Despite
the
potentialities
of
electrochemical
sensors,
these
devices
still
encounter
challenges
in
devising
high-throughput
and
accurate
drug
susceptibility
testing.
The
lack
platforms
for
providing
analyses
over
preclinical
trials
candidates
remains
a
significant
barrier
to
developing
medicines.
In
this
way,
ultradense
chips
are
combined
with
machine
learning
(ML)
enable
high-throughput,
user-friendly,
determination
viability
2D
tumor
cells
(breast
colorectal)
aiming
at
assays.
effect
doxorubicin
(anticancer
model)
was
assessed
through
cell
detachment
assays
by
interrogating
Ru(NH3)63+
square
wave
voltammetry
(SWV).
This
positive
probe
is
presumed
imply
sensitive
monitoring
on-sensor
cellular
death
because
its
electrostatic
preconcentration
so-called
nanogap
zone
between
electrode
surface
adherent
cells.
High-throughput
were
obtained
merging
fast
individual
SWV
measurements
(9
s)
ability
yield
series.
approach's
applicability
demonstrated
across
two
analysis
formats,
drop-casting
microfluidic
One
should
also
mention
that
fitting
multivariate
descriptor
from
selected
input
data
via
ML
proved
be
essential
determinations
(98
104%)
half-maximal
lethal
concentration
drug.
achieved
results
underscore
potential
method
steering
sensors
toward
enabling
screening
practical
applications.
Advanced Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 25, 2025
This
review
article
explores
the
transformative
potential
of
smart
dust
systems
by
examining
how
existing
chemical
sensing
technologies
can
be
adapted
and
advanced
to
realize
their
full
capabilities.
Smart
dust,
characterized
submillimeter-scale
autonomous
platforms,
offers
unparalleled
opportunities
for
real-time,
spatiotemporal
mapping
across
diverse
environments.
introduces
technological
advancements
underpinning
these
systems,
critically
evaluates
current
limitations,
outlines
new
avenues
development.
Key
challenges,
including
multi-compound
detection,
system
control,
environmental
impact,
cost,
are
discussed
alongside
solutions.
By
leveraging
innovations
in
miniaturization,
wireless
communication,
AI-driven
data
analysis,
sustainable
materials,
this
highlights
promise
address
critical
challenges
monitoring,
healthcare,
agriculture,
defense
sectors.
Through
lens,
provides
a
strategic
roadmap
advancing
from
concept
practical
application,
emphasizing
its
role
transforming
understanding
management
complex
systems.
Frontiers in Medicine,
Год журнала:
2025,
Номер
12
Опубликована: Апрель 8, 2025
Modern
healthcare
depends
fundamentally
on
clinical
biochemistry
for
disease
diagnosis
and
therapeutic
guidance.
The
discipline
encounters
operational
constraints,
including
sampling
inefficiencies,
precision
limitations,
expansion
difficulties.
Recent
advancements
in
established
technologies,
such
as
mass
spectrometry
the
development
of
high-throughput
screening
point-of-care
are
revolutionizing
industry.
biosensor
technology
wearable
monitors
facilitate
continuous
health
tracking,
Artificial
Intelligence
(AI)/machine
learning
(ML)
applications
enhance
analytical
capabilities,
generating
predictive
insights
individualized
treatment
protocols.
However,
concerns
regarding
algorithmic
bias,
data
privacy,
lack
transparency
decision-making
("black
box"
models),
over-reliance
automated
systems
pose
significant
challenges
that
must
be
addressed
responsible
AI
integration.
limitations
remain-substantial
implementation
expenses,
system
incompatibility
issues,
information
security
vulnerabilities
intersect
with
ethical
considerations
fairness
protected
information.
Addressing
these
demands
coordinated
efforts
between
clinicians,
scientists,
technical
specialists.
This
review
discusses
current
biochemistry,
explicitly
addressing
reference
intervals
barriers
to
implementing
innovative
biomarkers
medical
settings.
discussion
evaluates
how
advanced
technologies
multidisciplinary
collaboration
can
overcome
constraints
while
identifying
research
priorities
diagnostic
accessibility
better
delivery.
Advanced Healthcare Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 10, 2025
Abstract
Biological
Field
Effect
Transistors
(Bio‐FETs)
are
redefining
the
standard
of
biosensing
by
enabling
label‐free,
real‐time,
and
extremely
sensitive
detection
biomolecules.
At
center
this
innovation
is
fundamental
empowering
role
advanced
materials,
such
as
graphene,
molybdenum
disulfide,
carbon
nanotubes,
silicon.
These
when
harnessed
with
downstream
biomolecular
probes
like
aptamers,
antibodies,
enzymes,
allow
Bio‐FETs
to
offer
unrivaled
sensitivity
precision.
This
review
an
exposition
how
advancements
in
materials
science
have
permitted
detect
biomarkers
low
concentrations,
from
femtomolar
attomolar
levels,
ensuring
device
stability
reliability.
Specifically,
examines
incorporation
cutting‐edge
architectures,
flexible
/
stretchable
multiplexed
designs,
expanding
frontiers
contributing
development
more
adaptable
user‐friendly
Bio‐FET
platforms.
A
key
focus
placed
on
synergy
artificial
intelligence
(AI),
Internet
Things
(IoT),
sustainable
approaches
fast‐tracking
toward
transition
research
into
practical
healthcare
applications.
The
also
explores
current
challenges
material
reproducibility,
operational
durability,
cost‐effectiveness.
It
outlines
targeted
strategies
address
these
hurdles
facilitate
scalable
manufacturing.
By
emphasizing
transformative
played
their
cementing
position
Bio‐FETs,
positions
a
cornerstone
technology
for
future
solution
precision
would
lead
era
where
herald
massive
strides
biomedical
diagnostics
subsume.
The
performance
of
electrochemical
sensors
is
influenced
by
various
factors.
To
enhance
the
effectiveness
these
sensors,
it
crucial
to
find
right
balance
among
Researchers
and
engineers
continually
explore
innovative
approaches
sensitivity,
selectivity,
reliability.
Machine
learning
(ML)
techniques
facilitate
analysis
predictive
modeling
sensor
establishing
quantitative
relationships
between
parameters
their
effects.
This
work
presents
a
case
study
on
developing
molecularly
imprinted
polymer
(MIP)-based
for
detecting
doxorubicin
(Dox),
emphasizing
use
ML-based
ensemble
models
improve
Four
ML
models,
including
Decision
Tree
(DT),
eXtreme
Gradient
Boosting
(XGBoost),
Random
Forest
(RF),
K-Nearest
Neighbors
(KNN),
are
used
evaluate
effect
each
parameter
prediction
performance,
using
SHapley
Additive
exPlanations
(SHAP)
method
determine
feature
importance.
Based
analysis,
removing
less
influential
introducing
new
significantly
improved
model's
capabilities.
By
applying
min-max
scaling
technique,
ensured
that
all
features
contribute
proportionally
model
process.
Additionally,
multiple
models─Linear
Regression
(LR),
KNN,
DT,
RF,
Adaptive
(AdaBoost),
(GB),
Support
Vector
(SVR),
XGBoost,
Bagging,
Partial
Least
Squares
(PLS),
Ridge
Regression─are
applied
data
set
in
predicting
output
current
compared.
further
novel
proposed
integrates
GB,
Bagging
regressors,
leveraging
combined
strengths
offset
individual
weaknesses.
main
benefit
this
lies
its
ability
MIP-based
stacking
regressor
model,
which
improves
methodology
broadly
applicable
development
other
with
different
transducers
sensing
elements.
Through
extensive
simulation
results,
demonstrated
superior
compared
models.
achieved
an
R-squared
(R2)
0.993,
reducing
root-mean-square
error
(RMSE)
0.436
mean
absolute
(MAE)
0.244.
These
improvements
enhanced
sensitivity
reliability
sensor,
demonstrating
substantial
gain
over
Heliyon,
Год журнала:
2024,
Номер
11(1), С. e41338 - e41338
Опубликована: Дек. 18, 2024
AI-optimized
electrochemical
aptasensors
are
transforming
diagnostic
testing
by
offering
high
sensitivity,
selectivity,
and
rapid
response
times.
Leveraging
data-driven
AI
techniques,
these
sensors
provide
a
non-invasive,
cost-effective
alternative
to
traditional
methods,
with
applications
in
detecting
molecular
biomarkers
for
neurodegenerative
diseases,
cancer,
coronavirus.
The
performance
metrics
outlined
the
comparative
table
illustrate
significant
advancements
enabled
integration.
Sensitivity
increases
from
60
75
%
ordinary
85-95
%,
while
specificity
improves
70-80
90-98
%.
This
enhanced
allows
ultra-low
detection
limits,
such
as
10
fM
carcinoembryonic
antigen
(CEA)
20
mucin-1
(MUC1)
using
Electrochemical
Impedance
Spectroscopy
(EIS),
1
pM
prostate-specific
(PSA)
Differential
Pulse
Voltammetry
(DPV).
Similarly,
Square
Wave
(SWV)
potentiometric
have
detected
alpha-fetoprotein
(AFP)
at
5
epithelial
cell
adhesion
molecule
(EpCAM)
100
fM,
respectively.
integration
also
enhances
reproducibility,
reduces
false
positives
negatives
(from
15-20
5-10
%),
significantly
decreases
times
10-15
s
2-3
s).
These
improve
data
processing
speeds
min
per
sample
2-5
min)
calibration
accuracy
(<2
margin
of
error
compared
expanding
application
scope
multi-target
biomarker
detection.
review
highlights
how
position
powerful
tools
personalized
treatment,
point-of-care
testing,
continuous
health
monitoring.
Despite
higher
cost
($500-$1,500/unit),
their
portability
promise
revolutionize
healthcare,
environmental
monitoring,
food
safety,
ultimately
improving
public
outcomes.
Micromachines,
Год журнала:
2024,
Номер
15(11), С. 1358 - 1358
Опубликована: Ноя. 8, 2024
Technologies
based
on
digital
microfluidics
(DMF)
have
made
significant
advancements
in
the
automated
manipulation
of
microscale
liquids
and
complex
multistep
processes.
Due
to
their
numerous
benefits,
such
as
automation,
speed,
cost-effectiveness,
minimal
sample
volume
requirements,
these
systems
are
particularly
well
suited
for
immunoassays.
In
this
review,
an
overview
is
provided
diverse
DMF
platforms
applications
immunological
analysis.
Initially,
droplet-driven
electrowetting
dielectric
(EWOD),
magnetic
manipulation,
surface
acoustic
wave
(SAW),
other
related
technologies
briefly
introduced.
The
preparation
then
described,
including
material
selection,
fabrication
techniques
droplet
generation.
Subsequently,
a
comprehensive
account
integration
with
various
immunoassay
offered,
encompassing
colorimetric,
direct
chemiluminescence,
enzymatic
electrosensory,
Ultimately,
potential
challenges
future
perspectives
burgeoning
field
delved
into.
Timely
monitoring
of
circulating
tumor
DNA
(ctDNA)
in
serum
is
meaningful
for
personalized
diagnosis
and
treatment
lung
cancer.
Cheap
efficient
point-of-care
testing
(POCT)
has
emerged
as
a
promising
method,
especially
low-resource
setting.
Herein,
(i)
3D
pop-up
paper-based
POCT
device
was
designed
manufactured
via
cheap
method;
it
used
saving
time
efficiently
building
biosensor;
(ii)
novel
cobalt
boride
nanosheet
(CoB
NS)
nanozyme
with
abundant
groups
dual-mode
signal
transduction
then
portable
smartphone/pressure
meter
to
readout;
(iii)
user-friendly
smartphone
app
fabricated
achieving
more
convenient
POCT.
Detailly,
the
generated
based
on
CoB
NS
peroxidase
activity
catalyze
chromogenic
agent
develop
color
catalase
decomposition
H
