Immunoglobulin heavy variable (IGHV) genes and alleles: new entities, new names and implications for research and prognostication in chronic lymphocytic leukaemia.

Νext generation sequencing studies in Homo sapiens have identified novel immunoglobulin heavy variable (IGHV) genes and alleles necessitating changes in the international ImMunoGeneTics information system (IMGT) GENE-DB and reference directories of IMGT/V-QUEST. In chronic lymphocytic leukaemia (CLL), the somatic hypermutation (SHM) status of the clonotypic rearranged IGHV gene is strongly associated with patient outcome. Correct determination of this parameter strictly depends on the comparison of the nucleotide sequence of the clonotypic rearranged IGHV gene with that of the closest germline counterpart. Consequently, changes in the reference directories could, in principle, affect the correct interpretation of the IGHV mutational status in CLL. To this end, we analyzed 8066 productive IG heavy chain (IGH) rearrangement sequences from our consortium both before and after the latest update of the IMGT/V-QUEST reference directory. Differences were identified in 405 cases (5 % of the cohort). In 291/405 sequences (71.9 %), changes concerned only the IGHV gene or allele name, whereas a change in the percent germline identity (%GI) was noted in 114/405 (28.1 %) sequences; in 50/114 (43.8 %) sequences, changes in the %GI led to a change in the mutational set. In conclusion, recent changes in the IMGT reference directories affected the interpretation of SHM in a sizeable number of IGH rearrangement sequences from CLL patients. This indicates that both physicians and researchers should consider a re-evaluation of IG sequence data, especially for those IGH rearrangement sequences that, up to date, have a GI close to 98 %, where caution is warranted.

Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock.

BACKGROUND: Even though temperature is a continuous quantitative variable, its measurement has been considered a snapshot of a process, indicating whether a patient is febrile or afebrile. Recently, other diagnostic techniques have been proposed for the association between different properties of the temperature curve with severity of illness in the Intensive Care Unit (ICU), based on complexity analysis of continuously monitored body temperature. In this study, we tried to assess temperature complexity in patients with systemic inflammation during a suspected ICU-acquired infection, by using wavelets transformation and multiscale entropy of temperature signals, in a cohort of mixed critically ill patients. METHODS: Twenty-two patients were enrolled in the study. In five, systemic inflammatory response syndrome (SIRS, group 1) developed, 10 had sepsis (group 2), and seven had septic shock (group 3). All temperature curves were studied during the first 24 hours of an inflammatory state. A wavelet transformation was applied, decomposing the signal in different frequency components (scales) that have been found to reflect neurogenic and metabolic inputs on temperature oscillations. Wavelet energy and entropy per different scales associated with complexity in specific frequency bands and multiscale entropy of the whole signal were calculated. Moreover, a clustering technique and a linear discriminant analysis (LDA) were applied for permitting pattern recognition in data sets and assessing diagnostic accuracy of different wavelet features among the three classes of patients. RESULTS: Statistically significant differences were found in wavelet entropy between patients with SIRS and groups 2 and 3, and in specific ultradian bands between SIRS and group 3, with decreased entropy in sepsis. Cluster analysis using wavelet features in specific bands revealed concrete clusters closely related with the groups in focus. LDA after wrapper-based feature selection was able to classify with an accuracy of more than 80% SIRS from the two sepsis groups, based on multiparametric patterns of entropy values in the very low frequencies and indicating reduced metabolic inputs on local thermoregulation, probably associated with extensive vasodilatation. CONCLUSIONS: We suggest that complexity analysis of temperature signals can assess inherent thermoregulatory dynamics during systemic inflammation and has increased discriminating value in patients with infectious versus noninfectious conditions, probably associated with severity of illness.

Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials.

BACKGROUND: Separation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients. RESULTS: Thirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H2O) and 2. weaning trials with PS: 5 cm H2O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P0.1 and RSBI* P0.1 (conventional model, R(2) = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05). CONCLUSIONS: We suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment.

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.

The effect of hypobaric hypoxia on multichannel EEG signal complexity.

OBJECTIVE: The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity. METHODS: Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined. RESULTS: A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions. CONCLUSIONS: The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies. SIGNIFICANCE: The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.

Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents.

OBJECTIVE: The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system. METHODS: Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the macroscopic analysis that estimates the on-going temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements' alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools. RESULTS: We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback-Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks' number and duration before driving errors. CONCLUSIONS: EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepiness in occupational settings incorporated in a sleepiness countermeasure device. SIGNIFICANCE: The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.

Non-linear analysis for the sleepy drivers problem.

The problem addressed in this work is sleepiness during driving, which often leads to accidents in the streets. Experiments with sleepy drivers took place and the EEG data were analysed in terms of non-linear methods. Sample entropy and phase synchronization variations were investigated within the signal sections corresponding to "driving events", i.e. driving mistakes or loss of control, as well as to periods of drowsiness and sleepiness, as compared to the periods of normal driving. Decreased sample entropy, indicating loss of complexity, and an increased phase synchronisation have been found in the preliminary study presented. The results are encouraging towards developing an alerting system for predicting and preventing driving accidents.

Chronic Lymphocytic Leukemia with Mutated IGHV4-34 Receptors: Shared and Distinct Immunogenetic Features and Clinical Outcomes.

Purpose: We sought to investigate whether B cell receptor immunoglobulin (BcR IG) stereotypy is associated with particular clinicobiological features among chronic lymphocytic leukemia (CLL) patients expressing mutated BcR IG (M-CLL) encoded by the IGHV4-34 gene, and also ascertain whether these associations could refine prognostication.Experimental Design: In a series of 19,907 CLL cases with available immunogenetic information, we identified 339 IGHV4-34-expressing cases assigned to one of the four largest stereotyped M-CLL subsets, namely subsets #4, #16, #29 and #201, and investigated in detail their clinicobiological characteristics and disease outcomes.Results: We identified shared and subset-specific patterns of somatic hypermutation (SHM) among patients assigned to these subsets. The greatest similarity was observed between subsets #4 and #16, both including IgG-switched cases (IgG-CLL). In contrast, the least similarity was detected between subsets #16 and #201, the latter concerning IgM/D-expressing CLL. Significant differences between subsets also involved disease stage at diagnosis and the presence of specific genomic aberrations. IgG subsets #4 and #16 emerged as particularly indolent with a significantly (P < 0.05) longer time-to-first-treatment (TTFT; median TTFT: not yet reached) compared with the IgM/D subsets #29 and #201 (median TTFT: 11 and 12 years, respectively).Conclusions: Our findings support the notion that BcR IG stereotypy further refines prognostication in CLL, superseding the immunogenetic distinction based solely on SHM load. In addition, the observed distinct genetic aberration landscapes and clinical heterogeneity suggest that not all M-CLL cases are equal, prompting further research into the underlying biological background with the ultimate aim of tailored patient management. Clin Cancer Res; 23(17); 5292-301. ©2017 AACR.

Quantitative multichannel EEG measure predicting the optimal weaning from ventilator in ICU patients with acute respiratory failure.

OBJECTIVE: The objective of this study was to develop a novel quantitative multichannel EEG (qEEG) based analysis method, called Global Field Damping Time (GFDT), in order to detect potential EEG changes of patients admitted to the ICU with acute respiratory failure, and correlate them to the patients' recovery outcome predicting the optimal time-point to disconnect the patient from mechanical ventilation. METHODS: Twenty-nine adult patients with acute respiratory failure out of 98 admitted to the Intensive Care Unit of Saint Paul General Hospital were enrolled, and among them only 15 completed the study. The patients were classified in 3 groups according to their outcome after 3 months follow-up. The patients were intubated with fraction of inspired oxygen (FiO2) of 100%. Neurological Deficit Scores (NDS) were measured 24 h after intubation to assess patients' neurological condition. Twenty-four hours after patient's intubation, FiO2 was decreased to 40% (weaning session), followed by a 5 min early recovery session, a 5 min recovery 1 session and a 5 min recovery 2 session. EEG recordings were performed during this experimental procedure. Multichannel EEG segments were processed and fitted into a multivariate autoregressive (mAR) model, and single channel EEG segments into a scalar autoregressive (sAR) model. The mAR and the sAR models of arbitrary order p were decomposed into mp and p oscillators and relaxators, respectively. Damping time of each oscillator and each relaxator, and the Global Field Damping Time (GFDT) as a weighted damping time were estimated for both mAR and sAR models. RESULTS: A statistically significant increase of mAR model's GFDT during the weaning session was observed in the subjects of all groups. Comparing the 3 patients' groups, statistically significant differences for mAR model's GFDT were observed for the weaning and early recovery session. Linear regression analysis between NDS and mean mAR model's GFDT showed statistical significance during weaning session, early recovery session, and recovery 1 session. There was no statistical significance for SaO2 in the regression analysis with NDS. The sAR model's GFDT presented worst results in comparison with the mAR modelling GFDT in the identification of hypoxic conditions during weaning session and in the discrimination of patients with acute respiratory failure according to their neurological outcome. CONCLUSIONS: Global Field Damping Time as correlated to the patients' neurological outcome appears to be a simple, compact, and substantial novel indicator of cerebral hypoxia and a potential predictor of the optimal time-point to disconnect the patient from the ventilator. SIGNIFICANCE: Quantitative EEG seems to be an important tool for ICU clinicians assisting them to decide for the patients' optimal time-point to disconnect the patient from the ventilator.

Investigation of altered heart rate variability, nonlinear properties of heart rate signals, and organ dysfunction longitudinally over time in intensive care unit patients.

PURPOSE: To investigate longitudinally over time heart rate dynamics and relation with mortality and organ dysfunction alterations in patients admitted to a multidisciplinary intensive care unit. METHODS: Data from 53 patients were used, with heart rate recorded from monitors and analyzed on a daily basis (every morning) for 600 seconds and sampling rate at 250 Hz, from admission to the intensive care unit until final discharge from the unit. Variance, which is a measure of heart rate variability; exponent alpha2; and approximate entropy (ApEn), which assess long-range correlations and periodicity within a signal, respectively; were measured and compared with every day Sequential Organ Failure Assessment Score (SOFA) and mortality. RESULTS: Nonsurvivors had lower ApEn mean (greater periodicity in their signals) and minimum values compared to survivors (0.53 +/- 0.25 vs 0.62 +/- 0.23, P = .04; 0.24 +/- 0.23 vs 0.48 +/- 0.23, P = .01, respectively). Patients in better conditions with SOFA of less than 7 (mean value) had higher variance and ApEn (more variable, less periodic signals) than those with SOFA of 7 or higher (0.47 +/- 0.51 vs 0.10 +/- 0.65, P < .001; 0.67 +/- 0.28 vs 0.49 +/- 0.24, P < .001, respectively). The alpha2 exponent and variance were correlated with length of stay (r = 0.55, P = .02, and r = 0.53, P = .02, respectively) and minimum ApEn with mortality (r = 0.41, P = .01). CONCLUSIONS: Loss of variability and increase in periodicity in heart rate of critically ill patients are linked with parallel deterioration of organ dysfunction and high mortality.

Association of heart rate variability and inflammatory response in patients with cardiovascular diseases: current strengths and limitations.

Many experimental and clinical studies have confirmed a continuous cross-talk between both sympathetic and parasympathetic branches of autonomic nervous system and inflammatory response, in different clinical scenarios. In cardiovascular diseases, inflammation has been proven to play a pivotal role in disease progression, pathogenesis and resolution. A few clinical studies have assessed the possible inter-relation between neuro-autonomic output, estimated with heart rate variability analysis, which is the variability of R-R in the electrocardiogram, and different inflammatory biomarkers, in patients suffering from stable or unstable coronary artery disease (CAD) and heart failure. Moreover, different indices derived from heart rate signals' processing, have been proven to correlate strongly with severity of heart disease and predict final outcome. In this review article we will summarize major findings from different investigators, evaluating neuro-immunological interactions through heart rate variability analysis, in different groups of cardiovascular patients. We suggest that markers originating from variability analysis of heart rate signals seem to be related to inflammatory biomarkers. However, a lot of open questions remain to be addressed, regarding the existence of a true association between heart rate variability and autonomic nervous system output or its adoption for risk stratification and therapeutic monitoring at the bedside. Finally, potential therapeutic implications will be discussed, leading to autonomic balance restoration in relation with inflammatory control.

Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: a study of physiologic complexity in surgical critically ill patients.

PURPOSE: The aim of the study was to investigate heart rate (HR) and respiratory rate (RR) complexity in patients with weaning failure or success, using both linear and nonlinear techniques. MATERIALS AND METHODS: Forty-two surgical patients were enrolled in the study. There were 24 who passed and 18 who failed a weaning trial. Signals were analyzed for 10 minutes during 2 phases: (1) pressure support (PS) ventilation (15-20 cm H(2)O) and (2) weaning trials with PS (5 cm H(2)O). Low- and high-frequency (LF, HF) components of HR signals, HR multiscale entropy (MSE), RR sample entropy, cross-sample entropy between cardiorespiratory signals, Poincaré plots, and α1 exponent were computed in all patients and during the 2 phases of PS. RESULTS: Weaning failure patients exhibited significantly decreased RR sample entropy, LF, HF, and α1 exponent, compared with weaning success subjects (P < .001). Their changes were opposite between the 2 phases, except for MSE that increased between and within groups (P < .001). A new model including rapid shallow breathing index (RSBI), α1 exponent, RR, and cross-sample entropies predicted better weaning outcome compared with RSBI, airway occlusion pressure at 0.1 second (P(0.1)), and RSBI × P(0.1) (conventional model, R(2) = 0.887 vs 0.463; P < .001). Areas under the curve were 0.92 vs 0.86, respectively (P < .005). CONCLUSIONS: We suggest that nonlinear analysis of cardiorespiratory dynamics has increased prognostic impact upon weaning outcome in surgical patients.

Investigation of heart rate and blood pressure variability, baroreflex sensitivity, and approximate entropy in acute brain injury patients.

PURPOSE: The purpose of the study was to investigate longitudinally over time heart rate (HR) and blood pressure variability and baroreflex sensitivity in acute brain injury patients and relate them with the severity of neurologic dysfunction and outcome. METHODS: Data from 20 brain injured patients due to multiple causes and treated in the intensive care unit were used, with HR and blood pressure recorded from monitors and analyzed on a daily basis. We performed power spectral analysis estimating low frequencies (LF: 0.04-0.15 Hz), high frequencies (HF: 0.15-0.4 Hz), and their ratio and calculated the approximate entropy, which assesses periodicity within a signal and transfer function (TF), that estimates baroreflex sensitivity. Heart rate variance was considered as a measure of HR variability. RESULTS: Nonsurvivors (brain dead) had lower approximate entropy (0.65 +/- 0.24 vs 0.84 +/- 0.26, P < .05) and lower variance mean values (0.48 +/- 0.54 vs 1.29 +/- 0.42 ms(2)/Hz, P < .01), lower LF and HF minimum values (0.31 +/- 0.88 vs 1.11 +/- 0.46, P < .01; and 0.27 +/- 0.42 vs 0.86 +/- 0.30, P < .01, respectively), lower LF/HF (0.22 +/- 0.29 vs 0.62 +/- 0.28, P < .01), and lower TF mean values (0.43 +/- 0.29 vs 1.11 +/- 0.74, P < .05) during their whole stay in the intensive care unit in relation with survivors. The mean variance (P < .05), mean TF (P < .05), and mean LF/HF (P < .05) were significantly successful in separating survivors from nonsurvivors. CONCLUSIONS: We conclude that in acute brain injury patients, low variability, low baroreflex sensitivity, and sustained decrease in LF/HF of HR signals are linked with a high mortality rate.

Indicators of sleepiness in an ambulatory EEG study of night driving.

Driver sleepiness due to sleep deprivation is a causative factor in 1% to 3% of all motor vehicle crashes. In recent studies, the importance of developing driver fatigue countermeasure devices has been stressed, in order to help prevent driving accidents and errors. Although numerous physiological indicators are available to describe an individual's level of alertness, the EEG signal has been shown to be one of the most predictive and reliable, since it is a direct measure of brain activity. In the present study, multichannel EEG data that were collected from 20 sleep-deprived subjects during real environmental conditions of driving are presented for the first time. EEG data's annotation made by two independent Medical Doctors revealed an increase of slowing activity and an acute increase of the alpha waves 5-10 seconds before driving events. From the EEG data that were collected, the Relative Band Ratio (RBR) of the EEG frequency bands, the Shannon Entropy, and the Kullback-Leibler (KL) Entropy were estimated for each one second segment. The mean values of these measurements were estimated for 5 minutes periods. Analysis revealed a significant increase of alpha waves relevant band ratios (RBR), a decrease of gamma waves RBR, and a significant decrease of KL entropy when the first and the last 5-min periods were compared. A rapid decrease of both Shannon and K-L entropies was observed just before the driving events. Conclusively, EEG can assess effectively the brain activity alterations that occur a few seconds before sleeping/drowsiness events in driving, and its quantitative measurements can be used as potential sleepiness indicators for future development of driver fatigue countermeasure devices.

Effects of mental workload and caffeine on catecholamines and blood pressure compared to performance variations.

Caffeine is characterised as a central nervous system stimulant, also affecting metabolic and cardiovascular functions. A number of studies have demonstrated an effect of caffeine on the excretion of catecholamines and their metabolites. Urinary epinephrine and norepinephrine have been shown to increase after caffeine administration. Similar trends were observed in our study in adrenaline (ADR) and noradrenaline (NORADR) levels and additionally a dose dependent effect of caffeine. The effect of caffeine on cognitive performance, blood pressure, and catecholamines was tested under resting conditions and under mental workload. Each subject performed the test after oral administration of 1 cup and then 3 cups of coffee. Root mean square error (RMSE) for the tracking task was continuously monitored. Blood pressure was also recorded before and after each stage of the experiment. Catecholamines were collected and measured for three different conditions as: at rest, after mental stress alone, after one dose of caffeine under stress, and after triple dose of caffeine under stress. Comparison of the performance of each stage with the resting conditions revealed statistically significant differences between group of smokers/coffee drinkers compared with the other two groups of non-coffee drinkers/non-smokers and non-smokers/coffee drinkers. There was no statistically significant difference between the last two groups. There was an increase of urine adrenaline with 1 cup of coffee and statistically significant increase of urine noradrenaline. Both catecholamines were significantly increased with triple dose of caffeine. Mental workload increased catecholamines. There was a dose dependent effect of caffeine on catecholamines.