Natural Compounds for Preventing Age-Related Diseases and Cancers
Mi‐Ran Ki,
No information about this author
Sol Youn,
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Dong Hyun Kim
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et al.
International Journal of Molecular Sciences,
Journal Year:
2024,
Volume and Issue:
25(14), P. 7530 - 7530
Published: July 9, 2024
Aging
is
a
multifaceted
process
influenced
by
hereditary
factors,
lifestyle,
and
environmental
elements.
As
time
progresses,
the
human
body
experiences
degenerative
changes
in
major
functions.
The
external
internal
signs
of
aging
manifest
various
ways,
including
skin
dryness,
wrinkles,
musculoskeletal
disorders,
cardiovascular
diseases,
diabetes,
neurodegenerative
cancer.
Additionally,
cancer,
like
aging,
complex
disease
that
arises
from
accumulation
genetic
epigenetic
alterations.
Circadian
clock
dysregulation
has
recently
been
identified
as
an
important
risk
factor
for
cancer
development.
Natural
compounds
herbal
medicines
have
gained
significant
attention
their
potential
preventing
age-related
diseases
inhibiting
progression.
These
demonstrate
antioxidant,
anti-inflammatory,
anti-proliferative,
pro-apoptotic,
anti-metastatic,
anti-angiogenic
effects
well
circadian
regulation.
This
review
explores
cancers,
specific
natural
targeting
key
features
these
conditions.
Language: Английский
Décalage horaire, perturbations circadiennes et santé des athlètes
Bulletin de l Académie Nationale de Médecine,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 1, 2025
Achilles tendinopathy treatment via circadian rhythm regulation
Journal of Advanced Research,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 1, 2024
Language: Английский
Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data
Yanchan Wu,
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Changyuan Yu,
No information about this author
Meijia Zhang
No information about this author
et al.
Journal of Translational Medicine,
Journal Year:
2024,
Volume and Issue:
22(1)
Published: Dec. 20, 2024
With
the
increasing
burden
of
dental
diseases
and
limited
availability
healthcare
resources,
traditional
triage
methods
are
inadequate
in
efficiently
utilizing
resources
meeting
patient
needs.
The
aim
this
study
is
to
develop
an
advanced
system
that
combines
oral
radiomics
biological
multi-omics
data,
which
enables
accurate
departmental
referral
patients
by
automatically
interpreting
information
X-ray
images.
Using
a
multi-label
learning
algorithm,
we
analyzed
data
from
3,942
with
three
cohorts
between
July
1,
2023
August
18,
2023,
continuously
monitored
classification
accuracy
(ACC)
metrics.
In
test
cohort
external
validation
cohort,
used
DenseNet121
model
analyze
achieved
accuracies
0.80
0.82,
respectively.
main
contribution
propose
new
treatment
process
incorporates
reduces
workload
physicians
while
providing
timely
medical
care
patients.
Through
comparative
experiments,
demonstrate
more
efficient
than
existing
processes.
addition,
intelligent
demonstrates
high
prediction
practical
applications,
ideas
for
research.
Language: Английский