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.