Journal of Personalized Medicine,
Год журнала:
2023,
Номер
13(9), С. 1386 - 1386
Опубликована: Сен. 15, 2023
Alzheimer's
disease
(AD)
is
the
most
common
form
of
neurodegenerative
disorder.
The
prodromal
phase
AD
mild
cognitive
impairment
(MCI).
capacity
to
predict
transitional
from
MCI
represents
a
challenge
for
scientific
community.
adoption
artificial
intelligence
(AI)
useful
diagnostic,
predictive
analysis
starting
clinical
epidemiology
disorders.
We
propose
Machine
Learning
Model
(MLM)
where
algorithms
were
trained
on
set
neuropsychological,
neurophysiological,
and
data
diagnosis
decline
in
both
patients.We
built
dataset
with
neuropsychological
4848
patients,
which
2156
had
AD,
2684
MCI,
Model,
60
patients
enrolled
test
dataset.
an
ML
algorithm
using
RoboMate
software
based
training
dataset,
then
calculated
its
accuracy
dataset.The
Receiver
Operating
Characteristic
(ROC)
revealed
that
diagnostic
was
86%,
appropriate
cutoff
value
1.5;
sensitivity
72%;
specificity
reached
91%
prediction
MMSE.This
method
may
support
clinicians
provide
second
opinion
concerning
high
prognostic
power
progression
impairment.
MLM
used
this
study
big
confirmed
given
credibility
about
presence
determinant
risk
factors
also
supported
by
score.
Cells,
Год журнала:
2024,
Номер
13(10), С. 790 - 790
Опубликована: Май 7, 2024
Translational
research
in
neurological
and
psychiatric
diseases
is
a
rapidly
advancing
field
that
promises
to
redefine
our
approach
these
complex
conditions
[...]
PeerJ Computer Science,
Год журнала:
2024,
Номер
10, С. e2031 - e2031
Опубликована: Май 13, 2024
Neurodegenerative
conditions
significantly
impact
patient
quality
of
life.
Many
do
not
have
a
cure,
but
with
appropriate
and
timely
treatment
the
advance
disease
could
be
diminished.
However,
many
patients
only
seek
diagnosis
once
condition
progresses
to
point
at
which
life
is
impacted.
Effective
non-invasive
readily
accessible
methods
for
early
can
considerably
enhance
affected
by
neurodegenerative
conditions.
This
work
explores
potential
convolutional
neural
networks
(CNNs)
gain
freezing
associated
Parkinson’s
disease.
Sensor
data
collected
from
wearable
gyroscopes
located
sole
patient’s
shoe
record
walking
patterns.
These
patterns
are
further
analyzed
using
accurately
detect
abnormal
The
suggested
method
assessed
on
public
real-world
dataset
parents
as
well
individuals
control
group.
To
improve
accuracy
classification,
an
altered
variant
recent
crayfish
optimization
algorithm
introduced
compared
contemporary
metaheuristics.
Our
findings
reveal
that
modified
(MSCHO)
outperforms
other
in
accuracy,
demonstrated
low
error
rates
high
Cohen’s
Kappa,
precision,
sensitivity,
F1-measures
across
three
datasets.
results
suggest
CNNs,
combined
advanced
techniques,
early,
conditions,
offering
path
Alzheimer's
and
Parkinson's
diseases
are
among
the
most
prevalent
neurodegenerative
conditions
affecting
aging
populations
globally,
presenting
significant
challenges
in
early
diagnosis
management.
This
narrative
review
explores
pivotal
role
of
advanced
neuroimaging
techniques
detecting
managing
these
at
stages,
potentially
slowing
their
progression
through
timely
interventions.
Recent
advancements
MRI,
such
as
ultra-high-field
systems
functional
have
enhanced
sensitivity
for
subtle
structural
changes.
Additionally,
development
novel
amyloid-beta
tracers
other
emerging
modalities
like
optical
imaging
transcranial
ultrasonography
improved
diagnostic
accuracy
capability
existing
methods.
highlights
clinical
applications
technologies
diseases,
where
they
shown
performance,
enabling
earlier
intervention
better
prognostic
outcomes.
Moreover,
integration
artificial
intelligence
(AI)
longitudinal
research
is
a
promising
enhancement
to
refine
detection
strategies
further.
However,
this
also
addresses
technical,
ethical,
accessibility
field,
advocating
more
extensive
use
overcome
barriers.
Finally,
we
emphasize
need
holistic
approach
that
incorporates
both
neurological
psychiatric
perspectives,
which
crucial
optimizing
patient
care
outcomes
management
diseases.
Frontiers in Molecular Neuroscience,
Год журнала:
2024,
Номер
17
Опубликована: Май 31, 2024
Neurodegenerative
diseases
(NDs)
are
characterized
by
abnormalities
within
neurons
of
the
brain
or
spinal
cord
that
gradually
lose
function,
eventually
leading
to
cell
death.
Upon
examination
affected
tissue,
pathological
changes
reveal
a
loss
synapses,
misfolded
proteins,
and
activation
immune
cells—all
indicative
disease
progression—before
severe
clinical
symptoms
become
apparent.
Early
detection
NDs
is
crucial
for
potentially
administering
targeted
medications
may
delay
advancement.
Given
their
complex
pathophysiological
features
diverse
symptoms,
there
pressing
need
sensitive
effective
diagnostic
methods
NDs.
Biomarkers
such
as
microRNAs
(miRNAs)
have
been
identified
potential
tools
detecting
these
diseases.
We
explore
pivotal
role
miRNAs
in
context
NDs,
focusing
on
Alzheimer’s
disease,
Parkinson’s
Multiple
sclerosis,
Huntington’s
Amyotrophic
Lateral
Sclerosis.
The
review
delves
into
intricate
relationship
between
aging
highlighting
structural
functional
alterations
implications
development.
It
elucidates
how
RNA-binding
proteins
implicated
pathogenesis
underscores
importance
investigating
expression
function
aging.
Significantly,
exert
substantial
influence
post-translational
modifications
(PTMs),
impacting
not
just
nervous
system
but
wide
array
tissues
types
well.
Specific
found
target
involved
ubiquitination
de-ubiquitination
processes,
which
play
significant
regulating
protein
stability.
discuss
link
miRNA,
PTM,
Additionally,
discusses
significance
biomarkers
early
detection,
offering
insights
strategies.
Journal of NeuroEngineering and Rehabilitation,
Год журнала:
2024,
Номер
21(1)
Опубликована: Авг. 1, 2024
Abstract
Background
The
increase
in
cases
of
mild
cognitive
impairment
(MCI)
underlines
the
urgency
finding
effective
methods
to
slow
its
progression.
Given
limited
effectiveness
current
pharmacological
options
prevent
or
treat
early
stages
this
deterioration,
non-pharmacological
alternatives
are
especially
relevant.
Objective
To
assess
a
cognitive-motor
intervention
based
on
immersive
virtual
reality
(VR)
that
simulates
an
activity
daily
living
(ADL)
functions
and
impact
depression
ability
perform
such
activities
patients
with
MCI.
Methods
Thirty-four
older
adults
(men,
women)
MCI
were
randomized
experimental
group
(
n
=
17;
75.41
±
5.76)
control
77.35
6.75)
group.
Both
groups
received
motor
training,
through
aerobic,
balance
resistance
Subsequently,
training
VR,
while
traditional
training.
Cognitive
functions,
depression,
(ADLs)
assessed
using
Spanish
versions
Montreal
Assessment
(MoCA-S),
Short
Geriatric
Depression
Scale
(SGDS-S),
Instrumental
Activities
Daily
Living
(IADL-S)
before
after
6-week
(a
total
twelve
40-minutes
sessions).
Results
Between
comparison
did
not
reveal
significant
differences
either
function
geriatric
depression.
intragroup
effect
was
both
p
<
0.001),
large
sizes.
There
no
statistically
improvement
any
when
evaluating
their
performance
ADLs
(control,
0.28;
experimental,
0.46)
as
expected.
completion
rate
higher
(82.35%)
compared
(70.59%).
Likewise,
participants
reached
level
difficulty
application
needed
less
time
complete
task
at
each
level.
Conclusions
dual
intervention,
prior
Immersive
VR
shown
be
beneficial
strategy
improve
reduce
Similarly,
benefited
from
improvements.
Trial
registration
ClinicalTrials.gov
NCT06313931;
https://clinicaltrials.gov/study/NCT06313931
.
AIMS neuroscience,
Год журнала:
2023,
Номер
10(2), С. 154 - 171
Опубликована: Янв. 1, 2023
<abstract>
<p>Mild
cognitive
impairment
(MCI)
is
often
considered
a
precursor
to
Alzheimer's
disease
(AD)
and
early
diagnosis
may
help
improve
treatment
effectiveness.
To
identify
accurate
MCI
biomarkers,
researchers
have
utilized
various
neuroscience
techniques,
with
electroencephalography
(EEG)
being
popular
choice
due
its
low
cost
better
temporal
resolution.
In
this
scoping
review,
we
analyzed
2310
peer-reviewed
articles
on
EEG
between
2012
2022
track
the
research
progress
in
field.
Our
data
analysis
involved
co-occurrence
using
VOSviewer
Patterns,
Advances,
Gaps,
Evidence
of
Practice,
Research
Recommendations
(PAGER)
framework.
We
found
that
event-related
potentials
(ERP),
EEG,
epilepsy,
quantitative
(QEEG),
EEG-based
machine
learning
were
primary
themes.
The
study
showed
ERP/EEG,
QEEG,
frameworks
provide
high-accuracy
detection
seizure
MCI.
These
findings
main
themes
suggest
promising
avenues
for
future
field.</p>
</abstract>
