Machine learning-assisted image-based optical devices for health monitoring and food safety
Maryam Mousavizadegan,
Farzaneh Shalileh,
Saba Mostajabodavati
и другие.
TrAC Trends in Analytical Chemistry,
Год журнала:
2024,
Номер
177, С. 117794 - 117794
Опубликована: Июнь 5, 2024
Язык: Английский
Machine Learning assisted Paper-Based Fluorescent Sensor Array with Metal-Doped Multicolor Carbon Quantum Dots for Identification and Inactivation of Bacteria
Talanta,
Год журнала:
2025,
Номер
unknown, С. 128035 - 128035
Опубликована: Март 1, 2025
Язык: Английский
Species- and subspecies-level differentiation of cancer cells based on viscosity sensitive fluorescence sensor array and machine learning
Shouning Yang,
Hongyan Jia,
Yide Liu
и другие.
Microchemical Journal,
Год журнала:
2025,
Номер
unknown, С. 113682 - 113682
Опубликована: Апрель 1, 2025
Язык: Английский
A novel strategy for ultrasensitive detection and effective inactivation of Staphylococcus aureus based on Fe3O4-QCS-PEI-Cu-aptamer and ladder-branch HCR
Microchimica Acta,
Год журнала:
2025,
Номер
192(6)
Опубликована: Май 7, 2025
Язык: Английский
Non-invasive cortisol monitoring via a machine leaning-enabled smartphone liquid crystal aptasensor
Microchemical Journal,
Год журнала:
2025,
Номер
unknown, С. 113944 - 113944
Опубликована: Май 1, 2025
Язык: Английский
Gold nanorod etching for sensitive aptamer-mediated colorimetric detection of Escherichia coli in water
Microchemical Journal,
Год журнала:
2024,
Номер
208, С. 112368 - 112368
Опубликована: Дек. 10, 2024
Язык: Английский
Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water
ACS Applied Materials & Interfaces,
Год журнала:
2024,
Номер
16(51), С. 70419 - 70428
Опубликована: Дек. 12, 2024
The
challenge
of
rapid
identification
bacteria
in
food
and
water
still
persists
as
a
major
health
problem.
To
tackle
this
matter,
we
have
developed
single-probe
liquid
crystal
(LC)-based
optical
sensing
platform
for
the
differentiation
five
common
bacterial
strains,
including
Bacillus
cereus,
Escherichia
coli,
Pseudomonas
aeruginosa,
Staphylococcus
aureus,
S.
typhimurium,
using
cysteine-functionalized
silver
nanotriangles
signal
enhancers.
Unique
patterns
were
generated
from
interaction
samples
with
LC
interface
captured
by
camera
under
polarized
light.
Pattern
recognition
was
carried
out
based
on
image
analysis
machine
learning
(ML)
calculations.
Among
various
ML
algorithms
trained,
Support
Vector
Machines
had
best
performance
able
to
successfully
discern
98.89%
accuracy.
A
linear
range
10–106
CFU
mL–1
detection
limits
10
attained
all
strains.
proposed
method
tested
water,
juice,
milk
samples,
prediction
accuracies
95.83,
97.92,
89.58%,
respectively,
obtained.
offers
simple,
cost-efficient
solution
recognition.
Язык: Английский
Enhanced Antibacterial Effect of Kanamycin‐Stabilized Nanoclusters
Kimia Rezapour,
Maryam Mousavizadegan,
Seyed Mohammad Reza Mortazavi
и другие.
ChemistrySelect,
Год журнала:
2024,
Номер
9(48)
Опубликована: Дек. 1, 2024
Abstract
Pertaining
to
the
imminent
problem
of
antibiotics
resistance,
and
in
turn
urgent
need
for
new
antibiotics,
it
is
vital
generate
compounds
that
are
effective
against
resistant
species.
For
this,
we
have
aimed
develop
enhanced
antibacterial
by
merging
with
nanomaterials
strengthen
their
activity.
Hence,
kanamycin‐stabilized
gold,
silver,
copper
nanoclusters
(NCs)
were
synthesized
investigated
It
was
seen
gold
silver
NCs
had
increased
activity,
especially
Escherichia
coli
,
an
average
decrease
MIC
4:1
2:1
NCs.
The
activity
Staphylococcus
aureus
Salmonella
typhimurium
also
increased,
more
significantly
However,
NCs,
not
witnessed.
Thus,
can
be
concluded
incorporating
nanoclusters,
antibiotic
ligands
enhance
antimicrobial
Язык: Английский