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Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the \'type 1\' ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the first day of the workshop, the recordings, and presentations of which are publicly available for review. The topics covered included the report of the Medical Image De-Identification Initiative (MIDI) Task Group on best practices and recommendations, tools for conventional approaches to de-identification, international approaches to de-identification, and an industry panel.

Genome annotation has historically ignored small open reading frames (smORFs), which encode a class of proteins shorter than 100 amino acids, collectively referred to as microproteins. This cutoff was established to avoid thousands of false positives due to limitations of pure genomics pipelines. Proteogenomics, a computational approach that combines genomics, transcriptomics, and proteomics, makes it possible to accurately identify these short sequences by overlaying different levels of omics evidence. In this chapter, we showcase the use of μProteInS, a bioinformatics pipeline developed for the identification of unannotated microproteins encoded by smORFs in bacteria. The workflow covers all the steps from quality control and transcriptome assembly to the scoring and post-processing of mass spectrometry data. Additionally, we provide an example on how to apply the pipeline\'s machine learning method to identify high-confidence spectra and pinpoint the most reliable identifications from large datasets.

BACKGROUND: Intraoperative MRI (iMRI) has emerged as a useful tool in glioma surgery to safely improve the extent of resection. However, iMRI requires a dedicated operating room (OR) with an integrated MRI scanner solely for this purpose. Due to physical or economical restraints, this may not be feasible in all centers. The aim of this study was to investigate the feasibility of using a non-dedicated MRI scanner at the radiology department for iMRI and to describe the workflow with special focus on time expenditure and surgical implications.
METHODS: In total, 24 patients undergoing glioma surgery were included. When the resection was deemed completed, the wound was temporarily closed, and the patient, under general anesthesia, was transferred to the radiology department for iMRI, which was performed using a dedicated protocol on 1.5 or 3 T scanners. After performing iMRI the patient was returned to the OR for additional tumor resection or final wound closure. All procedural times, timestamps, and adverse events were recorded.
RESULTS: The median time from the decision to initiate iMRI until reopening of the wound after scanning was 68 (52-104) minutes. Residual tumors were found on iMRI in 13 patients (54%). There were no adverse events during the surgeries, transfers, transportations, or iMRI-examinations. There were no wound-related complications or infections in the postoperative period or at follow-up. There were no readmissions within 30 or 90 days due to any complication.
CONCLUSIONS: Performing intraoperative MRI using an MRI located outside the OR department was feasible and safe with no adverse events. It did not require more time than previously reported data for dedicated iMRI scanners. This could be a viable alternative in centers without access to a dedicated iMRI suite.

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell\'s streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.

UNASSIGNED: Mechanical thrombectomy is a time-sensitive treatment, with rapid initiation and reduced delays being associated with better patient outcomes. Several systematic reviews reported on various interventions to address delays. Hence, we performed an umbrella review of systematic reviews to summarise the current evidence.
UNASSIGNED: Medline, Embase, Cochrane Library and JBI were searched for published systematic reviews. Systematic Reviews that detailed outcomes related to time-to-thrombectomy or functional independence were included. Methodological quality was assessed using the JBI critical appraisal tool by two independent reviewers.
UNASSIGNED: A total of 17 systematic reviews were included in the review. These were all assessed as high-quality reviews. A total of 13 reviews reported on functional outcomes, and 12 reviews reported on time-to-thrombectomy outcomes. Various interventions were identified as beneficial. The most frequently reported beneficial interventions that improved functional and time-related outcomes included: direct-to-angio-suite and using a mothership model (compared to drip-and-ship). Only a few studies investigated other strategies including other pre-hospital and teamwork strategies.
UNASSIGNED: Overall, there were various strategies that can be used to reduce delays in the delivery of mechanical thrombectomy with different effectiveness. The mothership model appears to be superior to the drip-and-ship model in reducing delays and improving functional outcomes. Additionally, the direct-to-angiosuite approach appears to be beneficial, but further research is required for broader implementation of this approach and to determine which groups of patients would benefit the most.

UNASSIGNED: Over the past decade, the adoption of virtual wards has surged. Virtual wards aim to prevent unnecessary hospital admissions, expedite home discharge, and enhance patient satisfaction, which are particularly beneficial for the older adult population who faces risks associated with hospitalization. Consequently, substantial investments are being made in virtual rehabilitation wards (VRWs), despite evidence of varying levels of success in their implementation. However, the facilitators and barriers experienced by virtual ward staff for the rapid implementation of these innovative care models remain poorly understood.
UNASSIGNED: This paper presents insights from hospital staff working on an Australian VRW in response to the growing demand for programs aimed at preventing hospital admissions. We explore staff\'s perspectives on the facilitators and barriers of the VRW, shedding light on service setup and delivery.
UNASSIGNED: Qualitative interviews were conducted with 21 VRW staff using the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. The analysis of data was performed using framework analysis and the 7 domains of the NASSS framework.
UNASSIGNED: The results were mapped onto the 7 domains of the NASSS framework. (1) Condition: Managing certain conditions, especially those involving comorbidities and sociocultural factors, can be challenging. (2) Technology: The VRW demonstrated suitability for technologically engaged patients without cognitive impairment, offering advantages in clinical decision-making through remote monitoring and video calls. However, interoperability issues and equipment malfunctions caused staff frustration, highlighting the importance of promptly addressing technical challenges. (3) Value proposition: The VRW empowered patients to choose their care location, extending access to care for rural communities and enabling home-based treatment for older adults. (4) Adopters and (5) organizations: Despite these benefits, the cultural shift from in-person to remote treatment introduced uncertainties in workflows, professional responsibilities, resource allocation, and intake processes. (6) Wider system and (7) embedding: As the service continues to develop to address gaps in hospital capacity, it is imperative to prioritize ongoing adaptation. This includes refining the process of smoothly transferring patients back to the hospital, addressing technical aspects, ensuring seamless continuity of care, and thoughtfully considering how the burden of care may shift to patients and their families.
UNASSIGNED: In this qualitative study exploring health care staff\'s experience of an innovative VRW, we identified several drivers and challenges to implementation and acceptability. The findings have implications for future services considering implementing VRWs for older adults in terms of service setup and delivery. Future work will focus on assessing patient and carer experiences of the VRW.

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography-MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.