Molecular Alterations of Nucleocytoplasmic Transport in Patients on the Heart Transplantation Waiting List and Its Correlation With the Severity and Etiology of Heart Failure.

OBJECTIVES: To evaluate whether the levels of some molecules implicated in nucleocytoplasmic transport in human cardiomyocytes are related to the severity of heart failure (HF) in patients on the heart transplantation (HT) waiting list, and to determine whether there is a differential pattern of molecular alteration between ischemic cardiomyopathy (ICM) and non-ischemic dilated cardiomyopathy (DCM). METHODS: Sixty-three blood samples collected before HT were analyzed to identify the levels of IMPORTIN5 (IMP5); IMPORTINalpha2; ATPaseCaTransp (ATPCa); NUCLEOPORIN153kDa (Nup153); NUCLEOPORIN160kDa (Nup160); RANGTPaseAP1 (RanGAP1) and EXPORTIN4 (EXP4). These data were then compared between patients with advanced HF with or without the need for ventricular support with extracorporeal membrane oxygenation (ECMO) as a bridge for HT, as well as between patients with non-ischemic DCM and patients with ICM. RESULTS: Thirty-three patients had ICM, 26 had non-ischemic DCM, and 4 had heart disease. Seventeen patients required ventricular assistance as a bridge to HT. The levels of ATPCa, RanGAP1, and IMP5 were significantly higher in patients with ECMO, while EXP4 was significantly higher in patients without ECMO. Patients with DCM showed higher levels of IMP5, RanGAP1, and Nup153 than those with ICM. CONCLUSION: Patients with advanced HF in critical condition (with ECMO as a bridge for HT) presented with significantly higher levels of ATPCa, RanGAP1, and IMP5, while patients with DCM had significantly higher levels of RanGAP1, IMP5, and Nup153. It remains to be clarified whether the determination of these molecules would facilitate the early identification of this group or if their alteration occurs as consequence of circulatory support with ECMO.

Novel characterization method of impedance cardiography signals using time-frequency distributions.

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P = 0.780) and the extended modified beta distribution (P = 0.765) provided similar results, higher than the rest of analyzed kernels. Graphical abstract Flowchart for the optimization of time-frequency distribution kernels for impedance cardiography signals.

Efficacy of different methods used for dry socket prevention and risk factor analysis: A systematic review.

BACKGROUND: Dry socket is one of the most common complications that develops after the extraction of a permanent tooth, and its prevention is more effective than its treatment. OBJECTIVES: Analyze the efficacy of different methods used in preventing dry socket in order to decrease its incidence after tooth extraction. MATERIAL AND METHODS: A Cochrane and PubMed-MEDLINE database search was conducted with the search terms "dry socket", "prevention", "risk factors", "alveolar osteitis" and "fibrynolitic alveolitis", both individually and using the Boolean operator "AND". The inclusion criteria were: clinical studies including at least 30 patients, articles published from 2005 to 2015 and written in English. The exclusion criteria were case reports and nonhuman studies. RESULTS: 30 publications were selected from a total of 250. Six of the 30 were excluded after reading the full text. The final review included 24 articles: 9 prospective studies, 2 retrospective studies and 13 clinical trials. They were stratified according to their level of scientific evidence using SIGN criteria (Scottish Intercollegiate Guidelines Network). CONCLUSIONS: All treatments included in the review were aimed at decreasing the incidence of dry socket. Locally administering chlorhexidine or applying platelet-rich plasma reduces the likelihood of developing this complication. Antibiotic prescription does not avoid postoperative complications after lower third molar surgery. With regard to risk factors, all of the articles selected suggest that patient age, history of previous infection and the difficulty of the extraction are the most common predisposing factors for developing dry socket. There is no consensus that smoking, gender or menstrual cycles are risk factors. Taking the scientific quality of the articles evaluated into account, a level B recommendation has been given for the proposed-procedures in the prevention of dry socket.

Auditory and Nociceptive Stimuli Responses in the Electroencephalogram. A Non-linear Measures and Time-frequency Representation Based Analysis.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Neural Signals and Images". OBJECTIVES: An efficient way to investigate the neural basis of nociceptive responses is the analysis of the event-related brain potentials (ERPs). The main objective of this work was to study how adaptation and fatigue affect the ERPs to stimuli of different modalities, by characterizing the responses to infrequent and frequent stimulation in different recording periods. METHODS: In this work, series of averaged EEG epochs recorded after thermal, electrical and auditory stimulation were analyzed with time-frequency representation and non-linear measures as spectral entropy and auto-mutual information function. The study was performed by considering the traditional EEG frequency bands. RESULTS: The defined measures presented a statistical significance p-value < 0.01 and accuracy higher than 60% by differentiating windows of response to infrequent (I) and frequent (F) stimuli between the start and end of the EEG recording. CONCLUSIONS: These measures permitted to observe some aspects of the subject's adaptation and the nociceptive response.

Náuseas y vómitos postoperatorios en cirugía mayor traumatológica / Postoperative nausea and vomiting in major trauma surgery

Objetivo: Las náuseas y vómitos son complicaciones frecuentes durante el postoperatorio, causan un gran malestar en el paciente y pueden aumentar la morbilidad. El objetivo de nuestro estudio es determinar la prevalencia de náuseas y vómitos postoperatorios (NVPO), qué factores influyen en su aparición y obtener un modelo predictivo de factores pronóstico. Método Se realizó un estudio observacional prospectivo a 201 pacientes intervenidos de cirugía mayor Traumatológica y Ortopédica en el año 2008. Para la recogida de datos se elaboró un cuestionario que constaba de los datos demográficos del paciente, de los intraoperatorios, de los postoperatorios y del registro del tratamiento antiemético postoperatorio según el protocolo del servicio. Resultados Presentaron NVPO el 39,8% de los pacientes. Del total de mujeres padecieron NVPO el 46,6% y el 75% eran pacientes con antecedentes de NVPO previos. Se obtuvo mayor prevalencia en cirugías consideradas más agresivas. El horario en el que hubo mayor número de episodios fue a las 17 y 19 h PM y a las 8 h a. m. Obtuvimos nuestro modelo predictivo a partir de la siguiente fórmula: Y (probabilidad de náuseas y vómitos)=−1,334+0,753*S+1,5602*NVP+0,769*IQa. Conclusiones La prevalencia de NVPO en este estudio ha sido elevada, ya que más de un tercio de la población estudiada las presentaron. El modelo predictivo nos permitirá saber cuál es el riesgo de cada paciente de padecer NVPO, y por lo tanto marcará la estrategia terapéutica tanto preoperatoria como postoperatoria. Ser mujer, tener antecedentes de náuseas y vómitos previos y estar sometido a una cirugía agresiva son factores de riesgo. La movilización del paciente y las visitas de los familiares producen mayor número de episodios de NVPO (AU)

Objective: Postoperative nausea and vomiting (PONV) are common complications during the postoperative period, causing important discomfort to the patient and also can increase morbidity. The objective of our article is to predict the prevalence of postoperative nausea and vomiting, the factors that have an influence on its appearance, and to obtain a predictive model based on prognostic factors. Method: A prospective observational study was conducted on 201 patients who underwent major Orthopaedic and Trauma surgery during the year 2008. A questionnaire was designed to collect the required data as established previously by a standardized protocol, in which was requested, patient demographics, intraoperative and postoperative data, as well as details on any antiemetic treatment that was needed in the recovery ward. Results: A total of 39.8% patients suffered PONV. Of the females, 46.6% suffered PONV, and 75% had previous history of PONV. A higher prevalence was observed in patients who were subjected to more aggressive surgery. There was a concentration of cases between 5 pm and 7 pm, and also at 8 am. The predictive model was obtained from this formula: Y = −1,334 + 0,753*S + 1,5602*NVP + 0,769*IQa. Conclusions: The prevalence of PONV in this study has been high, as more a third of the studied population suffered from it. The predictive model should help determine the specific risk of each patient of suffering from PONV, thus being able to define a therapeutic strategy during the preoperative period as well as during the postoperative period. Being female, a previous history of PONV, and undergoing an aggressive surgical procedure are risk factors. Patient mobilization and family visits increase the number of PONV episodes (AU)

[Postoperative nausea and vomiting in major trauma surgery]. / Náuseas y vómitos postoperatorios en cirugía mayor traumatológica.

OBJECTIVE: Postoperative nausea and vomiting (PONV) are common complications during the postoperative period, causing important discomfort to the patient and also can increase morbidity. The objective of our article is to predict the prevalence of postoperative nausea and vomiting, the factors that have an influence on its appearance, and to obtain a predictive model based on prognostic factors. METHOD: A prospective observational study was conducted on 201 patients who underwent major Orthopaedic and Trauma surgery during the year 2008. A questionnaire was designed to collect the required data as established previously by a standardized protocol, in which was requested, patient demographics, intraoperative and postoperative data, as well as details on any antiemetic treatment that was needed in the recovery ward. RESULTS: A total of 39.8% patients suffered PONV. Of the females, 46.6% suffered PONV, and 75% had previous history of PONV. A higher prevalence was observed in patients who were subjected to more aggressive surgery. There was a concentration of cases between 5 pm and 7 pm, and also at 8 am. The predictive model was obtained from this formula: Y= -1,334 + 0,753*S + 1,5602*NVP + 0,769*IQa CONCLUSIONS: The prevalence of PONV in this study has been high, as more a third of the studied population suffered from it. The predictive model should help determine the specific risk of each patient of suffering from PONV, thus being able to define a therapeutic strategy during the preoperative period as well as during the postoperative period. Being female, a previous history of PONV, and undergoing an aggressive surgical procedure are risk factors. Patient mobilization and family visits increase the number of PONV episodes.

Symbolic dynamic analysis of relations between cardiac and breathing cycles in patients on weaning trials.

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure.

DNA binding site characterization by means of Rényi entropy measures on nucleotide transitions.

In this work, parametric information-theory measures for the characterization of binding sites in DNA are extended with the use of transitional probabilities on the sequence. We propose the use of parametric uncertainty measures such as Rényi entropies obtained from the transition probabilities for the study of the binding sites, in addition to nucleotide frequency-based Rényi measures. Results are reported in this work comparing transition frequencies (i.e., dinucleotides) and base frequencies for Shannon and parametric Rényi entropies for a number of binding sites found in E. Coli, lambda and T7 organisms. We observe that the information provided by both approaches is not redundant. Furthermore, under the presence of noise in the binding site matrix we observe overall improved robustness of nucleotide transition-based algorithms when compared with nucleotide frequency-based method.

Heart rate variability analysis based on time-frequency representation and entropies in hypertrophic cardiomyopathy patients.

In hypertrophic cardiomyopathy (HCM) patients there is an increased risk of premature death, which can occur with little or no warning. Furthermore, classification for sudden cardiac death on patients with HCM is very difficult. The aim of our study was to improve the prognostic value of heart rate variability (HRV) in HCM patients, giving insight into changes of the autonomic nervous system. In this way, the suitability of linear and nonlinear measures was studied to assess the HRV. These measures were based on time-frequency representation (TFR) and on Shannon and Rényi entropies, and compared with traditional HRV measures. Holter recordings of 64 patients with HCM and 55 healthy subjects were analyzed. The HCM patients consisted of two groups: 13 high risk patients, after aborted sudden cardiac death (SCD); 51 low risk patients, without SCD. Five-hour RR signals, corresponding to the sleep period of the subjects, were considered for the analysis as a comparable standard situation. These RR signals were filtered in the three frequency bands: very low frequency band (VLF, 0-0.04 Hz), low frequency band (LF, 0.04-0.15 Hz) and high frequency band (HF, 0.15-0.45 Hz). TFR variables based on instantaneous frequency and energy functions were able to classify HCM patients and healthy subjects (control group). Results revealed that measures obtained from TFR analysis of the HRV better classified the groups of subjects than traditional HRV parameters. However, results showed that nonlinear measures improved group classification. It was observed that entropies calculated in the HF band showed the highest statistically significant levels comparing the HCM group and the control group, p-value < 0.0005. The values of entropy measures calculated in the HCM group presented lower values, indicating a decreasing of complexity, than those calculated from the control group. Moreover, similar behavior was observed comparing high and low risk of premature death, the values of the entropy being lower in high risk patients, p-value < 0.05, indicating an increase of predictability. Furthermore, measures from information entropy, but not from TFR, seem to be useful for enhanced risk stratification in HCM patients with an increased risk of sudden cardiac death.

SNP sets selection under mutual information criterion, application to F7/FVII dataset.

One of the main goals of human genetics is to find genetic markers related to complex diseases. In blood coagulation process, it is known that genetic variability in F7 gene is the most responsible for observed variations in FVII levels in blood. In this work, we propose a method for selecting sets of Single Nucleotide Polymorphisms (SNPs) significantly correlated with a phenotype (FVII levels). This method employs a feature selection algorithm (variant of Sequential Forward Selection, SFS) based on a criterion of statistical significance of a mutual information functional. This algorithm is applied to a sample of independent individuals from the GAIT project. Main SNPs found by the algorithm are in correspondence with previous results published using family-based techniques.

Multiscale regularity analysis of the Heart Rate Variability: stratification of cardiac death risk.

Subjects with ischemic dilated cardiomiopathy tend to suffer episodes of sudden cardiac death, thus risk stratification is essential to establish an adequate therapy for the patients. In this work, a new methodology was proposed for the study of the heart rate variability by using a multiscale analysis based on the concept of entropy rates, for improving risk prediction in cardiac patients. Symbolic dynamics were applied to RR time series and sets of words in several scales were constructed. The multiscale regularity analysis was proposed by comparing the entropies, calculated using Shannon and Renyi definitions, of the series of words in different scales. The study considered the selection of the best parameters for the length of the words (l) and the order of the entropies (q). Statistical analysis with repeated measures and discriminant analysis revealed statistically significant differences (p-value<0.05) and a high percentage of well classified subjects in their different risk groups, with sensitivity, specificity and positive predictive values of 100%.

Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders.

Discrete hidden Markov models (HMMs) were applied to classify pregnancy disorders. The observation sequence was generated by transforming RR and systolic blood pressure time series using symbolic dynamics. Time series were recorded from 15 women with pregnancy-induced hypertension, 34 with preeclampsia and 41 controls beyond 30th gestational week. HMMs with five to ten hidden states were found to be sufficient to characterize different blood pressure variability, whereas significant classification in RR-based HMMs was found using fifteen hidden states. Pregnancy disorders preeclampsia and pregnancy induced hypertension revealed different patho-physiological autonomous regulation supposing different etiology of both disorders.

Information flow to assess cardiorespiratory interactions in patients on weaning trials.

Nonlinear processes of the autonomic nervous system (ANS) can produce breath-to-breath variability in the pattern of breathing. In order to provide assess to these nonlinear processes, nonlinear statistical dependencies between heart rate variability and respiratory pattern variability are analyzed. In this way, auto-mutual information and cross-mutual information concepts are applied. This information flow analysis is presented as a short-term non linear analysis method to investigate the information flow interactions in patients on weaning trials. 78 patients from mechanical ventilation were studied: Group A of 28 patients that failed to maintain spontaneous breathing and were reconnected; Group B of 50 patients with successful trials. The results show lower complexity with an increase of information flow in group A than in group B. Furthermore, a more (weakly) coupled nonlinear oscillator behavior is observed in the series of group A than in B.

Association between short term and long term communication in pathological autonomic control.

Autonomic Information Flow (AIF) reflects the time scale dependence of autonomic communications such as vagal, sympathetic, and slower rhythms and their complex interplay. We investigated the hypothesis that pathologically disturbed short term control is associated with simplified complex long term control. This particular characteristic of altered autonomic communication was evaluated in different medical patient groups. Holter recordings were assessed in patients with multiple organ dysfunction (MODS) (26 survivors, 10 non-survivors); with heart failure (14 low risk-without history of aborted cardiac arrest (CA), 13 high risk--with history of CA); with idiopathic dilated cardiomyopathy (IDC) (26 low risk, 11 high risk of CA), after myocardial infarction (MI) (1221 low risk--survivors, 55 high risk--non-survivors); after abdominal aorta surgery (AAS, 32 length of stay in hospital LOS>7 days, 62 LOS < or =7 days). AIF of short and long time scales was investigated. We found a fundamental association of increased short term randomness and decreased long term randomness due to pathology. Concerning risk, high risk patients were characterized by increased short term complexity and decreased long term complexity in all patients groups with the exception of the IDC patients. We conclude that different time scales of AIF represent specific pathophysiological aspects of altered autonomic communication and control. The association of altered short term control with simplified long term behavior might be a pathophysiologically relevant compensation mechanism in the case of a disturbed fastest actuator. This knowledge might be useful for the development of comprehensive therapeutic strategies besides the predictive implications.

Optimized symbolic dynamics approach for the analysis of the respiratory pattern.

Traditional time domain techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this paper, the respiratory pattern variability is analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation are studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of inspiratory time, expiratory time, breathing duration, fractional inspiratory time, tidal volume and mean inspiratory flow are analyzed. Two different symbol alphabets, with three and four symbols, are considered to characterize the respiratory pattern variability. Assessment of the method is made using the 40 respiratory volume signals classified using clinical criteria into two classes: low variability (LV) or high variability (HV). A discriminant analysis using single indexes from symbolic dynamics has been able to classify the respiratory volume signals with an out-of-sample accuracy of 100%.

Joint symbolic dynamic analysis of cardiorespiratory interactions in patients on weaning trials.

Assessing autonomic control provides information about patho-physiological imbalances. Measures of variability of the cardiac interbeat duration RR(n) and the variability of the breath duration TTot(n) are sensitive to those changes. The interactions between RR(n) and TTot(n) are complex and strongly non-linear. A study of joint symbolic dynamics is presented as a new short-term non-linear analysis method to investigate these interactions in patients on weaning trials. 78 patients from mechanical ventilation are studied: Group A (patients that failed to maintain spontaneous breathing and were reconnected) and Group B (patients with successful trials). Using the concept of joint symbolic dynamics, cardiac and respiratory changes were transformed into a word series, and the probability of occurrence of each word type was calculated and compared between both groups. Significant differences were found in 13 words, and the most significant pn(Wc010, r010): 0.0041 ± 0.0036 (group A) against 0.0012 ± 0.0024 (group B), p-value = 0.00001. The number of seldom occurring word types (forbidden words) also presents significant differences fwcr: 6.9 ± 6.6 against 13.5 ± 5.3, p-value = 0.00004. Joint symbolic dynamics provides an efficient non-linear representation of cardiorespiratory interactions that offers simple physiological interpretations.

Hidden markov models and mutual information analysis to characterize nonlinear dynamics in heart rate variability.

A study of nonlinear dynamics of the heart rate variability (HRV) was performed using hidden Markov models (HMM) and Mutual Information (MI). A methodology based on HMM has been developed in the present work. Cardiac RR series were analyzed in the three frequency bands: HF (0.15-0.45Hz), high frequency band; LF (0.04-0.15Hz), low frequency band; VLF (0.003-0.04Hz), very low frequency band. These series (0, observations) were modeled using HMM. The model λ=(A,B,∏) was selected so that P(O/λ) was locally maximized. Ergodic topology and N=10 states were also considered for this analysis. Different measures based on HMM were defined and obtained from RR time series of 37 Idiopathic Dilated Crdiomyopathy (IDC) patients and 46 healthy subjects (NRM), during awake and sleep stages. Two groups of IDC patients were considered: 11 high risk (HR) patients, after aborted sudden cardiac death (SCD) or who died during the follow up; 26 low risk (LR) patients, without SCD. Some HMM measures showed high percentages (up to 100%) of well classified subjects in all groups.

Estimating respiratory pattern variability by symbolic dynamics.

OBJECTIVES: The traditional techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this study the respiratory pattern variability was analyzed using symbolic dynamics. METHODS: A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels. Breath duration (T(TOT)) time series and the relation T(I)/T(TOT), that contains the influence of inspiratory time (T(I)), were considered. Length-3 words and 3 different symbols were proposed. The incidence of the overlapping tau and the parameter alpha were analyzed. RESULTS: From the breath duration time series, the distribution of words with probability of occurrence higher than 6% was concentrated on one word for low respiratory variability, whereas high variability was characterized by 4 words, presenting a statistically significant difference (p

Variability analysis of the respiratory volume based on non-linear prediction methods.

This work proposed and studied a method of automatically classifying respiratory volume signals as high or low variability by means of non-linear analysis of the respiratory volume. The analysis used volume signals generated by the respiratory system to construct a model of its dynamics and to estimate the quality of the predictions made with the model. Different methods of prediction evaluation, prediction horizons and embedding dimensions were also analysed. Assessment of the method was made using a database that contained 40 respiratory volume signals classified using clinical criteria into two classes: low or high variability. The results obtained using the method of surrogate data provided evidence of non-linear determinism in the respiratory volume signals. A discriminant analysis carried out using non-linear prediction variables classified the respiratory volume signals with an accuracy of 95%.

Analysis of the nonlinear autodependencies of respiratory pattern in patients on weaning trials.

Traditional time domain techniques of data analysis are often not sufficient to characterize the nonlinear dynamics of respiration. In this study, the respiratory pattern variability was analyzed using auto mutual information measures. These provide access to nonlinear statistical autodependencies of respiratory pattern variability. A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of breathing duration, inspiratory time, fractional inspiratory time, tidal volume and mean inspiratory flow were analyzed. Different measures based on auto-mutual information were studied to characterize the respiratory pattern variability with regard to its complex organization.