Zinc
nanoparticles
(ZnNPs)
are
a
viable
option
in
number
of
disciplines,
including
cancer
treatment,
due
to
their
special
features.
Among
the
several
techniques
for
synthesizing
ZnNP,
biosynthesis
with
natural
extracts
is
highly
effective
and
environmentally
benign
method,
especially
uses
biomedicine.
Using
an
aqueous
extract
marine
red
seaweed
Jania
rubens,
we
created
unique
biosynthetic
technique
this
study
manufacture
ZnNPs.
The
produced
ZnNPs
have
characteristic
flower-like
form,
as
seen
by
scanning
electron
microscopy
(SEM)
transmission
(TEM).
production
involvement
biomolecules
synthesis
process
were
validated
energy-dispersive
X-ray
spectroscopy
(EDAX)
Fourier
transform
infrared
(FTIR)
techniques.
MTT
assay,
cytotoxic
effects
biosynthesized
evaluated,
indicating
ability
inhibit
MCF-7
breast
cells.
Furthermore,
ZnNPs'
cytotoxicity
against
cells
was
live/dead
imaging
experiments,
which
supported
results.
Bioengineering,
Год журнала:
2025,
Номер
12(1), С. 65 - 65
Опубликована: Янв. 14, 2025
This
paper
reviews
the
main
research
on
Optical
Coherence
Tomography
(OCT),
focusing
progress
and
advancements
made
by
researchers
over
past
three
decades
in
its
methods
medical
imaging
applications.
By
analyzing
existing
studies
developments,
this
review
aims
to
provide
a
foundation
for
future
field.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 19, 2025
Abstract
The
most
dangerous
form
of
cancer
is
breast
cancer.
This
disease
life-threatening
because
its
aggressive
nature
and
high
death
rates.
Therefore,
early
discovery
increases
the
patient’s
survival.
Mammography
has
recently
been
recommended
as
diagnosis
technique.
Mammography,
expensive
exposure
person
to
radioactivity.
Thermography
a
less
invasive
affordable
technique
that
becoming
increasingly
popular.
Considering
this,
recent
deep
learning-based
approach
executed
by
thermography
images.
Initially,
images
are
chosen
from
online
sources.
collected
being
preprocessed
Contrast
Limited
Adaptive
Histogram
Equalization
(CLAHE)
contrasting
enhancement
methods
improve
quality
brightness
Then,
optimal
binary
thresholding
done
segment
images,
where
optimized
value
using
developed
Rock
Hyraxes
Dandelion
Algorithm
Optimization
(RHDAO).
A
newly
implemented
learning
structure
StackVRDNet
used
for
further
processing
diagnosing
segmented
fed
framework,
Visual
Geometry
Group
(VGG16),
Resnet,
DenseNet
employed
constructing
this
model.
relevant
features
extracted
usingVGG16,
DenseNet,
then
obtain
stacked
weighted
feature
pool
features,
weight
optimization
with
help
RHDAO.
final
classification
performed
StackVRDNet,
results
obtained
at
layer
VGG16,
DenseNet.
higher
scoring
method
rated
ensuring
results.
Here,
parameters
present
within
via
RHDAO
simulation
outcomes
model
achieve
97.05%
86.86%
in
terms
accuracy
precision,
respectively.
effectiveness
designed
methd
analyzed
conventional
models
various
performance
measures.
Microscopy Research and Technique,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 2, 2025
Microscopic
imaging
aids
disease
diagnosis
by
describing
quantitative
cell
morphology
and
tissue
size.
However,
the
high
spatial
resolution
of
these
images
poses
significant
challenges
for
manual
evaluation.
This
project
proposes
using
computer-aided
analysis
methods
to
address
challenges,
enabling
rapid
precise
clinical
diagnosis,
course
analysis,
prognostic
prediction.
research
introduces
advanced
deep
learning
frameworks
such
as
squeeze-and-excitation
dilated
dense
convolution
blocks
tackle
complexities
quantifying
small
intricate
breast
cancer
tissues
meeting
real-time
requirements
pathological
image
analysis.
Our
proposed
framework
integrates
a
convolutional
network
(DenseNet)
with
an
attention
mechanism,
enhancing
capability
accurate
assessments.
These
multi-classification
models
facilitate
prediction
segmentation
lesions
in
microscopic
leveraging
lightweight
multi-scale
feature
extraction,
dynamic
region
attention,
sub-region
classification,
regional
regularization
loss
functions.
will
employ
transfer
paradigms
data
enhancement
enhance
models'
further
prevent
overfitting.
We
propose
fine-tuning
employing
pre-trained
architectures
VGGNet-19,
ResNet152V2,
EfficientNetV2-B1,
DenseNet-121,
modifying
final
pooling
layer
each
model's
last
block
SPP
associated
BN
layer.
The
study
uses
labeled
unlabeled
robust
features
classification
abilities.
method
reduces
costs
time
traditional
methods,
alleviating
burden
labeling
computational
pathology.
goal
is
provide
sophisticated,
efficient
solution,
improving
outcomes
advancing
field.
model,
trained,
validated,
tested
on
microscope
dataset,
achieved
recognition
accuracy
99.6%
benign
malignant
secondary
99.4%
eight
subtypes
classification.
approach
demonstrates
substantial
improvement
compared
existing
which
generally
report
lower
accuracies
subtype
ranging
between
85%
94%.
level
underscores
potential
our
reliable
diagnostic
support,
precision
decision-making.
Structured
illumination
microscopy
(SIM)
is
a
robust
wide-field
optical
nanoscopy
technique.
Several
approaches
are
implemented
to
improve
SIM's
resolution
capability
(∼2-fold).
However,
achieving
high
with
large
field
of
view
(FOV)
still
challenging.
We
present
tilt-mirror-based
multi-periodic
SIM
for
large-FOV
super-resolution
microscopy.
The
sample
illuminated
by
structured
pattern
generated
six-beam
interference
using
custom-designed
mirror
mount.
achieve
3.16-fold
improvement
while
20×/0.40
numerical-aperture
objective
that
supports
FOV
(0.53
mm
×
0.34
mm).
This
overcomes
the
high-space-bandwidth
product
challenge,
9.98-fold
improvement.
mMP-SIM
decouples
and
collection
paths,
enabling
scalable
over
FOV.
By
28×/0.80
lens,
an
170
nm
0.40
0.25
imaging
area
demonstrated.
proof-of-principle
experimental
demonstration
performed
both
fluorescent
beads
biosample
like
U2OS
(human
bone
osteosarcoma)
cells.
Cancers,
Год журнала:
2025,
Номер
17(3), С. 473 - 473
Опубликована: Янв. 31, 2025
Background/Objectives:
Validation
of
predictive
models
(PMs)
is
crucial
to
be
implemented
in
new
populations
or
when
advances
diagnostic
approaches
occurred.
The
aim
this
study
validate
the
BCN-MRI
PM
for
sPCa
a
highly
effective
prostate
biopsy
protocol
used.
Methods:
A
prospective
cohort
457
men
suspected
having
PCa,
whom
MRI
results
were
reported
with
Prostate
Imaging-Reporting
and
Data
System
(PI-RADS)
v
2.1,
underwent
per
0.5
mm-core
mapping
targeted
suspicious
lesions
perilesional
areas,
followed
by
12-core-systematic
biopsy.
These
procedures
took
place
between
1
February
2022,
29
2024,
at
reference
center
individual
likelihood
was
assessed
through
risk
calculator.
Results:
overall
detection
rate
58.3%.
calibration
curve
showed
an
appropriate
accuracy
expected
observed
probabilities
discrimination
ability
yielding
area
under
(AUC)
0.862
(95%
CI
0.828-0.896)
comparable
AUC
0.858
0.833-0.883)
development
cohort.
application
provided
net
benefit
over
performing
biopsies
on
all
men,
avoiding
24.9%
95%
sensitivity
sPCa,
compared
23.7%
reduction
Conclusions:
We
conclude
that
ready
employed.