Severinia buxifolia-isolated acridones inhibit lung cancer invasion and decrease HIFα protein synthesis involving 5'UTR-mediated translation inhibition.

BACKGROUND: Lung cancer is one of the most common cancers worldwide and is by far the leading cause of cancer death attributed to its rapid metastasis and poor prognosis. Given that hypoxia-inducible factors (HIFs) are associated with cancer metastasis, discovering agents to inhibit HIF-mediated invasive cancer is highly desired. PURPOSE: This study aimed to investigate the natural acridone compounds isolated from Severinia buxifolia for the potential to delay hypoxia-induced lung cancer invasiveness by HIF inhibition. METHODS: Using a hypoxia-responsive element (HRE) luciferase reporter, cell migration and invasion assays, real-time PCR, Western blot, and DNA recombinant clones, compound effect on HIF activity, cancer metastasis, HIF-1α mRNA transcription, HIFs protein stability, and HIF-1α translation were observed under hypoxia conditions. RESULTS: Atalaphyllidine (Sbs-A) and atalaphyllinine (Sbs-B) were found to show the most potent effects on HIF transcriptional activity and HIF-1α protein expression in NSCLC cell line A549, although Sbs-A and Sbs-B might not attribute decreasing HIF-1α mRNA expression to potent inhibition of HIF activity. HIF-1α protein stability was not affected by Sbs-A; also, prolyl hydroxylase and proteasome inhibitors could not reverse the inhibitory effect from compounds. Furthermore, 3 - 10 µM low concentrations of Sbs-A inhibited HIF target gene expression, gelatin zymography activity, and A549 cancer invasion. Ultimately, Sbs-A inhibited HIF-1α 5'UTR-mediated translation independent of oxygen concentration, underlying the mechanism of compounds inhibiting HIF-1α protein expression. CONCLUSION: Our study proposed Severinia buxifolia-isolated acridone compounds inhibited 5'-mRNA HIFA-mediated translation and provided evidence supporting the ability of acridone compounds in targeting HIFα for delayed lung cancer metastasis.

Detecting clinical practice guideline-recommended wheelchair propulsion patterns with wearable devices following a wheelchair propulsion intervention.

Wheelchair propulsion interventions typically teach manual wheelchair users to perform wheelchair propulsion biomechanics as recommended by the Clinical Practice Guidelines (CPG). Outcome measures for these interventions are primarily laboratory based. Discrepancies remain between manual wheelchair propulsion (MWP) in laboratory-based examinations and propulsion in the real-world. Current developments in machine learning (ML) allow for monitoring of MWP in the real world. In this study, we collected data from participants enrolled in two wheelchair propulsion interventions, then built an ML algorithm to distinguish CPG recommended MWP patterns from non-CPG-recommended patterns. Eight primary manual wheelchair users did not initially follow CPG recommendations but learned and performed CPG propulsion after the interventions. Participants each wore two inertial measurement units as they propelled their wheelchairs on a roller system, indoors overground, and outdoors. ML models were trained to classify propulsion patterns as following the CPG or not following the CPG. Video recordings were used for reference. For indoor detection, we found that a subject-independent model was able to achieve 85% accuracy. For outdoor detection, we found that the subject-independent model achieved 75.4% accuracy. These results provide further evidence that CPG and non-CPG-recommended MWP patterns can be predicted with wearable sensors using an ML algorithm.

Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors.

Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.

Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity ("atomic" activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.

Interhemispheric transfer of post-amputation cortical plasticity within the human somatosensory cortex.

Animal models reveal that deafferenting forelimb injuries precipitate reorganization in both contralateral and ipsilateral somatosensory cortices. The functional significance and duration of these effects are unknown, and it is unclear whether they also occur in injured humans. We delivered cutaneous stimulation during functional magnetic resonance imaging (fMRI) to map the sensory cortical representation of the intact hand and lower face in a group of chronic, unilateral, upper extremity amputees (N = 19) and healthy matched controls (N = 29). Amputees exhibited greater activity than controls within the deafferented former sensory hand territory (S1f) during stimulation of the intact hand, but not of the lower face. Despite this cortical reorganization, amputees did not differ from controls in tactile acuity on their intact hands. S1f responses during hand stimulation were unrelated to tactile acuity, pain, prosthesis usage, or time since amputation. These effects appeared specific to the deafferented somatosensory modality, as fMRI visual mapping paradigm failed to detect any differences between groups. We conclude that S1f becomes responsive to cutaneous stimulation of the intact hand of amputees, and that this modality-specific reorganizational change persists for many years, if not indefinitely. The functional relevance of these changes, if any, remains unknown.

Toward community-based wheelchair evaluation with machine learning methods.

INTRODUCTION: Upper extremity pain among manual wheelchair users induces functional decline and reduces quality of life. Research has identified chronic overuse due to wheelchair propulsion as one of the factors associated with upper limb injuries. Lack of a feasible tool to track wheelchair propulsion in the community precludes testing validity of wheelchair propulsion performed in the laboratory. Recent studies have shown that wheelchair propulsion can be tracked through machine learning methods and wearable accelerometers. Better results were found in subject-specific machine learning method. To further develop this technique, we conducted a pilot study examining the feasibility of measuring wheelchair propulsion patterns. METHODS: Two participants, an experienced manual wheelchair user and an able-bodied individual, wore two accelerometers on their arms. The manual wheelchair user performed wheelchair propulsion patterns on a wheelchair roller system and overground. The able-bodied participant performed common daily activities such as cooking, cleaning, and eating. RESULTS: The support vector machine built from the wrist and arm acceleration of wheelchair propulsion pattern recorded on the wheelchair roller system predicted the wheelchair propulsion patterns performed overground with 99.7% accuracy. The support vector machine built from additional rotation data recorded overground predicted wheelchair propulsion patterns (F1 = 0.968). CONCLUSIONS: These results further demonstrate the possibility of tracking wheelchair propulsion in the community.