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Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient's sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz's algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform (TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method.The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of 97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.The history of insulin is rightly considered one of the most beautiful stories in medicine which goes even further than the extraordinary result of tens of millions of lives saved. Without a doubt, it constitutes a major achievement for medical science which, especially in the last 50 years, has led to an impressive acceleration in the succession of new treatment opportunities. We are going to describe the history of insulin therapy, the history we lived from two different angles as people living with type 1 diabetes, and obviously also as diabetologists, but as diabetologists with diabetes. Without a doubt, insulin and his story constitutes a major achievement for medical science which has led to an impressive acceleration in the succession of new treatment opportunities. Care opportunities that have not only allowed fundamental improvements in outcomes, but have also and above all impacted the quality of life of people with diabetes. Summarizing one hundred years of insulin is no simple endeavor. In our view, it would be easier, and probably more befitting, to focus on the last 50 years, namely the period we have lived closely and personally together with insulin. More to the point, these last 50 years have witnessed a dramatic acceleration of research and innovation. In our opinion, it is precisely the innovations in insulin therapy introduced from the last decades that fully justify the description of events in this incredible period as "the miracle of insulin". We'll describe how the most important innovations introduced in the last decades had impact on what we have nowadays, as patients and diabetologits today, we can finally adapt insulin therapy to the patient's life or lifestyle, reversing what was the perception of patients until 20 years, when insulin was considered, by the most, as an obstacle, which seemed insurmountable to some, to a free and unconstrained life.

Peripheral neuropathy (PN) affects two-thirds of type 2 diabetes patients (T2DM). According to diabetic PN length-dependent pattern, neurophysiological evaluation of foot-sole nerves might increase NCS diagnostic sensitivity, hence allowing early diagnosis of PN. Thus, we aim to assess the ability of whole plantar nerve (WPN) conduction in diabetic PN early diagnosis.

This is a single center prospective observational cohort study on 70 T2DM patients referred to Internal Medicine Unit of A.O.U. "Luigi Vanvitelli" between October 2019/October 2020. Primary endpoint was WPN efficacy assessment in PN early detection. As secondary, we evaluated (i) a potential cut-off of SNAPs amplitude by WPN and (ii) WPN diagnostic accuracy vs. gold-standard distal sural nerve conduction.

ROC curve analysis allowed to establish two potential cut-offs for people aged ≤60years (AUROC 0.83, 95%CI 0.69-0.96, p<0.001) and ≤60years (AUROC 0.76, 95%CI 0.59-0.93, p=0.017). In depth, we fixed a cut-off of WPN-SNAP amplitude of 4.55μV and 2.65μV, respectively, with subsequent 48 patients classified as PN-T2DM.

Our data support WPN conduction study reliability in characterizing the most distal sensory nerve fibers at lower limbs. Thus, WPN may represent an extremely useful diagnostic tool for diabetic PN early detection.

Our data support WPN conduction study reliability in characterizing the most distal sensory nerve fibers at lower limbs. www.selleckchem.com/Proteasome.html Thus, WPN may represent an extremely useful diagnostic tool for diabetic PN early detection.Having a psychiatric disorder may increase the risk of developing type 2 diabetes[T2D] and this umbrella review aims to determine whether people with a psychiatric disorder have an increased risk of developing T2D and to investigate potential underlying mechanisms. A literature search was performed to identify systematic reviews of longitudinal studies investigating different psychiatric disorders as risk factors for incident T2D in humans (≥18 years). A total of 8612 abstracts were identified, 180 full-text articles were read, and 25 systematic reviews were included. Six categories of psychiatric disorders were identified. Except for eating disorders, all psychiatric disorders were associated with increased risk of incident T2D ranging from RR = 1.18 [95% CI 1.12-1.24] to RR = 1.60 [95% CI 1.37-1.88] for depression; from RR = 1.27 [95% CI 1.19-1.35] to OR = 1.50 [95% CI 1.08-2.10] for use of antidepressant medication; from OR = 1.93 [1.37-2.73] to OR = 1.94 [1.34-2.80] for use of antipsychotic medication; from RR = 1.