Synthesis, preclinical evaluation and pilot clinical study of a P2Y12 receptor targeting radiotracer [18F]QTFT for imaging brain disorders by visualizing anti-inflammatory microglia
Bolin Yao,
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Yanyan Kong,
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Jianing Li
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et al.
Acta Pharmaceutica Sinica B,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
As
the
brain's
resident
immune
cells,
microglia
perform
crucial
functions
such
as
phagocytosis,
neuronal
network
maintenance,
and
injury
restoration
by
adopting
various
phenotypes.
Dynamic
imaging
of
these
phenotypes
is
essential
for
accessing
brain
diseases
therapeutic
responses.
Although
numerous
probes
are
available
pro-inflammatory
microglia,
no
PET
tracers
have
been
developed
specifically
to
visualize
anti-inflammatory
microglia.
In
this
study,
we
present
an
18F-labeled
tracer
(QTFT)
that
targets
P2Y12,
a
receptor
highly
expressed
on
[18F]QTFT
exhibited
high
binding
affinity
P2Y12
(14.43
nmol/L)
superior
blood-brain
barrier
permeability
compared
other
candidates.
Micro-PET
in
IL-4-induced
neuroinflammation
models
showed
higher
uptake
lesions
contralateral
normal
tissues.
Importantly,
specific
could
be
blocked
QTFT
or
antagonist.
Furthermore,
visualized
mouse
epilepsy,
glioma,
aging
targeting
aberrantly
pilot
clinical
successfully
located
epileptic
foci,
showing
enhanced
radioactive
signals
patient
with
epilepsy.
Collectively,
studies
suggest
serve
valuable
diagnostic
tool
disorders
overexpressed
Language: Английский
Emerging TSPO-PET Radiotracers for Imaging Neuroinflammation: A Critical Analysis
Grace A. Cumbers,
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Edward D. Harvey-Latham,
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Michael Kassiou
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et al.
Seminars in Nuclear Medicine,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 1, 2024
Language: Английский
Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging
Frontiers in Human Neuroscience,
Journal Year:
2025,
Volume and Issue:
19
Published: March 21, 2025
Introduction
Combining
many
types
of
imaging
data—especially
structural
MRI
(sMRI)
and
functional
(fMRI)—may
greatly
assist
in
the
diagnosis
treatment
brain
disorders
like
Alzheimer’s.
Current
approaches
are
less
helpful
for
forecasting,
however,
as
they
do
not
always
blend
spatial
temporal
patterns
from
different
sources
properly.
This
work
presents
a
novel
mixed
deep
learning
(DL)
method
combining
data
using
CNN,
GRU,
attention
techniques.
introduces
hybrid
Dynamic
Cross-Modality
Attention
Module
to
help
more
efficiently
data.
Through
working
around
issues
with
current
multimodal
fusion
techniques,
our
approach
increases
accuracy
readability
diagnoses.
Methods
Utilizing
CNNs
models
dynamics
fMRI
connection
measures
utilizing
GRUs,
proposed
extracts
characteristics
sMRI.
Strong
integration
is
made
possible
by
including
an
mechanism
give
diagnostically
important
features
top
priority.
Training
evaluation
model
took
place
Human
Connectome
Project
(HCP)
dataset
behavioral
data,
fMRI,
Measures
include
accuracy,
recall,
precision
F1-score
used
evaluate
performance.
Results
It
was
correct
96.79%
time
combined
structure.
Regarding
identification
disorders,
successful
than
existing
ones.
Discussion
These
findings
indicate
that
strategy
makes
sense
complimentary
information
several
kinds
photos.
detail
helped
one
choose
which
aspects
concentrate
on,
thereby
enhancing
diagnostic
accuracy.
Conclusion
The
offers
fresh
benchmark
neuroimaging
analysis
has
great
potential
use
real-world
assessment
prediction.
Researchers
will
investigate
future
applications
this
technique
new
picture
clinical
Language: Английский