Utilization of Artificial Intelligence Coupled with a High-Throughput, High-Content Platform in the Exploration of Neurodevelopmental Toxicity of Individual and Combined PFAS
Journal of Xenobiotics,
Год журнала:
2025,
Номер
15(1), С. 24 - 24
Опубликована: Фев. 2, 2025
Per-
and
polyfluoroalkyl
substances
(PFAS)
are
synthetic
chemicals
used
in
various
products,
such
as
firefighting
foams
non-stick
cookware,
due
to
their
resistance
heat
degradation.
However,
these
same
properties
make
them
persistent
the
environment
human
body,
raising
public
health
concerns.
This
study
selected
eleven
PFAS
commonly
found
drinking
water
exposed
Caenorhabditis
elegans
concentrations
ranging
from
0.1
200
µM
assess
neurodevelopmental
toxicity
using
a
high-throughput,
high-content
screening
(HTS)
platform
coupled
with
artificial
intelligence
for
image
analysis.
Our
findings
showed
that
6:2
FTS,
HFPO-DA,
PFBA,
PFBS,
PFHxA,
PFOS
inhibited
dopaminergic
neuron
activity,
fluorescence
intensity
reductions
observed
across
100
µM.
PFBS
also
disrupted
synaptic
transmission,
causing
reduced
motility
increased
paralysis
aldicarb-induced
assays,
most
pronounced
effects
at
higher
concentrations.
These
impairments
both
activity
function
led
behavioral
deficits.
Notably,
was
one
of
toxic
PFAS,
affecting
multiple
endpoints.
results
emphasize
developmental
risks
exposure,
highlighting
impact
individual
compounds
mixtures
on
neurodevelopment.
knowledge
is
essential
assessing
PFAS-related
informing
mitigation
strategies.
Язык: Английский
NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis
Metrics,
Год журнала:
2025,
Номер
2(1), С. 4 - 4
Опубликована: Март 10, 2025
This
study
aims
to
provide
a
comprehensive
knowledge
mapping
and
extensive
analysis
of
NeuroIS
research,
elucidating
global
trends
directions
within
this
field
from
January
2007
2024.
A
visual
256
research
articles
sourced
the
Scopus
database
is
conducted.
The
mapping,
utilizing
CiteSpace
(CiteSpace
3.6
R1)
VOSviewer
(VOSviewer
1.6.19),
illustrates
current
landscape,
encompassing
collaboration
networks,
co-citation
references
exhibiting
citation
bursts,
keyword
analysis.
findings
highlight
United
States
Germany
as
leading
nations
in
exploration
NeuroIS,
with
Karlsruher
Institut
für
Technologie
identified
prominent
institution
domain.
René
Riedl,
Pierre-Majorique
Léger,
Marc
T.
P.
Adam,
Christof
Weinhardt
emerge
most
prolific
authors
field.
Noteworthy
themes
that
have
garnered
attention
recent
years
include
customer
experience,
information
systems,
processing.
Document
reveals
by
Dimoka
et
al.
2012
cited
work,
providing
overview
research.
Analysis
document
network
identifies
electroencephalography
(EEG)
context
technostress,
social
impact
security
alerts,
user
experience
human–computer
interaction
key
areas
focus.
Riedl
recognized
researcher,
while
MIS
Quarterly
distinguished
journal
Twelve
papers
exhibit
high
counts,
significant
activity
noted
2021
2022.
timeline
delineates
evolution
topics
such
neuroscience,
fMRI,
cognitive
media,
trust,
eye
tracking,
interaction.
pioneers
examination
status
through
bibliometric
latest
available
data.
It
advocates
for
enhanced
collaborations
among
scholars
institutions
improve
systems
management
foster
development
NeuroIS.
underscores
importance
ongoing
cooperation
deepen
our
understanding
how
neuroscience
can
inform
design
management,
thereby
enhancing
human–technology
By
identifying
trends,
influential
authors,
themes,
lays
groundwork
further
innovation
interdisciplinary
As
technology
continues
advance
reliance
on
intensifies,
insights
derived
valuable
perspectives
experiences,
optimizing
processing,
applying
neuroscientific
principles
develop
more
effective
IT
artifacts.
Through
sustained
sharing,
community
drive
progress
shape
future
an
increasingly
dynamic
digital
landscape.
Язык: Английский
Integration of AI With ML for Neuropsychological Applications
Advances in psychology, mental health, and behavioral studies (APMHBS) book series,
Год журнала:
2025,
Номер
unknown, С. 93 - 106
Опубликована: Янв. 3, 2025
This
chapter
examines
how
Artificial
Intelligence
(AI)
and
Machine
Learning
(ML)
are
being
used
in
neuropsychology,
focusing
on
they
can
significantly
improve
the
study
treatment
of
cognitive
issues
like
Mild
Cognitive
Impairment
(MCI)
Alzheimer's
Disease
(AD).
Traditional
methods
neuropsychology
often
depend
subjective
evaluations,
which
reduce
accuracy
diagnoses
delay
necessary
treatments.
AI
ML
use
large
amounts
data
to
find
early
signs
problems
provide
better
predictive
analysis,
helping
with
detection
more
accurate
treatment.
From
a
research
standpoint,
offers
new
tools
examine
complex
from
brain
scans,
genetic
information,
behaviour
tests.
learning
identify
patterns
that
suggest
diseases
might
progress,
could
lead
important
discoveries
finding
markers
for
creating
treatments
tailored
individual
patients.
The
indicates
should
focus
making
systems
fair
easy
understand
using
them
many
medical
situations.
Язык: Английский
A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry
Computers in Biology and Medicine,
Год журнала:
2025,
Номер
189, С. 109984 - 109984
Опубликована: Март 14, 2025
Язык: Английский
Integration of AI-Based Nano Synergy in Bayesian Uncertainty Quantification for Advanced Engineering Design
Nanotechnology Perceptions,
Год журнала:
2024,
Номер
unknown, С. 77 - 89
Опубликована: Дек. 1, 2024
The
advancement
in
artificial
intelligence
and
nanotechnology
has
provided
new
solutions
for
tackling
problems
enhanced
engineering
design.
This
research
focuses
on
both
AI
assisted
observational
methodologies
Bayesian
uncertainty
quantification
(BUQ)
improving
the
predictive
models,
material
properties,
design
procedures.
Four
complex
techniques
of
estimating
managing
are
following:
Neural
Networks
(BNN),
Gaussian
Processes
(GP),
Monte
Carlo
Dropout
(MCD),
Ensemble
Learning
(EL).
Numerical
studies
revealed
that
forecast
accuracy
proposed
framework
is
94.6%
with
BNN
93.1%
GP,
which
makes
excellent
improvements
over
prior
arts
up
to
15%
quantification.
Besides,
computational
resources
less
by
20%
EL
compared
standalone
approaches,
while
incorporation
nanoscale
information
increase
AT
RT
17%.
To
demonstrate
AI-driven
BUQ
addresses
limitations
existing
a
comparative
discussion
provided.
results
reinforce
its
viability
providing
sustainable
efficient
under
conditions
risk.
work
may
be
used
as
platform
subsequent
synergies
between
AI,
nanotechnology,
advanced
materials
systems
drive
progress
well
Язык: Английский