Isoginkgetin protects against degeneration of ALS motor neurons via regulating the GSK-3β-TFEB signaling axis.

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[1] § Materials and methods › Receptor preparation ↔ Receptor Preparation and Structure-based Screeening.ipynb, lines 166 to 211 · score 0.71 · PDBFixer, missing atoms, hydrogen, residues, AlphaFold, Receptor · Report a mismatch

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The authors' code

Jupyter notebook · 493 lines · 14 KB · no license · 1 match

# %% [markdown]
# # Data preparation
# %% [markdown]
# ## download af structures via uniport id
# %%
import pandas as pd
file = pd.read_csv('data/2_Autophagy_OR_Mitophagy.csv')
print(file.shape)
file = file.rename(columns={'Uniprot ID':'Uniprot'})
file.head(2)
# %%
import requests
from pathlib import Path
from tqdm import tqdm
ids = file.Uniprot.tolist()
error_list = []
output_dir = Path("alphafold_mouse")
output_dir.mkdir(exist_ok=True)
for uniprot_id in tqdm(ids):
url = f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v6.pdb"
out_path = output_dir / f"AF-{uniprot_id}-F1-model_v4.pdb"
#print(f"Downloading {uniprot_id} ...")
try:
r = requests.get(url, timeout=10)
r.raise_for_status()
out_path.write_bytes(r.content)
except Exception as e:
print(f"Failed to download {uniprot_id}: {e}")
error_list.append(uniprot_id)
# %%
ids = file.Uniprot.tolist()
# %%
import os
import pandas as pd
dir_path = 'alphafold_mouse'
filenames = [f for f in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, f))]
df = pd.DataFrame(filenames, columns=['filename'])
print(df.shape)
df.head()
# %%
# %%
df['name'] = df["filename"].str.split("-").str[1]
ids_df = pd.DataFrame(ids, columns=["name"])
print(ids_df.shape)
ids_df = ids_df[~ids_df.name.isin(df['name'])]
print(ids_df.shape)
# %%
df.to_csv('data/downloaded_AFpdb.csv',index=False)
# %%
ids_df.to_csv('data/missing_AFpdb.csv',index=False)
# %%
ids_df.shape
# %% [markdown]
# # Pocket prediction and fliter
# %% [markdown]
# ## use p2rank to seg pockets
# %%
import pandas as pd
# %%
df = pd.read_csv('data/downloaded_AFpdb.csv')
# %%
ds = f"protein_list.ds"
with open(ds, "w") as out:
for i in df.filename:
out.write(f"alphafold_mouse/{i}\n")
# %%
shutil.unpack_archive('p2rank_23_archive.zip', 'p2rank_23')
# %%
import os
import time
start_time = time.time()
p2rank = "p2rank_23/prank"
cmd = f"{p2rank} predict {ds} -o af_workspace1/p2rank -threads 64"
os.system(cmd)
# Record end time
end_time = time.time()
# Calculate elapsed time
p2rank_time = end_time - start_time
# %%
print(p2rank_time)
# %% [markdown]
# ## fliter none pocket proteins
# %%
import pandas as pd
tmp = pd.read_csv("af_workspace/p2rank/AF-Q14005-F1-model_v4.pdb_predictions.csv")
print(tmp[[' center_x', ' center_y', ' center_z']])
tmp.head(2)
# %%
import pandas as pd
import os
import re
from tqdm import tqdm
workspace = "af_workspace/p2rank"
non_empty_ids = []
matching_files = [f for f in os.listdir(workspace) if re.match(r'AF-.+-F1-model_v4\.pdb_predictions\.csv', f)]
matching_files.sort()
for filename in tqdm(matching_files, desc="Checking files", unit="file"):
match = re.match(r'AF-(.+)-F1-model_v4\.pdb_predictions\.csv', filename)
if match:
file_id = match.group(1)
file_path = os.path.join(workspace, filename)
try:
df = pd.read_csv(file_path)
if len(df) > 0:
non_empty_ids.append(file_id)
except pd.errors.EmptyDataError:
continue
result_df = pd.DataFrame({"non_empty_ids": non_empty_ids})
print(f"Found {len(non_empty_ids)} non-empty files")
# %%
result_df.to_csv('data/downloaded_AFpdb_wpocket.csv',index=False)
# %% [markdown]
# # Docking
# %% [markdown]
# ## sample code for docking with predicted pocket info
# %%
import pandas as pd
result_df = pd.read_csv('data/downloaded_AFpdb_wpocket.csv')
df = pd.read_csv('data/downloaded_AFpdb.csv')
df = df[df.name.isin(result_df.non_empty_ids)]
# %%
import pandas as pd
from pdbfixer import PDBFixer
from openmm.app import PDBFile
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
from tqdm import tqdm
def add_h(protein_file, outpath):
try:
fixer = PDBFixer(protein_file)
fixer.findMissingResidues()
fixer.findNonstandardResidues()
fixer.replaceNonstandardResidues()
fixer.removeHeterogens(True)
fixer.findMissingAtoms()
fixer.addMissingAtoms()
fixer.addMissingHydrogens(7.4)
with open(outpath, "w") as f:
PDBFile.writeFile(fixer.topology, fixer.positions, f)
return (protein_file, True, "")
except Exception as e:
return (protein_file, False, str(e))
def process_file(filename, prep_dir, out_dir):
protein_path = f"{prep_dir}/{filename}"
outpath = f"{out_dir}/{filename}"
return add_h(protein_path, outpath)
if __name__ == "__main__":
outpath = 'alphafold_mouse'
output_dir = Path(outpath)
output_dir.mkdir(exist_ok=True)
prep = 'alphafold_mouse'
filenames = df.filename.tolist()
# 并行处理
results = []
with ProcessPoolExecutor(max_workers=8) as executor:
futures = [executor.submit(process_file, fname, prep, outpath) for fname in filenames]
for future in tqdm(as_completed(futures), total=len(futures)):
result = future.result()
results.append(result)
if not result[1]:
print(f"❌ Failed: {result[0]}\nError: {result[2]}")
# %%
import subprocess
from tqdm import tqdm
from pathlib import Path
outpath = 'debug'
output_dir = Path(outpath)
output_dir.mkdir(exist_ok=True)
ligand_o = f'af_workspace/Keto.sdf'
ligand_h = f'af_workspace/NAce.sdf'
prep='alphafold_mouse'
for i in tqdm(df.filename):
try:
df1 = pd.read_csv(f'af_workspace/p2rank/{i}_predictions.csv')
protein_path = f'{prep}/{i}'
i=i.split("-")[1]
print(f'start docking {i}')
out_dict_o = f'{outpath}/{i}/'
out_dict_h = f'{outpath}/{i}/'
#add_h(protein_path, protein_path)
'''for j in range(df.shape[0]):
x,y,z = df1.loc[j][[' center_x', ' center_y', ' center_z']].values
subprocess.run(f'unidocktools unidock_pipeline -r {protein_path} -l {ligand_o} -sd {out_dict_o} -nm 10 -cx {x} -cy {y} -cz {z}', shell=True, check=True)
subprocess.run(f'unidocktools unidock_pipeline -r {protein_path} -l {ligand_h} -sd {out_dict_h} -nm 10 -cx {x} -cy {y} -cz {z}', shell=True, check=True)
'''
x,y,z = df1[' center_x'][0],df1[' center_y'][0],df1[' center_z'][0]
subprocess.run(f'unidocktools unidock_pipeline -r {protein_path} -l {ligand_o} -sd {out_dict_o} -nm 25 -cx {x} -cy {y} -cz {z}', shell=True, check=True)
subprocess.run(f'unidocktools unidock_pipeline -r {protein_path} -l {ligand_h} -sd {out_dict_h} -nm 25 -cx {x} -cy {y} -cz {z}', shell=True, check=True)
except Exception as e:
print(e)
print(i)
# %%
import tempfile
import os
import shutil
def filter_pdb(input_file, output_file=None):
if output_file is None or input_file == output_file:
input_dir = os.path.dirname(os.path.abspath(input_file))
temp_fd, temp_path = tempfile.mkstemp(
suffix='.pdb',
dir=input_dir
)
os.close(temp_fd)
else:
temp_path = None
try:
with open(input_file, 'r') as infile, open(temp_path or output_file, 'w') as outfile:
for line in infile:
if line.startswith(('ATOM', 'TER', 'END')): #, 'CONECT'
outfile.write(line)
if temp_path:
os.replace(temp_path, input_file)
#print(f"{input_file}")
finally:
if temp_path and os.path.exists(temp_path):
os.remove(temp_path)
for subdir in tqdm(list(docking_dir.iterdir()), desc="Collecting docking info"):
if subdir.is_dir():
pdb_id = subdir.name
protein_file = protein_dir / f"AF-{pdb_id}-F1-model_v4.pdb"
#print(protein_file)
if not protein_file.exists():
print(f"[Warning] Missing protein file: {protein_file}")
continue
filter_pdb(protein_file)
# %% [markdown]
# ## prepare ligand config
# %%
import pandas as pd
ligand = pd.read_csv('data/1_first_stage_compound.csv')
ligand['Source_idx'] = ligand['Source_idx'].astype(str)
ligand['path'] = "af_workspace/"+ligand.Source_idx+'.sdf'
print(ligand.shape)
ligand.head(1)
# %%
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem, SDWriter
from tqdm import tqdm
def smiles_to_individual_sdf(df, smiles_col, name_col, out_dir="./sdf_files"):
import os
os.makedirs(out_dir, exist_ok=True)
for idx, row in tqdm(df.iterrows(), total=len(df)):
smi = row[smiles_col]
name = str(row[name_col]) # 用 name_col 来命名
mol = Chem.MolFromSmiles(smi)
if mol is None:
print(smi)
continue
mol = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol, AllChem.ETKDG())
AllChem.UFFOptimizeMolecule(mol)
mol_noH = Chem.RemoveHs(mol)
mol_noH.SetProp("_Name", name)
file_path = os.path.join(out_dir, f"{name}.sdf")
with SDWriter(file_path) as writer:
writer.write(mol_noH)
#print(f"✅ {name}.sdf saved")
smiles_to_individual_sdf(ligand, smiles_col="Smiles", name_col="Source_idx", out_dir="./af_workspace")
# %%
import os
import pandas as pd
folder = "af_workspace"
sdf_files = [f for f in os.listdir(folder) if f.endswith(".sdf")]
df1 = pd.DataFrame({"ligand": sdf_files})
df1['ligand'] = "af_workspace/"+df1['ligand']
df1.to_csv('data/ligands.csv',index=False, header=None)
# %%
import subprocess
import pandas as pd
import time
start = time.time()
file_path = 'alphafold_mouse/AF-O60356-F1-model_v4.pdb'
df1 = pd.read_csv(f'af_workspace1/p2rank/AF-A1A4Y4-F1-model_v4.pdb_predictions.csv')
df1.columns = [col.strip() for col in df1.columns]
x, y, z = df1.loc[0][['center_x', 'center_y', 'center_z']].values
x, y, z
subprocess.run(f'unidock --receptor alphafold_mouse/AF-A1A4Y4-F1-model_v4.pdb --ligand 20883.sdf --search_mode balance --center_x {x} --center_y {y} --center_z {z} --size_x 25 --size_y 25 --size_z 25 --dir tmp', shell=True, check=True)
end = time.time()
# %%
import glob
import os
import subprocess
from tqdm import tqdm
from pathlib import Path
import time
start = time.time()
for i in tqdm(range(df.shape[0])):#
file_path = 'alphafold_mouse/'+df.filename.iloc[i]
df1 = pd.read_csv(f'af_workspace/p2rank/{df.filename.iloc[i]}_predictions.csv')
df1.columns = [col.strip() for col in df1.columns]
x, y, z = df1.loc[0][['center_x', 'center_y', 'center_z']].values
outputdir = f"dock_record/{file_path.split('/')[-1][:-4]}"
os.makedirs(outputdir, exist_ok=True)
subprocess.run(f'unidock --receptor {file_path} --ligand_index ligands.csv --search_mode balance --center_x {x} --center_y {y} --center_z {z} --size_x 25 --size_y 25 --size_z 25 --dir {outputdir}', shell=True, check=True)
base_path = './'
output_folder = os.path.join(base_path, "files")
os.makedirs(output_folder, exist_ok=True)
save_name = f"{file_path.split('/')[-1][:-5]}.txt"
file_save_path = os.path.join(output_folder, save_name)
with open(file_save_path, "w", encoding="utf-8") as f:
f.write('finished')
end = time.time()
# %% [markdown]
# ## Check result
# %%
import os
import glob
import pandas as pd
import re
from concurrent.futures import ProcessPoolExecutor, as_completed
from tqdm import tqdm
base_dir = "dock_record"
def get_target_folders(base_dir):
pattern = os.path.join(base_dir, "AF-*")
folders = [f for f in glob.glob(pattern) if os.path.isdir(f)]
return folders
def process_folder(folder):
results = []
folder_name = os.path.basename(folder)
try:
protein_id = folder_name.split('-')[1] # e.g., Q92985
except IndexError:
protein_id = "UNKNOWN"
sdf_files = glob.glob(os.path.join(folder, "*.sdf"))
for sdf_file in sdf_files:
try:
with open(sdf_file, "r") as f:
content = f.read()
except Exception as e:
continue # silent skip or log if needed
blocks = content.split("$$$$")
for block in blocks:
match = re.search(r"> <Uni-Dock RESULT>.*?ENERGY=\s*([-\d\.]+)", block, re.DOTALL)
if match:
try:
energy = float(match.group(1))
results.append({
"file": os.path.basename(sdf_file),
"energy": energy,
"protein": protein_id,
"folder": folder_name
})
except ValueError:
pass
break
if not results:
return pd.DataFrame()
df = pd.DataFrame(results)
df = df.sort_values(by="energy", ascending=True).reset_index(drop=True)
df = df.drop_duplicates(subset=["file", "protein"], keep="first").reset_index(drop=True)
return df
def main():
folders = get_target_folders(base_dir)
print(f"Found {len(folders)} folders to process.")
if not folders:
print("No folders found. Check base_dir path.")
return
all_dfs = []
with ProcessPoolExecutor(max_workers=min(16, os.cpu_count())) as executor:
future_to_folder = {executor.submit(process_folder, folder): folder for folder in folders}
with tqdm(total=len(future_to_folder), desc="Processing folders") as pbar:
for future in as_completed(future_to_folder):
try:
df = future.result()
if not df.empty:
all_dfs.append(df)
except Exception as e:
folder = future_to_folder[future]
print(f"\nError processing {folder}: {e}")
finally:
pbar.update(1)
if all_dfs:
final_df = pd.concat(all_dfs, ignore_index=True)
final_df.to_csv("data/merged_docking_results1.csv", index=False)
print(f"\n✅ Saved merged results to merged_docking_results.csv (total rows: {len(final_df)})")
else:
print("\n❌ No valid results found.")
if __name__ == "__main__":
main()
final_df = pd.read_csv('data/merged_docking_results1.csv')
final_df['ligand'] = final_df.file.str[:-8].astype('int')
final_df = final_df[final_df['ligand']<30]
final_df[['energy', 'protein', 'ligand']].to_csv('data/3_docking_results.csv',index=False)

Receptor Preparation and Structure-based Screeening.ipynb at commit a53cfe6, no license · at the source

Overview

Authors: Ang Li¹, Xianglu Xiao², Guopan Liu^3,4, Krinos Li², Yixia Ling³, Shenglong Deng², Chunzuan Xu¹, Shu-Qin Cao⁵, Jing Wen¹, Guang Lu⁶, Guang Yang^2,7,8,9, Evandro F. Fang^5,10, Dajiang Qin^3,4, Huanxing Su¹

ORCID iDs: Guang Lu, Huanxing Su

Institutions: University of Macau (Macao SAR China); Imperial College London (United Kingdom); Chinese Academy of Sciences (China); Guangzhou Medical University (China); University of Oslo (Norway); Akershus University Hospital (Norway); Sun Yat-sen University (China); King's College London (United Kingdom); Royal Brompton Hospital (United Kingdom)

Journal: Pharmacological research, volume 227, article 108172

Dates: received 23 February 2026; accepted 24 March 2026; published online 28 March 2026; in print May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.phrs.2026.108172 · PMID 41912102 · PMCID PMC13132972 · OpenAlex W7141807343

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)

Methods: Statistics, Spectral & time-frequency, Evoked potentials

Keywords: Amyotrophic lateral sclerosis, GSK-3β–TFEB axis, Isoginkgetin, Lysosome, Artificial intelligence

MeSH: Amyotrophic Lateral Sclerosis*, Basic Helix-Loop-Helix Leucine Zipper Transcription Factors*, Glycogen Synthase Kinase 3 beta*, Motor Neurons*, Neuroprotective Agents*, Animals, Humans, Induced Pluripotent Stem Cells, Lysosomes, Mice, Signal Transduction (* major topic)

Topic: Amyotrophic Lateral Sclerosis Research (Clinical Neurology, Medicine), according to OpenAlex

Funding: Macau Science and Technology Development Fund (0093/2024/AFJ); Science and Technology Development Fund; SKL-QRCM(UM) (2023–2025, 001/2023/ALC, MYRG-GRG2023–00152-ICMS-UMDF); Guangdong Provincial Postdoctoral Research Matching Fund (RSK-602–06–04, B202600301); Guangzhou Municipal Science and Technology Project (2025B03J0160); Research Funds from Program launched by Innovation Technology Commission of the Hong Kong SAR; National Natural Science Foundation of China (82471386, 81971327); ERC IMI (101005122); UK Research and Innovation (MC/PC/21013, EP/Z002206/1, MR/U506710/1); Royal Society (IEC\NSFC\211235); Wellcome Leap Dynamic resilience program, Swiss National Science Foundation (220785); UKRI Future Leaders Fellowship (MR/V023799/1, UKRI2738); National Natural Science Foundation of China, Young Scientists Fund-Type C (32501096); Cure Alzheimer’s Fund (282952, 284930); Helse sør-øst (2020001, 2021021, 2023093); Norwegian Research Council (262175, 334361); Molecule AG/VITADAO (282942); NordForsk Foundation (119986); Akershus University Hospital (269901, 261973, 262960); Civitan Norges Forskningsfond for Alzheimers sykdom (281931); Czech Republic-Norway KAPPA programme (TO01000215); Rosa sløyfe/Norwegian Cancer Society & Norwegian Breast Cancer Society (207819); horizon-TMA-MSCA-DN (101073251); Wellcome Leap’s Dynamic Resilience Program (jointly funded by Temasek Trust) (104617); Alzheimer's Association (PTC-Gene-25–1439553); Nasjonalforeningen for Folkehelsen (43622)

Citations: cited by 6 papers (Europe PMC); 67 references in the paper

Abstract

Lysosomal dysfunction is a core pathological driver of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS). Transcription factor EB (TFEB) serves as a master regulator of lysosomal biogenesis, and its pharmacological activation represents a strategy to restore lysosomal function in disease and aging. Here, using a series of artificial intelligence-powered computational virtual screening workflows, we have identified isoginkgetin (ISO), a small-molecule compound, as a potent TFEB activator that promotes mechanistic target of rapamycin complex 1 (mTORC1)–independent TFEB nuclear translocation to enhance lysosomal biogenesis and function. Mechanistically, ISO functions as an ATP-competitive inhibitor that binds to the key Lys85 residue within the ATP-binding pocket of glycogen synthase kinase 3β (GSK-3β), thereby regulating the GSK-3β–TFEB signaling axis to activate TFEB nuclear translocation. Functionally, ISO improves lysosomal function and protects motor neurons differentiated from induced pluripotent stem cells derived from patients with ALS from degeneration. Collectively, these results support the hypothesis that lysosomal dysfunction is a druggable target for ALS.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

XiangLuXiao/mTORIndependentCompoundScreen

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: a53cfe601a83501e47ff804dd49c62cf1fd51215, 20 January 2026

Languages: Jupyter (2)

Size: 9 files, 2 scripts

Software Heritage: not archived

Found in: the text, “Code, hardware, and runtime”

Holds: README, 2 notebooks

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: pandas (2 files), RDKit (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

3 files

Post-Docking Analysis.ipynb, Jupyter, 38 lines
Receptor Preparation and Structure-based Screeening.ipynb, Jupyter, 493 lines, 1 match
readme.md, Text, 53 lines

At the source: github.com/XiangLuXiao/mTORIndependentCompoundScreen

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
2 scripts, each with its path and the digest of its content;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Data will be made available on request.

All raw data for the paper can be downloaded via the WeTransfer link.all the original data (https://we.tl/t-RLe7u06Ajr)

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 14 authors, 5 keywords, 11 MeSH terms, 26 funders, 67 references.

Cite

This paper

Li, A., Xiao, X., Liu, G., Li, K., Ling, Y., Deng, S., Xu, C., Cao, S.-Q., Wen, J., Lu, G., Yang, G., Fang, E. F., Qin, D., & Su, H. (2026). Isoginkgetin protects against degeneration of ALS motor neurons via regulating the GSK-3β-TFEB signaling axis. Pharmacological research, 227, 108172. https://doi.org/10.1016/j.phrs.2026.108172

BibTeX

@article{li2026isoginkgetin, author = {Li, Ang and Xiao, Xianglu and Liu, Guopan and Li, Krinos and Ling, Yixia and Deng, Shenglong and Xu, Chunzuan and Cao, Shu-Qin and Wen, Jing and Lu, Guang and Yang, Guang and Fang, Evandro F. and Qin, Dajiang and Su, Huanxing}, title = {{Isoginkgetin protects against degeneration of ALS motor neurons via regulating the GSK-3β-TFEB signaling axis}}, journal = {Pharmacological research}, year = {2026}, month = mar, volume = {227}, pages = {108172}, publisher = {Elsevier BV}, issn = {1043-6618}, doi = {10.1016/j.phrs.2026.108172}, url = {https://doi.org/10.1016/j.phrs.2026.108172}, pmid = {41912102}, pmcid = {PMC13132972} }

RIS

TY - JOUR AU - Li, Ang AU - Xiao, Xianglu AU - Liu, Guopan AU - Li, Krinos AU - Ling, Yixia AU - Deng, Shenglong AU - Xu, Chunzuan AU - Cao, Shu-Qin AU - Wen, Jing AU - Lu, Guang AU - Yang, Guang AU - Fang, Evandro F. AU - Qin, Dajiang AU - Su, Huanxing TI - Isoginkgetin protects against degeneration of ALS motor neurons via regulating the GSK-3β-TFEB signaling axis T2 - Pharmacological research J2 - Pharmacol Res PY - 2026 DA - 2026/03/28 VL - 227 SP - 108172 SN - 1043-6618 PB - Elsevier BV DO - 10.1016/j.phrs.2026.108172 UR - https://doi.org/10.1016/j.phrs.2026.108172 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.phrs.2026.108172", "type": "article-journal", "title": "Isoginkgetin protects against degeneration of ALS motor neurons via regulating the GSK-3β-TFEB signaling axis", "container-title": "Pharmacological research", "author": [ { "family": "Li", "given": "Ang" }, { "family": "Xiao", "given": "Xianglu" }, { "family": "Liu", "given": "Guopan" }, { "family": "Li", "given": "Krinos" }, { "family": "Ling", "given": "Yixia" }, { "family": "Deng", "given": "Shenglong" }, { "family": "Xu", "given": "Chunzuan" }, { "family": "Cao", "given": "Shu-Qin" }, { "family": "Wen", "given": "Jing" }, { "family": "Lu", "given": "Guang" }, { "family": "Yang", "given": "Guang" }, { "family": "Fang", "given": "Evandro F." }, { "family": "Qin", "given": "Dajiang" }, { "family": "Su", "given": "Huanxing" } ], "container-title-short": "Pharmacol Res", "volume": "227", "page": "108172", "DOI": "10.1016/j.phrs.2026.108172", "PMID": "41912102", "PMCID": "PMC13132972", "ISSN": "1043-6618", "publisher": "Elsevier BV", "URL": "https://doi.org/10.1016/j.phrs.2026.108172", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 28 ] ] } }

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