<｜begin▁of▁sentence｜>### Instruction:
Imagine we're examining the structural integrity of a patient's skeletal system, with a particular focus on the interconnected network of bones and joints. In this context, the bones are represented by points or 'nodes' labeled 0 through 7, and the joints could be viewed as the directed 'edges' or connections between these nodes.

We are presented with a series of connections akin to the musculoskeletal relationships: bone '0' is joined to bone '1' (0 -> 1), bone '1' to bone '2' (1 -> 2), and so forth, constructing a network of connections: (0 -> 1), (1 -> 2), (2 -> 0), (2 -> 3), (4 -> 5), (3 -> 4), (5 -> 6), (6 -> 3), and (6 -> 7). 

In our analysis, we aim to determine the number of distinct skeletal groups that are interconnected within themselves but not with other groups. In orthopedic terms, how many isolated segments of the skeleton – let's call them 'strongly connected components' – can be identified in this network, where each segment represents a series of bones that are connected in such a way that you can traverse from any bone to another within the same segment, ensuring the continuity of that part of the skeletal system? 

To put the question in a practical scenario, consider this network as a diagram or a patient's medical imaging data file (perhaps named "skeletal_network.gml") that you need to analyze. How would you assess the number of such isolated segments based on the given connections between the 'bones'?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph([(0, 1), (1, 2), (2, 0), (2, 3), (4, 5), (3, 4), (5, 6), (6, 3), (6, 7)])

# Calculate the number of strongly connected components
num_strongly_connected = nx.number_strongly_connected_components(G)

print('Number of strongly connected components:', num_strongly_connected)
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