Unraveling Elastic Fiber-Derived Signaling in Arterial Aging and Related Arterial Diseases
Mingyi Wang,
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Kimberly R. McGraw,
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Robert E. Monticone
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et al.
Biomolecules,
Journal Year:
2025,
Volume and Issue:
15(2), P. 153 - 153
Published: Jan. 21, 2025
Arterial
stiffening
is
a
significant
risk
factor
for
the
development
of
cardiovascular
diseases,
including
hypertension,
atherosclerosis,
and
arteriopathy.
The
destruction
elastic
fibers,
accompanied
by
vascular
inflammatory
remodeling,
key
process
in
progression
arterial
related
pathologies.
In
young,
healthy
arteries,
intact
fibers
create
resilient
microenvironment
that
maintains
quiescence
cells.
However,
with
advancing
age,
these
undergo
post-translational
modifications,
such
as
oxidation,
glycosylation,
calcification,
leading
to
their
eventual
degeneration.
This
degeneration
results
release
degraded
peptides
formation
an
inflammatory,
stiffened
niche.
Elastic
fiber
profoundly
impacts
proinflammatory
phenotypes
behaviors
various
cells,
endothelial
smooth
muscle
macrophages,
fibroblasts,
mast
Notably,
elastin-derived
(EDPs),
which
act
potent
molecules.
EDPs
activate
cellular
processes,
secretion,
cell
migration,
proliferation,
interacting
elastin
receptor
complex
(ERC).
These
elastin-related
events
are
commonly
observed
aging
diseased
arteries.
findings
suggest
meshwork
primary
event
driving
inflammation,
stiffening,
adverse
remodeling
age.
Therefore,
preserving
blocking
EDP/ERC
signaling
pathways
may
offer
promising
therapeutic
strategies
mitigating
age-related
diseases.
Language: Английский
Integrating Artificial Intelligence in the Diagnosis and Management of Metabolic Syndrome: A Comprehensive Review
Diabetes/Metabolism Research and Reviews,
Journal Year:
2025,
Volume and Issue:
41(4)
Published: March 27, 2025
ABSTRACT
Background
Metabolic
syndrome
(MetS)
is
a
progressive
chronic
pathophysiological
state
characterised
by
abdominal
obesity,
hypertension,
hyperglycaemia,
and
dyslipidaemia.
It
recognised
as
one
of
the
major
clinical
syndromes
affecting
human
health,
with
approximately
one‐quarter
global
population
impacted.
MetS
increases
risk
developing
cardiovascular
diseases
(CVDs),
stroke,
type
2
diabetes
mellitus
(T2DM),
diverse
metabolic
diseases.
Early
diagnosis
could
potentially
reduce
prevalence
these
However,
care
for
faces
significant
challenges
due
to
(i)
lack
comprehensive
understanding
full
spectrum
associated
diseases,
stemming
from
unclear
mechanisms
(ii)
frequent
underdiagnosis
or
misdiagnosis
in
settings
inconsistent
screening
guidelines,
limited
medical
resources,
time
constraints
practice,
insufficient
awareness
training.
The
increasing
availability
healthcare
data
presents
opportunities
apply
innovate
artificial
intelligence
(AI)
addressing
challenges.
This
review
aims
summarise
AI
models
applied
syndrome‐related
(MetSRD),
where
MetSRD
collectively
refers
conditions
directly
MetS.
Methods
Our
consists
two
phases.
Initially,
we
conducted
literature
on
narrow
down
based
strength
evidence.
We
then
used
terms
‘Metabolic
Syndrome’
‘Machine
Learning’
combination
identified
further
refinement.
In
total,
52
related
first
phase
36
articles
second
phase.
Results
total
after
phase,
T2DM,
CVDs,
cancer
being
top
three.
Among
obtained
observed
following:
criteria
were
across
studies.
primary
purpose
applications
was
identify
factors
thereby
improving
predictions
MetSRD.
Traditional
machine
learning
models,
such
Random
Forest
Logistic
Regression,
found
be
most
effective.
(iii)
addition
criteria,
explored
other
factors,
including
demographic
physiological
variables,
dietary
influences,
lipidomic
proteomic
indicators,
more.
Conclusion
underscores
link
between
particular
focus
underreported
non‐alcoholic
fatty
liver
disease
stroke.
Through
analysis
sources,
diagnostic
additional
indicators
beyond
traditional
measures
have
been
identified,
emphasising
importance
combining
both
non‐traditional
markers
enhance
predictive
capabilities
shows
great
potential
research,
particularly
through
integration
multi‐source
data,
metrics,
genetic
information,
omics
data.
amalgamation
modern
promising,
offering
balanced
approach
model
performance
complexity.
While
international
definitions
provide
applicability,
they
may
not
suitable
all
populations
scenarios,
necessitating
flexible
adaptive,
explainable
algorithms.
Ultimately,
will
enable
personalised
diagnostics
targeted
interventions.
Language: Английский
Liposomal encapsulation of Chenopodium berlandieri extracts rich in oleanolic acid: Improved bioactivities targeting metabolic syndrome prevention
Food & Function,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
Chronic
inflammation
and
oxidative
stress
are
major
contributors
to
the
development
of
metabolic
syndrome
conditions,
including
obesity,
insulin
resistance,
dyslipidemia,
hypertension.
Language: Английский