{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Literature overview\n", "\n", "First, how were the studies selected? We used the [Medline database](https://pubmed.ncbi.nlm.nih.gov) and retrieved all the records mentioning (1) myelin, (2) MRI and (3) histology (or a related technique). The full list of keywords is provided in the preprint.\n", "\n", "\n", "```{admonition} Figure 1\n", ":class: tip\n", "The Sankey diagram shows the screening procdess. You can hover with the mouse on each block and connection to see details about the number of studies and exclusion criteria.\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide_input" ] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import plotly.graph_objects as go\n", "from IPython.core.display import display, HTML\n", "from plotly.offline import plot\n", "import plotly.express as px\n", "import plotly.colors\n", "from plotly.subplots import make_subplots\n", "\n", "from rpy2.robjects.packages import importr\n", "import rpy2.robjects\n", "import subprocess\n", "subprocess.call('curl https://raw.githubusercontent.com/Notebook-Factory/brand/main/insertLogo.py --output /tmp/insertLogo.py', shell=True)\n", "%run /tmp/insertLogo.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure 1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [ "hide_input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", " \n", " \n", "