Advanced Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: May 16, 2025
Abstract
The
rapidly
advancing
field
of
theranostics
aims
to
integrate
therapeutic
and
diagnostic
functionalities
into
a
single
platform
for
precision
medicine,
enabling
the
simultaneous
treatment
monitoring
diseases.
Photo‐energy
conversion‐based
nanomaterials
have
emerged
as
versatile
that
utilizes
unique
properties
light
activate
with
high
spatial
temporal
precision.
This
review
provides
comprehensive
overview
recent
developments
in
photo‐energy
conversion
using
nanomaterials,
highlighting
their
applications
disease
theranostics.
discussion
begins
by
exploring
fundamental
principles
including
types
materials
used
various
light‐triggered
mechanisms,
such
photoluminescence,
photothermal,
photoelectric,
photoacoustic,
photo‐triggered
SERS,
photodynamic
processes.
Following
this,
delves
broad
spectrum
emphasizing
role
diagnosis
major
diseases,
cancer,
neurodegenerative
disorders,
retinal
degeneration,
osteoarthritis.
Finally,
challenges
opportunities
technologies
are
discussed,
aiming
advance
personalized
medicine.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 54816 - 54852
Published: Jan. 1, 2024
Raman
spectroscopy
(RS)
is
a
label-free
molecular
vibrational
technique
that
able
to
identify
the
fingerprint
of
various
samples
making
use
inelastic
scattering
monochromatic
light.
Because
its
advantages
non-destructive
and
accurate
detection,
RS
finding
more
for
benign
malignant
tissues,
tumor
differentiation,
subtype
classification,
section
pathology
diagnosis,
operating
either
in
vivo
or
vitro
.
However,
high
specificity
comes
at
cost.
The
acquisition
rate
low,
depth
information
cannot
be
directly
accessed,
sampling
area
limited.
Such
limitations
can
contained
if
data
pre-
post-processing
methods
are
combined
with
current
Artificial
Intelligence
(AI),
essentially,
Machine
Learning
(ML)
Deep
(DL).
latter
modifying
approach
cancer
diagnosis
currently
used
automate
many
analyses,
it
has
emerged
as
promising
option
improving
healthcare
accuracy
patient
outcomes
by
abiliting
prediction
diseases
tools.
In
very
broad
context,
applications
in
oncology
include
risk
assessment,
early
prognosis
estimation,
treatment
selection
based
on
deep
knowledge.
application
autonomous
datasets
generated
analysis
tissues
could
make
rapid
stand-alone
help
pathologists
diagnose
accuracy.
This
review
describes
milestones
achieved
applying
AI-based
algorithms
analysis,
grouped
according
seven
major
types
cancers
(Pancreatic,
Breast,
Skin,
Brain,
Prostate,
Ovarian
Oral
cavity).
Additionally,
provides
theoretical
foundation
tackle
both
present
forthcoming
challenges
this
domain.
By
exploring
achievements
discussing
relative
methodologies,
offers
recapitulative
insights
recent
ongoing
efforts
position
effective
screening
tool
pathologists.
Accordingly,
we
aim
encourage
future
research
endeavors
facilitate
realization
full
potential
AI
grading.
Journal of Materials Chemistry B,
Journal Year:
2024,
Volume and Issue:
12(20), P. 4785 - 4808
Published: Jan. 1, 2024
This
review
focuses
on
the
versatile
applications
of
near-infrared
(NIR)-responsive
smart
carriers
in
biomedical
applications,
particularly
drug
delivery
and
photothermal
chemotherapy.
ACS Sensors,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 10, 2025
Artificial
intelligence
(AI)-based
surface-enhanced
Raman
scattering
(SERS)
is
a
powerful
system
for
cancer
diagnosis,
leveraging
its
unique
advantages
by
combining
the
high
sensitivity
of
SERS
technique
with
advanced
classification
capabilities
provided
computing
power.
While
previous
studies
have
yielded
significant
results
through
using
exosomes,
miRNA,
and
phenotypic
biomarkers
detecting
breast
cancer,
these
methods
frequently
entail
time-consuming
complex
pretreatment
steps,
demanding
highly
skilled
handling.
Here,
we
present
free-label
platform
faster
sampling
without
any
pretreat
blood
plasma
diagnosis.
In
this
study,
cluster
structure
gold
nanoparticles
within
confines
space
microcapillary
was
fabricated
to
generate
close-packing
enhancing
electromagnetic
field
large
number
"hot
spot."
We
demonstrate
that
our
can
significantly
amplify
signal
standard
chemical
detection
R6G
molecules.
Consequently,
solution
mixed
appropriately
between
collected
from
participants
build
hybrid
in
measurement.
With
support
machine
learning
model,
diagnosis
has
successfully
classified
patients
normal
accuracy
87.5%.
