Current Approaches Research Articles

Targeting GM2 Ganglioside Accumulation in Dementia: Current Therapeutic Approaches and Future Directions.

Dementia in neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), and dementia with Lewy bodies (DLB) is a progressive neurological condition affecting millions worldwide. The amphiphilic molecule GM2 gangliosides are abundant in the human brain and play important roles in neuronal development, intercellular recognition, myelin stabilization, and signal transduction. GM2 ganglioside's degradation requires hexosaminidase A (HexA), a heterodimer composed of an α subunit encoded by HEXA and a β subunit encoded by HEXB. The hydrolysis of GM2 also requires a non-enzymatic protein, the GM2 activator protein (GM2-AP), encoded by GM2A. Pathogenic mutations of HEXA, HEXB, and GM2A are responsible for autosomal recessive diseases known as GM2 gangliosidosis, caused by the excessive intralysosomal accumulation of GM2 gangliosides. In AD, PD and DLB, GM2 ganglioside accumulation is reported to facilitate Aβ and α-synuclein aggregation into toxic oligomers and plaques through activation of downstream signaling pathways, such as protein kinase C (PKC) and oxidative stress factors. This review explored the potential role of GM2 ganglioside alteration in toxic protein aggregations and its related signaling pathways leading to neurodegenerative diseases. Further review explored potential therapeutic approaches, which include synthetic and phytomolecules targeting GM2 ganglioside accumulation in the brain, holding a promise for providing new and effective management for dementia.

Split-Level Evolutionary Neural Architecture Search With Elite Weight Inheritance.

Neural architecture search (NAS) has recently gained extensive interest in the deep learning community because of its great potential in automating the construction process of deep models. Among a variety of NAS approaches, evolutionary computation (EC) plays a pivotal role with its merit of gradient-free search ability. However, a massive number of the current EC-based NAS approaches evolve neural architectures in an absolutely discrete manner, which makes it tough to flexibly handle the number of filters for each layer, since they often reduce it to a limit set rather than searching for all possible values. Moreover, EC-based NAS methods are often criticized for their inefficiency in performance evaluation, which usually requires laborious full training for hundreds of candidate architectures generated. To address the inflexible search issue on the number of filters, this work proposes a split-level particle swarm optimization (PSO) approach. Each dimension of the particle is subdivided into an integer part and a fractional part, encoding the configurations of the corresponding layer, and the number of filters within a large range, respectively. In addition, the evaluation time is greatly saved by a novel elite weight inheritance method based on an online updating weight pool, and a customized fitness function considering multiple objectives is developed to well control the complexity of the searched candidate architectures. The proposed method, termed split-level evolutionary NAS (SLE-NAS), is computationally efficient, and outperforms many state-of-the-art peer competitors at much lower complexity across three popular image classification benchmark datasets.

Meta-Reinforcement Learning in Nonstationary and Nonparametric Environments.

Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills. Meta-reinforcement learning (meta-RL) addresses this challenge by leveraging knowledge learned from training tasks to perform well in previously unseen tasks. However, current meta-RL approaches limit themselves to narrow parametric and stationary task distributions, ignoring qualitative differences and nonstationary changes between tasks that occur in the real world. In this article, we introduce a Task-Inference-based meta-RL algorithm using explicitly parameterized Gaussian variational autoencoders (VAEs) and gated Recurrent units (TIGR), designed for nonparametric and nonstationary environments. We employ a generative model involving a VAE to capture the multimodality of the tasks. We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective. We establish a zero-shot adaptation procedure to enable the agent to adapt to nonstationary task changes. We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared with state-of-the-art meta-RL approaches in terms of sample efficiency (three to ten times faster), asymptotic performance, and applicability in nonparametric and nonstationary environments with zero-shot adaptation. Videos can be viewed at https://videoviewsite.wixsite.com/tigr.

Weakly Supervised Temporal Action Localization With Bidirectional Semantic Consistency Constraint.

Weakly supervised temporal action localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the training datasets. Due to the lack of boundary information during training, existing approaches formulate WTAL as a classification problem, i.e., generating the temporal class activation map (T-CAM) for localization. However, with only classification loss, the model would be suboptimized, i.e., the action-related scenes are enough to distinguish different class labels. Regarding other actions in the action-related scene (i.e., the scene same as positive actions) as co-scene actions, this suboptimized model would misclassify the co-scene actions as positive actions. To address this misclassification, we propose a simple yet efficient method, named bidirectional semantic consistency constraint (Bi-SCC), to discriminate the positive actions from co-scene actions. The proposed Bi-SCC first adopts a temporal context augmentation to generate an augmented video that breaks the correlation between positive actions and their co-scene actions in the inter-video. Then, a semantic consistency constraint (SCC) is used to enforce the predictions of the original video and augmented video to be consistent, hence suppressing the co-scene actions. However, we find that this augmented video would destroy the original temporal context. Simply applying the consistency constraint would affect the completeness of localized positive actions. Hence, we boost the SCC in a bidirectional way to suppress co-scene actions while ensuring the integrity of positive actions, by cross-supervising the original and augmented videos. Finally, our proposed Bi-SCC can be applied to current WTAL approaches and improve their performance. Experimental results show that our approach outperforms the state-of-the-art methods on THUMOS14 and ActivityNet. The code is available at https://github.com/lgzlIlIlI/BiSCC.

MTC-CSNet: Marrying Transformer and Convolution for Image Compressed Sensing.

Image compressed sensing (ICS) has been extensively applied in various imaging domains due to its capability to sample and reconstruct images at subNyquist sampling rates. The current predominant approaches in ICS, specifically pure convolutional networks (ConvNets)-based ICS methods, have demonstrated their effectiveness in capturing local features for image recovery. Simultaneously, the Transformer architecture has gained significant attention due to its capability to model global correlations among image features. Motivated by these insights, we propose a novel hybrid network for ICS, named MTC-CSNet, which effectively combines the strengths of both ConvNets and Transformer architectures in capturing local and global image features to achieve high-quality image recovery. Particularly, MTC-CSNet is a dual-path framework that consists of a ConvNets-based recovery branch and a Transformer-based recovery branch. Along the ConvNets-based recovery branch, we design a lightweight scheme to capture the local features in natural images. Meanwhile, we implement a Transformer-based recovery branch to iteratively model the global dependencies among image patches. Ultimately, the ConvNets-based and Transformer-based recovery branches collaborate through a bridging unit, facilitating the adaptive transmission and fusion of informative features for image reconstruction. Extensive experimental results demonstrate that our proposed MTC-CSNet surpasses the state-of-the-art methods on various public datasets. The code and models are publicly available at MTC-CSNet.

Buzhong Yiqi decoction attenuates acquired myasthenia by regulating the JAK2/STAT3/AKT signaling pathway, inhibiting inflammation, and improving mitochondrial function.

Acquired myasthenia (AM), a debilitating autoimmune disease, is typically characterized by skeletal muscle fatigue and weakness. Despite advances in myasthenia gravis treatment, current approaches remain unsatisfactory and many result in unexpected side effects. Traditional Chinese medicine has shown great potential in the treatment of myasthenia gravis, including relieving myasthenic symptoms, improving patients' quality of life, and reducing Western medicine side effects. This study investigates the protective effects and mechanism of BZYQD in mice with acquired myasthenia. BZYQD alleviates the reduced grip strength and increased expression of MAFbx and MuRF-1 in mice with acquired myasthenia. It also reduces levels of pro-inflammatory factors IL-1β, IL-6, and TNF-α in the mouse serum. In addition, BZYQD reduces ROS accumulation and the mitochondrial ROS production rate, while increasing ATP levels and mitochondrial membrane potential in mice with acquired myasthenia. Moreover, BZYQD decreases the expression of p-JAK2, p-STAT3, and p-AKT in the skeletal muscle of mice with acquired myasthenia. In summary, BZYQD reduces inflammation, enhances mitochondrial function, and regulates the JAK2/STAT3/AKT signaling pathway to treat acquired myasthenia.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

Towards Better Modeling With Missing Data: A Contrastive Learning-Based Visual Analytics Perspective.

Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to enhance ML performance. These approaches rely on the observed data to estimate the missing values and therefore encounter three main shortcomings in imputation, including the need for different imputation methods for various missing data mechanisms, heavy dependence on the assumption of data distribution, and potential introduction of bias. This study proposes a Contrastive Learning (CL) framework to model observed data with missing values, where the ML model learns the similarity between an incomplete sample and its complete counterpart and the dissimilarity between other samples. Our proposed approach demonstrates the advantages of CL without requiring any imputation. To enhance interpretability, we introduce CIVis, a visual analytics system that incorporates interpretable techniques to visualize the learning process and diagnose the model status. Users can leverage their domain knowledge through interactive sampling to identify negative and positive pairs in CL. The output of CIVis is an optimized model that takes specified features and predicts downstream tasks. We provide two usage scenarios in regression and classification tasks and conduct quantitative experiments, expert interviews, and a qualitative user study to demonstrate the effectiveness of our approach. In short, this study offers a valuable contribution to addressing the challenges associated with ML modeling in the presence of missing data by providing a practical solution that achieves high predictive accuracy and model interpretability.

HPRN: Holistic Prior-Embedded Relation Network for Spectral Super-Resolution.

Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to tackle this ill-posed problem is to plug into multisource prior information such as the natural spatial context prior of RGB images, deep feature prior, or inherent statistical prior of HSIs so as to effectively alleviate the degree of ill-posedness. However, most current approaches only consider the general and limited priors in their customized convolutional neural networks (CNNs), which leads to the inability to guarantee the confidence and fidelity of reconstructed spectra. In this article, we propose a novel holistic prior-embedded relation network (HPRN) to integrate comprehensive priors to regularize and optimize the solution space of SSR. Basically, the core framework is delicately assembled by several multiresidual relation blocks (MRBs) that fully facilitate the transmission and utilization of the low-frequency content prior of RGBs. Innovatively, the semantic prior of RGB inputs is introduced to mark category attributes, and a semantic-driven spatial relation module (SSRM) is invented to perform the feature aggregation of clustered similar ranges for refining recovered characteristics. In addition, we develop a transformer-based channel relation module (TCRM), which breaks the habit of employing scalars as the descriptors of channelwise relations in the previous deep feature prior and replaces them with certain vectors to make the mapping function more robust and smoother. In order to maintain the mathematical correlation and spectral consistency between hyperspectral bands, the second-order prior constraints (SOPCs) are incorporated into the loss function to guide the HSI reconstruction. Finally, extensive experimental results on four benchmarks demonstrate that our HPRN can reach the state-of-the-art performance for SSR quantitatively and qualitatively. Furthermore, the effectiveness and usefulness of the reconstructed spectra are verified by the classification results on the remote sensing dataset. Codes are available at https://github.com/Deep-imagelab/HPRN.

Therapeutic Potential of Indian Medicinal Herbs and Current Therapeutic Approach used to Mitigate the Symptoms of Chronic Fatigue Syndrome: A Review

Abstract: Chronic fatigue syndrome (CFS) is a complex condition marked by severe exhaustion that lasts at least 6 months. The global prevalence of CFS ranging between 0.4% and 2.5% is growing. Women are affected by CFS more often than men. It is considered a common condition in developed countries. There is no approved treatment for CFS but symptoms can be managed and controlled persistent exhaustion causes significant impairment in daily routine activities. Lowered ATP synthesis, mitochondrial impairment, decreased oxidative phosphorylation, disruption of the hypothalamic–pituitary–adrenal (HPA) axis, and an imbalance of brain neurotransmitters play a major role in the pathophysiology of CFS. The purpose of the present study is to figure out the several plants that are used as a source of medication in chronic fatigue syndrome (CFS) and its current therapeutic approach. The Indian medicinal herbs described in this article are very efficacious in the management of chronic fatigue syndrome symptoms due to the presence of phytochemicals. This review article also covers the current therapeutic approach for chronic fatigue syndrome in a concise form that comprises CBT (Cognitive based therapy), GET (Graded exercise therapy), usage of immunoglobins, psychodynamic counseling, and yoga therapy that includes isometric yoga and yoga nidra are very beneficial in alleviating the symptoms of chronic fatigue syndrome. Antidepressants, immunomodulatory agents, and corticosteroids come under conventional medication for CFS. This article explores different Indian medicinal herbs, their pharmacological properties, and their potential role and current treatments for reducing the severity of symptoms of chronic fatigue syndrome.

PanVA: Pangenomic Variant Analysis.

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.

Cross-Image Pixel Contrasting for Semantic Segmentation.

This work studies the problem of image semantic segmentation. Current approaches focus mainly on mining "local" context, i.e., dependencies between pixels within individual images, by specifically-designed, context aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization objectives (e.g., IoU-like loss). However, they ignore "global" context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive algorithm, dubbed as PiCo, for semantic segmentation in the fully supervised learning setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely studied before. Our training algorithm is compatible with modern segmentation solutions without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCRNet, SegFormer, Segmenter, MaskFormer) and backbones (i.e., MobileNet, ResNet, HRNet, MiT, ViT), our algorithm brings consistent performance improvements across diverse datasets (i.e., Cityscapes, ADE20 K, PASCAL-Context, COCO-Stuff, CamVid). We expect that this work will encourage our community to rethink the current de facto training paradigm in semantic segmentation.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

Catalyst-free Approach to Dihydropyrimidones Using Glycerol/Ethyl Lactate as a Recyclable and Biodegradable Promoting Medium

Abstract: A variety of dihydropyrimidone compounds were synthesised using an effective one-pot, multicomponent, environmentally friendly reaction of aromatic aldehydes, urea/thiourea, ethyl acetoacetate, and glycerol/ethyl lactate. To the best of our knowledge, this is the first catalyst-free strategy for the synthesis of this key scaffold with medicinal chemistry applications. Other significant aspects of the current approach consist of the employment of glycerol/ethyl lactate as a biodegradable and environmentally friendly reaction medium-cum-promoter, the use of easily available substrates, moderate reaction conditions, ease of use, a wide substrate scope, a short reaction time, easy workup, and excellent yields, and atom efficiency, which make the disclosed procedure an excellent alternative to existing methods.

Immunologic Crosstalk of Endoplasmic Reticulum Stress Signaling in Bladder Cancer.

Bladder cancer (BC) is a common malignant tumor of the urinary system. While current approaches involving adjuvant chemotherapy, radiotherapy, and immunotherapy have shown significant progress in BC treatment, challenges, such as recurrence and drug resistance, persist, especially in the case of muscle-invasive bladder cancer (MIBC). It is mainly due to the lack of pre-existing immune response cells in the tumor immune microenvironment. Micro-environmental changes (such as hypoxia and under-nutrition) can cause the aggregation of unfolded and misfolded proteins in the lumen, which induces endoplasmic reticulum (ER) stress. ER stress and its downstream signaling pathways are closely related to immunogenicity and tumor drug resistance. ER stress plays a pivotal role in a spectrum of processes within immune cells and the progression of BC cells, encompassing cell proliferation, autophagy, apoptosis, and resistance to therapies. Recent studies have increasingly recognized the potential of natural compounds to exhibit anti-BC properties through ER stress induction. Still, the efficacy of these natural compounds remains less than that of immune checkpoint inhibitors (ICIs). Currently, the ER stress-mediated immunogenic cell death (ICD) pathway is more encouraging, which can enhance ICI responses by mediating immune stemness. This article provides an overview of the recent developments in understanding how ER stress influences tumor immunity and its implications for BC. Targeting this pathway may soon emerge as a compelling therapeutic strategy for BC.

Delivery of A Jagged1-PEG-MAL hydrogel with Pediatric Human Bone Cells Regenerates Critically-Sized Craniofacial Bone Defects.

Treatments for congenital and acquired craniofacial (CF) bone abnormalities are limited and expensive. Current reconstructive methods include surgical correction of injuries, short-term bone stabilization, and long-term use of bone grafting solutions, including implantation of (i) allografts which are prone to implant failure or infection, (ii) autografts which are limited in supply. Current bone regenerative approaches have consistently relied on BMP2 application with or without addition of stem cells. BMP2 treatment can lead to severe bony overgrowth or uncontrolled inflammation, which can accelerate further bone loss. Bone marrow-derived mesenchymal stem cell-based treatments, which do not have the side effects of BMP2, are not currently FDA approved, and are time and resource intensive. There is a critical need for novel bone regenerative therapies to treat CF bone loss that have minimal side effects, are easily available, and are affordable. In this study we investigated novel bone regenerative therapies downstream of JAGGED1 (JAG1). We previously demonstrated that JAG1 induces murine cranial neural crest (CNC) cells towards osteoblast commitment via a NOTCH non-canonical pathway involving JAK2-STAT5 (1) and that JAG1 delivery with CNC cells elicits bone regeneration in vivo. In this study, we hypothesize that delivery of JAG1 and induction of its downstream NOTCH non-canonical signaling in pediatric human osteoblasts constitute an effective bone regenerative treatment in an in vivo murine bone loss model of a critically-sized cranial defect. Using this CF defect model in vivo, we delivered JAG1 with pediatric human bone-derived osteoblast-like (HBO) cells to demonstrate the osteo-inductive properties of JAG1 in human cells and in vitro we utilized the HBO cells to identify the downstream non-canonical JAG1 signaling intermediates as effective bone regenerative treatments. In vitro, we identified an important mechanism by which JAG1 induces pediatric osteoblast commitment and bone formation involving the phosphorylation of p70 S6K. This discovery enables potential new treatment avenues involving the delivery of tethered JAG1 and the downstream activators of p70 S6K as powerful bone regenerative therapies in pediatric CF bone loss.

Auditors’ Risk Perception of Process Control Deficiencies: A Discrete Choice Experiment

ABSTRACT In risk-based auditing, data-driven analyses are often used to automatically detect process deficiencies. This introduces a challenge: the number of deficiencies is too large to inspect manually. Current approaches addressing this challenge neglect integrating the risk dimension or rely on auditors to manually integrate it. This study aims to increase the effectiveness of such data-driven analysis approaches by including the risk dimension when presenting process deficiencies for further inspection. We investigate how the deficiency type and the affected control activity are associated with perceived risk. We run a discrete choice experiment with 58 auditors interpreting deficiencies that occur in a procure-to-pay or an order-to-cash process and find that (1) deficiencies of type “missing” or deficiencies related to asset-decrementing activities are perceived as the riskiest, (2) the control activity contributes 75 percent of the risk perception, and (3) external and internal auditors share a similar risk perception.

We need to change how we compute RMSEA for nested model comparisons in structural equation modeling.

Comparison of nested models is common in applications of structural equation modeling (SEM). When two models are nested, model comparison can be done via a chi-square difference test or by comparing indices of approximate fit. The advantage of fit indices is that they permit some amount of misspecification in the additional constraints imposed on the model, which is a more realistic scenario. The most popular index of approximate fit is the root mean square error of approximation (RMSEA). In this article, we argue that the dominant way of comparing RMSEA values for two nested models, which is simply taking their difference, is problematic and will often mask misfit, particularly in model comparisons with large initial degrees of freedom. We instead advocate computing the RMSEA associated with the chi-square difference test, which we call RMSEAD. We are not the first to propose this index, and we review numerous methodological articles that have suggested it. Nonetheless, these articles appear to have had little impact on actual practice. The modification of current practice that we call for may be particularly needed in the context of measurement invariance assessment. We illustrate the difference between the current approach and our advocated approach on three examples, where two involve multiple-group and longitudinal measurement invariance assessment and the third involves comparisons of models with different numbers of factors. We conclude with a discussion of recommendations and future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

"It feels terrible that people are making decisions for me": Reflections and experiences of individuals with psychiatric disability who have substitute decision makers for treatment.

In Canada and elsewhere, making treatment decisions for a person with serious mental illness (SMI) who was found incapable for treatment decisions via a substitute decision maker (SDM) is the norm. This practice is often called into question from a rights-based perspective. The literature on the views of affected individuals is limited. We explore the experiences of adults with SMI who have had SDMs to gain more in-depth understanding. We conducted semistructured interviews with 11 consumers of psychiatric services who have had experiences with SDM (range 1-12 years) at an urban hospital in Toronto, Canada. Thematic analysis showed five main themes and related subthemes, including: (1) strong dissatisfaction with and rejection of the SDM's role and purpose; (2) pervasive sense of stigma associated with having a SDM; (3) ongoing struggles to gain autonomy; (4) mixed changes in relationship with and views about SDM; and (5) views on how to improve SDM process. Our study highlights substantial dissatisfaction with the current SDM system and approaches among adults with SMI who have had SDMs. Issues of stigma and struggles to regain autonomy are prevalent. We discuss the personal, clinical, and social-legal contexts in which they occur, particularly in light of the United Nations' Convention on the Rights of Persons with Disabilities that calls for replacing SDMs with supported decision making. Rights-based approaches to care carry substantial practice implications and call for thoughtful change management. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

High variability in physician estimations of flow-diverting stent deployment versus PreSize Neurovascular software simulation: a comparison study

BackgroundPhysician variablity in preoperative planning of endovascular implant deployment and associated inaccuracies have not been documented. This study aimed to quantify the variability in accuracy of physician flow diverter (FD)...