNA (miRNA)) onto an Argonaute protein to form the functional center of an RNA-induced silencing complex (RISC). In humans, Argonaute2 (AGO2) assembles with the guide RNA-generating enzyme Dicer and the RNA-binding protein TRBP to form a RISC-loading complex (RLC), which is necessary for efficient transfer of nascent siRNAs and miRNAs from Dicer to AGO2. Here, using single-particle EM analysis, we show that human Dicer has an L-shaped structure. The RLC Dicer's N-terminal DExH/D domain, located in a short 'base branch', interacts with TRBP, whereas its C-terminal catalytic domains in the main body are proximal to AGO2. A model generated by docking the available atomic structures of Dicer and Argonaute homologs into the RLC reconstruction suggests a mechanism for siRNA transfer from Dicer to AGO2

Automatic recognition of pain, anxiety, engagement and tiredness for virtual rehabilitation from stroke: A marginalization approach

Virtual rehabilitation taps affective computing to personalize therapy. States of anxiety, pain and engagement (affective) and tiredness (physical or psychological) were studied to be inferable from metrics of 3D hand location-proxy of hand movement- and fingers' pressure relevant for upper limb motor recovery. Features from the data streams characterized the motor dynamics of 2 stroke patients attending 10 sessions of motor virtual rehabilitation. Experts tagged states manifestations from videos. We aid classification contributing with a marginalization mechanism whereby absent input is reconstructed. With the hand movement information absent, marginalization statistically outperformed a base model where such input is ignored. Marginalized classification performance was (Area below ROC curve: μ ± σ) 0.880 ± 0.173 and 0.738 ± 0.177 for each patient. Marginalization aid classification sustaining performance under input failure or permitting different sensing settings

CMR for Assessment of Diastolic Function

Prevalence of heart failure with preserved left ventricular ejection fraction amounts to 50% of all cases with heart failure. Diagnosis assessment requires evidence of left ventricular diastolic dysfunction. Currently, echocardiography is the method of choice for diastolic function testing in clinical practice. Various applications are in use and recommended criteria are followed for classifying the severity of dysfunction. Cardiovascular magnetic resonance (CMR) offers a variety of alternative applications for evaluation of diastolic function, some superior to echocardiography in accuracy and reproducibility, some being complementary. In this article, the role of the available CMR applications for diastolic function testing in clinical practice and research is reviewed and compared to echocardiography

Bilateral asynchronous acute epidural hematoma : a case report

BACKGROUND: Bilateral extradural hematomas have only rarely been reported in the literature. Even rarer are cases where the hematomas develop sequentially, one after removal of the other. Among 187 cases of operated epidural hematomas during past 4 years in our hospital, we found one case of sequentially developed bilateral epidural hematoma. CASE PRESENTATION: An 18-year-old conscious male worker was admitted to our hospital after a fall. After deterioration of his consciousness, an emergency brain CT scan showed a right temporoparietal epidural hematoma. The hematoma was evacuated, but the patient did not improve afterwards. Another CT scan showed contralateral epidural hematoma and the patient was reoperated. Postoperatively, the patient recovered completely. CONCLUSIONS: This case underlines the need for monitoring after an operation for an epidural hematoma and the need for repeat brain CT scans if the patient does not recover quickly after removal of the hematoma, especially if the first CT scan has been done less than 6 hours after the trauma. Intraoperative brain swelling can be considered as a clue for the development of contralateral hematoma