Electrohysterogram signal component cataloging with spectral and time-frequency methods

The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3].

Evolução, a curto prazo, do perfil do estado de nutrição no transplante hepático, em adultos : contributo para otimização da abordagem clínica e nutricional

RESUMO:Contexto: A avaliação do estado de nutrição do doente com indicação para transplante hepático (TH) deve ser abrangente, considerando o amplo espetro de situações clínicas e metabólicas. As alterações metabólicas relacionadas com a doença hepática podem limitar a aplicação de métodos de avaliação nutricional, subestimando a desnutrição. Após o TH, é expectável a reversão dos distúrbios metabólicos da doença hepática, pela melhoria da função do fígado. No entanto, algumas complicações metabólicas podem surgir após o TH, relacionadas com a má-nutrição, a desnervação hepática e o uso prolongado de imunossupressão, comprometendo os resultados clínicos a longo-prazo. A medição longitudinal e confiável do metabolismo energético e dos compartimentos corporais após o TH, avaliada em conjunto com fatores influentes no estado de nutrição, pode identificar precocemente situações de risco e otimizar e individualizar estratégias clínicas e nutricionais com vantagens no prognóstico. Objetivo: Avaliar longitudinalmente, a curto prazo, o estado de nutrição após o TH em doentes com insuficiência hepática por doença crónica e identificar os fatores, para além da cirurgia, que determinam diferentes evoluções do metabolismo energético e da composição corporal. Métodos: Foi estudada uma coorte de indivíduos com indicação para TH por doença hepática crónica, admitidos consecutivamente para TH ortotópico eletivo, durante 2 anos. Foram programados 3 momentos de avaliação: na última consulta pré-TH (T0), logo que adquirida autonomia respiratória e funcional após o TH (T1) e um mês após o TH (T2). Nesses momentos, foram medidos no mesmo dia: o suprimento nutricional por recordatório das últimas 24 horas, o estado de nutrição por Avaliação Subjetiva Global (ASG), o gasto energético em repouso (GER) por calorimetria indireta, a antropometria, a composição corporal por bioimpedância elétrica tetrapolar multifrequências e a força muscular por dinamometria de preensão palmar. O índice de massa magra (IMM) e a massa celular corporal (MCC) foram usados como indicadores do músculo esquelético e a percentagem de massa gorda (%MG) e o índice de massa gorda (IMG) como indicadores de adiposidade. O GER foi comparado com o estimado pelas fórmulas de Harris-Benedict para classificação do estado metabólico em:hipermetabolismo (GER medido >120% do GER estimado), normometabolismo (GER medido entre 80 e 120% do GER estimado) e hipometabolismo (GER medido <80% do GER estimado). Foi utilizada análise multivariável: por regressão logística, para identificar variáveis associadas à possibilidade (odds ratio – OR) de pertencer a cada grupo metabólico pré-TH; por regressão linear múltipla, para identificar variáveis associadas à variação dos compartimentos corporais no período pós-TH; e por modelos de efeitos mistos generalizados, para identificar variáveis associadas à evolução do GER e dos compartimentos corporais entre o período pré- e pós-TH. Resultados: Foram incluídos 56 indivíduos com idade, média (DP), 53,7 (8,5) anos, 87,5% do sexo masculino, 23,2% com doença hepática crónica de etiologia etanólica. Após o TH, em 60,7% indivíduos foi administrado regime imunossupressor baseado no tacrolimus. Os indivíduos foram avaliados [mediana (AIQ)] 90,5 (P25: 44,2; P75: 134,5) dias antes do TH (T0), 9,0 (P25: 7,0; P75: 12,0) dias após o TH (T1) e 36,0 (P25: 31,0; P75: 43,0) dias após o TH (T2). Após o TH houve melhoria significativa do estado de nutrição, com diminuição da prevalência de desnutrição classificada pela ASG (37,5% em T0, 16,1% em T2, p<0,001). Antes do TH, 41,1% dos indivíduos eram normometabólicos, 37,5% hipometabólicos e 21,4% hipermetabólicos. A possibilidade de pertencer a cada grupo metabólico pré-TH associou-se à: idade (OR=0,899, p=0,010) e desnutrição pela ASG (OR=5,038, p=0,015) para o grupo normometabólico; e índice de massa magra (IMM, OR=1,264, p=0,049) e etiologia viral da doença hepática (OR=8,297, p=0,019) para o grupo hipermetabólico. Não se obteve modelo múltiplo para o grupo de hipometabólico pré-TH, mas foram identificadas associações univariáveis com a história de toxicodependência (OR=0,282, p=0,047) e com a sarcopénia pré- TH (OR=8,000, p=0,040). Após o TH, houve normalização significativa e progressiva do estado metabólico, indicada pelo aumento da prevalência de normometabolismo (41,1% em T0, 57,1% em T2, p=0,040). Foram identificados diferentes perfis de evolução do GER após o TH, estratificado pelo estado metabólico pré-TH: no grupo hipometabólico pré-TH, o GER (Kcal) aumentou significativa e progressivamente (1030,6 em T0; 1436,1 em T1, p=0,001; 1659,2 em T2, p<0,001); no grupo hipermetabólico pré-TH o GER diminuiu significativa e progressivamente (2097,1 em T0; 1662,5 em T1, p=0,024; 1493,0 em T2, p<0.001); no grupo normometabólico não houve variações significativas. Os perfis de evolução do GER associaram-se com: peso corporal (β=9,6, p<0,001) e suprimento energético (β=13,6, p=0,005) na amostra total; com peso corporal (β=7,1, p=0,018) e contributo energético dos lípidos (β=18,9, p=0,003) no grupo hipometabólico pré-TH; e com peso corporal (β=14,1, p<0,001) e desnutrição pela ASG (β=-171,0, p=0,007) no grupo normometabólico pré-TH.Houve redução transitória dos compartimentos corporais entre T0 e T1, mas a maioria destes recuperou para valores semelhantes aos pré-TH. As exceções foram a água extracelular, que diminuiu entre T0 e T2 (média 18,2 L e 17,8 L, p=0,042), a massa gorda (média 25,1 Kg e 21,7 Kg, p<0,001) e o IMG (média 10,6 Kg.m-2 e 9,3 Kg.m-2, p<0,001) que diminuíram entre T1 e T2. Relativamente à evolução dos indicadores de músculo esquelético e adiposidade ao longo do estudo: a evolução do IMM associou-se com força de preensão palmar (β=0,06, p<0,001), creatininémia (β=2,28, p<0,001) e número total de fármacos administrados (β=-0,21, p<0,001); a evolução da MCC associou-se com força de preensão palmar (β=0,16, p<0,001), creatininémia (β=4,17, p=0,008) e número total de fármacos administrados (β=-0,46, p<0,001); a evolução da %MG associou-se com força de preensão palmar (β=-0,11, p=0,028), história de toxicodependência (β=-5,75, p=0,024), creatininémia (β=-5,91, p=0,004) e suprimento proteico (β=-0,06, p=0,001); a evolução do IMG associou-se com história de toxicodependência (β=- 2,64, p=0,019), creatininémia (β=-2,86, p<0,001) e suprimento proteico (β=-0,02, p<0,001). A variação relativa (%Δ) desses compartimentos corporais entre T1 e T2 indicou o impacto da terapêutica imunossupressora na composição corporal: o regime baseado na ciclosporina associou-se positivamente com a %Δ do IMM (β=23,76, p<0,001) e %Δ da MCC (β=26,58, p<0,001) e negativamente com a %Δ MG (β=-25,64, p<0,001) e %Δ do IMG (β=-25,62, p<0,001), relativamente ao regime baseado no tacrolimus. Os esteróides não influenciaram a evolução do GER nem com a dos compartimentos corporais. Conclusões: O estado de nutrição, avaliado por ASG, melhorou significativamente após o TH, traduzida pela diminuição da prevalência de desnutrição. O normometabolismo pré-TH foi prevalente e associou-se à menor idade e à desnutrição pré- TH. O hipometabolismo pré-TH associou-se à história de toxicodependência e à sarcopénia pré-TH. O hipermetabolismo pré-TH associou-se ao maior IMM e à etiologia viral da doença hepática. Após o TH, houve normalização progressiva do estado metabólico. Foram identificados três perfis de evolução do GER, associando-se com: peso corporal e suprimento energético na amostra total; peso corporal e contributo energético dos lípidos no grupo hipometabólico pré- TH; e peso corporal e desnutrição pela ASG no grupo normometabólico pré-TH. Foram identificados diferentes perfis de evolução da composição corporal após TH. A evolução do músculo esquelético associou-se positivamente com a força de preensão palmar e a creatininémia e negativamente com o número total de fármacos administrados. A evolução da adiposidade (%MG e IMG) associou-se inversamente com a história de toxicodependência, a creatininémia e o suprimento proteico; adicionalmente, a %MG associou-se inversamente com a força de preensão palmar. O regime baseado na ciclosporina associou-se independentemente com diminuição da adiposidade e aumento do músculo esquelético, comparativamente ao regime baseado no tacrolimus.---------------------------ABSTRACT:Background: The assessment of nutritional status in patients undergoing liver transplantation (LTx) should be comprehensive, accounting for the wide spectrum of the clinical and metabolic conditions. The metabolic disturbances related to liver disease may limit the precision and accuracy of traditional nutritional assessment methods underestimating the undernourishment. After LTx, it is expected that many metabolic derangements improve with the recovery of liver function. However, some metabolic complications arising after LTx, related to nutritional status, hepatic denervation, and prolonged immunosuppression, may compromise the longterm outcome. A reliable longitudinal assessment of both energy metabolism and body compartments after LTx, combined with assessments of other factors potentially affecting the nutritional status, may enable a better interpretation on the relationship between the metabolic and the nutritional status. These reliable assessments may precociously identify nutritional risk conditions and optimize and customize clinical and nutritional strategies improving the prognosis. Objective: To assess longitudinally the nutritional status shortly after orthotopic LTx in patients with chronic liver disease, and identify factors, beyond surgery, determining different energy metabolism and body composition profiles.Methods: A cohort of consecutive patients who underwent LTx due to chronic liver disease was studied within a period of two years. The assessments were performed in three occasions: at the last visit before LTx (T0), after surgery as soon as respiratory and functional autonomy was established (T1), and approximately one month after surgery (T2). On each occasion all assessments were performed on the same day, and included: the dietary assessment by 24- hour dietary recall, nutritional status by the Subjective Global Assessment (SGA), the resting energy expenditure (REE) by indirect calorimetry, anthropometry, body composition by multifrequency bioelectrical impedance analysis, and muscle strength by handgrip strength. Both the lean mass index (LMI) and body cell mass (BCM) were used as surrogates of skeletal muscle, and both the percentage of fat mass (%FM) and fat mass index (FMI) of adiposity. The REE was predicted according to the Harris and Benedict equation. Hypermetabolism was defined as a measured REE more than 120% of the predicted value; normometabolism as a measured REE within 80-120% of the predicted value; and hypometabolism as a measured REE less than 80% of the predicted value. Multiple regression analysis was used: by logistic regression to identify variables associated with odds of belong each pre-LTx metabolic groups; by linear multiple regression analysis to identify variables associated with body compartments relative variations (%Δ) in the post-LTx period; and by mixed effects models to identify variables associated with the REE and body compartments profiles pre- and post-LTx. Results: Fifty six patients with a mean (SD) of 53.7 (8.5) years of age were included, 87.5% were men and 23.2% with alcoholic liver disease. After LTx 60.7% individuals were assigned to tacrolimus-based immunosuppressive regimen. The patients were assessed at a median time (inter-quartil range) of 90.5 (P25 44.2; P75 134.5) days before LTx (T0), at a median time of 9.0 (P25 7.0; P75 12.0) (T1) and 36 (P25 31.0; P75 43.0) (T2) days after LTx. After LTx the nutritional status significantly improved: the SGA-undernourishment decreased from 37.5% (T0) to 16.1% (T2) (p<0.001). Before LTx, 41.1% patients were normometabolic, 37.5% hypometabolic, and 21.4% hypermetabolic. The predictors of each pre-LTx metabolic group were: age (OR=0.899, p=0.010) and SGA-undernourishment (OR=5.038, p=0.015) for the normometabolic group; and LMI (OR=1.264, p=0.049) and viral etiology of liver disease (OR=8.297, p=0.019) for the hypermetabolic group. No multiple model was found for the pre-LTx hypometabolic group, but univariate association was found with history of drug addiction (OR=0.282, p=0.047) and pre- LTx sarcopenia (OR=8.000, p=0.040). After LTx a significant normalization of the metabolic status occurred, indicated by the increase in the prevalence of normometabolic patients (from T0: 41.1% to T2: 57.1%, p=0.040). Different REE profiles were found with REE stratified by preoperative metabolic status: in the hypometabolic group a significant progressive increase in mean REE (Kcal) was observed (T0: 1030.6; T1: 1436.1, p=0.001; T2: 1659.2, p<0.001); in the hypermetabolic group, a significant progressive decrease in mean REE (Kcal) was observed (T0: 2097.1; T1: 1662.5, p=0.024; T2: 1493.0, p<0.001); and in the normometabolic group, no significant differences were found. The REE profiles were associated with: body weight (β- estimate=9.6, p<0.001) and energy intake (β-estimate=13.6, p=0.005) in the whole sample; with body weight (β-estimate=7.1, p=0.018) and %TEV from lipids (β-estimate=18.9, p=0.003) in the hypometabolic group; and with body weight (β-estimate=14.1, p<0.001), and SGAundernourishment (β-estimate=-171, p=0.007) in the normometabolic group. A transient decrease in most body compartments occurred from T0 to T1, with subsequent catch-up to similar preoperative values. Exceptions were the extracellular water, decreasing from T0 to T2 (mean 18.2 L to 17.8 L, p=0.042), the fat mass (mean 25.1 Kg to 21.7 Kg, p<0.001) and FMI (mean 10.6 Kg.m-2 to 9.3 Kg.m-2, p<0.001), decreasing from T1 to T2. Significant predictors of skeletal muscle and adiposity profiles were found: LMI evolution was associated with handgrip strength (β-estimate=0.06, p<0.001), serum creatinine (β- estimate=2.28, p<0.001) and number of medications (β-estimate=-0.21, p<0.001); BCM evolution was associated with handgrip strength (β-estimate=0.16, p<0.001), serum creatinine (β-estimate=4.17, p<0.001) and number of medications (β-estimate=-0.46, p<0.001); the %FM evolution was associated with handgrip strength (β-estimate=-0.11, p=0.028), history of drug addiction (β-estimate=-5.75, p=0.024), serum creatinine (β-estimate=-5.91, p=0.004) and protein intake (β-estimate=-0.06, p=0.001); and FMI evolution was associated with history of drug addiction (β-estimate=-2.64, p=0.019), serum creatinine (β-estimate=-2.86, p<0.001) and protein intake (β-estimate=-0.02, p<0.001). The %Δ of the aforementioned body compartments from T1 to T2 indicated the influence of immunosuppressive agents on body composition: the cyclosporine-based regimen, compared with tacrolimus-based regimen, was positively associated with %Δ LMI (β-estimate=23.76, p<0.001) and %Δ BCM (β- estimate=26.58, p<0.001), and inversely associated with %Δ FM (β-estimate=-25.64, p<0.001) and %Δ FMI (β-estimate=-25.62, p<0.001). No significant changes in REE or body composition were observed associated with dose or duration of steroid therapy. Conclusions: The SGA-assessed nutritional status improved shortly after LTx, with significant decrease in prevalence undernourished individuals. XXI Preoperative normometabolism was prevalent and was associated with younger age and SGAundernourishment before LTx. Preoperative hypometabolism was associated with history of drug addiction and pre-LTx sarcopenia. Preoperative hypermetabolism was associated with higher LMI and viral etiology of liver disease. A significant normalization of the metabolic status was observed after LTx. The REE profiles were positively predicted by body weight and energy intake in the whole sample, by body weight and percentage of energy intake from lipids in the preoperative hypometabolic patients, and by body weight and SGA–undernourishment in the preoperative normometabolic patients. Different body composition profiles were found after LTx. Skeletal muscle profile was positively associated with handgrip strength and serum creatinine, and inversely with the number of medications. The adiposity profile was inversely associated with history of drug addiction, serum creatinine and protein intake. Additionally, the %FM evolution was inversely associated with handgrip strength. The cyclosporine-based regimen, compared with tacrolimus-based regimen, was independently associated with skeletal muscle increase and adiposity decrease.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.