Heart proteomic profiling discovers MYH6 and COX5B as biomarkers for sudden unexplained death.

Sudden unexplained death (SUD) is not uncommon in forensic pathology. Yet, diagnosis of SUD remains challenging due to lack of specific biomarkers. This study aimed to screen differentially expressed proteins (DEPs) and validate their usefulness as diagnostic biomarkers for SUD cases. We designed a three-phase investigation, where in the discovery phase, formalin-fixed paraffin-embedded (FFPE) heart specimens were screened through label-free proteomic analysis of cases dying from SUD, mechanical injury and carbon monoxide (CO) intoxication. A total of 26 proteins were identified to be DEPs for the SUD cases after rigorous criterion. Bioinformatics and Adaboost-recursive feature elimination (RFE) analysis further revealed that three of the 26 proteins (MYH6, COX5B and TNNT2) were potential discriminative biomarkers. In the training phase, MYH6 and COX5B were verified to be true DEPs in cardiac tissues from 29 independent SUD cases as compared with a serial of control cases (n = 42). Receiver operating characteristic (ROC) analysis illustrated that combination of MYH6 and COX5B achieved optimal diagnostic sensitivity (89.7 %) and specificity (84.4 %), with area under the curve (AUC) being 0.91. A diagnostic software based on the logistic regression formula derived from the training phase was then constructed. In the validation phase, the diagnostic software was applied to eight authentic SUD cases, seven (87.5 %) of which were accurately recognized. Our study provides a valid strategy towards practical diagnosis of SUD by integrating cardiac MYH6 and COX5B as dual diagnostic biomarkers.

Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔB0 shim array fields.

PURPOSE: Developing a general framework with a novel stochastic offset strategy for the design of optimized RF pulses and time-varying spatially non-linear ΔB0 shim array fields for restricted slice excitation and refocusing with refined magnetization profiles within the intervals of the fixed voxels. METHODS: Our framework uses the decomposition property of the Bloch equations to enable joint design of RF-pulses and shim array fields for restricted slice excitation and refocusing with auto-differentiation optimization. Bloch simulations are performed independently on orthogonal basis vectors, Mx, My, and Mz, which enables designs for arbitrary initial magnetizations. Requirements for refocusing pulse designs are derived from the extended phase graph formalism obviating time-consuming sub-voxel isochromatic simulations to model the effects of crusher gradients. To refine resultant slice-profiles because of voxelwise optimization functions, we propose an algorithm that stochastically offsets spatial points at which loss is computed during optimization. RESULTS: We first applied our proposed design framework to standard slice-selective excitation and refocusing pulses in the absence of non-linear ΔB0 shim array fields and compared them against pulses designed with Shinnar-Le Roux algorithm. Next, we demonstrated our technique in a simulated setup of fetal brain imaging in pregnancy for restricted-slice excitation and refocusing of the fetal brain. CONCLUSIONS: Our proposed framework for optimizing RF pulse and time-varying spatially non-linear ΔB0 shim array fields achieve high fidelity restricted-slice excitation and refocusing for fetal MRI, which could enable zoomed fast-spin-echo-MRI and other applications.

Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI.

PURPOSE: To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model. METHODS: Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T2 -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints. RESULTS: An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T2 -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments. CONCLUSION: Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.

Sudden unexplained death in schizophrenia patients: An autopsy-based comparative study from China.

Explainable sudden deaths in schizophrenia patients due to both cardiac (SCD) and non-cardiac causes (SNCD) have been extensively documented. However, sudden unexplained death (SUD) in this cohort remains to be elucidated. This study retrospectively analyzed 18 SUD cases that underwent systematic autopsy at our institutes during the period 2010-2022. The etiological, demographic, and autopsy features of the SUD cases were then compared with 37 year-matched sudden explainable deaths (23 SCD cases and 14 SNCD cases). Our results showed that the average age of the SUD was 39.0 ( ± 8.4) years, with the disease duration of 11.8 ( ± 8.1) years and a male/female ratio of 11:7. Most cases occurred during daytime (72.2%) and outside of hospital (77.8%). A large proportion of the SUD cases (77.8%) had persistent psychiatric episodes before death. Clozapine was found to be the most commonly used antipsychotic (33.3%), followed by Olanzapine (27.8%), Risperidone (27.8%) and Chlorpromazine (27.8%) in the SUD cases. When compared among groups, the SUD cases showed significantly younger ages (p = 0.035), lower heart weight (p = 0.004) and lower proportion of Clozapine use (p = 0.045). The presence of persistent psychiatric episodes was significantly higher in the SUD group than in any explainable deaths (p = 0.018) and was an independent risk factor for SUD (OR = 4.205, p = 0.040). This is the first autopsy-based study of SUD cases from China. We conclude that a stable mental state maintained by antipsychotics (i.e., Clozapine) is vital to schizophrenia patients.

Cardiotoxicity of current antipsychotics: Newer antipsychotics or adjunct therapy?

Use of newer antipsychotics for substitution of current antipsychotics might be one way awaiting to be clinically verified to address antipsychotic cardiotoxic effects. Alternatively, the combination of existing antipsychotics with cardioprotective agents is also beneficial for patients with mental disorders for avoiding cardiotoxicity to the maximum.

Use of potassium ion channel and spliceosome proteins as diagnostic biomarkers for sudden unexplained death in schizophrenia.

Sudden unexplained death in schizophrenia (SUD-SCZ) is not uncommon and its incidence is approximately three times higher than that in the general population. However, diagnosis of SUD-SCZ remains a great challenge in forensic pathology. This study designed a two-phase study to investigate whether three proteins, namely two potassium ion channel proteins (KCNJ3 and KCNAB1) and one spliceosome protein (SF3B3) that were identified in our previous work, could be applied in the postmortem diagnosis of SUD-SCZ. Immunohistochemical staining of the three biomarkers, followed by a rigorous quantitative analysis, was performed on heart specimens from both SUD-SCZ and control groups. A diagnostic software based on the logistic regression formula derived from the test phase data was then constructed. In the test phase, we found that the staining intensities of KCNJ3, KCNAB1, and SF3B3 were all significantly lower in the SUD-SCZ group (n = 20) as compared with the control group that died from non-natural causes (n = 25), with fold-changes being 14.85 (p < 0.001), 4.13 (p = 0.028) and 2.12 (p = 0.048), respectively. Receiver operating characteristic analysis further illustrated that combination of the three biomarkers achieved the optimal diagnostic specificity (92%) and area under the curve (0.886). In the validation phase, the diagnostic software was confirmed to be a promising tool for predicting the risk of SUD-SCZ in authentic cases. Our study provided a valid strategy towards the practical diagnosis of SUD-SCZ by using KCNJ3, KCNAB1, and SF3B3 proteins as diagnostic biomarkers.

Selective RF excitation designs enabled by time-varying spatially non-linear ΔB0 fields with applications in fetal MRI.

PURPOSE: To demonstrate, through numerical simulations, novel designs of spatially selective radiofrequency (RF) excitations of the fetal brain by both a restricted 2D slice and 3D inner-volume selection. These designs exploit a single-channel RF pulse, conventional gradient fields, and the spatially non-linear ΔB0 fields of a multi-coil shim array, using an auto-differentiation optimization algorithm. METHODS: The design algorithm jointly optimizes the RF pulse and the time-varying ΔB0 fields, which is produced by a 64-channel multi-coil ΔB0 body array to augment the RF and the linear gradient fields, using an auto-differentiation approach. Two design targets were specified, one a 4-mm thick slice with a limited in-slice extent in one dimension ("restricted slice"), and the other a 3D inner-volume selection encompassing the fetal brain ("inner volume"). The RF duration was limited to 2 ms for the restricted slice excitation and 6 ms for the inner-volume excitation. RESULTS: Excitation profiles were achieved for both the restricted slice excitation task (one-minus-minimum magnitude, 8%) within the region of interest (ROI) and (maximum-minus-zero magnitude, 8%) in the suppressed regions and the fetal brain volume excitation task (13% and 9%, respectively). CONCLUSIONS: The proposed joint design of RF and time-varying, spatially non-linear ΔB0 fields achieves the target excitation profiles with short RF pulse durations and demonstrates the potential to enhance fetal MRI with multi-channel body shim arrays.

Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy.

Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making. Current developments of deep reinforcement learning (DRL), offer a novel approach for fetal landmarks detection. In this task 15 agents are deployed to detect 15 landmarks simultaneously by DRL. The optimization is challenging, and here we propose an improved DRL that incorporates priors on physical structure of the fetal body. First, we use graph communication layers to improve the communication among agents based on a graph where each node represents a fetal-body landmark. Further, additional reward based on the distance between agents and physical structures such as the fetal limbs is used to fully exploit physical structure. Evaluation of this method on a repository of 3-mm resolution in vivo data demonstrates a mean accuracy of landmark estimation within 10 mm of ground truth as 87.3%, and a mean error of 6.9 mm. The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.

Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network.

The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI observations of the gravid abdomen that are acquired at low spatial resolution but relatively high temporal resolution and over long durations (10-30 minutes). We estimate fetal pose per frame in MRI volumes of the pregnant abdomen via deep learning algorithms that detect key fetal landmarks. Evaluation of the proposed method shows that our framework achieves quantitatively an average error of 4.47 mm and 96.4% accuracy (with error less than 10 mm). Fetal pose estimation in MRI time series yields novel means of quantifying fetal movements in health and disease, and enables the learning of kinematic models that may enhance prospective mitigation of fetal motion artifacts during MRI acquisition.