New multiple matched filter: design and experimental realization

A method of making a multiple matched filter which allows the recognition of different characters in successive planes in simple conditions is proposed. The generation of the filter is based on recording on the same plate the Fourier transforms of the different patterns to be recognized, each of which is affected by different spherical phase factors because the patterns have been placed at different distances from the lens. This is proved by means of experiments with a triple filter which allows satisfactory recognition of three characters.

New multiple matched filter: design and experimental realization

A method of making a multiple matched filter which allows the recognition of different characters in successive planes in simple conditions is proposed. The generation of the filter is based on recording on the same plate the Fourier transforms of the different patterns to be recognized, each of which is affected by different spherical phase factors because the patterns have been placed at different distances from the lens. This is proved by means of experiments with a triple filter which allows satisfactory recognition of three characters.

Multiple-model fault tolerant control of terminal unites of HVAC Systems

In this work, a fault-tolerant control scheme is applied to a air handling unit of a heating, ventilation and air-conditioning system. Using the multiple-model approach it is possible to identify faults and to control the system under faulty and normal conditions in an effective way. Using well known techniques to model and control the process, this work focuses on the importance of the cost function in the fault detection and its influence on the reconfigurable controller. Experimental results show how the control of the terminal unit is affected in the presence a fault, and how the recuperation and reconfiguration of the control action is able to deal with the effects of faults.

MULTIPLE MODEL EVALUATION ABSENT THE GOLD STANDARD VIA MODEL COMBINATION

We describe a method for evaluating an ensemble of predictive models given a sample of observations comprising the model predictions and the outcome event measured with error. Our formulation allows us to simultaneously estimate measurement error parameters, true outcome — aka the gold standard — and a relative weighting of the predictive scores. We describe conditions necessary to estimate the gold standard and for these estimates to be calibrated and detail how our approach is related to, but distinct from, standard model combination techniques. We apply our approach to data from a study to evaluate a collection of BRCA1/BRCA2 gene mutation prediction scores. In this example, genotype is measured with error by one or more genetic assays. We estimate true genotype for each individual in the dataset, operating characteristics of the commonly used genotyping procedures and a relative weighting of the scores. Finally, we compare the scores against the gold standard genotype and find that Mendelian scores are, on average, the more refined and better calibrated of those considered and that the comparison is sensitive to measurement error in the gold standard.

Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

Clustering techniques applied to multiple-models structures

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

Dimension and coverage of multiple-models structures using clustering techniques

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

Blur adaptation explained by a norm-based model of contrast adaptation

Adapting to blurred images makes in-focus images look too sharp, and vice-versa (Webster et al, 2002 Nature Neuroscience 5 839 - 840). We asked how such blur adaptation is related to contrast adaptation. Georgeson (1985 Spatial Vision 1 103 - 112) found that grating contrast adaptation followed a subtractive rule: perceived (matched) contrast of a grating was fairly well predicted by subtracting some fraction k(~0.3) of the adapting contrast from the test contrast. Here we apply that rule to the responses of a set of spatial filters at different scales and orientations. Blur is encoded by the pattern of filter response magnitudes over scale. We tested two versions - the 'norm model' and 'fatigue model' - against blur-matching data obtained after adaptation to sharpened, in-focus or blurred images. In the fatigue model, filter responses are simply reduced by exposure to the adapter. In the norm model, (a) the visual system is pre-adapted to a focused world and (b) discrepancy between observed and expected responses to the experimental adapter leads to additional reduction (or enhancement) of filter responses during experimental adaptation. The two models are closely related, but only the norm model gave a satisfactory account of results across the four experiments analysed, with one free parameter k. This model implies that the visual system is pre-adapted to focused images, that adapting to in-focus or blank images produces no change in adaptation, and that adapting to sharpened or blurred images changes the state of adaptation, leading to changes in perceived blur or sharpness.

Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Can type of school be used as an alternative indicator of socioeconomic status in dental caries studies? A cross-sectional study

Background: Despite the importance of collecting individual data of socioeconomic status (SES) in epidemiological oral health surveys with children, this procedure relies on the parents as respondents. Therefore, type of school (public or private schools) could be used as an alternative indicator of SES, instead of collecting data individually. The aim of this study was to evaluate the use of the variable type of school as an indicator of socioeconomic status as a substitute of individual data in an epidemiological survey about dental caries in Brazilian preschool children. Methods: This study followed a cross-sectional design, with a random sample of 411 preschool children aged 1 to 5 years, representative of Catalao, Brazil. A calibrated examiner evaluated the prevalence of dental caries and parents or guardians provided information about several individual socioeconomic indicators by means of a semi-structured questionnaire. A multilevel approach was used to investigate the association among individual socioeconomic variables, as well as the type of school, and the outcome. Results: When all significant variables in the univariate analysis were used in the multiple model, only mother's schooling and household income (individual socioeconomic variables) presented significant associations with presence of dental caries, and the type of school was not significantly associated. However, when the type of school was used alone, children of public school presented significantly higher prevalence of dental caries than those enrolled in private schools. Conclusions: The type of school used as an alternative indicator for socioeconomic status is a feasible predictor for caries experience in epidemiological dental caries studies involving preschool children in Brazilian context.

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.