Data_Sheet_1_Home-based transcranial direct current stimulation in dual active treatments for symptoms of depression and anxiety: A case series.docx

Transcranial direct current stimulation (tDCS) is a potential treatment strategy across some psychiatric conditions. However, there is high heterogeneity in tDCS efficacy as a stand-alone treatment. To increase its therapeutic potential, researchers have begun to explore the efficacy of combining tDCS with psychological and pharmacological interventions. The current case series details the effect of 6–10 weeks of self-administered tDCS paired with a behavioral therapy smartphone app (Flow™), on depressive and anxiety symptoms, in seven patients (26–51 years old; four female) presenting distinctive psychiatric disorders (major depression, dysthymia, illness anxiety disorder, obsessive-compulsive disorder, and anxiety disorders). tDCS protocol consisted of an acute phase of daily 30 min sessions, across 10 workdays (2 weeks Monday-to-Friday; Protocol 1) or 15 workdays (3 weeks Monday-to-Friday; Protocol 2). A maintenance phase followed, with twice-weekly sessions for 4 or 3 weeks, corresponding to 18 or 21 sessions in total (Protocol 1 or 2, respectively). The Flow tDCS device uses a 2 mA current intensity, targeting the bilateral dorsolateral prefrontal cortex. The Flow app offers virtually guided behavioral therapy courses to be completed during stimulation. We assessed depressive symptoms using MADRS-S and BDI-II, anxious symptoms using STAI-Trait, acceptability using ACCEPT-tDCS, and side effects using the Adverse Effects Questionnaire, at baseline and week 6 of treatment. Six patients underwent simultaneous cognitive-behavioral psychotherapy and two were on antidepressants and benzodiazepines. According to the Reliable Change Index (RCI), for depressive symptoms, we found clinically reliable improvement in five patients using MADRS-S (out of seven; RCI: −1.45, 80% CI; RCI: −2.17 to −4.82, 95% CI; percentage change: 37.9–66.7%) and in four patients using BDI-II (out of five; RCI: −3.61 to −6.70, 95% CI; percentage change: 57.1–100%). For anxiety symptoms, clinically reliable improvement was observed in five patients (out of six; RCI: −1.79, 90% CI; RCI: −2.55 to −8.64, 95% CI; percentage change: 12.3–46.4%). Stimulation was well-tolerated and accepted, with mild tingling sensation and scalp discomfort being the most common side effects. This case series highlights the applicability, acceptability, and promising results when combining home-based tDCS with psychotherapy and pharmacotherapy to manage depression and anxiety symptoms in clinical practice.</p

Additional file 2: of INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

Table S1. A. INSaFLU genetic markers for type and subtype/lineage identification (“influenza_typing” database). B. INSaFLU genetic markers for the assignment of segments (and references) to draft contigs (“influenza_assign_segments2contigs” database). GISAID acknowledgement tables are included in these tables. (XLSX 45 kb

Additional file 4: of INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

Table S2. A. Matrix of pairwise nucleotide differences between whole-genome consensus sequences obtained using INSaFLU versus IRMA for 137 A(H3N2) viruses from dataset 1. B. Matrix of pairwise nucleotide differences between whole-genome consensus sequences obtained using INSaFLU versus IRMA for 39 A(H1N1pdm09) viruses from dataset 2. (XLSX 100 kb

Additional file 3: of INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

Figure S1. INSaFLU graphical output plotting the number of iSNVs at frequencies 1–50% (minor iSNVs) and 50–90% obtained for dataset 1. Figure S2. INSaFLU testing with artificial mixtures of A(H3N2) viruses. A. INSaFLU graphical output plotting the number of iSNVs at frequencies 1–50% (minor iSNVs) and 50–90%. B. Phylogenetic tree based on “whole-genome” consensus sequences obtained for dataset 3. (PDF 977 kb

Schematic representation of the sampling process.

<p>Schematic representation of the sampling process.</p

Patients characterization regarding age and gender variables.

<p>Patients characterization regarding age and gender variables.</p

<i>IFITM3</i> rs12252 genotypic and allelic frequencies among the four groups of patients and its association with hospitalization, assuming a dominant model.

<p><i>IFITM3</i> rs12252 genotypic and allelic frequencies among the four groups of patients and its association with hospitalization, assuming a dominant model.</p

Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries of Northern hemisphere.

<p>Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries of Northern hemisphere.</p

Influenza cases reported to the national influenza surveillance system by each participating country (from southern- to northern-most) and percentages of cases due to influenza type B virus.

<p>Influenza cases reported to the national influenza surveillance system by each participating country (from southern- to northern-most) and percentages of cases due to influenza type B virus.</p

Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries in the inter-tropical belt.

<p>Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries in the inter-tropical belt.</p