Journal of Alzheimer s Disease,
Год журнала:
2023,
Номер
97(1), С. 89 - 100
Опубликована: Ноя. 24, 2023
The
accumulation
of
amyloid-β
(Aβ)
plaques
in
the
brain
is
considered
a
hallmark
Alzheimer’s
disease
(AD).
Mathematical
modeling,
capable
predicting
motion
and
Aβ,
has
obtained
increasing
interest
as
potential
alternative
to
aid
diagnosis
AD
predict
prognosis.
These
mathematical
models
have
provided
insights
into
pathogenesis
progression
that
are
difficult
obtain
through
experimental
studies
alone.
modeling
can
also
simulate
effects
therapeutics
on
Aβ
levels,
thereby
holding
for
drug
efficacy
simulation
optimization
personalized
treatment
approaches.
In
this
review,
we
provide
an
overview
been
used
levels
(oligomers,
protofibrils,
and/or
plaques).
We
classify
five
categories:
general
ordinary
differential
equation
models,
partial
network
linear
optimal
modified
(i.e.,
Smoluchowski
models).
assumptions,
advantages
limitations
these
discussed.
Given
popularity
using
our
review
summarizes
history
major
advancements
(e.g.,
their
application
onset
combined
use
with
This
intended
bring
attention
more
scientists
clinical
researchers
working
promote
cross-disciplinary
research.
Annual Review of Biomedical Engineering,
Год журнала:
2024,
Номер
26(1), С. 529 - 560
Опубликована: Апрель 10, 2024
Despite
the
remarkable
advances
in
cancer
diagnosis,
treatment,
and
management
over
past
decade,
malignant
tumors
remain
a
major
public
health
problem.
Further
progress
combating
may
be
enabled
by
personalizing
delivery
of
therapies
according
to
predicted
response
for
each
individual
patient.
The
design
personalized
requires
integration
patient-specific
information
with
an
appropriate
mathematical
model
tumor
response.
A
fundamental
barrier
realizing
this
paradigm
is
current
lack
rigorous
yet
practical
theory
initiation,
development,
invasion,
therapy.
We
begin
review
overview
different
approaches
modeling
growth
including
mechanistic
as
well
data-driven
models
based
on
big
data
artificial
intelligence.
then
present
illustrative
examples
manifesting
their
utility
discuss
limitations
stand-alone
models.
potential
not
only
predicting
but
also
optimizing
therapy
basis.
describe
efforts
future
possibilities
integrate
conclude
proposing
five
challenges
that
must
addressed
fully
realize
care
patients
driven
computational
Frontiers in Artificial Intelligence,
Год журнала:
2023,
Номер
6
Опубликована: Окт. 11, 2023
We
develop
a
methodology
to
create
data-driven
predictive
digital
twins
for
optimal
risk-aware
clinical
decision-making.
illustrate
the
as
an
enabler
anticipatory
personalized
treatment
that
accounts
uncertainties
in
underlying
tumor
biology
high-grade
gliomas,
where
heterogeneity
response
standard-of-care
(SOC)
radiotherapy
contributes
sub-optimal
patient
outcomes.
The
twin
is
initialized
through
prior
distributions
derived
from
population-level
data
literature
mechanistic
model's
parameters.
Then
using
Bayesian
model
calibration
assimilating
patient-specific
magnetic
resonance
imaging
data.
calibrated
used
propose
regimens
by
solving
multi-objective
risk-based
optimization
under
uncertainty
problem.
solution
leads
suite
of
exhibiting
varying
levels
trade-off
between
two
competing
objectives:
(i)
maximizing
control
(characterized
minimizing
risk
volume
growth)
and
(ii)
toxicity
radiotherapy.
proposed
framework
illustrated
generating
Frontiers in Immunology,
Год журнала:
2023,
Номер
14
Опубликована: Май 9, 2023
The
tumor
microenvironment
(TME),
which
includes
both
cellular
and
non-cellular
elements,
is
now
recognized
as
one
of
the
major
regulators
development
primary
tumors,
metastasis
occurs
to
specific
organs,
response
therapy.
Development
immunotherapy
targeted
therapies
have
increased
knowledge
cancer-related
inflammation
Since
blood-brain
barrier
(BBB)
blood-cerebrospinal
fluid
(BCB)
limit
immune
cells
from
entering
periphery,
it
has
long
been
considered
an
immunological
refuge.
Thus,
that
make
their
way
"to
brain
were
believed
be
protected
body's
normal
mechanisms
monitoring
eliminating
them.
In
this
process,
at
different
stages
interact
depend
on
each
other
form
basis
evolution
metastases.
This
paper
focuses
pathogenesis,
microenvironmental
changes,
new
treatment
methods
types
Through
systematic
review
summary
macro
micro,
occurrence
rules
key
driving
factors
disease
are
revealed,
clinical
precision
medicine
metastases
comprehensively
promoted.
Recent
research
shed
light
potential
TME-targeted
treatments
for
treating
Brain
metastases,
we'll
use
discuss
advantages
disadvantages
these
approaches.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences,
Год журнала:
2025,
Номер
383(2293)
Опубликована: Апрель 2, 2025
We
have
developed
a
family
of
biology-based
mathematical
models
high-grade
glioma
(HGG),
capturing
the
key
features
tumour
growth
and
response
to
chemoradiation.
now
seek
quantify
accuracy
parameter
estimation
determine,
when
given
virtual
patient
cohort,
which
model
was
used
generate
tumours.
In
this
way,
we
systematically
test
both
identifiability.
Virtual
patients
are
generated
from
unique
parameters
whose
dynamics
determined
by
family.
then
assessed
ability
recover
select
tumour.
evaluated
predictions
using
selected
at
four
weeks
post-chemoradiation.
observed
median
errors
0.04%
72.96%.
Our
selection
framework
that
data
in
82%
cases.
Finally,
predicted
tumours
resulting
low
error
voxel-level
(concordance
correlation
coefficient
(CCC)
ranged
0.66
0.99)
global
level
(percentage
total
cellularity
−12.35%
0.07%).
These
results
demonstrate
reliability
our
identify
most
appropriate
under
noisy
conditions
expected
clinical
setting.
This
article
is
part
theme
issue
'Uncertainty
quantification
for
healthcare
biological
systems
(Part
2)'.
Procedia Computer Science,
Год журнала:
2024,
Номер
235, С. 456 - 467
Опубликована: Янв. 1, 2024
Recent
strides
in
artificial
intelligence
(AI)
and
deep
learning
techniques
have
propelled
the
development
of
an
AI-powered
brain
tumour
detection
model.
This
study
blends
multilevel
thresholding,
neural
network
optimisation,
image
preprocessing
to
craft
a
robust
AI
model
capable
accurately
categorising
diverse
types
normal
cases.
Through
rigorous
testing
with
comprehensive
dataset
1747
images,
achieves
accuracy
92%.
Its
integration
into
user-friendly
smartphone
app,
MediScan,
enhances
accessibility
practicality.
The
app
provides
heatmap
visualisations
generates
diagnostic
reports,
supporting
medical
professionals
making
swift
decisions.
prioritises
interpretability
enhancement
has
potential
cultivate
collaboration
between
experts
practitioners,
thus
advancing
field
diagnosis.
While
promising,
demands
computational
resources
datasets.
research
also
highlights
AI's
transform
healthcare
diagnostics,
ensuring
precise
efficient
identification.
Journal of Oleo Science,
Год журнала:
2025,
Номер
74(3), С. 261 - 274
Опубликована: Янв. 1, 2025
Among
primary
brain
tumors,
glioma
has
one
of
the
highest
fatality
rates.
Routine
chemotherapy
often
faces
off-target
drug
loss
and
sub-optimal
availability
at
tissue.
The
present
study
aims
development
transferrin-conjugated
gemcitabine
loaded
poly
(lactic
co
glycolic
acid)
nanoparticles
(Tf-GB-PLGA-NPs)
targeted
strategy
for
cancer
cell.
GB-PLGA-NPs
were
prepared
using
solvent
evaporation
nanoprecipitation
method
then
conjugated
with
Tf.
formulation
was
characterized
physicochemical
parameters,
in-vitro
release,
cytotoxicity,
apoptosis
(U87MG
cell
line),
in-vivo
pharmacokinetic
study.
Tf-GB-PLGA-NPs
showed
143±6.23
nm
particle
size,
0.213
PDI,
-25
mV
zeta
potential,
77.53±1.43%
entrapment
efficiency,
respectively.
exhibited
spherical
morphology
sustained
release
GB
(76.54±4.08%)
over
24
h.
significant
(p
<
0.05)
inhibition
against
line
(U87MG)
than
pure
GB.
higher
U87MG
(61.25%)
(31.61%).
a
significantly
concentration
in
GB-PLGA-NPs.
11.16-fold
AUC0-t
(bioavailability)
solution
2.23-fold
bioavailability
finding
concludes
that
are
an
alternative
potent
carrier
to
delivery
treating
cancer.
Radiation Oncology,
Год журнала:
2025,
Номер
20(1)
Опубликована: Апрель 4, 2025
Mathematical
modeling
has
long
been
a
cornerstone
of
radiotherapy
for
cancer,
guiding
treatment
prescription,
planning,
and
delivery
through
versatile
applications.
As
we
enter
the
era
medical
big
data,
where
integration
molecular,
imaging,
clinical
data
at
both
tumor
patient
levels
could
promise
more
precise
personalized
cancer
treatment,
role
mathematical
become
even
critical.
This
comprehensive
narrative
review
aims
to
summarize
main
applications
in
radiotherapy,
bridging
gap
between
classical
models
latest
advancements.
The
covers
wide
range
applications,
including
radiobiology,
workflows,
stereotactic
radiosurgery/stereotactic
body
(SRS/SBRT),
spatially
fractionated
(SFRT),
FLASH
(FLASH-RT),
immune-radiotherapy,
emerging
concept
digital
twins.
Each
these
areas
is
explored
depth,
with
particular
focus
on
how
newer
trends
innovations
are
shaping
future
radiation
treatment.
By
examining
diverse
this
provides
overview
current
state
radiotherapy.
It
also
highlights
growing
importance
context
medicine
multi-scale,
multi-modal
integration,
offering
insights
into
they
can
be
leveraged
enhance
precision
outcomes.
continues
evolve,
gained
from
will
help
guide
research
practice,
ensuring
that
propel
