The
ubiquitous
gut
microbiotas
acquired
from
the
environment
contribute
to
host
health.
of
soil
invertebrates
are
gradually
assembled
microecological
region
ecosystem
which
they
inhabit,
but
little
is
known
about
their
characteristics
when
hosts
under
environmental
stress.
rapid
development
high-throughput
DNA
sequencing
in
last
decade
has
provided
unprecedented
insights
and
opportunities
characterize
invertebrates.
Here,
we
characterized
core,
transient,
rare
bacterial
taxa
guts
using
core
index
(CI)
developed
a
new
theory
global
microbial
diversity
ecological
microregions.We
found
that
Gammaproteobacteria
could
respond
indiscriminately
exposure
concentrations
pollutants
were
closely
associated
with
physiology
function
host.
Meanwhile,
machine-learning
models
based
on
metadata
calculated
bacteria
highest
colonization
potential
gut,
further
identified
best
indicator
taxon
response
pollution.
also
correlated
abundance
antibiotic
resistance
genes.Our
results
determined
an
responded
pollutants,
thus
providing
effective
theoretical
basis
for
subsequent
assessments
risk.
physiological
biochemical
analyses
microbial-community
functions,
Gammaproteobacteria,
provide
evaluating
Video
abstract.
Abstract
Deep
learning
is
a
subdiscipline
of
artificial
intelligence
that
uses
machine
technique
called
neural
networks
to
extract
patterns
and
make
predictions
from
large
data
sets.
The
increasing
adoption
deep
across
healthcare
domains
together
with
the
availability
highly
characterised
cancer
datasets
has
accelerated
research
into
utility
in
analysis
complex
biology
cancer.
While
early
results
are
promising,
this
rapidly
evolving
field
new
knowledge
emerging
both
learning.
In
review,
we
provide
an
overview
techniques
how
they
being
applied
oncology.
We
focus
on
applications
for
omics
types,
including
genomic,
methylation
transcriptomic
data,
as
well
histopathology-based
genomic
inference,
perspectives
different
types
can
be
integrated
develop
decision
support
tools.
specific
examples
may
diagnosis,
prognosis
treatment
management.
also
assess
current
limitations
challenges
application
precision
oncology,
lack
phenotypically
rich
need
more
explainable
models.
Finally,
conclude
discussion
obstacles
overcome
enable
future
clinical
utilisation
Frontiers in Immunology,
Год журнала:
2021,
Номер
12
Опубликована: Фев. 23, 2021
The
past
decade
has
witnessed
groundbreaking
advances
in
the
field
of
microbiome
research.
An
area
where
immense
implications
have
been
demonstrated
is
tumor
biology.
affects
initiation
and
progression
through
direct
effects
on
cells
indirectly
manipulation
immune
system.
It
can
also
determine
response
to
cancer
therapies
predict
disease
survival.
Modulation
be
harnessed
potentiate
efficacy
immunotherapies
decrease
their
toxicity.
In
this
review,
we
comprehensively
dissect
recent
evidence
regarding
interaction
anti-tumor
machinery
outline
critical
questions
which
need
addressed
as
further
explore
dynamic
colloquy.
Biomedicine & Pharmacotherapy,
Год журнала:
2023,
Номер
164, С. 114985 - 114985
Опубликована: Июнь 11, 2023
The
gut
microbiota
is
indispensable
for
maintaining
host
health
by
enhancing
the
host's
digestive
capacity,
safeguarding
intestinal
epithelial
barrier,
and
preventing
pathogen
invasion.
Additionally,
exhibits
a
bidirectional
interaction
with
immune
system
promotes
of
to
mature.
Dysbiosis
microbiota,
primarily
caused
factors
such
as
genetic
susceptibility,
age,
BMI,
diet,
drug
abuse,
significant
contributor
inflammatory
diseases.
However,
mechanisms
underlying
diseases
resulting
from
dysbiosis
lack
systematic
categorization.
In
this
study,
we
summarize
normal
physiological
functions
symbiotic
in
healthy
state
demonstrate
that
when
occurs
due
various
external
factors,
are
lost,
leading
pathological
damage
lining,
metabolic
disorders,
barrier
damage.
This,
turn,
triggers
disorders
eventually
causes
systems.
These
discoveries
provide
fresh
perspectives
on
how
diagnose
treat
unrecognized
variables
might
affect
link
between
illnesses
need
further
studies
extensive
basic
clinical
research
will
still
be
required
investigate
relationship
future.
Cancers,
Год журнала:
2022,
Номер
14(6), С. 1370 - 1370
Опубликована: Март 8, 2022
Lung
cancer
is
the
leading
cause
of
malignancy-related
mortality
worldwide
due
to
its
heterogeneous
features
and
diagnosis
at
a
late
stage.
Artificial
intelligence
(AI)
good
handling
large
volume
computational
repeated
labor
work
suitable
for
assisting
doctors
in
analyzing
image-dominant
diseases
like
lung
cancer.
Scientists
have
shown
long-standing
efforts
apply
AI
screening
via
CXR
chest
CT
since
1960s.
Several
grand
challenges
were
held
find
best
model.
Currently,
FDA
approved
several
programs
reading,
which
enables
systems
take
part
detection.
Following
success
application
radiology
field,
was
applied
digitalized
whole
slide
imaging
(WSI)
annotation.
Integrating
with
more
information,
demographics
clinical
data,
could
play
role
decision-making
by
classifying
EGFR
mutations
PD-L1
expression.
also
help
clinicians
estimate
patient's
prognosis
predicting
drug
response,
tumor
recurrence
rate
after
surgery,
radiotherapy
side
effects.
Though
there
are
still
some
obstacles,
deploying
workflow
vital
foreseeable
future.
Signal Transduction and Targeted Therapy,
Год журнала:
2023,
Номер
8(1)
Опубликована: Окт. 9, 2023
Abstract
Individual
variability
in
drug
response
(IVDR)
can
be
a
major
cause
of
adverse
reactions
(ADRs)
and
prolonged
therapy,
resulting
substantial
health
economic
burden.
Despite
extensive
research
pharmacogenomics
regarding
the
impact
individual
genetic
background
on
pharmacokinetics
(PK)
pharmacodynamics
(PD),
diversity
explains
only
limited
proportion
IVDR.
The
role
gut
microbiota,
also
known
as
second
genome,
its
metabolites
modulating
therapeutic
outcomes
human
diseases
have
been
highlighted
by
recent
studies.
Consequently,
burgeoning
field
pharmacomicrobiomics
aims
to
explore
correlation
between
microbiota
variation
IVDR
or
ADRs.
This
review
presents
an
up-to-date
overview
intricate
interactions
classical
agents
for
systemic
diseases,
including
cancer,
cardiovascular
(CVDs),
endocrine
others.
We
summarise
how
directly
indirectly,
modify
absorption,
distribution,
metabolism,
excretion
(ADME)
drugs.
Conversely,
drugs
modulate
composition
function
leading
changes
microbial
metabolism
immune
response.
discuss
practical
challenges,
strategies,
opportunities
this
field,
emphasizing
critical
need
develop
innovative
approach
multi-omics,
integrate
various
data
types,
genomic
data,
well
translate
lab
into
clinical
practice.
To
sum
up,
represents
promising
avenue
address
improve
patient
outcomes,
further
is
imperative
unlock
full
potential
precision
medicine.
The
field
of
human
space
travel
is
in
the
midst
a
dramatic
revolution.
Upcoming
missions
are
looking
to
push
boundaries
travel,
with
plans
for
longer
distances
and
durations
than
ever
before.
Both
National
Aeronautics
Space
Administration
(NASA)
several
commercial
companies
(e.g.,
Blue
Origin,
SpaceX,
Virgin
Galactic)
have
already
started
process
preparing
long-distance,
long-duration
exploration
currently
plan
explore
inner
solar
planets
Mars)
by
2030s.
With
emergence
tourism,
has
materialized
as
potential
new,
exciting
frontier
business,
hospitality,
medicine,
technology
coming
years.
However,
current
evidence
regarding
health
very
limited,
particularly
pertaining
short-term
long-term
travel.
This
review
synthesizes
developments
across
continuum
including
prior
studies
unpublished
data
from
NASA
related
each
individual
organ
system,
medical
screening
We
categorized
extraterrestrial
environment
into
exogenous
radiation
microgravity)
endogenous
processes
alteration
humans'
natural
circadian
rhythm
mental
due
confinement,
isolation,
immobilization,
lack
social
interaction)
their
various
effects
on
health.
aim
this
challenges
associated
how
they
may
be
overcome
order
enable
new
paradigms
health,
well
use
emerging
Artificial
Intelligence
based
(AI)
propel
future
research.
Advances in Nutrition,
Год журнала:
2022,
Номер
13(6), С. 2573 - 2589
Опубликована: Сен. 27, 2022
Data
currently
generated
in
the
field
of
nutrition
are
becoming
increasingly
complex
and
high-dimensional,
bringing
with
them
new
methods
data
analysis.
The
characteristics
machine
learning
(ML)
make
it
suitable
for
such
analysis
thus
lend
itself
as
an
alternative
tool
to
deal
this
nature.
ML
has
already
been
applied
important
problem
areas
nutrition,
obesity,
metabolic
health,
malnutrition.
Despite
this,
experts
often
without
understanding
ML,
which
limits
its
application
therefore
potential
solve
open
questions.
current
article
aims
bridge
knowledge
gap
by
supplying
researchers
a
resource
facilitate
use
their
research.
is
first
explained
distinguished
from
existing
solutions,
key
examples
applications
literature
provided.
Two
case
studies
domains
particularly
applicable,
precision
metabolomics,
then
presented.
Finally,
framework
outlined
guide
interested
integrating
into
work.
By
acting
can
refer,
we
hope
support
integration
modern
