Quantification of Visceral Fat at the L5 Vertebral Body Level in Patients with Crohn's Disease Using T2-Weighted MRI.

The umbilical or L3 vertebral body level is often used for body fat quantification using computed tomography. To explore the feasibility of using clinically acquired pelvic magnetic resonance imaging (MRI) for visceral fat measurement, we examined the correlation of visceral fat parameters at the umbilical and L5 vertebral body levels. We retrospectively analyzed T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) MR axial images from Crohn's disease patients who underwent MRI enterography of the abdomen and pelvis over a three-year period. We determined the area/volume of subcutaneous and visceral fat from the umbilical and L5 levels and calculated the visceral fat ratio (VFR = visceral fat/subcutaneous fat) and visceral fat index (VFI = visceral fat/total fat). Statistical analyses involved correlation analysis between both levels, inter-rater analysis between two investigators, and inter-platform analysis between two image-analysis platforms. Correlational analysis of 32 patients yielded significant associations for VFI (r = 0.85; p < 0.0001) and VFR (r = 0.74; p < 0.0001). Intraclass coefficients for VFI and VFR were 0.846 and 0.875 (good agreement) between investigators and 0.831 and 0.728 (good and moderate agreement) between platforms. Our study suggests that the L5 level on clinically acquired pelvic MRIs may serve as a reference point for visceral fat quantification.

Using natural language processing to compare task-specific verbal cues in coached versus noncoached cardiac arrest teams during simulated pediatrics resuscitation.

OBJECTIVES: Coaches improve cardiopulmonary (CPR) outcomes in real-world and simulated settings. To explore verbal feedback that targets CPR quality, we used natural language processing (NLP) methodologies on transcripts from a published pediatric randomized trial (coach vs. no coach in simulated CPR). Study objectives included determining any differences by trial arm in (1) overall communication and (2) metrics over minutes of CPR and (3) exploring overall frequencies and temporal patterns according to degrees of CPR excellence. METHODS: A human-generated transcription service produced 40 team transcripts. Automated text search with manual review assigned semantic category; word count; and presence of verbal cues for general CPR, compression depth or rate, or positive feedback to transcript utterances. Resulting cue counts per minute (CPM) were corresponded to CPR quality based on compression rate and depth per minute. CPMs were compared across trial arms and over the 18 min of CPR. Adaptation to excellence was analyzed across four patterns of CPR excellence determined by k-shape methods. RESULTS: Overall coached teams experienced more rate-directive, depth-directive, and positive verbal cues compared with noncoached teams. The frequency of coaches' depth cues changed over minutes of CPR, indicating adaptation. In coached teams, the number of depth-directive cues differed among the four patterns of CPR excellence. Noncoached teams experienced fewer utterances by type, with no adaptation over time or to CPR performance. CONCLUSION: NLP extracted verbal metrics and their patterns in resuscitation sessions provides insight into communication patterns and skills used by CPR coaches and other team members. This could help to further optimize CPR training, feedback, excellence, and outcomes.

Probing the nature of black holes: Deep in the mHz gravitational-wave sky.

Black holes are unique among astrophysical sources: they are the simplest macroscopic objects in the Universe, and they are extraordinary in terms of their ability to convert energy into electromagnetic and gravitational radiation. Our capacity to probe their nature is limited by the sensitivity of our detectors. The LIGO/Virgo interferometers are the gravitational-wave equivalent of Galileo's telescope. The first few detections represent the beginning of a long journey of exploration. At the current pace of technological progress, it is reasonable to expect that the gravitational-wave detectors available in the 2035-2050s will be formidable tools to explore these fascinating objects in the cosmos, and space-based detectors with peak sensitivities in the mHz band represent one class of such tools. These detectors have a staggering discovery potential, and they will address fundamental open questions in physics and astronomy. Are astrophysical black holes adequately described by general relativity? Do we have empirical evidence for event horizons? Can black holes provide a glimpse into quantum gravity, or reveal a classical breakdown of Einstein's gravity? How and when did black holes form, and how do they grow? Are there new long-range interactions or fields in our Universe, potentially related to dark matter and dark energy or a more fundamental description of gravitation? Precision tests of black hole spacetimes with mHz-band gravitational-wave detectors will probe general relativity and fundamental physics in previously inaccessible regimes, and allow us to address some of these fundamental issues in our current understanding of nature.

The missing link in gravitational-wave astronomy: A summary of discoveries waiting in the decihertz range.

Since 2015 the gravitational-wave observations of LIGO and Virgo have transformed our understanding of compact-object binaries. In the years to come, ground-based gravitational-wave observatories such as LIGO, Virgo, and their successors will increase in sensitivity, discovering thousands of stellar-mass binaries. In the 2030s, the space-based LISA will provide gravitational-wave observations of massive black holes binaries. Between the ∼ 10 -103 Hz band of ground-based observatories and the ∼ 1 0 - 4 -10- 1 Hz band of LISA lies the uncharted decihertz gravitational-wave band. We propose a Decihertz Observatory to study this frequency range, and to complement observations made by other detectors. Decihertz observatories are well suited to observation of intermediate-mass ( ∼ 1 0 2 -104 M ⊙) black holes; they will be able to detect stellar-mass binaries days to years before they merge, providing early warning of nearby binary neutron star mergers and measurements of the eccentricity of binary black holes, and they will enable new tests of general relativity and the Standard Model of particle physics. Here we summarise how a Decihertz Observatory could provide unique insights into how black holes form and evolve across cosmic time, improve prospects for both multimessenger astronomy and multiband gravitational-wave astronomy, and enable new probes of gravity, particle physics and cosmology.

Detectable Environmental Effects in GW190521-like Black-Hole Binaries with LISA.

GW190521 is the compact binary with the largest masses observed to date, with at least one black hole in the pair-instability gap. This event has also been claimed to be associated with an optical flare observed by the Zwicky Transient Facility in an active galactic nucleus (AGN), possibly due to the postmerger motion of the merger remnant in the AGN gaseous disk. The Laser Interferometer Space Antenna (LISA) may detect up to ten such gas-rich black-hole binaries months to years before their detection by Laser Interferometer Gravitational Wave Observatory or Virgo-like interferometers, localizing them in the sky within ≈1°^{2}. LISA will also measure directly deviations from purely vacuum and stationary waveforms arising from gas accretion, dynamical friction, and orbital motion around the AGN's massive black hole (acceleration, strong lensing, and Doppler modulation). LISA will therefore be crucial to enable us to point electromagnetic telescopes ahead of time toward this novel class of gas-rich sources, to gain direct insight on their physics, and to disentangle environmental effects from corrections to general relativity that may also appear in the waveforms at low frequencies.

Machine learning to extract communication and history-taking skills in OSCE transcripts.

OBJECTIVES: Observed Structured Clinical Exams (OSCEs) allow assessment of, and provide feedback to, medical students. Clinical examiners and standardised patients (SP) typically complete itemised checklists and global scoring scales, which have known shortcomings. In this study, we applied machine learning (ML) to label some communication skills and interview content information in OSCE transcripts and to compare several ML methodologies by performance and transferability. METHODS: One-hundred and twenty-one transcripts of two OSCE scenarios were manually annotated per utterance across 19 communication skills and content areas. Utterances were converted to two types of numeric sentence vector representations and were paired with three types of ML algorithms. First, ML models (MLMs) were evaluated using a five K-fold cross-validation technique on all transcripts in one scenario to generate precision and recall, and their harmonic mean, F1 scores. Second, ML models were trained on all 101 transcripts from scenario 1 and tested for transferability on 20 scenario 2 transcripts. RESULTS: Performance testing in the K-fold cross-validation demonstrated relatively high mean F1 scores: median 0.87 and range 0.53-0.98 across all 19 labels. Transferability testing demonstrated success: F1 median 0.76 and range 0.46-0.97. The combination of a bi-directional long short-term memory neural network (biLSTM) algorithm with GenSen numeric sentence vector representations was associated with greater F1 scores across both performance and transferability (P < .005). CONCLUSIONS: We report the first application of ML in the context of student-SP OSCEs. We demonstrated that several MLMs automatically labelled OSCE transcripts for a range of interview content and some clinical communications skills. Some MLMs achieved greater performance and transferability. Optimised MLMs could provide automated and accurate assessment of OSCEs with potential to track student progress and identify areas for further practice.