Biosensor-based methods for exosome detection with applications to disease diagnosis
Weikang Ge,
Zheying Mu,
Shiao Yang
и другие.
Biosensors and Bioelectronics,
Год журнала:
2025,
Номер
unknown, С. 117362 - 117362
Опубликована: Март 1, 2025
Язык: Английский
Exosomes and their roles in major depressive disorder: an overview of the research progress and perspectives
Deleted Journal,
Год журнала:
2025,
Номер
unknown, С. 100035 - 100035
Опубликована: Март 1, 2025
Язык: Английский
A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm
Mathematics,
Год журнала:
2025,
Номер
13(8), С. 1218 - 1218
Опубликована: Апрель 8, 2025
Multisource
and
multimodal
data
fusion
plays
a
pivotal
role
in
large-scale
artificial
intelligence
applications
involving
big
data.
However,
the
choice
of
strategies
for
different
scenarios
is
often
based
on
experimental
comparisons,
which
leads
to
increased
computational
costs
during
model
training
suboptimal
performance
testing.
In
this
paper,
we
present
theoretical
analysis
early
fusion,
late
gradual
methods.
We
derive
equivalence
conditions
between
fusions
within
framework
generalized
linear
models.
Moreover,
analyze
failure
presence
nonlinear
feature-label
relationships.
Furthermore,
propose
an
approximate
equation
evaluating
accuracy
methods
as
function
sample
size,
feature
quantity,
modality
number.
also
critical
size
threshold
at
dominance
models
undergoes
reversal.
Finally,
introduce
method
selection
paradigm
selecting
most
appropriate
prior
task
execution
demonstrate
its
effectiveness
through
extensive
numerical
experiments.
Our
expected
solve
problems
resource
construction,
improving
scalability
efficiency
Язык: Английский
Exosomal Biomarkers: A Comprehensive Overview of Diagnostic and Prognostic Applications in Malignant and Non-Malignant Disorders
Biomolecules,
Год журнала:
2025,
Номер
15(4), С. 587 - 587
Опубликована: Апрель 15, 2025
Exosomes
are
small
extracellular
vesicles,
ranging
from
30
to
150
nm,
that
essential
in
cell
biology,
mediating
intercellular
communication
and
serving
as
biomarkers
due
their
origin
cells.
for
diagnosing
various
illnesses
have
gained
significant
investigation
the
high
cost
invasive
nature
of
current
diagnostic
procedures.
a
clear
advantage
diagnosis
diseases
because
they
include
certain
signals
indicative
genetic
proteomic
profile
ailment.
This
feature
gives
them
potential
be
useful
liquid
biopsies
real-time,
noninvasive
monitoring,
enabling
early
cancer
identification
creation
individualized
treatment
plans.
According
our
analysis,
trend
toward
utilizing
exosomes
prognostic
tools
has
raised
since
2012.
In
this
regard,
proportion
malignant
indications
is
higher
compared
with
non-malignant
ones.
To
precise,
been
used
most
gastrointestinal,
thoracic,
urogenital
cancers,
along
cardiovascular,
diabetic,
breathing,
infectious,
brain
disorders.
best
knowledge,
first
research
examine
all
registered
clinical
trials
look
at
biomarker.
Язык: Английский
Construction of geriatric hypoalbuminemia predicting model for hypoalbuminemia patients with and without pneumonia and explainability analysis
Frontiers in Medicine,
Год журнала:
2024,
Номер
11
Опубликована: Дек. 31, 2024
Background
and
objectives
Pneumonia
portrays
a
critical
health
concern
in
geriatrics.
Geriatric
pneumonia
can
lead
to
changes
on
other
complications,
which
hypoalbuminemia
is
common
complication.
However,
few
studies
have
looked
at
the
impact
of
course
predicting.
This
study
aims
predicting
geriatric
non-pneumonia
patients
exploring
clinical
difference
between
two
groups.
Materials
methods
retrospective
enrolled
42
group
76
group.
The
indicators
different
groups
were
analyzed,
then
mutual
information-grey
relational
coefficient
gradual
fusion
model
was
constructed
predict
future
by
vital
signs,
N-Terminal
Pro-Brain
Natriuretic
Peptide,
blood
routine
examination
urine
admission.
Through
sensitivity
analysis
model,
we
analysed
important
four
examines
with
without
pneumonia.
Results
predicted
accuracy
our
0.954,
improve
prediction
nearly
17.6%
compared
classical
machine
learning
method.
AUC
0.96
0.9
showed
examine
most
pneumonia,
while
patients.
Conclusion
combined
more
significant
than
that
alone,
characterized
abnormal
excretion
due
low
protein.
We
suggested
doctors
should
pay
attention
results
when
preventing
Язык: Английский