Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification.

Background: Transcranial direct current stimulation (tDCS) is a promising treatment for Alzheimer's Disease (AD). However, identifying objective biomarkers that can predict brain stimulation efficacy, remains a challenge. The primary aim of this investigation is to delineate the cerebral regions implicated in AD, taking into account the existing lacuna in comprehension of these regions. In pursuit of this objective, we have employed a supervised machine learning algorithm to prognosticate the neurophysiological outcomes resultant from the confluence of tDCS therapy plus cognitive intervention within both the cohort of responders and non-responders to antecedent tDCS treatment, stratified on the basis of antecedent cognitive outcomes. Methods: The data were obtained through an interventional trial. The study recorded high-resolution electroencephalography (EEG) in 70 AD patients and analyzed spectral power density during a 6 min resting period with eyes open focusing on a fixed point. The cognitive response was assessed using the AD Assessment Scale-Cognitive Subscale. The training process was carried out through a Random Forest classifier, and the dataset was partitioned into K equally-partitioned subsamples. The model was iterated k times using K-1 subsamples as the training bench and the remaining subsample as validation data for testing the model. Results: A clinical discriminating EEG biomarkers (features) was found. The ML model identified four brain regions that best predict the response to tDCS associated with cognitive intervention in AD patients. These regions included the channels: FC1, F8, CP5, Oz, and F7. Conclusion: These findings suggest that resting-state EEG features can provide valuable information on the likelihood of cognitive response to tDCS plus cognitive intervention in AD patients. The identified brain regions may serve as potential biomarkers for predicting treatment response and maybe guide a patient-centered strategy. Clinical Trial Registration: https://classic.clinicaltrials.gov/ct2/show/NCT02772185?term=NCT02772185&draw=2&rank=1, identifier ID: NCT02772185.

Impact of Pelvic Floor Muscle Training Isolated and Associated with Game Therapy on Mixed Urinary Incontinence: A Randomized Controlled Trial.

Objective: To verify whether pelvic floor muscle training (PFMT) associated with game therapy (GT) can potentiate improvements in PFM pressure, urinary loss, and perception of improvement in women with mixed urinary incontinence (MUI). Materials and Methods: A randomized and blinded trial was conducted with 32 women aged between 45 to 70 years presenting diagnosis of MUI. They were randomly divided into two groups: PFMT group and PFMT+GT group. Interventions occurred twice a week during 8 weeks. Primary outcome was PFM pressure, assessed by manometry, and secondary outcomes were 1-hour pad-test, International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), and patients global impression of improvement for incontinence (PGI-I). Two-way analysis of variance and post hoc Tukey analysis were performed. Results: Initially, no significant difference between groups was found in variables of age, body mass index, educational level, marital status, gynecological and obstetric variables, life habits, and sexual activity. Besides, at baseline clinical variables also showed similar results between groups for PFM pressure, 1-hour pad-test, and ICIQ-SF. Time-group interaction did not present statistically significant differences for PFM pressure (P = 0.56), 1-hour pad-test (P = 0.75), and ICIQ-SF (P = 0.30) in intergroup analysis. All women reported being "much better or better," considering the comparison of urinary complaints in the beginning and end of treatment. Conclusion: There were no statistically significant differences between groups for PFM pressure, 1-hour pad-test, and ICIQ-SF. However, both treatments proved to be effective for MUI symptoms. Perception of improvement was highly improved, according to women's report.

Low-level laser therapy improves pain in postcesarean section: a randomized clinical trial.

This study aimed to evaluate the effect of low-level laser therapy (LLLT) on immediate postpartum pain relief during cesarean section. A randomized, parallel controlled trial was carried out. In total, 88 women with immediate postpartum were divided into 4 groups: control group (n = 22), placebo group (n = 22), experimental group I (n = 22, dose of 4 J/cm2), and experimental group II (n = 22, dose of 2 J/cm2). The pain measured by Numeric Rating Scale (NRS), algometry, and Global Change Perception Scale (GCPS) was assessed at 12, 20-24, and 44-48 h postpartum. Two LLLT sessions were performed at 12 and 24 h postpartum. A significant interaction was observed between time versus group for NRS F (2.40) = 36.80, p < 0.001 and algometry F (1.70) = 27.18, p < 0.001. GCPS revealed a significant difference between the groups during second (p = 0.04) and third evaluation (p = 0.04). The NRS and algometry presented a large effect size for the experimental groups. LLLT is an efficient method to reduce pain and enhance the GCP in postcesarean section. No significant clinical differences were found between the laser doses.