7 research outputs found
Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients
IMPORTANCE The successful implementation of artificial intelligence (AI) in health care depends on
its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of
AI-driven outcomes.
OBJECTIVES To survey hospital patients to investigate their trust, concerns, and preferences
toward the use of AI in health care and diagnostics and to assess the sociodemographic factors
associated with patient attitudes.
DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study developed and implemented an
anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability
sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older
who agreed with voluntary participation in the survey presented in 1 of 26 languages.
EXPOSURE Information sheets and paper surveys handed out by hospital staff and posted in
conspicuous hospital locations.
MAIN OUTCOMES AND MEASURES The primary outcome was participant responses to a 26-item
instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis,
preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link
mixed and binary mixed-effects models.
RESULTS In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855
(35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were
male. The survey results indicated a predominantly favorable general view of AI in health care, with
57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited
notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine
than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited
fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views)
than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely,
higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing
information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients
preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652
[72.9%]), even if it meant slightly compromised accuracy.
CONCLUSIONS AND RELEVANCE In this cross-sectional study of patient attitudes toward AI use in
health care across 6 continents, findings indicated that tailored AI implementation strategies should
take patient demographics, health status, and preferences for explainable AI and physician oversight
into account
<i>N</i><sup>ε</sup>‑Acryloyllysine Piperazides as Irreversible Inhibitors of Transglutaminase 2: Synthesis, Structure–Activity Relationships, and Pharmacokinetic Profiling
Transglutaminase
2 (TGase 2)-catalyzed transamidation represents
an important post-translational mechanism for protein modification
with implications in physiological and pathophysiological conditions,
including fibrotic and neoplastic processes. Consequently, this enzyme
is considered a promising target for the diagnosis of and therapy
for these diseases. In this study, we report on the synthesis and
kinetic characterization of Nε-acryloyllysine
piperazides as irreversible inhibitors of TGase 2. Systematic structural
modifications on 54 new compounds were performed with a major focus
on fluorine-bearing substituents due to the potential of such compounds
to serve as radiotracer candidates for positron emission tomography.
The determined inhibitory activities ranged from 100 to 10 000
M–1 s–1, which resulted in comprehensive
structure–activity relationships. Structure–activity
correlations using various substituent parameters accompanied by covalent
docking studies provide an advanced understanding of the molecular
recognition for this inhibitor class within the active site of TGase
2. Selectivity profiling of selected compounds for other transglutaminases
demonstrated an excellent selectivity toward transglutaminase 2. Furthermore,
an initial pharmacokinetic profiling of selected inhibitors was performed,
including the assessment of potential membrane permeability and liver
microsomal stability
<i>N</i><sup>ε</sup>‑Acryloyllysine Piperazides as Irreversible Inhibitors of Transglutaminase 2: Synthesis, Structure–Activity Relationships, and Pharmacokinetic Profiling
Transglutaminase
2 (TGase 2)-catalyzed transamidation represents
an important post-translational mechanism for protein modification
with implications in physiological and pathophysiological conditions,
including fibrotic and neoplastic processes. Consequently, this enzyme
is considered a promising target for the diagnosis of and therapy
for these diseases. In this study, we report on the synthesis and
kinetic characterization of <i>N</i><sup>ε</sup>-acryloyllysine
piperazides as irreversible inhibitors of TGase 2. Systematic structural
modifications on 54 new compounds were performed with a major focus
on fluorine-bearing substituents due to the potential of such compounds
to serve as radiotracer candidates for positron emission tomography.
The determined inhibitory activities ranged from 100 to 10 000
M<sup>–1</sup> s<sup>–1</sup>, which resulted in comprehensive
structure–activity relationships. Structure–activity
correlations using various substituent parameters accompanied by covalent
docking studies provide an advanced understanding of the molecular
recognition for this inhibitor class within the active site of TGase
2. Selectivity profiling of selected compounds for other transglutaminases
demonstrated an excellent selectivity toward transglutaminase 2. Furthermore,
an initial pharmacokinetic profiling of selected inhibitors was performed,
including the assessment of potential membrane permeability and liver
microsomal stability
<i>N</i><sup>ε</sup>‑Acryloyllysine Piperazides as Irreversible Inhibitors of Transglutaminase 2: Synthesis, Structure–Activity Relationships, and Pharmacokinetic Profiling
Transglutaminase
2 (TGase 2)-catalyzed transamidation represents
an important post-translational mechanism for protein modification
with implications in physiological and pathophysiological conditions,
including fibrotic and neoplastic processes. Consequently, this enzyme
is considered a promising target for the diagnosis of and therapy
for these diseases. In this study, we report on the synthesis and
kinetic characterization of <i>N</i><sup>ε</sup>-acryloyllysine
piperazides as irreversible inhibitors of TGase 2. Systematic structural
modifications on 54 new compounds were performed with a major focus
on fluorine-bearing substituents due to the potential of such compounds
to serve as radiotracer candidates for positron emission tomography.
The determined inhibitory activities ranged from 100 to 10 000
M<sup>–1</sup> s<sup>–1</sup>, which resulted in comprehensive
structure–activity relationships. Structure–activity
correlations using various substituent parameters accompanied by covalent
docking studies provide an advanced understanding of the molecular
recognition for this inhibitor class within the active site of TGase
2. Selectivity profiling of selected compounds for other transglutaminases
demonstrated an excellent selectivity toward transglutaminase 2. Furthermore,
an initial pharmacokinetic profiling of selected inhibitors was performed,
including the assessment of potential membrane permeability and liver
microsomal stability
<i>N</i><sup>ε</sup>‑Acryloyllysine Piperazides as Irreversible Inhibitors of Transglutaminase 2: Synthesis, Structure–Activity Relationships, and Pharmacokinetic Profiling
Transglutaminase
2 (TGase 2)-catalyzed transamidation represents
an important post-translational mechanism for protein modification
with implications in physiological and pathophysiological conditions,
including fibrotic and neoplastic processes. Consequently, this enzyme
is considered a promising target for the diagnosis of and therapy
for these diseases. In this study, we report on the synthesis and
kinetic characterization of <i>N</i><sup>ε</sup>-acryloyllysine
piperazides as irreversible inhibitors of TGase 2. Systematic structural
modifications on 54 new compounds were performed with a major focus
on fluorine-bearing substituents due to the potential of such compounds
to serve as radiotracer candidates for positron emission tomography.
The determined inhibitory activities ranged from 100 to 10 000
M<sup>–1</sup> s<sup>–1</sup>, which resulted in comprehensive
structure–activity relationships. Structure–activity
correlations using various substituent parameters accompanied by covalent
docking studies provide an advanced understanding of the molecular
recognition for this inhibitor class within the active site of TGase
2. Selectivity profiling of selected compounds for other transglutaminases
demonstrated an excellent selectivity toward transglutaminase 2. Furthermore,
an initial pharmacokinetic profiling of selected inhibitors was performed,
including the assessment of potential membrane permeability and liver
microsomal stability
A deep-learning algorithm to classify skin lesions from mpox virus infection
A deep-learning algorithm was developed to identify skin lesions caused by the mpox virus and was then implemented in a web-based app designed for patient use.Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation
Platinum Group Metal-Doped Tungsten Phosphates for Selective C–H Activation of Lower Alkanes
Platinum group metal (PGM)-based catalysts are known
to be highly
active in the total combustion of lower hydrocarbons. However, through
an alternative catalyst design reported in this paper by isolating
PGM-based active sites in a tungsten phosphate matrix, we present
a class of catalysts for selective oxidation of n-butane, propane, and propylene that do not contain Mo or V as redox-active
elements. Two different catalyst concepts have been pursued. Concept
A: isolating Ru-based active sites in a tungsten phosphate matrix
coming upon as ReO3-type structure. Concept B: dilution
of PGM-based active sites through the synthesis of X-ray amorphous
Ru tungsten phosphates supported on SiO2. Using a high-throughput
screening approach, model catalysts over a wide compositional range
were evaluated for C3 and C4 partial oxidation. Bulk crystalline and
supported XRD amorphous phases with similar Ru/W/P compositions showed
comparable performance. Hence, for these materials, composition is
more crucial than the degree of crystallinity. Further studies for
optimization on second-generation supported systems revealed even
better results. High selectivity for n-butane oxidation
to maleic anhydride and propane oxidation to an acrolein/acrylic acid
has been achieved
