Frontiers in Physiology,
Год журнала:
2024,
Номер
15
Опубликована: Июнь 10, 2024
Purpose
This
study
aims
to
explore
the
variations
in
external
and
internal
loads
on
a
quarter-by-quarter
basis
among
professional
Chinese
basketball
players.
It
emphasizes
crucial
impact
of
these
optimizing
athletic
performance
match
strategies.
Method
An
observational
longitudinal
design
was
employed,
involving
sixteen
male
players
from
National
Basketball
League
during
2024
season
China.
Data
collection
facilitated
through
use
Catapult
S7
devices
for
measuring
session
ratings
perceived
exertion
(sRPE)
assessing
loads.
Linear
mixed-effects
models
were
utilized
statistical
analysis
identify
differences
workload
intensities
across
game
quarters
based
player
positions.
The
Pearson
correlation
coefficient
used
examine
relationship
between
load
throughout
game.
Results
uncovered
significant
positional
quarters.
Guards
found
have
higher
PlayerLoad™
(PL)
per
minute
first
quarter,
while
centers
demonstrated
an
increase
high-intensity
accelerations
jumps
fourth
quarter.
Furthermore,
moderate
sRPE
PL
observed
all
quarters,
indicating
link
physical
athletes’
perceptions
effort.
Conclusion
offers
new
insights
into
dynamic
demands
faced
by
importance
using
both
objective
subjective
measures
comprehensive
assessment
athlete
wellbeing.
findings
underscore
interconnectedness
perception,
providing
foundation
future
research
practical
applications
field
science.
Abstract
With
the
rising
incidence
of
benign
prostatic
hyperplasia
(BPH)
due
to
societal
aging,
accurate
and
early
diagnosis
has
become
increasingly
critical.
The
clinical
challenges
associated
with
BPH
diagnosis,
particularly
lack
specific
biomarkers
that
can
differentiate
from
other
causes
lower
urinary
tract
symptoms
(LUTS).
Here,
matrix‐assisted
laser
desorption/ionization
mass
spectrometry
(MALDI
MS)
metabolomic
detection
platform
utilizing
urine
serum
samples
is
applied
explore
metabolic
information
identify
potential
in
designed
cohort.
nanoparticle‐assisted
demonstrated
rapid
analysis,
minimal
sample
consumption,
high
reproducibility.
Employing
a
two‐step
grouping
screening
approach,
identification
patterns
(UMPs)
automated
distinguish
healthy
individuals
LUTS
group,
followed
by
use
(SMPs)
accurately
cases
within
cohort,
achieving
an
area
under
curve
(AUC)
0.830
(95%
CI:
0.802‐0.851).
Furthermore,
eight
BPH‐sensitive
markers
are
identified,
confirming
their
uniform
distribution
across
age
groups
(
p
>
0.05).
This
research
contributes
valuable
insights
for
personalized
treatment
BPH,
enhancing
practice
patient
care.
Analytical Chemistry,
Год журнала:
2024,
Номер
96(36), С. 14688 - 14696
Опубликована: Авг. 29, 2024
Metabolomics
analysis
based
on
body
fluids,
combined
with
high-throughput
laser
desorption
and
ionization
mass
spectrometry
(LDI-MS),
holds
great
potential
promising
prospects
for
disease
diagnosis
screening.
On
the
other
hand,
chronic
obstructive
pulmonary
(COPD)
currently
lacks
innovative
powerful
diagnostic
screening
methods.
In
this
work,
CoFeNMOF-D,
a
metal-organic
framework
(MOF)-derived
metal
oxide
nanomaterial,
was
synthesized
utilized
as
matrix
to
assist
LDI-MS
extracting
serum
metabolic
fingerprints
of
COPD
patients
healthy
controls
(HC).
Through
machine
learning
algorithms,
successful
discrimination
between
HC
achieved.
Furthermore,
four
biomarkers
significantly
downregulated
in
were
screened
out.
The
models
demonstrated
excellent
performance
across
different
area
under
curve
(AUC)
values
reaching
0.931
0.978
training
validation
sets,
respectively.
Finally,
pathways
mechanisms
associated
identified
markers
explored.
This
work
advances
application
LDI-based
molecular
diagnostics
clinical
settings.
Analytical Chemistry,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 23, 2024
Exosomes
have
emerged
as
a
revolutionary
tool
for
liquid
biopsy
(LB),
they
carry
specific
cargo
from
cells.
Profiling
the
metabolites
of
exosomes
is
crucial
cancer
diagnosis
and
biomarker
discovery.
Herein,
we
propose
versatile
platform
exosomal
metabolite
assay
endometrial
(EC).
The
based
on
nanostructured
composite
material
comprising
gold
nanoparticle-coated
magnetic
COF
with
aptamer
modification
(Fe
The
rarity
and
heterogeneity
of
liposarcomas
(LPS)
pose
significant
challenges
in
their
diagnosis
management.
In
this
work,
a
series
metal-organic
frameworks
(MOFs)
engineering
is
designed
implemented.
Through
comprehensive
characterization
performance
evaluations,
such
as
stability,
thermal-driven
desorption
efficiency,
well
energy-
charge-transfer
capacity,
the
group
IV
bimetallic
MOFs
emerges
particularly
noteworthy.
This
especially
true
for
derivative
products,
which
exhibit
superior
across
range
laser
desorption/ionization
mass
spectrometry
(LDI
MS)
tests,
including
those
involving
practical
sample
assessments.
top-performing
product
utilized
to
enable
high-throughput
recording
LPS
metabolic
fingerprints
(PMFs)
within
seconds
using
LDI
MS.
With
machine
learning
on
PMFs,
both
LPSrecognizer
LPSclassifier
are
developed,
achieving
accurate
recognition
classification
with
area
under
curves
(AUCs)
0.900-1.000.
Simplified
versions
also
developed
by
screening
biomarker
panels,
considerable
predictive
performance,
conducting
basic
pathway
exploration.
work
highlights
matrix
design
potential
application
developing
analysis
tools
rare
diseases
clinical
settings.
ACS Applied Materials & Interfaces,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 12, 2025
Pancreatic
ductal
adenocarcinoma
(PDAC)
is
a
highly
aggressive
and
lethal
cancer,
typically
diagnosed
at
advanced
stages
due
to
its
asymptomatic
onset
challenges
in
early
detection.
To
address
the
critical
need
for
diagnosis
of
PDAC,
we
developed
laser
desorption/ionization
mass
spectrometry
(LDI-MS)
platform
based
on
mesoporous
silica-modified
magnetic
graphene
(MG@mSiO2).
MG@mSiO2
exhibited
exceptional
ultraviolet
(UV)
absorption,
efficient
ionization,
minimal
background
interference,
enabling
high-resolution
profiling
serum
metabolic
fingerprints
(SMFs).
Based
extracted
SMFs,
constructed
Random
Forest
(RF)
model
classify
PDAC
patients,
high-risk
(HR)
individuals,
healthy
controls
(HC),
achieving
an
accuracy
97.5%
independent
test
set.
Additionally,
six-metabolite
biomarker
panel
was
identified,
showing
strong
diagnostic
potential
with
sensitivity
exceeding
89.1%
distinguishing
HC
from
PDAC.
When
coupled
serological
marker
carbohydrate
antigen
19-9
(CA19-9),
integrated
strategy
delivered
significantly
improved
performance,
high
ranging
95.3%
100%
HR
patients
HC.
Furthermore,
pathway
analysis
revealed
key
pathways
associated
progression,
providing
mechanistic
insights
into
disease.
This
work
provides
powerful
tool
screening,
establishing
foundation
detection
precision
medicine
clinical
practice.
ACS Applied Materials & Interfaces,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 15, 2025
Hepatocellular
carcinoma
(HCC)
is
a
common
malignancy
and
generally
develops
from
liver
cirrhosis
(LC),
which
primarily
caused
by
the
chronic
hepatitis
B
(CHB)
virus.
Reliable
liquid
biopsy
methods
for
HCC
screening
in
high-risk
populations
are
urgently
needed.
Here,
we
establish
porous
silicon-assisted
laser
desorption
ionization
mass
spectrometry
(PSALDI-MS)
technology
to
profile
metabolite
information
hidden
human
serum
high
throughput
manner.
Serum
metabolites
can
be
captured
pore
channel
of
APTES-modified
silicon
(pSi)
particles
well-preserved
during
storage
or
transportation.
Furthermore,
APTES-pSi
directly
detected
on
LDI-MS
without
addition
an
organic
matrix,
thus
greatly
accelerating
acquisition
metabolic
fingerprints
samples.
The
PSALDI-MS
displays
capability
(5
min
per
96
samples),
reproducibility
(coefficient
variation
<15%),
sensitivity
(LOD
∼
1
pmol),
tolerance
background
salt
proteins.
In
multicenter
cohort
study,
1433
subjects
including
healthy
controls
(HC),
CHB,
LC,
volunteers
were
enrolled
nontargeted
metabolomic
analysis
was
performed
platform.
After
selection
feature
metabolites,
stepwise
diagnostic
model
classification
different
disease
stages
constructed
machine
learning
algorithm.
external
testing,
accuracy
91.2%
HC,
71.4%
70.0%
95.3%
achieved
chemometrics.
Preliminary
studies
indicated
that
fingerprint
also
good
predictive
performance
prospective
observation.
We
believe
combination
may
serve
as
efficient
tool
clinical
practice.
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