Video versus audio telehealth in safety net clinic patients: Changes by rurality and time.

BACKGROUND: Understanding the mix of video versus audio telehealth modality is critical to informing care for low-income safety net clinic patients. Our study examined whether telehealth modality and continued use of telehealth varied by rurality and whether that changed over time. METHODS: Encounters from adults in the OCHIN national network of primary care safety net clinics were identified by encounter type (in-person vs telehealth) and telehealth modality (video vs audio) from 4/1/2021 to 3/31/2023. Our main outcome was an interaction between patient rurality (defined using Rural Urban Commuting Area codes) and time. Linear probability models with clinic fixed effects were used to estimate predicted probabilities. RESULTS: The predicted probability of a telehealth visit decreased from 37.9% to 24.7% among urban patients (P <.001) and remained stable (29.5%-29.8%; P = .82) among patients in small rural areas. By March 2023, telehealth use among patients in small rural areas was 5.1 percentage points higher than among urban patients (P = .02). The predicted probability of an audio-only visit ranged from 63.5% to 70.5% for patients across all levels of rurality, but no significant differences by rurality or time were found. CONCLUSIONS: Safety net clinic patients were more likely to use audio-only than video telehealth visits. Telehealth in urban and large rural areas decreased since the first year of the pandemic. By the end of the study, patients in small rural communities used significantly more telehealth than urban patients. Elimination of reimbursement for audio telehealth visits may exacerbate existing health care inequities.

Benign Giant Phyllodes Tumours Presenting as Fungating Breast Masses.

Breast masses presenting as fungating growths usually represent advanced malignancy. One remarkable exception is a benign phyllodes tumour. These tumours of stromal origin often exhibit rapid growth, resulting in pressure necrosis at the summit of the tumor causing fungation. It is difficult to differentiate between benign and malignant types clinically. Here, we describe two cases in which patients presented with fungating growth similar to a case of carcinoma breast, which turned out to be cases of benign phyllodes tumours. We would like to highlight the clinical features that differentiate between benign and malignant fungating growth and provide a brief update on the latest treatment modalities for phyllodes tumours.

TRPV4 Channel in Neurological Disease: from Molecular Mechanisms to Therapeutic Potential.

Transient Receptor Potential Vanilloid 4 (TRPV4) is a non-selective cation channel with pivotal roles in various physiological processes, including osmosensitivity, mechanosensation, neuronal development, vascular tone regulation, and bone homeostasis in human bodies. Recent studies have made significant progress in understanding the structure and functional role of TRPV4, shedding light on its involvement in pathological processes, particularly in the realm of neurological diseases. Here, we aim to provide a comprehensive exploration of the multifaceted contributions of TRPV4 to neurological diseases, spanning its intricate molecular mechanisms to its potential as a target for therapeutic interventions. We delve into the structural and functional attributes of TRPV4, scrutinize its expression profile, and elucidate the possible mechanisms through which it participates in the pathogenesis of neurological disorders. Furthermore, we discussed recent years' progress in therapeutic strategies aimed at harnessing TRPV4 for the treatment of these diseases. These insights will provide a basis for understanding and designing modality-specific pharmacological agents to treat TRPV4-associated disorders.

Investigation of the Clinical Value of Four Visualization Modalities for Congenital Heart Disease.

Diagnosing congenital heart disease (CHD) remains challenging because of its complex morphology. Representing the intricate structures of CHD on conventional two-dimensional flat screens is difficult owing to wide variations in the pathologies. Technological advancements, such as three-dimensional-printed heart models (3DPHMs) and virtual reality (VR), could potentially address the limitations of viewing complex structures using conventional methods. This study aimed to investigate the usefulness and clinical value of four visualization modalities across three different cases of CHD, including ventricular septal defect, double-outlet right ventricle, and tetralogy of Fallot. Seventeen cardiac specialists were invited to participate in this study, which was aimed at assessing the usefulness and clinical value of four visualization modalities, namely, digital imaging and communications in medicine (DICOM) images, 3DPHM, VR, and 3D portable document format (PDF). Out of these modalities, 76.4% of the specialists ranked VR as the best for understanding the spatial associations between cardiac structures and for presurgical planning. Meanwhile, 94.1% ranked 3DPHM as the best modality for communicating with patients and their families. Of the various visualization modalities, VR was the best tool for assessing anatomical locations and vessels, comprehending the spatial relationships between cardiac structures, and presurgical planning. The 3DPHM models were the best tool for medical education as well as communication. In summary, both 3DPHM and VR have their own advantages and outperform the other two modalities, i.e., DICOM images and 3D PDF, in terms of visualizing and managing CHD.

Regulating Modality Utilization within Multimodal Fusion Networks.

Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network's utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ±10% of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method's average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications.

Visualizing Mitochondrial Membrane Potential with FRET Probes: Integrating Fluorescence Intensity Ratio and Lifetime Imaging.

Mitochondrial membrane potential (MMP) is crucial for mitochondrial function and serves as a key indicator of cellular health and metabolic activity. Traditional lipophilic cationic fluorescence intensity probes are unavoidably influenced by probe concentration, laser intensity, and photobleaching, limiting their accuracy. To address these issues, we designed and synthesized a pair of fluorescence molecules, OR-C8 and SiR-BA, based on the Förster Resonance Energy Transfer (FRET) mechanism, for dual-modality visualization of MMP. OR-C8 anchors to the inner mitochondrial membrane through strong hydrophobic interactions, while SiR-BA is expelled from mitochondria when MMP decreases, thereby regulating the FRET process. During MMP reduction, the fluorescence intensity and lifetime of OR-C8 increase, while the fluorescence intensity of SiR-BA decreases. By combining changes in fluorescence intensity ratio and fluorescence lifetime, dual-modality visualization of MMP was achieved. This method not only accurately reflects MMP changes but also provides a novel tool for in-depth studies of mitochondrial function and related disease mechanisms, offering significant potential for advancing mitochondrial research and therapeutic development.

Noise-induced modality-specific pretext learning for pediatric chest X-ray image classification.

Introduction: Deep learning (DL) has significantly advanced medical image classification. However, it often relies on transfer learning (TL) from models pretrained on large, generic non-medical image datasets like ImageNet. Conversely, medical images possess unique visual characteristics that such general models may not adequately capture. Methods: This study examines the effectiveness of modality-specific pretext learning strengthened by image denoising and deblurring in enhancing the classification of pediatric chest X-ray (CXR) images into those exhibiting no findings, i.e., normal lungs, or with cardiopulmonary disease manifestations. Specifically, we use a VGG-16-Sharp-U-Net architecture and leverage its encoder in conjunction with a classification head to distinguish normal from abnormal pediatric CXR findings. We benchmark this performance against the traditional TL approach, viz., the VGG-16 model pretrained only on ImageNet. Measures used for performance evaluation are balanced accuracy, sensitivity, specificity, F-score, Matthew's Correlation Coefficient (MCC), Kappa statistic, and Youden's index. Results: Our findings reveal that models developed from CXR modality-specific pretext encoders substantially outperform the ImageNet-only pretrained model, viz., Baseline, and achieve significantly higher sensitivity (p < 0.05) with marked improvements in balanced accuracy, F-score, MCC, Kappa statistic, and Youden's index. A novel attention-based fuzzy ensemble of the pretext-learned models further improves performance across these metrics (Balanced accuracy: 0.6376; Sensitivity: 0.4991; F-score: 0.5102; MCC: 0.2783; Kappa: 0.2782, and Youden's index:0.2751), compared to Baseline (Balanced accuracy: 0.5654; Sensitivity: 0.1983; F-score: 0.2977; MCC: 0.1998; Kappa: 0.1599, and Youden's index:0.1327). Discussion: The superior results of CXR modality-specific pretext learning and their ensemble underscore its potential as a viable alternative to conventional ImageNet pretraining for medical image classification. Results from this study promote further exploration of medical modality-specific TL techniques in the development of DL models for various medical imaging applications.

Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.

Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allowed us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2 %±2.22 % and 84.8 %±2.68 % for the diagnosis of SZ and ASD, respectively. We also compared with 6 DA, 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA+LOSMFS effectively integrated multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.

Multimodality Imaging for Rare Presentation of Placenta Increta in the First Trimester in a Patient with Previous Cesarean Section and Asherman Syndrome.

We report multi-modality imaging (Ultrasound and Magnetic Resonance Imaging) findings of a rare complication in a multi-gravida patient with history of Asherman syndrome presenting with placenta increta in a cesarean scar ectopic pregnancy. The appropriate diagnosis was established with imaging and patient was managed surgically with total abdominal hysterectomy and bilateral salpingectomy. Asherman syndrome and its management of hysteroscopic adhesiolysis are associated with increased odds of placenta accreta spectrum and postpartum hemorrhage. Patients with Asherman syndrome are considered high risk in pregnancy and should be closely monitored for placental site abnormalities during current and subsequent pregnancies.

Effect of Bimagrumab on body composition: a systematic review and meta-analysis.

BACKGROUND: Sarcopenia, a condition marked by progressive muscle mass and function decline, presents significant challenges in aging populations and those with chronic illnesses. Current standard treatments such as dietary interventions and exercise programs are often unsustainable. There is increasing interest in pharmacological interventions like bimagrumab, a monoclonal antibody that promotes muscle hypertrophy by inhibiting muscle atrophy ligands. Bimagrumab has shown effectiveness in various conditions, including sarcopenia. AIM: The primary objective of this meta-analysis is to evaluate the impact of bimagrumab treatment on both physical performance and body composition among patients diagnosed with sarcopenia. MATERIALS AND METHODS: This meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We systematically searched PubMed, Ovid/Medline, Web of Science, and the Cochrane Library databases up to June 2024 using appropriate Medical Subject Headings (MeSH) terms and keywords related to bimagrumab and sarcopenia. Eligible studies were randomized controlled trials (RCTs) that assessed the effects of bimagrumab on physical performance (e.g., muscle strength, gait speed, six-minute walk distance) and body composition (e.g., muscle volume, fat-free body mass, fat body mass) in patients with sarcopenia. Data extraction was independently performed by two reviewers using a standardized form, with discrepancies resolved through discussion or consultation with a third reviewer. RESULTS: From an initial search yielding 46 records, we screened titles, abstracts, and full texts to include seven RCTs in our meta-analysis. Bimagrumab treatment significantly increased thigh muscle volume (mean difference [MD] 5.29%, 95% confidence interval [CI] 4.08% to 6.50%, P < 0.001; moderate heterogeneity χ2 = 6.41, I2 = 38%, P = 0.17) and fat-free body mass (MD 1.90 kg, 95% CI 1.57 kg to 2.23 kg, P < 0.001; moderate heterogeneity χ2 = 8.60, I2 = 30%, P = 0.20), while decreasing fat body mass compared to placebo (MD - 4.55 kg, 95% CI - 5.08 kg to - 4.01 kg, P < 0.001; substantial heterogeneity χ2 = 27.44, I2 = 89%, P < 0.001). However, no significant improvement was observed in muscle strength or physical performance measures such as gait speed and six-minute walk distance with bimagrumab treatment, except among participants with slower baseline walking speeds or distances. DISCUSSION AND CONCLUSION: This meta-analysis provides valuable insights into the effects of bimagrumab on sarcopenic patients, highlighting its significant improvements in body composition parameters but limited impact on functional outcomes. The observed heterogeneity in outcomes across studies underscores the need for cautious interpretation, considering variations in study populations, treatment durations, and outcome assessments. While bimagrumab shows promise as a safe pharmacological intervention for enhancing muscle mass and reducing fat mass in sarcopenia, its minimal effects on muscle strength and broader physical performance suggest potential limitations in translating body composition improvements into functional gains. Further research is needed to clarify its long-term efficacy, optimal dosing regimens, and potential benefits for specific subgroups of sarcopenic patients.

Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model.

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios.

Imaging Findings in Cardiovascular Involvements of IgG4 Related Disease: A Systematic Review Study.

BACKGROUND: Immunoglobulin G4-related disease (IgG4-RD) is a fibroinflammatory condition characterized by IgG4-positive plasma cell infiltration that can affect multiple organs, including the cardiovascular system. The diagnosis of IgG4-RD relies on a combination of clinical, serological, radiological, and pathological findings. However, due to the varied and insidious clinical presentations, normal IgG4 levels in a significant percentage of patients, and frequent multi-organ involvement, imaging plays a crucial role in the diagnosis of IgG4-RD. The aim of study is to comprehensively examine the imaging findings in IgG4-related cardiovascular disease for accurate diagnosis and appropriate treatment. METHODS: A systematic search was conducted across electronic databases, PubMed, Scopus, and Web of Sciences, until 1 September 2023, following PRISMA guidelines by searching major databases for studies reporting detailed cardiovascular imaging findings in IgG4-RD. RESULTS: The search yielded 68 studies (60 case reports, 5 case series, 2 cross-sectional, 1 case-control) with 120 cases of cardiovascular IgG4-RD. Most of the cases were male, averaging 62.8 years. The common initial symptoms were dyspnea and chest pain. The most common imaging finding was vasculopathy, including vessel wall thickening, periarteritits, periaortitis, aortitis, stenosis, ectasia, aneurysm formation, intramural hemorrhage, fistula formation, and dissection, followed by pericardial involvement and mediastinal masses. Case series and cross-sectional studies also showed vasculopathy being the most common finding on various imaging modalities, including angiography and PET/CT, highlighting the complex pathology of IgG4-RD. CONCLUSION: This study evaluated current IgG4-RD articles, revealing a higher prevalence in men and vasculopathy as the most common cardiovascular complication.

Comparing Two-Step Approaches to Measuring Gender Identity: The Reliability and Applications of Asking About Sex Assigned at Birth versus Transgender Self-Identification.

Inclusive measures of gender are critical for health equity research. This study compared the reliability and applications of two different approaches for measuring gender in response to emerging community concerns regarding the potential harms of asking about sex assigned at birth (SAAB) within transgender and gender diverse (TGD) populations. Using data from a 2021 survey of LGBTQ+ people in Washington state, we compared approaches for measuring gender via a two-step question that collected data on: (1) current gender and SAAB versus (2) current gender and transgender self-identification. Among 2,275 LGBTQ+ participants aged 9-81, 63% were cisgender, 35% TGD, and 2% were not categorized. There was near perfect agreement between the two methods in their ability to identify TGD participants (percent agreement=99.7%, unweighted Cohen's Kappa=0.99). Among gender diverse participants, stratification by SAAB revealed differences in sexual health outcomes, while stratification by transgender self-identification revealed differences in access to gender-affirming care and lifetime experiences of discrimination. Ascertaining SAAB may be most useful for identifying sexual health disparities while transgender self-identification may better illuminate healthcare needs and social determinants of health among TGD people. Researchers and public health practitioners should critically consider the acceptability and relevance of SAAB questions to their research goals.

B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion.

Learning using privileged information (LUPI) has shown its effectiveness to improve the B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) by transferring knowledge from the elasticity ultrasound (EUS). However, LUPI only performs transfer learning between the paired data with shared labels, and cannot handle the scenario of modality imbalance. In order to conduct the supervised transfer learning between the paired ultrasound data together with the additional single-modal BUS images, a novel multi-view LUPI algorithm with Dual-Level Modality Completion, named DLMC-LUPI, is proposed to improve the performance of BUS-based CAD. The DLMC-LUPI implements both image-level and feature-level (dual-level) completions of missing EUS modality, and then performs multi-view LUPI for knowledge transfer. Specifically, in the dual-level modality completion stage, a variational autoencoder (VAE) model for feature generation and a novel generative adversarial network (VAE-based GAN) model for image generation are sequentially trained. The proposed VAE-based GAN can improve the synthesis quality of EUS images by adopting the features generated by VAE from the BUS images as the model constrain to make the features generated from the synthesized EUS images more similar to them. In the multi-view LUPI stage, two feature vectors are generated from the real or pseudo images as two source domains, and then fed them to the multi-view support vector machine plus classifier for model training. The experiments on two ultrasound datasets indicate that the DLMC-LUPI outperforms all the compared algorithms, and it can effectively improve the performance of single-modal BUS-based CAD.

Radical cystectomy versus trimodal therapy for muscle-invasive bladder cancer: Analysis of an other-cause mortality matched cohort.

OBJECTIVE: Comparative effectiveness studies comparing trimodal therapy (TMT) to radical cystectomy (RC) are typically hindered by selection bias where TMT is usually reserved to patients with poor overall health status. We developed a novel approach by matching patients based on their calculated other-cause mortality (OCM) risk. Using this homogeneous cohort, we tested the impact of TMT vs RC on cancer-specific mortality (CSM). MATERIALS AND METHODS: The Surveillance, Epidemiology and End Results (SEER) 2004-2018 database was queried to identify patients diagnosed with cT2-4N0M0 muscle-invasive bladder cancer (MIBC). A Fine-Gray competing-risk regression model calculating the 5-year OCM risk was used to create a 1:1 propensity-score matched-cohort of patients treated with RC or TMT. Cumulative incidence and competing-risk regression analyses tested the impact of treatment type (RC vs TMT) on CSM. Patients were further stratified according to clinical T stage (cT2 vs cT3-4) in sensitivity analyses. RESULTS: We identified 6,587 patients (76%) treated with RC and 2,057 (24%) with TMT. The median follow-up was 3.0 years. In the unmatched-cohort, 5-year OCM and CSM rates were 14% and 40% for RC vs 23% and 47% in TMT group, respectively (all P < 0.001). Our matched-cohort included 4,074 patients, equally distributed for treatment type, with no difference in 5-year OCM (HR: 0.98, 95% CI: 0.86-1.11, P = 0.714). In clinical-stage specific sensitivity analyses, 5-year CSM rate was significantly worse for cT2N0M0 patients treated with TMT (HR: 1.52, 95% CI: 1.21-1.91, P < 0.001) than those treated with RC. For cT3-4N0M0 patients, there was no difference in CSM among the 2 approaches (HR: 0.98, 95% CI: 0.63-1.52, P = 0.900). CONCLUSIONS: Our findings demonstrate an oncologic advantage of RC over TMT for cT2 MIBC patients. Conversely, we did not find a cancer-specific survival difference for cT3-T4 MIBC patients, regardless of treatment.

Deep unfolding network with spatial alignment for multi-modal MRI reconstruction.

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly under-sampled k-space data with another fully-sampled reference modality is an efficient solution. However, the misalignment between modalities, which is common in clinic practice, can negatively affect reconstruction quality. Existing deep learning-based methods that account for inter-modality misalignment perform better, but still share two main common limitations: (1) The spatial alignment task is not adaptively integrated with the reconstruction process, resulting in insufficient complementarity between the two tasks; (2) the entire framework has weak interpretability. In this paper, we construct a novel Deep Unfolding Network with Spatial Alignment, termed DUN-SA, to appropriately embed the spatial alignment task into the reconstruction process. Concretely, we derive a novel joint alignment-reconstruction model with a specially designed aligned cross-modal prior term. By relaxing the model into cross-modal spatial alignment and multi-modal reconstruction tasks, we propose an effective algorithm to solve this model alternatively. Then, we unfold the iterative stages of the proposed algorithm and design corresponding network modules to build DUN-SA with interpretability. Through end-to-end training, we effectively compensate for spatial misalignment using only reconstruction loss, and utilize the progressively aligned reference modality to provide inter-modality prior to improve the reconstruction of the target modality. Comprehensive experiments on four real datasets demonstrate that our method exhibits superior reconstruction performance compared to state-of-the-art methods.

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, and prognosis assessment; however, the DLR on multi-modal glioma imaging has not been assessed. PURPOSE: To assess multi-modal MRI for glioma based on the DLR method. MATERIAL AND METHODS: We assessed multi-modal images of 107 glioma patients (49 preoperative and 58 postoperative). All the images were reconstructed with both DLR and conventional reconstruction methods, encompassing T1-weighted (T1W), contrast-enhanced T1W (CE-T1), T2-weighted (T2W), and T2 fluid-attenuated inversion recovery (T2-FLAIR). The image quality was evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Visual assessment and diagnostic assessment were performed blindly by neuroradiologists. RESULTS: In contrast with conventionally reconstructed images, (residual) tumor SNR for all modalities and tumor to white/gray matter CNR from DLR images were higher in T1W, T2W, and T2-FLAIR sequences. The visual assessment of DLR images demonstrated the superior visualization of tumor in T2W, edema in T2-FLAIR, enhanced tumor and necrosis part in CE-T1, and fewer artifacts in all modalities. Improved diagnostic efficiency and confidence were observed for preoperative cases with DLR images. CONCLUSION: DLR of multi-modal MRI reconstruction prototype for glioma has demonstrated significant improvements in image quality. Moreover, it increased diagnostic efficiency and confidence of glioma.

Current landscape of mRNA technologies and delivery systems for new modality therapeutics.

Realizing the immense clinical potential of mRNA-based drugs will require continued development of methods to safely deliver the bioactive agents with high efficiency and without triggering side effects. In this regard, lipid nanoparticles have been successfully utilized to improve mRNA delivery and protect the cargo from extracellular degradation. Encapsulation in lipid nanoparticles was an essential factor in the successful clinical application of mRNA vaccines, which conclusively demonstrated the technology's potential to yield approved medicines. In this review, we begin by describing current advances in mRNA modifications, design of novel lipids and development of lipid nanoparticle components for mRNA-based drugs. Then, we summarize key points pertaining to preclinical and clinical development of mRNA therapeutics. Finally, we cover topics related to targeted delivery systems, including endosomal escape and targeting of immune cells, tumors and organs for use with mRNA vaccines and new treatment modalities for human diseases.

Multimodal therapy and use of adjunctive therapies to BoNT-A in spasticity management: defining terminology to help enhance spasticity treatment.

Spasticity management should be provided within the context of a comprehensive person-centered rehabilitation program. Furthermore, active goal setting for specific spasticity interventions is also important, with a well-established "more is better" approach. It is critical to consider adjunctive therapy and multimodal approaches if patients are not attaining their treatment goals. Often used interchangeably, there may be confusion between the terms adjunctive and multimodal therapy. Yet it is imperative to understand the differences between these approaches to achieve treatment goals in spasticity management. Addition of a secondary pharmacologic or non-pharmacologic treatment to optimize the efficacy of the initial modality, such as adding electrical stimulation or casting to BoNT-A, is considered an adjunctive therapy. Adjunctive therapy is time-specific and requires the added therapy be initiated within a specific period to enhance the primary treatment; usually within 2 weeks. Multimodal therapy is an integrated, patient-centric program of pharmacologic and non-pharmacologic strategies utilized in a concurrent/integrated or sequential manner to enhance the overall treatment effect across a variety of spasticity-associated impairments (e.g., neural and non-neural components). Moreover, within a multimodal approach, adjunctive therapy can be used to help enhance the treatment effect of one specific modality. The objectives of this paper are to clarify the differences between adjunctive and multimodal therapies, provide a brief evidence-based review of such approaches, and highlight clinical insights on selecting multimodal and adjunctive therapies in spasticity management.

Enhancing prompt perception in dementia: a comparative study of mixed reality cue modalities.

Introduction: Dementia impacts millions worldwide and is challenging individuals' ability to engage in daily activities. Active living is crucial in mitigating dementia's neurodegenerative effects, yet people with dementia often struggle to initiate and complete tasks independently. Technologies offer promising solutions to engage people with dementia in activities of active living and improving their quality of life through prompting and cueing. It is anticipated that developments in sensor and wearable technologies will result in mixed reality technology becoming more accessible in everyday homes, making them more deployable. The possibility of mixed reality technologies to be programmed for different applications, and to adapt them to different levels of impairments, behaviours and contexts, will make them more scalable. Objective: The study aimed to develop a better understanding of modalities of prompts that people with dementia perceive successfully and correctly in mixed reality environments. It investigated interactions of people with dementia with different types of visual (graphics, animation, etc.) and sound (human voice, tones, etc.) prompts in mixed reality technologies. Methods: We used the Research through Design (RtD) method in this study. This paper describes the findings from the user research carried out in the study. We conducted observation studies with twenty-two people with dementia playing games on off-the-shelf mixed reality technologies, including both Augmented Reality (HoloLens, ArKit on iPhone) and Augmented Virtuality (Xbox Kinect and Osmo) technologies. The interactions with the technologies during the gameplay were video recorded for thematic analysis in Noldus Observer XT (version 16.0) for successful and correct perception of prompts. Results: A comparison of the probability estimates of correct perception of the prompts by people with dementia suggests that human voice, graphic symbols and text are the most prominently perceived modalities of prompts. Feedback prompts for every action performed by people with dementia on the technology are critical for successful perception and should always be provided in the design. Conclusion: The study has resulted in recommendations and guidelines for designers to design prompts for people with dementia in mixed-reality environments. The work lays the foundation for considering mixed reality technologies as assistive tools for people with dementia, fostering discussions on their accessibility and inclusive design in technology development.