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Neuronal ceroid lipofuscinoses (NCLs) comprise 13 hereditary neurodegenerative pathologies of very low frequency that affect individuals of all ages around the world. All NCLs share a set of symptoms that are similar to other diseases. The exhaustive collection of data from diverse sources (clinical, genetic, neurology, ophthalmology, etc.) would allow being able in the future to define this group with greater precision for a more efficient diagnostic and therapeutic approach. Despite the large amount of information worldwide, a detailed study of the characteristics of the NCLs in South America and the Caribbean region (SA&C) has not yet been done. Here, we aim to present and analyse the multidisciplinary evidence from all the SA&C with qualitative weighting and biostatistical evaluation of the casuistry. Seventy-one publications from seven countries were reviewed, and data from 261 individuals (including 44 individuals from the Cordoba cohort) were collected. Each NCL disease, as well as phenotypical and genetic data were described and discussed in the whole group. The CLN2, CLN6, and CLN3 disorders are the most frequent in the region. Eighty-seven percent of the individuals were 10 years old or less at the onset of symptoms. Seizures were the most common symptom, both at onset (51%) and throughout the disease course, followed by language (16%), motor (15%), and visual impairments (11%). Although symptoms were similar in all NCLs, some chronological differences could be observed. Sixty DNA variants were described, ranging from single nucleotide variants to large chromosomal deletions. The diagnostic odyssey was probably substantially decreased after medical education activities promoted by the pharmaceutical industry and parent organizations in some SA&C countries. There is a statistical deviation in the data probably due to the approval of the enzyme replacement therapy for CLN2 disease, which has led to a greater interest among the medical community for the early description of this pathology. As a general conclusion, it became clear in this work that the combined bibliographical/retrospective evaluation approach allowed a general overview of the multidisciplinary components and the epidemiological tendencies of NCLs in the SA&C region.

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Motivation: The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Results: Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results ( n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions. Availability and Implementation: Source code can be found in 'pbcmc' R package at Bioconductor. Contacts: cristobalfresno@gmail.com or efernandez@bdmg.com.ar. Supplementary information: Supplementary data are available at Bioinformatics online.

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The aims of this study were to analyse body composition, to detect the presence of undernutrition, and to establish a relationship between undernutrition and the biological markers routinely used as indicators of nutritional status in hemodialysis (HD) patients (pts). We used a body composition monitor (BCM) that expresses body weight in terms of lean tissue mass (LTM) and fat tissue mass (FTM) independent of hydration status. From nine HD units, 934 pts were included. Undernutrition was defined as having a lean tissue index (LTI = LTM/height(2)) below the 10th percentile of a reference population. Biochemical markers and parameters delivered by BCM were used to compare low LTI and normal LTI groups. Undernutrition prevalence was 58.8% of the population studied. Low LTI pts were older, were significantly more frequently overhydrated, and had been on HD for a longer period of time than the normal LTI group. FTI (FTI = FTM/ height(2)) was significantly higher in low LTI pts and increased according to BMI. LTI was not influenced by different BMI levels. Albumin and C-reactive protein correlated inversely (r = -0.28). However neither of them was statistically different when considering undernourished and normal LTI pts. Our BCM study was able to show a high prevalence of undernutrition, as expressed by low LTI. In our study, BMI and other common markers, such as albumin, failed to predict malnutrition as determined by BCM.

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The aim of this study was to analyse the biological response to different recombinant human FSH (rhFSH) glycosylation variants on the endocrine activity and gene expression at whole-genome scale in human granulosa-like tumor cell line, KGN. The effects of differences in rhFSH sialylation and oligosaccharide complexity were determined on steroid hormone and inhibin production. A microarray approach was used to explore gene expression patterns induced by rhFSH glycosylation variants. Set enrichment analysis revealed that hormone sialylation and oligosaccharide complexity in rhFSH differentially affected the expression of genes involved in essential biological processes and molecular functions of KGN cells. The relevance of rhFSH oligosaccharide structure on steroidogenesis was confirmed assessing gene expression by real time-PCR. The results demonstrate that FSH oligosaccharide structure affects expression of genes encoding proteins, growth factors and hormones essential for granulosa cells function.

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BACKGROUND: The low (LF) vs. high (HF) frequency energy ratio, computed from the spectral decomposition of heart beat intervals, has become a major tool in cardiac autonomic system control and sympatho-vagal balance studies. The (statistical) distributions of response variables designed from ratios of two quantities, such as the LF/HF ratio, are likely to non-normal, hence preventing e.g., from a relevant use of the t-test. Even using a non-parametric formulation, the solution may be not appropriate as the test statistics do not account for correlation and heteroskedasticity, such as those that can be observed when several measures are taken from the same patient. OBJECTIVES: The analyses for such type of data require the application of statistical models which do not assume a priori independence. In this spirit, the present contribution proposes the use of the Generalized Linear Mixed Models (GLMMs) framework to assess differences between groups of measures performed over classes of patients. METHODS: Statistical linear mixed models allow the inclusion of at least one random effect, besides the error term, which induces correlation between observations from the same subject. Moreover, by using GLMM, practitioners could assume any probability distribution, within the exponential family, for the data, and naturally model heteroskedasticity. Here, the sympatho-vagal balance expressed as LF/HF ratio of patients suffering neurogenic erectile dysfunction under three different body positions was analyzed in a case-control protocol by means of a GLMM under gamma and Gaussian distributed responses assumptions. RESULTS: The gamma GLMM model was compared with the normal linear mixed model (LMM) approach conducted using raw and log transformed data. Both raw GLMM gamma and log transformed LMM allow better inference for factor effects, including correlations between observations from the same patient under different body position compared to the raw LMM. The gamma GLMM provides a more natural distribution assumption of a response expressed as a ratio. CONCLUSIONS: A gamma distribution assumption intrinsically models quadratic relationships between the expected value and the variance of the data avoiding prior data transformation. SAS and R source code are available on request.

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The knowledge of the underlying molecular kinetics is a key point for the development of a dialysis treatment as well as for patient monitoring. In this work, we propose a kinetic inference method that is general enough to be used on different molecular types measured in the spent dialysate. It estimates the number and significance of the compartments involved in the overall process of dialysis by means of a spectral deconvolution technique, characterizing therefore the kinetic behavior of the patient. The method was applied to 52 patients to reveal the underlying kinetics from dialysate time-concentration profiles of urea, which has a well-known molecular kinetic. Three types of behaviors were found: one-compartmental (exponential decay Tau = 180 +/- 61.64 minutes), bicompartmental (Tau1 = 24.96 +/- 19.33 minutes, Tau2 = 222.32 +/- 76.59 minutes), and tricompartmental (Tau1 = 23.03 +/- 14.21 minutes; Tau2 = 85.75 +/- 27.48 minutes; and Tau3 = 337 +/- 85.52 minutes). In patients with bicompartmental kinetics, the Tau2 was related to the level of dialysis dose. The study concluded that spectral deconvolution technique can be considered a powerful tool for molecular kinetics inference that could be integrated in on-line molecular analysis devices. Furthermore, the method could be used in the analysis of poorly understood molecules as well as in new hemodialysis target biomarkers.

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The National Kidney Foundation and the European Renal Association recommend routine measurement of hemodialysis (HD) dose and have set standards for adequacy of treatment. We compare the results of five methods for HD dose estimation, classifying each result as adequate or inadequate on the basis of equilibrated (eq) Urea Reduction Ratio (URR(eq)) > or = 65% or Kt/V(eq) > or = 1.2, to assess the accuracy of each method as a diagnostic tool. Data from 113 patients from two different dialysis units were analyzed. Equilibrated postdialysis blood urea was measured 60 min after each hemodialysis session to calculate URR(eq) and Kt/V(eq), considered as gold standard indexes (GSI). URR and Kt/V were estimated by using the Smye formula, an artificial neural network (ANN), modified URR, the second generation Kt/V Daugirdas formula, and standard indexes based on postdialysis urea, then compared to the GSI. For URR, best estimator was ANN (error rate: ER% = 12.70), followed by modified URR (ER% = 17.46%), the Smye (ER% = 22.22), and standard URR (ER% = 23.81). For Kt/V, the Daugirdas equation and the ANN were similar (ER% = 9.52 and 11.11). The single-pool Kt/V (Kt/V(sp)) > or = 1.4 (ERA recommended) produced an ER% = 7.94 and a false positive rate (FPR%) equal to that shown by the ANN (FPR% = 3.17). According to the current threshold limits for HD dose adequacy, the ANN was a reliable and accurate tool for URR monitoring, better than the Smye and the modified URR methods. The use of the ANN urea estimation yields accurate results when used to calculate Kt/V. The Kt/V(sp) with an adequacy threshold of 1.4 is a superior approach for HD adequacy monitoring, suggesting that the current adequacy limits should be reviewed for both URR and Kt/V.

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