ACS Applied Materials & Interfaces,
Год журнала:
2024,
Номер
16(30), С. 38832 - 38851
Опубликована: Июль 17, 2024
Phenotypic
drug
discovery
(PDD),
which
involves
harnessing
biological
systems
directly
to
uncover
effective
drugs,
has
undergone
a
resurgence
in
recent
years.
The
rapid
advancement
of
artificial
intelligence
(AI)
over
the
past
few
years
presents
numerous
opportunities
for
augmenting
phenotypic
screening
on
microfluidic
platforms,
leveraging
its
predictive
capabilities,
data
analysis,
efficient
processing,
etc.
Microfluidics
coupled
with
AI
is
poised
revolutionize
landscape
discovery.
By
integrating
advanced
platforms
algorithms,
researchers
can
rapidly
screen
large
libraries
compounds,
identify
novel
candidates,
and
elucidate
complex
pathways
unprecedented
speed
efficiency.
This
review
provides
an
overview
advances
challenges
AI-based
microfluidics
their
applications
We
discuss
synergistic
combination
high-throughput
AI-driven
analysis
phenotype
characterization,
drug-target
interactions,
modeling.
In
addition,
we
highlight
potential
AI-powered
achieve
automated
system.
Overall,
represents
promising
approach
shaping
future
by
enabling
rapid,
cost-effective,
accurate
identification
therapeutically
relevant
compounds.
Langmuir,
Год журнала:
2022,
Номер
38(20), С. 6233 - 6248
Опубликована: Май 13, 2022
Over
the
past
decade,
droplet
microfluidics
has
attracted
growing
interest
in
biology,
medicine,
and
engineering.
In
this
feature
article,
we
review
advances
microfluidics,
primarily
focusing
on
research
conducted
by
our
group.
Starting
from
introduction
to
mechanisms
of
microfluidic
formation
strategies
for
cell
encapsulation
droplets,
then
focus
transformation
into
microgels.
Furthermore,
three
biomedical
applications
that
is,
3D
culture,
single-cell
analysis,
vitro
organ
disease
modeling.
We
conclude
with
perspective
future
directions
development
applications.
ACS Biomaterials Science & Engineering,
Год журнала:
2022,
Номер
8(3), С. 1215 - 1225
Опубликована: Фев. 15, 2022
Prolyl
hydroxylases
(PHD)
inhibitors
have
been
observed
to
improve
drug
distribution
in
mice
tumors
via
blood
vessel
normalization,
increasing
the
effectiveness
of
chemotherapy.
These
effects
are
yet
be
demonstrated
human
cell
models.
Tumor
spheroids
three-dimensional
clusters
that
great
potential
evaluation
for
personalized
medicine.
Here,
we
used
a
perfusable
vascularized
tumor
spheroid-on-a-chip
simulate
microenvironment
vivo
and
PHD
inhibitor
dimethylallyl
glycine
prevents
degradation
normal
vessels
while
enhancing
efficacy
anticancer
drugs
paclitaxel
cisplatin
esophageal
carcinoma
(Eca-109)
spheroids.
Our
results
point
this
model
evaluate
under
more
physiologically
relevant
conditions.
Lab on a Chip,
Год журнала:
2022,
Номер
22(9), С. 1714 - 1722
Опубликована: Янв. 1, 2022
A
successful
outcome
of
the
coupling
between
microfluidics
and
AI:
neural
networks
tackle
signal
processing
challenges
single-cell
microfluidic
impedance
cytometry.
Micromachines,
Год журнала:
2023,
Номер
14(5), С. 972 - 972
Опубликована: Апрель 29, 2023
Microfluidics
attracts
much
attention
due
to
its
multiple
advantages
such
as
high
throughput,
rapid
analysis,
low
sample
volume,
and
sensitivity.
has
profoundly
influenced
many
fields
including
chemistry,
biology,
medicine,
information
technology,
other
disciplines.
However,
some
stumbling
stones
(miniaturization,
integration,
intelligence)
strain
the
development
of
industrialization
commercialization
microchips.
The
miniaturization
microfluidics
means
fewer
samples
reagents,
shorter
times
results,
less
footprint
space
consumption,
enabling
a
throughput
parallelism
analysis.
Additionally,
micro-size
channels
tend
produce
laminar
flow,
which
probably
permits
creative
applications
that
are
not
accessible
traditional
fluid-processing
platforms.
reasonable
integration
biomedical/physical
biosensors,
semiconductor
microelectronics,
communications,
cutting-edge
technologies
should
greatly
expand
current
microfluidic
devices
help
develop
next
generation
lab-on-a-chip
(LOC).
At
same
time,
evolution
artificial
intelligence
also
gives
another
strong
impetus
microfluidics.
Biomedical
based
on
normally
bring
large
amount
complex
data,
so
it
is
big
challenge
for
researchers
technicians
analyze
those
huge
complicated
data
accurately
quickly.
To
address
this
problem,
machine
learning
viewed
an
indispensable
powerful
tool
in
processing
collected
from
micro-devices.
In
review,
we
mainly
focus
discussing
miniaturization,
portability,
technology.
Fluids,
Год журнала:
2023,
Номер
8(7), С. 212 - 212
Опубликована: Июль 19, 2023
The
significant
growth
of
artificial
intelligence
(AI)
methods
in
machine
learning
(ML)
and
deep
(DL)
has
opened
opportunities
for
fluid
dynamics
its
applications
science,
engineering
medicine.
Developing
AI
encompass
different
challenges
than
with
massive
data,
such
as
the
Internet
Things.
For
many
scientific,
biomedical
problems,
data
are
not
massive,
which
poses
limitations
algorithmic
challenges.
This
paper
reviews
ML
DL
research
dynamics,
presents
discusses
potential
future
directions.
International Journal of Pharmaceutics,
Год журнала:
2023,
Номер
636, С. 122818 - 122818
Опубликована: Март 11, 2023
A
new
technological
passage
has
emerged
in
the
pharmaceutical
field,
concerning
management,
application,
and
transfer
of
knowledge
from
humans
to
machines,
as
well
implementation
advanced
manufacturing
product
optimisation
processes.
Machine
Learning
(ML)
methods
have
been
introduced
Additive
Manufacturing
(AM)
Microfluidics
(MFs)
predict
generate
learning
patterns
for
precise
fabrication
tailor-made
treatments.
Moreover,
regarding
diversity
complexity
personalised
medicine,
ML
part
quality
by
design
strategy,
targeting
towards
development
safe
effective
drug
delivery
systems.
The
utilisation
different
novel
techniques
along
with
Internet
Things
sensors
AM
MFs,
shown
promising
aspects
well-defined
automated
procedures
production
sustainable
quality-based
therapeutic
Thus,
data
utilisation,
prospects
on
a
flexible
broader
“on
demand”
In
this
study,
thorough
overview
achieved,
scientific
achievements
past
decade,
which
aims
trigger
research
interest
incorporating
types
essential
enhancement
standards
customised
medicinal
applications,
reduction
variability
potency,
throughout
process.
Talanta Open,
Год журнала:
2023,
Номер
8, С. 100267 - 100267
Опубликована: Окт. 30, 2023
Recent
advances
in
noninvasive
portable
and
wearable
biosensors
have
attracted
significant
attention
due
to
their
capability
offer
continual
physiological
information
for
continuous
healthcare
monitoring
through
the
collection
of
biological
signals.
To
make
collected
data
understandable
improve
efficacy
these
biosensors,
scientists
integrated
machine
learning
(ML)
with
analyze
large
sensing
various
ML
algorithms.
In
this
article,
we
highlighted
recent
developments
ML-enabled
biosensors.
Initially,
introduced
discussed
basic
features
algorithms
used
processing
build
an
intelligent
biosensor
system
clinical
decisions.
Next,
principles
application
different
models
diverse
applications,
impact
on
performance
are
discussed.
The
last
section
highlights
challenges
(such
as
privacy,
consistency,
stability,
accuracy,
scalable
production,
adaptive
capacity),
future
prospects,
necessary
steps
required
address
issues,
spotlighting
revolutionizing
industry
development
next-generation
efficient
