Craniocaudal toggling increases the risk of screw loosening in osteoporotic vertebrae.

BACKGROUND AND OBJECTIVE: Screw loosening remains a prominent problem for osteoporotic patients undergoing pedicle screw fixation surgeries but its underlying mechanisms are not fully understood. This study sought to examine the interactive effect of craniocaudal or axial cyclic loading (toggling) and osteoporosis on screw fixation. METHODS: QCT-based finite element models of normal (n = 7; vBMD = 156 ± 13 mg/cm3) and osteoporotic vertebrae (n = 7; vBMD = 72 ± 6 mg/cm3) were inserted with pedicle screws and loaded with or without craniocaudal toggling. Among them, a representative normal vertebra (age: 55; BMD: 140 mg/cm3) and an osteoporotic vertebra (age: 64; BMD: 79 mg/cm3) were also loaded with or without axial toggling. The individual and interactive effects of craniocaudal toggling and osteoporosis on screw fixation strength (the force when the pull-up displacement of the screw head reached 1 mm) and bone tissue failure (characterized by equivalent plastic strain) were examined by repeated measure ANOVA. RESULTS: A significant interactive effect between craniocaudal toggling and osteoporosis on screw fixation strength was detected (p = 0.008). Specifically, craniocaudal toggling led to a marked decrease in the fixation strength (68%, p < 0.05) and stiffness (83%, p < 0.05) only in the osteoporotic vertebrae but had no effect on screw fixation strength and stiffness of the normal vertebrae (p > 0.05). Likewise, most of the bone tissues around the screw in the osteoporotic vertebrae yielded following craniocaudal toggling whereas this result was not seen in the normal vertebrae. The axial toggling had no significant effect on bone tissue failure as well as pedicle screw fixation in normal or osteoporotic vertebrae. CONCLUSIONS: Craniocaudal toggling substantially reduces the screw fixation strength of the osteoporotic vertebrae by progressively increasing tissue failure around the screw, and therefore may contribute to the higher rates of screw loosening in osteoporotic compared to normal patients, whereas axial toggling is not a risk factor for pedicle screw loosening in normal or osteoporotic patients.

A Novel Screw Modeling Approach to Study the Effects of Screw Parameters on Pullout Strength.

Screw loosening remains a prominent problem for osteoporotic patients undergoing pedicle screw fixation surgeries and is affected by screw parameters (e.g., diameter, pitch, and thread angle). However, the individual and interactive effects of these parameters on screw fixation are not fully understood. Furthermore, the current finite element modeling of a threaded screw is less computationally efficient. To address these issues, we (1) explored a novel "simulated threaded screw" approach (virtual threads assigned to the contact elements of a simplified screw) and compared its performance with threaded and simplified screws, and (2) examined this approach the individual and interactive effects of altering screw diameter (5.5-6.5 mm), pitch (1-2 mm) and half-thread angle (20-30 deg) on pullout strength of normal vertebrae. Results demonstrated that the "simulated threaded screw" approach equivalently predicted pullout strength compared to the "threaded screw" approach (R2 = 0.99, slope = 1). We further found that the pullout strength was most sensitive to the change in screw diameter, followed by thread angle, pitch, and interactions of diameter*pitch or diameter*angle. In conclusion, the "simulated threaded screw" approach can achieve the same predictive capability compared to threaded modeling of the screw. The current findings may serve as useful references for planning of screw parameters, so as to improve the complication of screw loosening.

Improved Classification Accuracy of Hand Movements Using Softmax Classifier and Kalman Filter.

Accurate identification of the intended hand movement from the surface Electromyography (sEMG) data is desired for effective control of myoelectric lower arm prostheses. This study improves the classification accuracy of hand gestures by using feature arrays, Kalman filter (KF), and a Softmax classifier. We use data from BioPatRec database to classify ten hand movements performed by 17 participants. The proposed classifier achieved 95.3% accuracy without KF, and 99.3% accuracy when KF was used to smooth the training data.

Automatic seizure detection with different time delays using SDFT and time-domain feature extraction.

Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram (EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform (SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response (IIR) structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.

Optimal time-varying postural control in a single-link neuromechanical model with feedback latencies.

Maintaining balance during quiet standing is a challenging task for the neural control mechanisms due to the inherent instabilities involved in the task. The feedback latencies and the lowpass characteristics of skeletal muscle add to the difficulty of regulating postural dynamics in real-time. Inverted-pendulum (IP) type robotic models have served as a popular paradigm to investigate control of postural balance. In this study, an in-depth neuromechanical postural control model is developed from physiological principles. The model comprises a single-segment IP robotic model, Hill-type muscle model, and proprioceptive feedback from the muscle spindle (MS) and golgi tendon organ (GTO). An optimal proportional-integral-derivative (PID) controller is proposed to realize effective postural control amid latencies in sensory feedback. The neural commands for postural stabilization are generated by a time-varying PID controller, tuned using linear quadratic regulator (LQR) principles. Computer simulations are used to assess the efficacy of the tuned PID-LQR controller. Sensitivity analysis of the controlled system shows a delay tolerance of 300ms. Preliminary empirical data in support of the mathematical model were obtained from perturbation experiments. The model response to perturbation torque, measured in terms of the center of mass (COM) excursion in the anterior-posterior (AP) direction, displays a high degree of correlation with the empirical data ([Formula: see text]).

Defining the Characteristics of Chronic Hypoparathyroidism Not Adequately Controlled on Conventional Therapy: Consensus Findings of Three European Delphi Panels.

INTRODUCTION: European Society of Endocrinology (ESE) guidelines provide goals for hypoparathyroidism management but do not define characteristics of chronic hypoparathyroidism that is not adequately controlled. Three European country-specific Delphi panels were conducted to gain consensus on these characteristics. METHODS: Delphi panels were conducted in the UK, Sweden, and Portugal using similar methodology. At each round, panellists considered patients with chronic hypoparathyroidism whose disease is not adequately controlled on conventional therapy according to a matrix of four presentations of patients with chronic hypoparathyroidism: normal biochemical levels/well (group 1), abnormal biochemical levels/well (group 2), normal biochemical levels/unwell (group 3), and abnormal biochemical levels/unwell (group 4), with wellness defined by the patient's persistent symptoms, comorbidities, and complications. For groups 2-4, panellists rated characteristics in five categories (patient characteristics, family history, comorbidities, biochemistry, and symptoms/impact on quality of life [QoL]) with respect to defining a patient as having chronic hypoparathyroidism that was not adequately controlled on conventional therapy. Consensus was achieved when more than 80% of respondents agreed. RESULTS: Among the three countries, panellists agreed that characteristics within four of the five categories (patient characteristics, comorbidities, biochemistry, and symptoms/impact on QoL) were important for defining inadequate control. Characteristics deemed important in groups 2-4 included a history of compliance problems and chronic kidney disease stages 4 and 5. In groups 2 and 4, the biochemical parameters deemed important were serum calcium, urinary calcium, and serum creatinine. In groups 3 and 4, tingling or numbness in the hands/feet and face was the only symptom deemed important in all three countries. CONCLUSION: Delphi panels conducted in three European countries provided national consensus on key parameters of patient characteristics, biochemistry, comorbidities, and symptoms/impact on QoL that define not adequately controlled chronic hypoparathyroidism. These characteristics should be tested more widely for their applicability in clinical practice. FUNDING: Shire International GmbH, Zug, Switzerland, a member of the Takeda group of companies.

Local edge-enhanced active contour for accurate skin lesion border detection.

BACKGROUND: Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. RESULT: Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. CONCLUSION: We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

The Impact of Transformational Leadership on Nurses' Organizational Commitment: A Multiple Mediation Model.

This study proposed that the transformational style of nursing staff supervisors inculcate commitment amongst nurses. Moreover, psychological empowerment and psychological well-being were posited as multiple meditators in the above-mentioned association, based on the tenant of conservation of resource (COR) theory. The authors have collected the survey data from the sample of (n = 299) hospital nurses working in Sargodha district of Pakistan. The bootstrap results have supported the direct, as well as indirect relationships. These findings imply that when the nursing staff perceives their leader as transformational, their psychological empowerment is enhanced, and they have higher well-being, which in turn increases their commitment to their hospitals. This study offers a better understanding of psychological states that mediate transformational leadership and organizational commitment linkage.

Glucocorticoid management of adrenal insufficiency in the United Kingdom: assessment using real-world data.

Background and objectives Glucocorticoids are used to manage adrenal insufficiency (AI). We describe treatments used in the United Kingdom and real-world clinical outcomes for each treatment. Methods We used 2010-2016 primary care data from The Health Improvement Network (THIN). Descriptive analyses were conducted, and differences in variables between patients prescribed immediate-release hydrocortisone (IR HC), prednisolone or modified-release hydrocortisone (MR HC) were assessed using Fisher's exact test. Results Overall, 2648 patients were included: 1912 on IR HC (72%), 691 on prednisolone (26%) and 45 (2%) on MR HC. A total of 1174 (44.3%) had primary and 1150 (43.4%) had secondary AI. Patients on prednisolone were older (P < 0.001) and had a greater history of smoking (292/691, P < 0.001) and CVD (275/691, P < 0.001). Patients on MR HC had more PCOS (3/45, P = 0.001) and diabetes (27/45, P = 0.004). The number of GP visits/patient/year was 6.50 in IR HC, 9.54 in prednisolone and 9.11 in MR HC cohorts. The mean number of A&E visits and inpatient and outpatient hospital admissions ranged from 0.42 to 0.93 visits/patient/year. The mean number of adrenal crises/patient/year was between 0.02 and 0.03 for all cohorts. Conclusion IR HC is most commonly used for the management of AI in the United Kingdom, followed by prednisolone. Few patients receive MR HC. The prednisolone and MR HC cohorts displayed a greater prevalence of vascular risk factors compared with IR HC. The occurrence of AC and primary and secondary resource use were similar between treatment cohorts, and they indicate significant resource utilisation. Improved treatment and management of patients with AI is needed.

A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool.

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

A Method for Locomotion Mode Identification Using Muscle Synergies.

Active lower limb transfemoral prostheses have enabled amputees to perform different locomotion modes such as walking, stair ascent, stair descent, ramp ascent and ramp descent. To achieve seamless mode transitions, these devices either rely on neural information from the amputee's residual limbs or sensors attached to the prosthesis to identify the intended locomotion modes or both. We present an approach for classification of locomotion modes based on the framework of muscle synergies underlying electromyography signals. Neural information at the critical instances (e.g., heel contact and toe-off) was decoded for this purpose. Non-negative matrix factorization was used to extract the muscles synergies from the muscle feature matrix. The estimation of the neural command was done using non-negative least squares. The muscle synergy approach was compared with linear discriminant analysis (LDA), support vector machine (SVM), and neural network (NN) and was tested on seven able-bodied subjects. There was no significant difference ( p > 0.05 ) in transitional and steady state classification errors during stance phase. The muscle synergy approach performed significantly better ( ) than NN and LDA during swing phase while results were similar to SVM. These results suggest that the muscle synergy approach can be used to discriminate between locomotion modes involving transitions.

Recurrent anti-GBM disease with pulmonary artery aneurysms.

We present the first known case of pulmonary artery aneurysms as a feature of Recurrent Anti-Glomerular Basement Membrane disease.

Real-Time Task Discrimination for Myoelectric Control Employing Task-Specific Muscle Synergies.

We present a novel formulation that employs task-specific muscle synergies and state-space representation of neural signals to tackle the challenging myoelectric control problem for lower arm prostheses. The proposed framework incorporates information about muscle configurations, e.g., muscles acting synergistically or in agonist/antagonist pairs, using the hypothesis of muscle synergies. The synergy activation coefficients are modeled as the latent system state and are estimated using a constrained Kalman filter. These task-dependent synergy activation coefficients are estimated in real-time from the electromyogram (EMG) data and are used to discriminate between various tasks. The task discrimination is helped by a post-processing algorithm that uses posterior probabilities. The proposed algorithm is robust as well as computationally efficient, yielding a decision with > 90% discrimination accuracy in approximately 3 ms . The real-time performance and controllability of the algorithm were evaluated using the targeted achievement control (TAC) test. The proposed algorithm outperformed common machine learning algorithms for single- as well as multi-degree-of-freedom (DOF) tasks in both off-line discrimination accuracy and real-time controllability (p < 0.01).

Task discrimination for non-weight-bearing movements using muscle synergies.

Myoelectric control of lower limb prostheses requires discrimination of task-specific muscle patterns. In this paper we present a method based on the notion of muscle synergies to discriminate between various non-weight-bearing movements such as knee extension/flexion, femur rotation in/out, tibia rotation in/out and ankle dorsiflexion/plantarflexion. Data is recorded from eight targeted muscle sites on the thigh. Non-negative matrix factorization is used to identify the muscle synergies using multiple features and estimation of electromyographic (EMG) patterns is done using non-negative least squares (NNLS). Classification accuracy for the movements involving the knee joint was higher than the movements involving the ankle joint. The proposed algorithm performs at par with the common machine learning algorithm Linear Discriminant Analysis (LDA) in offline analysis.

An analysis of TRITON-TIMI 38, based on the 12 month recommended length of therapy in the European label for prasugrel.

BACKGROUND: In TRITON-TIMI 38, patients with acute coronary syndromes were treated with prasugrel or clopidogrel, with aspirin, for a median of 14.5 (maximum of 15) months. Based on this trial, the EU label for prasugrel recommends treatment for up to 12 months and excludes patients with prior stroke/transient ischemic attack (TIA). Furthermore, the EU label recommends the 10 mg maintenance dose (MD) for patients with body weight ≥60 kg and age <75 years. A lower MD of 5 mg is recommended for those with body weight <60 kg; although generally not recommended, 5 mg can be prescribed to patients ≥75 years after individual risk-benefit evaluation. This paper presents the one-year outcome data for this '10 mg indicated cohort'. METHODS AND RESULTS: From the overall cohort of 13,608 patients in TRITON-TIMI 38, 10,804 fulfilled inclusion criteria for the 10 mg indicated cohort, of whom 22% had a history of diabetes, 73% an index diagnosis of unstable angina/non-ST-segment-elevation myocardial infarction (UA/NSTEMI), and 27% an index diagnosis of ST-segment-elevation myocardial infarction (STEMI). In this cohort at 12 months, those given prasugrel experienced significantly fewer ischemic events (cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke, 7.8% vs 10.5%, hazard ratio (HR) = 0.73, p < 0.001, than those given clopidogrel, with a non-significant increase in non-coronary artery bypass graft (CABG) TIMI major bleeding, 1.7% vs 1.5%, HR = 1.15, p = 0.40; similarly, in the overall cohort these frequencies were 9.4% vs 11.4%, HR = 0.81, p < 0.001, for cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke, and 2.2% vs 1.8%, HR = 1.24, p = 0.10, for non-CABG TIMI major bleeding. There was a significant reduction in stent thrombosis in the prasugrel group, with similar mortality rates and no excess of strokes. CONCLUSIONS: Treatment with prasugrel according to EU label recommendations results in a significant 27% and 57% relative risk reduction (absolute risk reductions of 2.7% and 1.2%) in ischemic events and stent thromboses respectively compared with clopidogrel, with a 15% relative risk increase (absolute risk increase of 0.2%) for major bleeds (p = 0.40), and no excess of strokes. LIMITATIONS: Although restricted to 365 days of follow-up, this analysis encapsulates 1366 of 1424 (95.9%) of all primary endpoint events and 244 of 257 (94.9%) of all first non-CABG TIMI major bleeds reported in the pivotal manuscript. Furthermore, the 10 mg indicated cohort was not a pre-specified subgroup in the study protocol, but due to European labeling restrictions, results for all outcomes in this cohort are presented through 12 months.

Locomotion mode identification for lower limbs using neuromuscular and joint kinematic signals.

Recent development in lower limb prosthetics has seen an emergence of powered prosthesis that have the capability to operate in different locomotion modes. However, these devices cannot transition seamlessly between modes such as level walking, stair ascent and descent and up slope and down slope walking. They require some form of user input that defines the human intent. The purpose of this study was to develop a locomotion mode detection system and evaluate its performance for different sensor configurations and to study the effect of locomotion mode detection with and without electromyography (EMG) signals while using kinematic data from hip joint of non-dominant/impaired limb and an accelerometer. Data was collected from four able bodied subjects that completed two circuits that contained standing, level-walking, ramp ascent and descent and stair ascent and descent. By using only the kinematic data from the hip joint and accelerometer data the system was able to identify the transitions, stance and swing phases with similar performance as compared to using only EMG and accelerometer data. However, significant improvement in classification error was observed when EMG, kinematic and accelerometer data were used together to identify the locomotion modes. The higher recognition rates when using the kinematic data along with EMG shows that the joint kinematics could be beneficial in intent recognition systems of locomotion modes.

A positive serum basophil histamine release assay is a marker for ciclosporin-responsiveness in patients with chronic spontaneous urticaria.

The electronic records of 398 patients with chronic spontaneous urticaria (CSU) who had had a serum basophil histamine release assay (BHRA) performed as a marker of functional autoantibodies were audited. The BHRA was positive in 105 patients (26.4%). Fifty eight were treated with ciclosporin because they were H1 anti-histamine unresponsive. CSU patients with a positive BHRA were more likely to respond clinically (P<0.001) and to have raised thyroid autoantibodies (P<0.02) than those with a negative BHRA. The BHRA offers a useful predictive biomarker for a good response of H1 antihistamine-unresponsive CSU patients to ciclosporin.

Muscle activity onset detection using energy detectors.

Muscle activity detection is important for clinical investigations leading to the identification of neuromuscular disorders. Myoelectric signal recorded via electrodes placed at skin surface can reveal important muscle excitation information about underlying limb movement. However, a primary difficulty in the detection of muscle activity period from myoelectric signals lies in the inherent variability of these signals and the noise added during the collection process. In the literature, the double threshold detector has been commonly used for detection of the muscle activity periods from myoelectric signals. In this study, we propose a new scheme based on the log-likelihood ratio test to detect muscle activity periods accurately. This scheme uses energy information contained in the myoelectric signal, which increases with the start of the activity. We demonstrate the viability of energy detection scheme via successful detection performed on synthetic as well as clinical myoelectric signals.