Introduction: The COVID-19 pandemic has forced governments to take exceptional measures to minimize its spread, imposing lockdown policies. The aim of this study was to evaluate the impact of lockdown on type 1 diabetes (T1D) glycemic control. Material and methods: People with T1D using flash glucose monitoring were included. Data from the 14 days before lockdown were compared with data from the last 14 days after 8 weeks of lockdown. Results: A total of 307 patients were included (age 45.8 ± 12.6 years, 50.2% male, diabetes duration 21.1 ± 12.3 years). Only one patient had COVID-19 infection. Mean glucose decreased from 166.89 ± 29.4 to 158.0 ± 29.0 mg/dL and estimated HbA1c declined from 7.4 ± 1.0 to 7.1 ± 1.0% (54 ± 10.9 vs 57 ± 10.9 mmol/mol; p < 0.001). Time in range increased from 57.8 ± 15.8 to 62.46 ± 16.1%. Time in hyperglycemia > 180 mg/dL and >250 mg/dL decreased from 37.3 ± 1.9% to 32.0 ± 17.1% and from 13.0 ± 11.3 to 10.3 ± 10.6%, respectively; (p < 0.001). Time in hypoglycaemia <70 mg/dL increased from 4.9 ± 4.0% to 5.5 ± 4.4% (p < 0.001). No differences in time <54 mg/dl, coefficient of variation (CV%) or number of scans per day were found. Conclusion: Despite the limitations of lockdown, glycemic control improved in patients with T1D. These results suggest that having more time for self-management may help improve glycemic control in the short term.

Among neural disorders related to movement, essential tremor has the highest prevalence; in fact, it is twenty times more common than Parkinson’s disease. The drawing of the Archimedes’ spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users; and taking into account social and economic points of view, it could be very helpful in real complex environments.

Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the socalled Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation.

The search for experimental models mimicking an early stage of Parkinson’s disease (PD) before motor manifestations is fundamental in order to explore early signs and get a better prognosis. Interestingly, our previous studies have indicated that 6-hydroxydopamine (6-OHDA) is a suitable model to induce an early degeneration of the nigrostriatal system without any gross motor impairment. Considering our previous findings, we aim to implement a novel system to monitor rats after intrastriatal injection of 6-OHDA to detect and analyze physiological changes underlying prodromal PD. Twenty male Sprague-Dawley rats were unilaterally injected with 6-OHDA ( n = 10) or saline solution ( n = 10) into the right striatum and placed in enriched environment cages where the activity was monitored. After 2 weeks, the amphetamine test was performed before the sacrifice. Immunohistochemistry was developed for the morphological evaluation and western blot analysis to assess molecular changes. Home-cage monitoring revealed behavioral changes in response to 6-OHDA administration including significant hyperactivity and hypoactivity during the light and dark phase, respectively, turning out in a change of the circadian timing. A preclinical stage of PD was functionally confirmed with the amphetamine test. Moreover, the loss of tyrosine hydroxylase expression was significantly correlated with the motor results, and 6-OHDA induced early proapoptotic events. Our findings provide evidence for a novel prodromal 6-OHDA model following a customized monitoring system that could give insights to detect non-motor deficits and molecular targets to test neuroprotective/neurorestorative agents.