Rapid
and
accurate
detection
plays
a
critical
role
in
improving
the
survival
prognosis
of
patients
with
cardiovascular
disease,
but
traditional
methods
are
far
from
ideal
for
those
suspected
conditions.
Metabolite
analysis
based
on
nanomatrix-assisted
laser
desorption/ionization
mass
spectrometry
(NMALDI-MS)
is
considered
to
be
promising
technique
disease
diagnosis.
However,
performance
core
nanomatrixes
has
limited
its
clinical
application.
In
this
study,
we
constructed
3D
flower-shaped
cages
controllable
structured
metal-organic
frameworks
iron
oxide
nanoparticles
low
thermal
conductivity
significant
photothermal
effects.
The
elongation
incident
light
path
through
multilayer
reflection
significantly
enhances
effective
absorption
area
nanomatrixes.
Concurrently,
alternating
layered
structure
confines
energy,
reducing
losses.
Moreover,
increases
affinity
sites,
expanding
coverage.
This
approach
effectively
ionization
desorption
efficiency
during
LDI
process.
We
applied
technology
analyze
serum
metabolomes
myocardial
infarction,
heart
failure,
failure
combined
achieving
cost-effective,
high-throughput,
highly
accurate,
user-friendly
diseases.
Subsequently,
deep
detected
fingerprints
via
artificial
intelligence
models
screens
potential
metabolic
biomarkers,
providing
new
paradigm
diagnosis
ACS Applied Materials & Interfaces,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 17, 2025
Matrix-assisted
laser
desorption/ionization
(MALDI)
mass
spectrometry
(MS)
holds
great
promise
for
the
rapid
and
sensitive
detection
of
biomolecules,
but
its
precise
small
molecule
metabolites
is
hindered
by
severe
background
interference
from
organic
matrix
in
low
molecular
weight
range.
To
address
this
issue,
nanomaterials
have
commonly
been
utilized
as
substrates
LDI-MS.
Among
them,
covalent
frameworks
(COFs),
known
their
unique
optical
absorption
structural
properties,
garnered
significant
attention.
Despite
these
advantages,
ionization
efficiency
remains
a
challenge.
Herein,
composite
material
COF-S@Au
nanoparticles
(NPs),
incorporating
Au
NPs
into
sulfur-functionalized
COF
(COF-S)
through
postsynthetic
modification,
was
designed
adopted
substrates.
This
hybrid
leverages
synergistic
effects
COF-S
to
improve
minimize
interference.
The
demonstrated
5-16-fold
improvement
MS
signals
biomolecules
along
with
clean
excellent
resistance
salt
protein
Their
corresponding
limits
(LODs)
were
achieved
at
∼pmol.
Furthermore,
applied
analyze
triclosan
(TCS)-exposed
mouse
model,
successfully
identifying
10
differential
associated
TCS
toxicity.
work
provides
foundation
developing
advanced
LDI-MS
materials
high-performance
metabolic
analysis
offers
valuable
insights
toxicity
potential
applications
environmental
toxicology.
Advanced Healthcare Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 9, 2024
Abstract
Metal‐organic
frameworks
(MOFs)
have
the
potential
to
revolutionize
biotechnological
and
medical
landscapes
due
their
easily
tunable
crystalline
porous
structure.
Herein,
study
presents
MOFs'
impact
on
proteomics,
unveiling
diverse
roles
MOFs
can
play
boost
it.
Although
are
excellent
catalysts
in
other
scientific
disciplines,
role
as
proteomics
applications
remains
largely
underexplored,
despite
protein
cleavage
being
of
crucial
importance
protocols.
Additionally,
discusses
evolving
MOF
materials
that
tailored
for
showcasing
structural
diversity
functional
advantages
compared
types
used
similar
applications.
be
developed
seamlessly
integrate
into
workflows
features,
contributing
separation,
peptide
enrichment,
ionization
mass
spectrometry.
This
review
is
meant
a
guide
help
bridge
gap
between
material
scientists,
engineers,
chemists
side
researchers
biology
or
bioinformatics
working
proteomics.
Journal of Materials Chemistry B,
Год журнала:
2024,
Номер
12(31), С. 7532 - 7542
Опубликована: Янв. 1, 2024
Hydrophilic
peptides
(HPs)
play
a
critical
role
in
the
pathogenesis
of
hepatocellular
carcinoma
(HCC).
However,
comprehensive
and
in-depth
high-throughput
analysis
specific
changes
HPs
associated
with
HCC
remains
unrealized,
due
to
complex
nature
biological
fluids
challenges
mining
patterns
large
data
sets.
The
clinical
diagnosis
still
lacks
non-destructive
accurate
classification
method,
given
limited
specificity
widely
used
biomarkers.
To
address
these
challenges,
we
have
established
multifunctional
platform
that
integrates
artificial
intelligence
computation,
hydrophilic
interaction
extraction
HPs,
MALDI-MS
testing.
This
aims
achieve
highly
sensitive
HP
fingerprinting
for
HCC.
method
not
only
facilitates
efficient
detection
but
also
achieves
remarkable
100.00%
diagnostic
accuracy
test
cohort,
supported
by
machine
learning
algorithms.
By
constructing
panel
10
characteristic
features,
achieved
98%
cohort
rapid
identified
62
deeply
involved
pathways
related
liver
diseases.
integrated
strategy
provides
new
research
directions
future
biomarker
studies
as
well
early
individualized
treatment
Advanced Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 26, 2025
Early
detection
of
hepatocellular
carcinoma
(HCC)
in
patients
with
liver
cirrhosis
(LC)
and/or
hepatitis
virus
B/C
infection
(HVI)
improves
survival,
highlighting
the
need
for
accurate,
affordable
diagnostic
tools.
Here,
11
methylated
DNA
markers
(MDMs)
are
identified
during
marker
discovery.
In
phase
I,
each
selected
MDM
is
validated
175
plasma
samples
(HCC,
n
=
85;
LC/HVI,
72)
by
CO-methylation
aMplification
rEal-Time
PCR
(COMET)
assay.
Of
these,
8
MDMs
qualified
II
study,
where
a
logistic
regression
model
(COMET-LR)
trained
and
336
211;
113;
training
vs
validation,
2:1).
COMET-LR
achieved
90.0%
sensitivity
at
97.4%
specificity.
Notably,
TNM
stage
diameter<3
cm,
AFP-negative
(<20
ng
mL-1),
PIVKA-II-negative
(<40
mAU
mL-1)
82.4%,
77.8%,
88.6%,
85.7%,
respectively.
The
outperformed
multiple
protein
(AFP,
AFP-L3,
PIVKA-II)
published
scores
HCC
screening
(GALAD,
Doylestown,
ASAP),
terms
both
assay
represents
significant
advancement
addressing
unmet
non-invasive,
accessible,
cost-effective
early
tools
LC/HVI
individuals.
Further
validation
prospective
cohort
warranted.
Computer Methods in Biomechanics & Biomedical Engineering,
Год журнала:
2025,
Номер
unknown, С. 1 - 13
Опубликована: Апрель 15, 2025
Mass
spectrometry
(MS)
serves
as
a
powerful
analytical
technique
in
metabolomics.
Traditional
MS
analysis
workflows
are
heavily
reliant
on
operator
experience
and
prone
to
be
influenced
by
complex,
high-dimensional
data.
This
study
introduces
deep
learning
framework
designed
enhance
the
classification
of
complex
data
facilitate
biomarker
screening.
The
proposed
integrates
preprocessing,
classification,
selection,
addressing
challenges
analysis.
Experimental
results
demonstrate
significant
improvements
tasks
compared
other
machine
approaches.
Additionally,
peak-preprocessing
module
is
validated
for
its
potential
screening,
identifying
biomarkers
from
