Hepatitis C Virus positivity prediction from serum samples using NIRS and L1-penalized classification.

AIMS: The hepatitis C virus (HCV) has developed a strategy to coexist with its host resulting in varying degrees of tissue and cell damage, which generate different pathological phenotypes, such as varying degrees of fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). However, there is no integrated information that can predict the evolutionary course of the infection. We propose to combine Near-infrared spectroscopy (NIRS) and machine learning techniques to provide a predictive model. In this work, we propose to discriminate HCV positivity in biobank patient serum samples. METHODS: 126 serum samples from 38 HCV patients in different stages of the disease were obtained from the Biobank of Hospital Universitario Fundación Alcorcon. NIRS spectrum was captured by a FT-NIRS Spectrum 100 (Perkin Elmer) device in reflectance mode. For each patient, the HCV positivity was identified (PCR) and labeled as detectable =1 and undetectable =0. We propose an L1-penalized logistic regression model to classify each spectrum as positive (1) or negative (0) for HCV presence (x). The regularization parameter is selected using 5- fold cross-validation. The penalized model will induce sparsity in the solution so that only a few relevant wavelengths will be different from zero. RESULTS: L1-penalized logistic regression model provided 167 wavelengths different from zero. The accuracy on an independent test set was 0.78. CONCLUSIONS: We present a straightforward promising approach to detect HCV positivity from patient serum samples combining NIRS and machine learning techniques. This result is encouraging to predict HCV progression, among other applications. Clinical relevance- We presented a simple while promising approach to use machine learning and NIRS to analyze viral presence on sample serums.

[Prevalence and clinical features of non-alcoholic steatohepatitis in a hypertensive population]. / Prevalencia y características clínicas de la esteatohepatitis no alcohólica en una población con hipertensión arterial esencial.

OBJECTIVE: Non-alcoholic fatty liver is a chronic liver disease in which fat is deposited in the liver, causing an inflammation called non-alcoholic steatohepatitis (NASH), and fibrosis. NASH is associated with metabolic syndrome (MS) and other cardiovascular risk factors. The aim of this study was to analyse the epidemiological features of NASH within a hypertensive population, with a high prevalence of MS, and to determine the features related to NASH. METHODS: The computerised records were collected from 3,473 patients from Mostoles University Hospital's Hypertension Unit in order to perform a retrospective, cross-sectional study. NASH was considered as ultrasound-detected fatty liver disease along with serum levels of alanine aminotransferase or aspartate aminotransferase 1.5 times above normal values, having ruled out other causes of liver disease: alcohol abuse, autoimmune hepatitis, drug toxicity, virus and hemochromatosis. A univariate, multivariate, and ANOVA analysis were performed to assess the effect of the studied features on the response of interest. RESULTS: The cohort included 2,242 patients (51.3% men). NASH was present in 255 patients (11.4%) of whom 71% were men. MS was detected in 52.6% of patients (69.4% in the NASH group, and 50.5% in the non-NASH group, P=.001). Prevalence of type 2 diabetes mellitus was 11.5% (16.5% in the NASH group, and 10.8% in the non-NASH group, P=.01). In a multivariate analysis, waist circumference, MS, body mass index, type 2 diabetes mellitus, age, fasting serum insulin, and serum ferritin were associated with NASH. ANOVA revealed that NASH and transaminases were also significantly associated with components of metabolic syndrome. CONCLUSIONS: In the population studied, MS, type 2 diabetes mellitus, and several components of MS were independently associated with NASH. Therefore, NASH can be considered as the liver manifestation of MS in patients with arterial hypertension.

Cystatin C as a predictor of cardiovascular outcomes in a hypertensive population.

Calculating the estimated glomerular filtration rate (eGFR) using creatinine-based equations may underestimate cardiovascular risk. Cystatin C-based eGFR may be a stronger prognostic biomarker than creatinine-based eGFR when assessing cardiovascular outcomes and mortality. Our aim was to determine whether levels of serum cystatin C, as an estimator of GFR, had a higher predictive value than creatinine-based eGFR for incident cardiovascular disease among hypertensive patients. We retrospectively analyzed the records of 2016 hypertensive patients from the Hypertension Unit at Mostoles University Hospital between 2006 and 2016. We calculated the eGFR using 3 CKD-EPI equations. The outcomes we included in our study were cardiovascular death, non-cardiovascular death, stroke, incident heart failure, and myocardial infarction. We used the Cox regression hazard model to estimate the hazard ratio. Our analysis found that, in terms of cardiovascular morbidity and mortality, both cystatin C-based eGFR (HR 2.88, 95% CI 1.86-4.47, P<0.0001) showed a higher prognostic value than creatinine-based eGFR (HR 2.83, 95% CI 1.73-4.63, P<0.0001). In terms of all-cause mortality, cystatin C-based eGFR (HR 4.24, 95% CI 2.38-7.53, P<0.0001) showed a higher prognostic value than creatinine-based eGFR (HR 2.77, 95% CI 1.43-5.36, P<0.0001). Our findings suggest that both cystatin C-based eGFRs may be stronger predictors of all-cause mortality and cardiovascular events in our hypertensive cohort when compared to creatinine-based eGFR, and may improve the risk assessment in certain populations.

Association between vitamin D supplementation and severity of tuberculosis in wild boar and red deer.

Tuberculosis (TB) is a chronic disease affecting humans and other mammal species. Severity of TB caused by Mycobacterium tuberculosis in humans seems to be influenced by nutritional factors like vitamin D3 intake. However, this relationship has been scarcely studied in cattle and other mammals infected with Mycobacterium bovis. The aim of this work was to assess if wildlife reservoirs of M. bovis show different levels of TB severity depending on the level of vitamin D found in serum after supplementation with vitamin D3. Forty hunted wildlife mammals were included in this study: 20 wild boar and 20 red deer. Ten wild boar and ten red deer had been supplemented with a vitamin D3-enriched food, whereas the remaining animals had received no supplementation. TB diagnosis was carried out in each animal based on microbiological isolation of M. bovis. Animals infected with M. bovis were then classified as animals with localized or generalized TB depending on the location and dissemination of the lesions. Furthermore, serum levels of vitamin D2 and D3 were determined in each animal to evaluate differences not only between supplemented and non-supplemented animals but also between those with localized and generalized TB. Levels of vitamin D3 found in both, supplemented wild boar and red deer, were significantly higher than those found in the non-supplemented animals. Interestingly, higher levels of vitamin D3 were observed in animals suffering localized TB when compared to animals with generalized TB suggesting that vitamin D3 concentration correlates negatively with TB severity in these wildlife reservoirs.

Nonparametric signal processing validation in T-wave alternans detection and estimation.

Although a number of methods have been proposed for T-Wave Alternans (TWA) detection and estimation, their performance strongly depends on their signal processing stages and on their free parameters tuning. The dependence of the system quality with respect to the main signal processing stages in TWA algorithms has not yet been studied. This study seeks to optimize the final performance of the system by successive comparisons of pairs of TWA analysis systems, with one single processing difference between them. For this purpose, a set of decision statistics are proposed to evaluate the performance, and a nonparametric hypothesis test (from Bootstrap resampling) is used to make systematic decisions. Both the temporal method (TM) and the spectral method (SM) are analyzed in this study. The experiments were carried out in two datasets: first, in semisynthetic signals with artificial alternant waves and added noise; second, in two public Holter databases with different documented risk of sudden cardiac death. For semisynthetic signals (SNR = 15 dB), after the optimization procedure, a reduction of 34.0% (TM) and 5.2% (SM) of the power of TWA amplitude estimation errors was achieved, and the power of error probability was reduced by 74.7% (SM). For Holter databases, appropriate tuning of several processing blocks, led to a larger intergroup separation between the two populations for TWA amplitude estimation. Our proposal can be used as a systematic procedure for signal processing block optimization in TWA algorithmic implementations.