“GoBabyGo!” – Rehab Engineering to Make Children Mobile

GoBabyGo! is a project to give children with movement difficulties a chance to move on their own. GoBabyGo! conducts community builds of modified ride-on-cars for these children and grants them mobility. Once issue is that once children have cars, there is no feedback to improve future builds. This project “GoBabyGo!” – Rehab Engineering to Make Children Mobile, wishes to collect quantifiable data on how these cars are being used and how they can be improved. This project aims to do this by collecting surveys from caretakers and through a programmed module to be installed in cars. The installed car module collects specific data about distance and time the child controls the vehicle. Through these collection methods, data can be analyzed to make future suggestions and improvements to new cars

Effect of Motor-Assisted Elliptical Training Speed and Body Weight Support on Center of Pressure Movement Variability

Background: A motor-assisted elliptical trainer is being used clinically to help individuals with physical disabilities regain and/or retain walking ability and cardiorespiratory fitness. Unknown is how the device’s training parameters can be used to optimize movement variability and regularity. This study examined the effect of motor-assisted elliptical training speed as well as body weight support (BWS) on center of pressure (CoP) movement variability and regularity during training. Methods: CoP was recorded using in-shoe pressure insoles as participants motor-assisted elliptical trained at three speeds (20, 40, and 60 cycles per minute) each performed at four BWS levels (0%, 20%, 40%, and 60%). Separate two-way repeated measures ANOVAs (3 × 4) evaluated impact of training speed and BWS on linear variability (standard deviation) and nonlinear regularity (sample entropy) of CoP excursion (anterior-posterior, medial-lateral) for 10 dominant limb strides. Findings: Training speed and BWS did not significantly affect the linear variability of CoP in the anterior-posterior or medial-lateral directions. However, sample entropy in both directions revealed the main effect of training speed (p \u3c 0.0001), and a main effect of BWS was observed in the medial-lateral direction (p = 0.004). Faster training speeds and greater levels of BWS resulted in more irregular CoP patterns. Interpretation: The finding that speed and BWS can be used to manipulate CoP movement variability when using a motor-assisted elliptical has significant clinical implications for promoting/restoring walking capacity. Further research is required to determine the impact of motor-assisted elliptical speed and BWS manipulations on functional recovery of walking in individuals who have experienced a neurologic injury or illness

Video capture and post-processing technique for approximating 3D projectile trajectory

In this paper we introduce a low-cost procedure and methodology for markerless projectile tracking in three-dimensional (3D) space. Understanding the 3D trajectory of an object in flight can often be essential in examining variables relating to launch and landing conditions. Many systems exist to track the 3D motion of projectiles but are often constrained by space or the type of object the system can recognize (Qualisys, Göteborg, Sweden; Vicon, Oxford, United Kingdom; Opti-Track, Corvallis, Oregon USA; Motion Analysis, Santa Rosa, California USA; Flight Scope, Orlando, Florida USA). These technologies can also be quite expensive, often costing hundreds of thousand dollars. The system presented in this paper utilizes two high-definition video cameras oriented perpendicular to each other to record the flight of an object. A postprocessing technique and subsequent geometrically based algorithm was created to determine 3D position of the object using the two videos. This procedure and methodology was validated using a gold standard motion tracking system resulting in a 4.5 ± 1.8% deviation from the gold standard

Video capture and post-processing technique for approximating 3D projectile trajectory

In this paper we introduce a low-cost procedure and methodology for markerless projectile tracking in three-dimensional (3D) space. Understanding the 3D trajectory of an object in flight can often be essential in examining variables relating to launch and landing conditions. Many systems exist to track the 3D motion of projectiles but are often constrained by space or the type of object the system can recognize (Qualisys, Göteborg, Sweden; Vicon, Oxford, United Kingdom; Opti-Track, Corvallis, Oregon USA; Motion Analysis, Santa Rosa, California USA; Flight Scope, Orlando, Florida USA). These technologies can also be quite expensive, often costing hundreds of thousand dollars. The system presented in this paper utilizes two high-definition video cameras oriented perpendicular to each other to record the flight of an object. A postprocessing technique and subsequent geometrically based algorithm was created to determine 3D position of the object using the two videos. This procedure and methodology was validated using a gold standard motion tracking system resulting in a 4.5 ± 1.8% deviation from the gold standard

Pilot Proof of Concept Clinical Trials of Stochastic Targeted (STAR) Glycemic Control

(open access)Introduction: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials. Methods: Seven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay ≥3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. Results: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. Conclusions: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT

Organ failure and tight glycemic control in the SPRINT study

INTRODUCTION: Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful tight glycemic control (TGC) intervention (SPRINT) on organ failure, morbidity, and thus mortality. METHODS: A retrospective analysis of 371 patients (3,356 days) on SPRINT (August 2005 - April 2007) and 413 retrospective patients (3,211 days) from two years prior, matched by Acute Physiology and Chronic Health Evaluation (APACHE) III. SOFA is calculated daily for each patient. The effect of the SPRINT TGC intervention is assessed by comparing the percentage of patients with SOFA 2) are also compared. Cumulative time in 4.0 to 7.0 mmol/L band (cTIB) was evaluated daily to link tightness and consistency of TGC (cTIB >/=0.5) to SOFA /=0.5 (37% Pre-SPRINT) reaching 100% by Day 7 (50% Pre-SPRINT). Conditional and joint probabilities indicate tighter, more consistent TGC under SPRINT (cTIB >/=0.5) increased the likelihood SOFA /=0.5 metric provides a first benchmark linking TGC quality to organ failure. These results support other physiological and clinical results indicating the role tight, consistent TGC can play in reducing organ failure, morbidity and mortality, and should be validated on data from randomised trials

Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control

ABSTRACT: INTRODUCTION: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) /=3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. RESULTS: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. CONCLUSIONS: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT

Epigenetic regulation of prostate cancer

Prostate cancer is a commonly diagnosed cancer in men and a leading cause of cancer deaths. Whilst the underlying mechanisms leading to prostate cancer are still to be determined, it is evident that both genetic and epigenetic changes contribute to the development and progression of this disease. Epigenetic changes involving DNA hypo- and hypermethylation, altered histone modifications and more recently changes in microRNA expression have been detected at a range of genes associated with prostate cancer. Furthermore, there is evidence that particular epigenetic changes are associated with different stages of the disease. Whilst early detection can lead to effective treatment, and androgen deprivation therapy has a high response rate, many tumours develop towards hormone-refractory prostate cancer, for which there is no successful treatment. Reliable markers for early detection and more effective treatment strategies are, therefore, needed. Consequently, there is a considerable interest in the potential of epigenetic changes as markers or targets for therapy in prostate cancer. Epigenetic modifiers that demethylate DNA and inhibit histone deacetylases have recently been explored to reactivate silenced gene expression in cancer. However, further understanding of the mechanisms and the effects of chromatin modulation in prostate cancer are required. In this review, we examine the current literature on epigenetic changes associated with prostate cancer and discuss the potential use of epigenetic modifiers for treatment of this disease