{
  "ranked_hypotheses": [
    {
      "title": "AMPK hypersensitivity in astrocytes creates enhanced mitochondrial rescue responses",
      "description": "Engineering astrocytes with constitutively active AMPK sensors will make them hyperresponsive to neuronal metabolic distress signals, triggering rapid mitochondrial transfer before irreversible neuronal damage occurs. This creates an early-warning system for metabolic neurodegeneration.",
      "target_gene": "PRKAA1",
      "dimensi...

import pandas as pd
import matplotlib.pyplot as plt

# Hypotheses data
hyp_data = [{'Title': 'AMPK hypersensitivity in astrocytes creates enhanced mitocho...', 'Target': 'PRKAA1', 'Score': '0.673', 'Mech': '0.75', 'Evid': '0.65', 'Novel': '0.80', 'Feas': '0.85'}, {'Title': 'Near-infrared light therapy stimulates COX4-dependent mitoch...', 'Target': 'COX4I1', 'Score': '0.629', 'Mech': '0.55', 'Evid': '0.50', 'Novel': '0.75', 'Feas': '0.90'}, {'Title': 'TFAM overexpression creates mitochondrial donor-recipient gr...', 'Target': 'TFAM', 'Score': '0.585', 'Mech': '0.70', 'Evid': '0.60', 'Novel': '0.70', 'Feas': '0.60'}, {'Title': 'RAB27A-dependent extracellular vesicle engineering for mitoc...', 'Target': 'RAB27A', 'Score': '0.531', 'Mech': '0.45', 'Evid': '0.40', 'Novel': '0.85', 'Feas': '0.45'}, {'Title': 'GAP43-mediated tunneling nanotube stabilization enhances neu...', 'Target': 'GAP43', 'Score': '0.451', 'Mech': '0.40', 'Evid': '0.35', 'Novel': '0.80', 'Feas': '0.30'}, {'Title': 'Designer TRAK1-KIF5 fusion proteins accelerate therapeutic m...', 'Target': 'TRAK1_KIF5A', 'Score': '0.427', 'Mech': '0.35', 'Evid': '0.30', 'Novel': '0.90', 'Feas': '0.25'}, {'Title': 'CX43 hemichannel engineering enables size-selective mitochon...', 'Target': 'GJA1', 'Score': '0.346', 'Mech': '0.40', 'Evid': '0.35', 'Novel': '0.60', 'Feas': '0.25'}]
df = pd.DataFrame(hyp_data)
print(df.to_string(index=False))

# Network visualization
import networkx as nx
import matplotlib.pyplot as plt

edges = [('', '', 'encodes'), ('', '', 'regulates'), ('', '', 'activates'), ('', '', 'protects_against'), ('', '', 'encodes'), ('', '', 'participates_in'), ('', '', 'encodes'), ('', '', 'regulates'), ('', '', 'encodes'), ('', '', 'regulates'), ('', '', 'encodes'), ('', '', 'regulates'), ('', '', 'encodes'), ('', '', 'forms')]

G = nx.DiGraph()
for source, target, relation in edges:
    G.add_edge(source, target, label=relation)

plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G, k=2, iterations=50)
nx.draw(G, pos, with_labels=True, node_color='lightblue',
        node_size=2000, font_size=8, font_weight='bold',
        arrows=True, arrowsize=20, edge_color='gray')
edge_labels = nx.get_edge_attributes(G, 'label')
nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=6)
plt.title('Knowledge Graph from Analysis')
plt.axis('off')
plt.tight_layout()
plt.show()

SciDEX Analysis: 2026 04 01 Gap V2 89432B95

Mitochondrial transfer between astrocytes and neurons¶

Debate Transcript Summary¶

Round 1: Theorist¶

Novel Therapeutic Hypotheses for Mitochondrial Transfer-Based Neurodegeneration Treatments¶

Hypothesis 1: Tunneling Nanotube Enhancement Therapy¶

Round 2: Skeptic¶

Critical Evaluation of Mitochondrial Transfer Therapeutic Hypotheses¶

Hypothesis 1: GAP43-Mediated Tunneling Nanotube Enhancement¶

Specific Weaknesses¶

Round 3: Domain Expert¶

Druggability Assessment of Mitochondrial Transfer Hypotheses¶

Hypothesis 2: TFAM Bioenergetic Gradient Amplification¶

Druggability Assessment¶

Existing Chemical Matter¶

Round 4: Synthesizer¶

Ranked Hypotheses¶

Top 3 Hypotheses Detail¶

Knowledge Graph Edges¶

Key Citations¶