Research on the prediction model of mastitis in dairy cows based on time series characteristics
Frontiers in Veterinary Science,
Год журнала:
2025,
Номер
12
Опубликована: Апрель 24, 2025
Mastitis
in
dairy
cows
is
a
significant
challenge
faced
by
the
global
industry,
significantly
affecting
quality
and
output
of
milk
from
enterprises
causing
them
to
suffer
severe
economic
losses.
With
increasing
public
concern
over
food
safety
rational
use
antibiotics,
how
identify
at
risk
disease
early
has
become
key
issue
that
needs
be
urgently
addressed.
Especially
subclinical
mastitis,
due
lack
obvious
external
symptoms,
makes
detection
more
difficult,
so
warning
it
particularly
important.
In
this
study,
time
series
prediction
method,
combined
with
machine
learning
techniques,
was
used
predict
mastitis
cows.
The
study
data
were
obtained
production
records
4000
large
farm
Hexi
region
Gansu.
By
constructing
time-series
features,
indicators
such
as
yield,
fat
rate
protein
each
cow
two
consecutive
months,
April
May,
utilized
its
health
status
June.
To
fully
exploit
value
we
designed
multidimensional
feature
set
included
raw
indicator
values,
monthly
change
rates,
statistical
features.
After
preprocessing
sample
balancing,
2821
selected
for
model
training.
Finally,
applicability
assessed
comparing
analyzing
performance
six
models,
namely
eXtreme
Gradient
Boosting(XGBoost),
Boosting
Decision
Tree
(GBDT),
Support
Vector
Machine
(SVM),
K
Nearest
Neighbors
(KNN),
Logistic
Regression,
Long
Short-Term
Memory
Network
(LSTM).
XGBoost
demonstrated
optimal
performance,
achieving
an
area
under
ROC
curve
(AUC)
0.75
accuracy
71.36%.
Feature
importance
analysis
revealed
three
temporal
influencing
outcomes:
May
yield
(22.29%),
standard
deviation
percentage
(20.27%),
(19.87%).
SHapley
Additive
exPlanations
(SHAP)
further
validated
predictive
these
providing
managers
clearly
defined
monitoring
priorities.
demonstrates
strong
potential
accurate
tool
This
presents
effective
early-warning
approach
through
modeling
offers
practical
prevention
management.
Язык: Английский
Colistin-Conjugated Selenium Nanoparticles: A Dual-Action Strategy Against Drug-Resistant Infections and Cancer
Pharmaceutics,
Год журнала:
2025,
Номер
17(5), С. 556 - 556
Опубликована: Апрель 24, 2025
Background/Objective:
Antimicrobial
resistance
(AMR)
and
therapy-resistant
cancer
cells
represent
major
clinical
challenges,
necessitating
the
development
of
novel
therapeutic
strategies.
This
study
explores
use
selenium
nanoparticles
(SeNPs)
colistin-conjugated
(Col-SeNPs)
as
a
dual-function
nanotherapeutic
against
multidrug-resistant
Pseudomonas
aeruginosa,
antifungal-drug-resistant
Candida
spp.,
human
breast
carcinoma
(MCF-7)
cells.
Methods:
SeNPs
were
synthesized
characterized
using
UV-Vis
spectroscopy,
atomic
force
microscopy
(AFM),
energy-dispersive
X-ray
spectroscopy
(EDX),
diffraction
(XRD),
field
emission
scanning
electron
(FESEM),
transmission
(TEM),
Fourier-transform
infrared
(FTIR),
confirming
their
nanoscale
morphology,
purity,
stability.
Results:
The
antimicrobial
activity
Col-SeNPs
was
assessed
based
on
minimum
inhibitory
concentration
(MIC)
bacterial
viability
assays.
exhibited
enhanced
antibacterial
effects
P.
along
with
significant
downregulation
mexY
efflux
pump
gene,
which
is
associated
colistin
resistance.
Additionally,
demonstrated
superior
antifungal
albicans,
C.
glabrata,
krusei
compared
to
alone.
anticancer
potential
evaluated
in
MCF-7
MTT
assay,
revealing
dose-dependent
cytotoxicity
through
apoptosis
oxidative
stress
pathways.
Although
not
inherently
drug-resistant,
this
model
used
explore
overcoming
mechanisms
commonly
encountered
therapy.
Conclusions:
these
findings
support
promise
approach
for
addressing
both
treatment
challenges.
Further
vivo
studies,
including
pharmacokinetics
combination
therapies,
are
warranted
advance
translation.
Язык: Английский