Human Prothrombin Matched Antibody Pair

In this paper, we propose a novel method for automatic k-complex (KC) detection in human sleep EEG, named MT-KCD. KCs are slow oscillations in the EEG signal characterized by a well-delineated, negative, sharp waves immediately followed by a positive component standing out from the background, with high-amplitude and total duration >= 0.5 s. Among the important aspects of the KC are its homeostatic and reactive functions in the brain, functioning as a sleep protection mechanism, and its practical use as a marker of N2 sleep stage during sleep studies. Given the importance of the KC, and the effort required from human experts to analyze EEG recordings visually, some recent research works have proposed automatic methods for KC detection. In comparison with existing methods, a key feature and novelty of MT-KCD is the use of multitaper spectral analysis to pre-process the EEG signal and automatically extract candidate KCs from it (characterized as 0-4 Hz power concentrations standing out from the background). After extraction, candidates are accepted/rejected depending on time domain characteristics (peak-to-peak amplitude >= 75 mu V, duration <= 2 s). The method overall time complexity is O(N logN). Regarding effectiveness, we have evaluated MT-KCD by using a public KC database (DREAMS) consisting of ten polysomnographic recordings of healthy patients (6 female and 4 male subjects with age range 20-47 years) partially annotated by two experts. Results have shown that MT-KCD improves detection metrics, especially F1 and F2 scores (harmonic averages of recall and precision), when compared to existing methods. Besides, improving F1 and F2 scores, MT-KCD also contributes to the automatic analysis of sleep EEG multitaper spectrograms, a technique recently proposed by researchers in the area of sleep studies as a complement to the traditional hypnogram (sleep stages diagram). (C) 2020 Elsevier Ltd. All rights reserved.

BACKGROUND & AIMS: Alcohol-related liver disease (ALD) causes chronic liver disease. We investigated how information on patients' drinking history and amount, stage of liver disease, and demographic feature can be used to determine risk of disease progression. METHODS: We collected data from 2334 heavy drinkers (50 g/day or more) with persistently abnormal results from liver tests who had been admitted to a hepato-gastroenterology unit in France from January 1982 through December 1997; patients with a recorded duration of alcohol abuse were assigned to the development cohort (n=1599; 75% men) or the validation cohort (n=735; 75% men), based on presence of a liver biopsy. We collected data from both cohorts on patient history and disease stage at the time of hospitalization. For the development cohort, severity of the disease was scored by the METAVIR (due to the availability of liver histology reports); in the validation cohort only the presence of liver complications was assessed. We developed a model of ALD progression and occurrence of liver complications (hepatocellular carcinoma and/or liver decompensation) in association with exposure to alcohol, age at the onset of heavy drinking, amount of alcohol intake, sex and body mass index. The model was fitted to the development cohort and then evaluated in the validation cohort. We then tested the ability of the model to predict disease progression for any patient profile (baseline evaluation). Patients with a 5-y weighted risk of liver complications greater than 5% were considered at high risk for disease progression. RESULTS: Model results are given for the following patient profiles: men and women, 40 y old, who started drinking at an age of 25 y, drank 150 g/day, and had a body mass index of 22 kg/m(2) according to the disease severity at baseline evaluation. For men with baseline F0-F2 fibrosis, the model estimated the probabilities of normal liver, steatosis, or steatohepatitis at baseline to be 31.8%, 61.5% and 6.7%, respectively. The 5-y weighted risk of liver complications was 1.9%, ranging from 0.2% for men with normal liver at baseline evaluation to 10.3% for patients with steatohepatitis at baseline. For women with baseline F0-F2 fibrosis, probabilities of normal liver, steatosis, or steatohepatitis at baseline were 25.1%, 66.5% and 8.4%, respectively; the 5-y weighted risk of liver complications was 3.2%, ranging from 0.5% for women with normal liver at baseline to 14.7% for patients with steatohepatitis at baseline. Based on the model, men with F3-F4 fibrosis at baseline have a 24.5% 5-y weighted risk of complications (ranging from 20.2% to 34.5%) and women have a 30.1% 5-y weighted risk of complications (ranging from 24.7% to 41.0%). CONCLUSIONS: We developed a Markov model that integrates data on level and duration of alcohol use to identify patients at high risk of liver disease progression. This model might be used to adapt patient care pathways.