Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care.

Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time. While many algorithms exist to assist in monitoring these patients, they often lack accuracy and specificity, leading to alarm fatigue among healthcare practitioners. In this study we propose a multimodal approach that incorporates salient physiological signals and EHR data to predict the onset of hemodynamic decompensation. A retrospective dataset of patients recovering from cardiac surgery was created and used to train predictive models. Advanced signal processing techniques were employed to extract complex features from physiological waveforms, while a novel tensor-based dimensionality reduction method was used to reduce the size of the feature space. These methods were evaluated for predicting the onset of decompensation at varying time intervals, ranging from a half-hour to 12 h prior to a decompensation event. The best performing models achieved AUCs of 0.87 and 0.80 for the half-hour and 12-h intervals respectively. These analyses evince that a multimodal approach can be used to develop clinical decision support systems that predict adverse events several hours in advance.

Réplica: Meningioma sincitial intramedular asociado a siringomielia cervical / Reply: Syncytial intramedullary meningioma associated with cervical syringomyelia

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Rapid isotropic resolution cartilage assessment using radial alternating repetition time balanced steady-state free-precession imaging.

PURPOSE: To compare a balanced steady-state free-precession sequence with a radial k-space trajectory and alternating repetition time fat suppression (Radial-ATR) with other currently used fat-suppressed 3D sequences for evaluating the articular cartilage of the knee joint at 3.0T. MATERIALS AND METHODS: Radial-ATR, fast spin-echo (FSE-Cube), gradient recall-echo acquired in the steady-state (GRASS), and spoiled gradient recall-echo (SPGR) sequences with similar voxel volumes and identical scan times were performed at 3.0T on both knee joints of five volunteers. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements were performed for all sequences using a double acquisition method and compared using Mann-Whitney Wilcoxon tests. Radial-ATR sequences with 0.3 mm and 0.4 mm isotropic resolution were also performed on the knee joints of seven volunteers and three patients with osteoarthritis. RESULTS: Average SNR values for cartilage, synovial fluid, and bone marrow were 54.7, 153.3, and 12.9, respectively, for Radial ATR, 30.8, 44.1, and 1.9, respectively, for FSE-Cube, 13.3, 46.9, and 3.3, respectively, for GRASS, and 19.1, 8.1, and 2.1, respectively, for SPGR. Average CNR values between cartilage and synovial fluid and between cartilage and bone marrow were 98.6 and 41.8, respectively, for VIPR-ATR, 13.4 and 28.8, respectively, for FSE-Cube, 33.6 and 10.0, respectively, for GRASS, and 11.0 and 16.9, respectively, for SPGR. Radial-ATR had significantly higher (P < 0.001) cartilage, synovial fluid, and bone marrow SNR and significantly higher (P < 0.01) CNR between cartilage and synovial fluid and between cartilage and bone marrow than FSE-Cube, GRASS, and SPGR. Radial-ATR provided excellent visualization of articular cartilage at high isotropic resolution with no image degradation due to off-resonance banding artifacts. CONCLUSION: Radial-ATR had superior SNR efficiency to other fat-suppressed 3D cartilage imaging sequences and produced high isotropic resolution images of the knee joint which could be used for evaluating articular cartilage at 3.0T.

Tracing atmospheric nitrate deposition in a complex semiarid ecosystem using delta17O.

The isotopic composition of nitrate collected from aerosols, fog, and precipitation was measured and found to have a large 17O anomaly with delta17O values ranging from 20 percent per thousand to 30% percent per thousand (delta17O = delta17O - 0.52(delta18O)). This 17O anomaly was used to trace atmospheric deposition of nitrate to a semiarid ecosystem in southern California. We demonstrate that the delta17O signal is a conserved tracer of atmospheric nitrate deposition and is a more robust indicator of N deposition relative to standard delta18O techniques. The data indicate that a substantial portion of nitrate found in the local soil, stream, and groundwater is of atmospheric origin and does not undergo biologic processing before being exported from the system.