Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop.

Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.

The relationship between plasma alpha-tocopherol concentration and vitamin E intake in patients with type 2 diabetes mellitus.

Type 2 diabetes mellitus (DM2) predisposes to an increased production of free radicals and a probable reduction in plasma antioxidants, including vitamin E. This cross-sectional study investigated the relationship between plasma alpha-Tocopherol concentration and vitamin E intake in 58 Brazilians with DM2. Plasma alpha-Tocopherol was determined by high-performance liquid chromatography. The intake of vitamin E-rich foods was assessed by a food frequency questionnaire. Total cholesterol and fractions were measured by colorimetric enzymatic methods. Data on demographic and socioeconomic factors, life habits, and anthropometry were obtained by a questionnaire and physical examination. The association between plasma alpha-Tocopherol and vitamin E intake was assessed by multiple linear regression analysis. The following variables were included in the regression model: alpha-Tocopherol, vitamin E intake, total cholesterol and fractions, body mass index, waist circumference, gender, age, education, occupation, income, smoking, alcohol intake, and blood pressure. There was no association between alpha-Tocopherol and vitamin E intake, but there were significant associations between alpha-Tocopherol and total cholesterol (p < 0.001) and waist circumference (p = 0.003). There were 36.2 % diabetics with low alpha-Tocopherol concentrations (< 12 µmol/L) and 32.7 % with a low alpha-Tocopherol/total cholesterol ratio (< 2.2). Further large, epidemiological, longitudinal studies, including measurements of gamma-tocopherol in blood, should be conducted to confirm our results.

Intraspecific variability of Bipolaris sorokiniana isolates determined by random-amplified polymorphic DNA (RAPD).

Isolates of Bipolaris sorokiniana were analyzed by random-amplified polymorphic DNA (RAPD) techniques to determine the amount of intraspecific genetic variability and to study host-pathogen interactions. Ten isolates originated from different regions of Brazil were examined. Plants of the wheat cultivars BR8, BH1146 (original host) and IAC-5 Maringá, classified as resistant, moderately resistant or susceptible to B. sorokiniana, respectively, were inoculated with these 10 isolates. Twenty-seven isolates were recovered from these cultivars and were analyzed by RAPD assay and compared to the RAPD of the original 10 isolates. According to the RAPD profiles there was a high level of genetic variability among the isolates. We detected 69 polymorphic fragments, ranging from 1.6 to 0.54 kb, in the original 10 isolates; 57 fragments with sizes between 1.98 and 0.38 kb from the isolates recovered from BH1146; 47 polymorphic bands, ranging from 1.96-0.54 kb, were detected in the isolates from BR8 and 32 fragments between 1.98 and 0.42 kb in isolates were recovered from IAC-5 Maringá. The number of polymorphic fragments varied, even for the same isolate, when the isolates were recovered from different cultivar hosts.

Serological and direct diagnosis of Helicobacter pylori in gastric carcinoma: a case-control study.

This study evaluated the sensitivity of serological and direct methods for the diagnosis of Helicobacter pylori infection in 127 patients with gastric carcinoma and in 127 controls without this disease, matched for age and sex. Antral and oxyntic mucosal specimens were obtained from all patients, at operation in patients with gastric carcinoma and at endoscopy from controls. The urease test, microscopy of stained smears and culture for H. pylori were performed on all specimens. Sera from all patients were tested for antibodies to H. pylori by a highly sensitive and specific IgG-ELISA. Culture, urease test, stained smear and ELISA were significantly less sensitive in the patients with gastric carcinoma than in control subjects. However, the combination of several methods improved the diagnosis of H. pylori infection in the gastric carcinoma group. Infection was significantly more frequent in the gastric carcinoma patients than in the controls. H. pylori infection was associated with an increased risk of developing gastric carcinoma.