Analysis of Muscle Fatigue Conditions in Surface EMG Signal with A Novel Hilbert Marginal Spectrum Entropy Method

Surface Electromyogram is the superposition of action potentials generated during muscle contraction that provides useful indices for biomechanics. Such signals are nonstationary and the measure of its time varying components are essentially needed to identify the progression of fatigue. In this work, an attempt has been made to identify the muscle fatigue condition using Hilbert-Huang Marginal Spectrum. The novelty of the proposed framework is that the Marginal Spectrum is computed by ordering the instantaneous frequency of an Intrinsic Mode Function obtained for a signal to be analyzed. For this purpose, signals are recorded from the biceps brachii muscles of 50 healthy volunteers using isometric and dynamic contraction exercise protocols. Initially, each signal is equally partitioned into 10 segments, where the 1^st, 5^th and 10^th segments are analyzed. Further, metrics estimated from marginal spectrum such as area under the curve, skewness and kurtosis are calculated for the considered three segments. The preliminary results show that the estimated metrics are able to distinguish the fatiguing conditions. The recorded signals show low amplitude and high frequencies in nonfatigue region and vice-versa. The obtained results are statistically significant with p <; 0.005. It appears that the instantaneous frequency based marginal spectrum estimator is able to measure the fatigue index. Therefore, the proposed method can be useful in analyzing fatigue condition of skeletal muscles.

Characterization of Alzheimer conditions in MR images using volumetric and sagittal brainstem texture features

BACKGROUND AND OBJECTIVE

Brainstem analysis in Magnetic Resonance Images is essential to detect Alzheimer's condition in the preclinical stages. In this work, an attempt has been made to segment the brainstem in sagittal (2D) and volumetric (3D) images and evaluate texture changes to differentiate Alzheimer's disease (AD) stages.

METHOD

The images obtained from a public access database are spatial normalized, skull stripped and contrast enhanced. Morphological Reconstruction based Fast and Robust Fuzzy 'C' Means technique is used to cluster the brain tissue in preprocessed images into three groups namely cerebrospinal fluid, grey matter and white matter. Brainstem is segmented from the white matter tissue using connected component labelling. Texture features from volumetric and sagittal brainstem slices are extracted and its statistical significance is evaluated.

RESULTS

Results show that the proposed approach is able to segment the brainstem from all the considered images. Variation in texture is observed to be less than 2% among sagittal brainstem slices. Additionally, midsagittal and volumetric features are correlated, suggesting that midsagittal brainstem structure gives an estimate of brainstem volume. Texture features extracted from midsagittal slice shows significant variation (p < 0.05) and is able to differentiate AD classes.

CONCLUSION

Midsagittal brainstem texture features are able to capture the changes occurring in the early stages of disease condition. As the distinction of AD in preclinical stage is complex and clinically significant, this approach could be useful for early diagnosis of the disease.

Ultrafine Pt Nanoparticles Stabilized by MoS2/N-Doped Reduced Graphene Oxide as a Durable Electrocatalyst for Alcohol Oxidation and Oxygen Reduction Reactions

Direct alcohol fuel cells play a pivotal role in the synthesis of catalysts because of their low cost, high catalytic activity, and long durability in half-cell reactions, which include anode (alcohol oxidation) and cathode (oxygen reduction) reactions. However, platinum catalysts suffer from CO tolerance, which affects their stability. The present study focuses on ultrafine Pt nanoparticles stabilized by flowerlike MoS₂/N-doped reduced graphene oxide (Pt@MoS₂/NrGO) architecture, developed via a facile and cost-competitive approach that was performed through the hydrothermal method followed by the wet-reflux strategy. Fourier transform infrared spectra, X-ray diffraction patterns, Raman spectra, X-ray photoelectron spectra, field-emission scanning electron microscopy, and transmission electron microscopy verified the conversion to Pt@MoS₂/NrGO. Pt@MoS₂/NrGO was applied as a potential electrocatalyst toward the anode reaction (liquid fuel oxidation) and the cathode reaction (oxygen reduction). In the anode reaction, Pt@MoS₂/NrGO showed superior activity toward electro-oxidation of methanol, ethylene glycol, and glycerol with mass activities of 448.0, 158.0, and 147.0 mA/mg_Pt, respectively, approximately 4.14, 2.82, and 3.34 times that of a commercial Pt-C (20%) catalyst. The durability of the Pt@MoS₂/NrGO catalyst was tested via 500 potential cycles, demonstrating less than 20% of catalytic activity loss for alcohol fuels. In the cathode reaction, oxygen reduction reaction results showed excellent catalytic activity with higher half-wave potential at 0.895 V versus a reversible hydrogen electrode for Pt@MoS₂/NrGO. The durability of the Pt@MoS₂/NrGO catalyst was tested via 30 000 potential cycles and showed only 15 mV reduction in the half-wave potential, whereas the Pt@NrGO and Pt-C catalysts experienced a much greater shift (Pt@NrGO, ∼23 mV; Pt-C, ∼20 mV).

Thermally Reduced Graphene Oxide as a Counter Electrode Material for Dye-Sensitized Solar Cells

In this present study, a highly conductive thermally reduced graphene oxide (TRGO) was synthesized by a low temperature thermal reduction method using RF heating, under an argon-hydrogen atmosphere. The crystallinity and morphology were examined by X-ray diffraction, Raman spectroscopy and TEM analysis. The chemical structure including the functional groups present on TRGO was studied using X-ray photoelectron spectroscopy and FTIR analysis. The studies reveal that thermal reduction of graphene oxide was successful under the experimental conditions and that the TRGO had high crystallinity. Further, the performance of the as-prepared TRGO was tested as a counter electrode (CE) in a dye-sensitized solar cell (DSSC). The maximum power conversion efficiency (PCE) obtained was 4.86% for TRGO under one sun illumination, which is comparable to that of a platinum CE-based DSSC (5.24%). The electrocatalytic activity and electron transfer kinetics were examined by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and Tafel measurements. The series resistance (R_s) and charge transfer resistance (R_ct) values were found to be 35.4 Ωcm^-2 and 56.40 Ωcm^-2 for TRGO. The results reveal that the TRGO had an electrocatalytic performance similar to that of Pt, making TRGO-CEs promising alternatives to the conventional Pt-CEs in DSSCs.

Randomized, double-blind, placebo-controlled, crossover study of the effects of repeated-dose caffeine on neurobehavioral performance during 48 h of total sleep deprivation

RATIONALE

Caffeine is widely used as a countermeasure against neurobehavioral impairment during sleep deprivation. However, little is known about the pharmacodynamic profile of caffeine administered repeatedly during total sleep deprivation.

OBJECTIVES

To investigate the effects of repeated caffeine dosing on neurobehavioral performance during sleep deprivation, we conducted a laboratory-based, randomized, double-blind, placebo-controlled, crossover, multi-dose study of repeated caffeine administration during 48 h of sleep deprivation. Twelve healthy adults (mean age 27.4 years, six women) completed an 18-consecutive-day in-laboratory study consisting of three 48 h total sleep deprivation periods separated by 3-day recovery periods. During each sleep deprivation period, subjects were awakened at 07:00 and administered caffeine gum (0, 200, or 300 mg) at 6, 18, 30, and 42 h of wakefulness. The Psychomotor Vigilance Test and Karolinska Sleepiness Scale were administered every 2 h.

RESULTS

The 200 and 300 mg doses of caffeine mitigated neurobehavioral impairment across the sleep deprivation period, approaching two-fold performance improvements relative to placebo immediately after the nighttime gum administrations. No substantive differences were noted between the 200 mg and 300 mg caffeine doses, and adverse effects were minimal.

CONCLUSIONS

The neurobehavioral effects of repeated caffeine dosing during sleep deprivation were most evident during the circadian alertness trough (i.e., at night). The difference between the 200 mg and 300 mg doses, in terms of the mitigation of performance impairment, was small. Neither caffeine dose fully restored performance to well-rested levels. These findings inform the development of biomathematical models that more accurately account for the time of day and sleep pressure-dependent effects of caffeine on neurobehavioral performance during sleep loss.

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions

In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.

Proposal of a Machine Learning Approach to Differentiate Mild and Alzheimer's Condition in MR Images Using Shape Changes in Corpus Callosum

The brain ventricles are surrounded by periventricular structures that are affected by dementia which results in neurodegenerative disorder such as Alzheimer's Disease (AD). The change in morphology of these structures must effect the shape and volume of Corpus Callosum (CC). These alterations in morphology of CC are considered to be a significant image biomarker for the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) subjects. Shape descriptors provide useful information about change in morphology of various brain structures during disease progression. In this work, Lattice Boltzmann criterion based hybrid level set method (LSM) is used to segment CC. Geometric and pseudo-Zernike moment measures are extracted from the segmented area of CC and are statistically analyzed using Statistical Package for Social Science (SPSS). The performance metric of significant moments is validated using machine learning algorithms. Results demonstrate that, hybrid level set is able to delineate CC and the segmented images are in high correlation with ground truth images. High accuracy value of 85.0% has been achieved using Multilayer Perceptron (MLP) classifier for Healthy Control (HC) versus AD subjects. Thus, moments are able to classify MCI from HC and AD subjects with high accuracy and hence the results are found to be clinically significant.

De-symmetrizing periodically grafted amphiphilic copolymers: design, synthesis and generation of Janus folded chains

The mutual immiscibility between the three segments, namely backbone HC, and pendant PEG and FC segments, causes zigzag folding of the polymer such that FC segments lie on one side and PEG on the other, thereby generating Janus-type folded chains.

Orthogonally clickable hyperbranched polymers: effect of reactant size and polarity on core-functionalization of peripherally jacketed HBPs

Using an orthogonally clickable strategy, the accessibility of internal allyl groups in jacketed HBPs, bearing either PEG or docosyl peripheral segments, was shown to depend both on the size and relative polarity of the reactant thiol.

Analysis of uterine electromyography signals in preterm condition using multifractal algorithm

In this work, an attempt has been made to analyze the preterm (gestation period $\leq 37$ weeks) condition using uterine electromyography (EMG) signals and multifractal detrended fluctuation analysis (MFDFA). The signals recorded from the electrodes placed on the surface of abdomen are used for this study and these are obtained from a publically available online database. These signals are preprocessed using 4-pole digital Butterworth filter. The preprocessed signals are subjected to MFDFA to extract multifractal features namely maximum singularity exponent, peak singularity exponent, strength of multifractality and exponent index. Generalized Hurst exponent extracted from the signals indicate that uterine EMG signals show multifractal behavior in preterm condition. Among the extracted features the coefficient of variation is found to be lower for peak singularity exponent. This indicates that this feature have lower inter-subject variability. Hence, it appears that the multifractal features can help in the assessment of uterine EMG signals for preterm detection.

Surface-Functionalized Polystyrene Latexes Using Itaconate-Based Surfmers

Itaconic acid was readily transformed to a series of amphiphilic diesters via stepwise esterification of itaconic anhydride; the diesters carry one alkyl (cetyl or octyl) group and either a PEG, glyceryl, or dopamine segment. These diesters were used as surfmers for the preparation of polystyrene (PS) emulsions, with the expectation that the surface of the emulsion particles would carry PEG, glyceryl or dopamine units. NMR spectroscopic studies revealed that the surfmers were covalently incorporated into the polystyrene chains; furthermore, NMR tube polymerization experiments also confirmed that when the PEG surfmer was used, the PEG segments are indeed present on the surface of the emulsion particles. The size of the PEGlyated PS emulsions was readily varied from 35 to 140 nm by changing the mole fraction of surfmer used. In the case of the glyceryl and dopamine carrying surfmers, an octyl unit was used as the hydrophobic segment to ensure appropriate hydrophobic-hydrophilic balance; it was noticed that significantly larger mole fractions of the surfmers were required (15-20 mol %) to generate stable emulsions with particle sizes of about 150 nm. The PS emulsions carrying dopamine units on the surface were found to adhere to glass surfaces; thus suggesting that such "sticky" emulsion particles could be used to functionalize different types of surfaces. Finally, itaconate diesters bearing cetyl and perfluorooctyl segments were also prepared and shown to copolymerize with styrene to generate fluoroalkyl-enriched PS copolymers; these were used to generate hydrophobic coatings, with water contact angles of over 120°. Thus, itaconate-based surfmers are readily accessible alternatives for the preparation of emulsions with tailored size and surface functionality.

Hybrasurfs-A New Class of Hyperbranched Surfactants

A hyperbranched (HB) polyester carrying peripheral allyl groups was prepared by melt-condensation of a suitably designed AB₂ monomer bearing two allyl ester groups and one hydroxyl group. The periphery of the hyperbranched polymer was co-clicked with two different organic thiols, namely, hexadecane thiol and 3-mercaptopropionic acid, using the thiol-ene reaction. Three different samples with varying mole fractions of the hydrophilic carboxylic acid groups were prepared; the conformational adaptability of the hyperbranched polymer backbone permitted these amphiphilic systems to form Janus structures that exhibit surfactant-like properties and, therefore, we have termed them hybrasurfs. These polymers behave like clusters of surfactants that have been stitched at the waist by the HB polymer backbone; the Langmuir isotherms revealed the formation of a monolayer, and in two of the samples having higher mole fractions of hexadecyl segments a weak inflection in the isotherm is seen. This suggests a densification, typically implying the crystallization of the alkyl segment at the air-water interface. The monolayers were transferred onto a substrate, and their heights were estimated using atomic force microscopy; the values thus obtained were in reasonable agreement with the expected value. The water contact angles of the substrates bearing the transferred monolayers of the three different samples (transferred at two different points along the isotherm) were measured; it was seen that the sample carrying the highest mole fraction of hexadecyl chains exhibited a significantly larger contact angle when compared to that of the other two samples. Interestingly, these hybrasurfs also formed vesicles in water and were shown to encapsulate water-soluble dyes, such as Eosin Y. Thus, this class of readily accessible amphiphilic HB polymers that behave as a cluster of surfactants opens some interesting possibilities for further exploration.

2B-Alert App: A mobile application for real-time individualized prediction of alertness

Knowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies. Here we describe and validate the 2B‐Alert App, the first mobile application that progressively learns an individual’s trait‐like response to sleep deprivation in real time, to generate increasingly more accurate individualized predictions of alertness. We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test. We implemented the resulting model and the psychomotor vigilance test as a smartphone application (2B‐Alert App), and prospectively validated its performance in a 62‐hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real‐time individualized predictions after each test. The temporal profiles of reaction times on the app‐conducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device. The app progressively learned each individual’s trait‐like response to sleep deprivation throughout the study, yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased. After only 12 psychomotor vigilance tests, the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests. With the ability to make real‐time individualized predictions of the effects of sleep deprivation on future alertness, the 2B‐Alert App can be used to tailor personalized fatigue management strategies, facilitating self‐management of alertness and safety in operational and non‐operational settings.

Clinical and angiographic profiles and six months outcomes of smokers with acute ST segment elevation myocardial infarction undergoing primary percutaneous coronary angioplasty

Background

Outcomes of primary percutaneous coronary intervention (PCI) for acute STEMI (ST-segment elevation myocardial infarction) in smokers are expected to be better than non-smokers as for patients of acute STEMI with or without fibrinolytic therapy.

Objectives

This comparative study was designed to evaluate the outcomes of primary PCI in patients with acute STEMI in smokers and non-smokers. Clinical and angiographic profile of the two groups was also compared.

Methods

Over duration of two year, a total of 150 consecutive patients of acute STEMI eligible for primary PCI were enrolled and constituted the two groups [Smokers (n = 90), Non-smokers (n = 60)] of the study population. There was no difference in procedure in two groups.

Results

In the present study of acute STEMI, current smokers were about a decade younger than non-smokers (p value = 0.0002), majority were male (98.9% vs 56.6%) were male with a higher prevalence of hypertension and diabetes mellitus (61.67% vs 32.28% and 46.67% vs 14.44%, p = 0.001) respectively. Smokers tended to have higher thrombus burden (p = 0.06) but less multi vessel disease (p = 0.028). Thirty day and six month mortality was non-significantly higher in smokers 4.66% vs 1.33% (p = 0.261) and 5.33% vs 2.66% (p = NS) respectively. Rate of quitting smoking among smokers was 80.90% at 6 months.

Conclusion

The study documents that smokers with acute STEMI have similar outcomes as compared to non smokers with higher thrombus burden and lesser non culprit artery involvement. Smokers present at much younger age emphasizing the role of smoking cessation for prevention of myocardial infarction.

Analysis of the Long Time Behavior of Enzymatic Cellulose Hydrolysis Kinetics

Enzymatic hydrolysis of biomass to produce sugars that can be converted to fuels and other valuable chemicals, is viewed as the prime technology for utilization of this renewable resource. To accelerate technology development, models are needed that are able to accurately predict the hydrolysis rate so that reactors can be tailored to the multitude of processing conditions and substrates that can be used. Of particular interest is the ability to predict the long time conversion in the hydrolysis reaction which dictates the maximum possible sugar concentration. It is our aim in this article to develop a simple model which is able to predict the long-term conversion of cellulose to soluble sugars. Drawing on the analogy from the theory of reactions in continuous mixtures, it is shown that analysis of the long time kinetics of hydrolysis by examining the behavior of the “lump” of the reacting material results in a simple expression which is capable of predicting the kinetics. Many features of actual enzyme systems can be included in the development of the hydrolysis model, such as the large size of the enzyme molecules, adsorption onto substrate, inhibition by different factors (solvent, glucose etc.), but, when the analysis is carried out to calculate the total sugar concentration, it is shown that the equations reduce to a simple expression. Analysis of this model is given with comparison to other models and experimental data available in the literature. In addition to predicting the long-term kinetics, it is shown that the model does a surprising job of predicting the initial hydrolysis rates as well.

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms

BACKGROUND AND OBJECTIVE

Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals.

METHODS

In this work, the analysis based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distribution (BD) and extended modified B-distribution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the biceps brachii of 52 healthy volunteers are preprocessed and subjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naïve Bayes, support vector machine (SVM) of polynomial and radial basis kernel, random forest and rotation forests are used for the classification.

RESULTS

The results show that all the proposed time-frequency distributions (TFDs) are able to show the nonstationary variations of sEMG signals. Most of the features exhibit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum number of features (66%) is reduced by GA and BPSO for EMBD and BD-TFD respectively. The combination of EMBD- polynomial kernel based SVM is found to be most accurate (91% accuracy) in classifying the conditions with the features selected using GA.

CONCLUSIONS

The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions.

One-Step RF-CVD Method for the Synthesis of Graphene Decorated with Metal and Metal Oxide Nanoparticles

Bilayer and few layer-graphene (Gr) with noble metal (Ag and Au) and TiO2 nanoparticles were synthesized using atmospheric pressure radio frequency chemical vapor deposition (APRF-CVD). The precursors for the formation of the respective nanoparticles were dissolved in ethanol and injected into the APRF-CVD containing a Cu foil catalyst at 1000 °C. The graphene obtained had a blistered morphology similar to bubble-wrap. The bubble-like protrusions on the graphene sheet were caused by large nanoparticle clusters (~220 nm) formed below the graphene sheets. Smaller nanoparticles (10-80 nm) were also observed on top of the graphene sheets. Encapsulation of the nanoparticles with graphene, to yield core-shell particles was observed. Similarly, secondary growth of carbon nanotubes (CNTs) from the Au nanoparticles was observed. The average full width half maxima (FWHM) of 2D bands in the Raman spectra indicate that the graphene formed was predominantly bilayer graphene for Gr-TiO2 (55 ± 1.72 cm-1), and few-layer graphene for Gr-Ag (76 ± 22 cm-1) and Gr-Au (88 ± 4.7 cm-1). Raman spectroscopy also showed evidence for the doping of graphene and surface-enhanced Raman sensitivity (SERS) in the materials. These electronic properties of graphene with nanoparticles are relevant to various applications such as optoelectronics, catalysis, chemical and biological sensing.

Management protocols for chronic heart failure in India

Heart failure is a common clinical syndrome and a global health priority. The burden of heart failure is increasing at an alarming rate worldwide as well as in India. Heart failure not only increases the risk of mortality, morbidity and worsens the patient's quality of life, but also puts a huge burden on the overall healthcare system. The management of heart failure has evolved over the years with the advent of new drugs and devices. This document has been developed with an objective to provide standard management guidance and simple heart failure algorithms to aid Indian clinicians in their daily practice. It would also inform the clinicians on the latest evidence in heart failure and provide guidance to recognize and diagnose chronic heart failure early and optimize management.

Detection of Left Ventricular Remodeling in Acute ST Elevation Myocardial Infarction after Primary Percutaneous Coronary Intervention by Two Dimensional and Three Dimensional Echocardiography

Background

Left ventricular remodeling (LVR) after ST-elevation myocardial infarction (STEMI) harbingers poor prognosis. Three-dimensional echocardiography (3DE) is more accurate than 2 D echo for the assessment of left ventricle (LV) shape. We assessed LV geometry with 3D ECHO 6 months after STEMI in patients who had primary angioplasty.

Materials and Methods

In this prospective study, morphological and functional analysis of LV with 3D ECHO (volumes, LVEF, 3D sphericity index [SI]) was assessed up to 7 days and 6 months in 42 STEMI patients. The LVR was considered for increase >15% of the end diastolic volume of the LV (LVEDV) 6 months after the STEMI, compared to the LVEDV up to 7 days of it.

Results

Sixteen (38%) patients had LVR. 3D Echocardiographic measurements up to 7 days after the acute myocardial infarction (AMI) 1-LVEDV in ventricular remodeling group was 99.8 ± 19.1 ml and in no ventricular remodeling group was 87 ± 18.2 mL (P = 0.037); 2-LVEF was 0.48 ± 0.01 and 51 ± 0.02 (P <.001); 3D-SI was 0.41 ± 0.05 and 31 ± 0.05 (P < 0.001) II-after 6 months: 1-LVEDV in remodeling group was 114.2 ± 19.5 mL and no remodeling group was 94.2 ± 18.6 (P = 0.002); 2-LVEF was 0.58 ± 0.01 and 59 ± .01 (P = 0.003); 3D-sphericity was 0.35 ± 0.05 and 28 ± .05 (P < 0.001).

Conclusion

LVR was observed in 38% of the patients 6 months after AMI. The 3D SI has been associated with occurrence of LVR and can differentiate patients with and without subsequent development of LVR accurately and early on its basis.

Assessment of Patient Pain at Colonoscopy: Are Nurses Better than Endoscopists?

The pain that patients recollect having experienced at colonoscopy is likely to influence uptake of the procedure. We used visual analogue scales to assess recollected pain shortly before discharge, and compared these scores with assessments by the endoscopist and the attending nurse. Data were complete for 426 procedures (90%). The mean perceived pain score for patients was 3.2, for endoscopists 2.8 and for nurses 3.1. On multivariate analysis, the endoscopists' assessments of pain had little predictive value over and above those of nurses, whereas nurses' assessments remained significant when adjusted for endoscopists' assessments. Nurses were more accurate than endoscopists in gauging the pain of colonoscopy. This may be because endoscopists are focused on the video monitor while nurses are focused on the patient. More active use of nurses' assessments might help keep pain to a minimum.

Unusual ground states in [Formula: see text] (R = rare earth; T = Rh, Ir; and X = Si, Ge, Sn): a review

Rare earth compounds of the type [Formula: see text] (R  =  rare earth; T  =  Rh, Ir, and X  =  Si, Ge, Sn) display a variety of phase transitions towards exotic states, including charge density waves (CDW), local moment magnetism, antiferromagnetism in the heavy fermion state, superconductivity and giant positive magnetoresistance. They support strongly correlated electron systems. In particular, R ₅Ir₄ [Formula: see text] (R  =  Dy-Lu) exhibit strong coupling CDWs with high transition temperatures, and superconductivity or magnetic ordering at lower temperatures. [Formula: see text] [Formula: see text] (R  =  Gd-Tm; T  =  Co, Rh, Ir) show multiple magnetic transitions with large magnetoresistance below the magnetic transitions. Finally, the light rare earth series [Formula: see text] [Formula: see text] (R  =  Ce, Pr, Nd; T  =  Rh, Ir) display heavy fermion behaviour (for Ce and Pr) or possess giant positive magnetoresistance (for Nd) at low temperatures. This review provides a comprehensive overview of compounds, crystal structures and phase transitions. This is followed by an in-depth discussion of the mechanisms of the phase transitions and the properties of the ordered states.

Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm

Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.

Circulating Monoclonal IgG1-kappa Antibodies Causing Anti-glomerular Basement Membrane Nephritis

Anti-glomerular basement membrane (GBM) antibody disease is a rare but well-characterized cause of glomerulonephritis. Patients present with rapidly progressive renal failure with hemoptysis. Early diagnosis is crucial in salvaging the renal damage and life-threatening pulmonary hemorrhage. Plasmapheresis and immunosuppression is the mode of therapy. Anti-GBM antibodies are polyclonal in nature. However, rare monoclonal antibodies can cause similar destruction of glomerular capillary walls. We describe distinct combination of circulating monoclonal and anti-GBM nephritis.