Entomopathogenic
nematodes
(EPNs)
are
effective
biocontrol
agents,
reducing
pesticide
impact
on
health
and
the
environment.
Understanding
their
physiology
ethology
is
crucial
for
optimizing
application.
This
study
offers
innovative
insights
about
Steinernema
carpocapsae
EPN
behavior,
contributing
to
interdisciplinary
field
of
engineering,
biology,
entomology.
The
proposed
hybrid
approach
combines
microfluidics,
deep
learning,
optical
flow.
A
Convolutional
Neural
Network
(CNN)
model
discerned
EPNs
in
presence
stimuli
within
a
specially
designed
microfluidic
arena,
highlighting
motor
behavior
differences.
At
video
level,
CNN
accurately
discriminated
context
characterized
by
host-borne
cues,
achieving
an
overall
accuracy
0.938,
precision
1,
f1-score
0.933.
Integrating
flow
analysis
unveiled
significant
difference
activity,
adding
novel
information
dynamic
responses.
showed
increased
activity
stimulus
presence.
comprehensive
advances
our
capability
detect
comprehend
responses
host
more
precise
targeted
strategies.
Biosensors and Bioelectronics,
Journal Year:
2024,
Volume and Issue:
263, P. 116632 - 116632
Published: Aug. 3, 2024
Microfluidic
devices
are
increasingly
widespread
in
the
literature,
being
applied
to
numerous
exciting
applications,
from
chemical
research
Point-of-Care
devices,
passing
through
drug
development
and
clinical
scenarios.
Setting
up
these
microenvironments,
however,
introduces
necessity
of
locally
controlling
variables
involved
phenomena
under
investigation.
For
this
reason,
literature
has
deeply
explored
possibility
introducing
sensing
elements
investigate
physical
quantities
biochemical
concentration
inside
microfluidic
devices.
Biosensors,
particularly,
well
known
for
their
high
accuracy,
selectivity,
responsiveness.
However,
signals
could
be
challenging
interpret
must
carefully
analysed
carry
out
correct
information.
In
addition,
proper
data
analysis
been
demonstrated
even
increase
biosensors'
mentioned
qualities.
To
regard,
machine
learning
algorithms
undoubtedly
among
most
suitable
approaches
undertake
job,
automatically
highlighting
biosensor
signals'
characteristics
at
best.
Interestingly,
it
was
also
benefit
themselves,
a
new
paradigm
that
is
starting
name
"intelligent
microfluidics",
ideally
closing
benefic
interaction
disciplines.
This
review
aims
demonstrate
advantages
triad
microfluidics-biosensors-machine
learning,
which
still
little
used
but
great
perspective.
After
briefly
describing
single
entities,
different
sections
will
benefits
dual
interactions,
applications
where
reviewed
employed.
Lab on a Chip,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
The
proposed
Bayesian
optimization-based
approach
enhances
micromixer
performance
by
optimizing
geometric
parameters,
significantly
reducing
required
number
of
simulations,
and
accelerating
the
design
process
compared
to
conventional
methods.
Biosensors,
Journal Year:
2024,
Volume and Issue:
14(12), P. 613 - 613
Published: Dec. 13, 2024
Microfluidic
devices
have
revolutionized
biosensing
by
enabling
precise
manipulation
of
minute
fluid
volumes
across
diverse
applications.
This
review
investigates
the
incorporation
machine
learning
(ML)
into
design,
fabrication,
and
application
microfluidic
biosensors,
emphasizing
how
ML
algorithms
enhance
performance
improving
design
accuracy,
operational
efficiency,
management
complex
diagnostic
datasets.
Integrating
microfluidics
with
has
fostered
intelligent
systems
capable
automating
experimental
workflows,
real-time
data
analysis,
supporting
informed
decision-making.
Recent
advances
in
health
diagnostics,
environmental
monitoring,
synthetic
biology
driven
are
critically
examined.
highlights
transformative
potential
ML-enhanced
systems,
offering
insights
future
trajectory
this
rapidly
evolving
field.
Catalysts,
Journal Year:
2025,
Volume and Issue:
15(1), P. 37 - 37
Published: Jan. 3, 2025
Nicotinamide
mononucleotide
(NMN)
has
emerged
as
a
promising
non-natural
cofactor
with
significant
potential
to
transform
biocatalysis,
synthetic
biology,
and
therapeutic
applications.
By
modulating
NAD⁺
metabolism,
NMN
offers
unique
advantages
in
enzymatic
reactions,
metabolic
engineering,
regenerative
medicine.
This
review
provides
comprehensive
analysis
of
NMN’s
biochemical
properties,
mechanisms
action,
diverse
Emphasis
is
placed
on
its
role
addressing
challenges
multi-enzyme
cascades,
biofuel
production,
the
synthesis
high-value
chemicals.
The
paper
also
highlights
critical
research
gaps,
including
need
for
scalable
methods,
improved
integration
into
systems,
toxicity
studies
use.
Emerging
technologies
such
AI-driven
enzyme
design
CRISPR-based
genome
engineering
are
discussed
transformative
tools
optimizing
NMN-dependent
pathways.
Furthermore,
synergistic
biology
innovations,
cell-free
systems
dynamic
regulatory
networks,
explored,
paving
way
precise
modular
biotechnological
solutions.
Looking
forward,
versatility
positions
it
pivotal
tool
advancing
sustainable
bioprocessing
precision
Addressing
current
limitations
through
interdisciplinary
approaches
will
enable
redefine
boundaries
innovation.
serves
roadmap
leveraging
across
scientific
industrial
domains.
Lab on a Chip,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 1, 2024
AI
is
revolutionizing
medicine
by
enhancing
diagnostics
and
patient
care.
Our
study
showed
ML
DL
models
excel
in
microchip
testing,
underscoring
AI's
potential
to
improve
precision
POC
diagnostics.
Cell Proliferation,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 26, 2025
ABSTRACT
Osteoarthritis
(OA)
is
the
most
prevalent
degenerative
joint
disease
worldwide,
imposing
a
substantial
global
burden.
However,
its
pathogenesis
remains
incompletely
understood,
and
effective
treatment
strategies
are
still
lacking.
Organoid
technology,
in
which
stem
cells
or
progenitor
self‐organise
into
miniature
tissue
structures
under
three‐dimensional
(3D)
culture
conditions,
provides
promising
vitro
platform
for
simulating
pathological
microenvironment
of
OA.
This
approach
can
be
employed
to
investigate
mechanisms,
carry
out
high‐throughput
drug
screening
facilitate
personalised
therapies.
review
summarises
structure,
OA
manifestations,
thereby
establishing
context
application
organoid
technology.
It
then
examines
components
arthrosis
system,
specifically
addressing
cartilage,
subchondral
bone,
synovium,
skeletal
muscle
ligament
organoids.
Furthermore,
it
details
various
constructing
organoids,
including
considerations
cell
selection,
classification
fabrication
techniques.
Notably,
this
introduces
concept
intelligent
manufacturing
organoids
by
incorporating
emerging
engineering
technologies
such
as
artificial
intelligence
(AI)
process,
forming
an
innovative
software
hardware
cluster.
Lastly,
discusses
challenges
currently
facing
highlights
future
directions
rapidly
evolving
field.
By
offering
comprehensive
overview
state‐of‐the‐art
methodologies
challenges,
anticipates
that
intelligent,
automated
will
expedite
fundamental
research,
discovery
translational
applications
orthopaedic
