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Commentary: Molecular responses in pig heart to human xenotransplantation unveiled by longitudinal multi-omic profiling.
No full textSuccessful Treatment of Candida auris Ventriculitis With Intravenous Liposomal Amphotericin B and Oral Flucytosine: A Case Report
No full textSearching for Sulfotyrosines (sY) in a HA(pY)STACK.
No full textProtein sulfation can be crucial in regulating protein-protein interactions but remains largely underexplored. Sulfation is nearly isobaric to phosphorylation, making it particularly challenging to investigate using mass spectrometry. The degree to which tyrosine sulfation (sY) is misidentified as phosphorylation (pY) is, thus, an unresolved concern. This study explores the extent of sY misidentification within the human phosphoproteome by distinguishing between sulfation and phosphorylation based on their mass difference. Using Gaussian mixture models (GMMs), we screened ∼45 M peptide-spectrum matches (PSMs) from the PeptideAtlas human phosphoproteome build for peptidoforms with mass error shifts indicative of sulfation. This analysis pinpointed 104 candidate sulfated peptidoforms, backed up by Gene Ontology (GO) terms and custom terms linked to sulfation. False positive filtering by manual annotation resulted in 31 convincing peptidoforms spanning 7 known and 7 novel sY sites. Y47 in calumenin was particularly intriguing since mass error shifts, acidic motif conservation, and MS2 neutral loss patterns characteristic of sulfation provided strong evidence that this site is sulfated rather than phosphorylated. Overall, although misidentification of sulfation in phosphoproteomics data sets derived from cell and tissue intracellular extracts can occur, it appears relatively rare and should not be considered a substantive confounding factor in high-quality phosphoproteomics data sets
2024 Update of the RECOVER-Adult Long COVID Research Index.
No full textIMPORTANCE: Classification of persons with long COVID (LC) or post-COVID-19 condition must encompass the complexity and heterogeneity of the condition. Iterative refinement of the classification index for research is needed to incorporate newly available data as the field rapidly evolves.
OBJECTIVE: To update the 2023 research index for adults with LC using additional participant data from the Researching COVID to Enhance Recovery (RECOVER-Adult) study and an expanded symptom list based on input from patient communities.
DESIGN, SETTING, AND PARTICIPANTS: Prospective, observational cohort study including adults 18 years or older with or without known prior SARS-CoV-2 infection who were enrolled at 83 sites in the US and Puerto Rico. Included participants had at least 1 study visit taking place 4.5 months after first SARS-CoV-2 infection or later, and not within 30 days of a reinfection. The study visits took place between October 2021 and March 2024.
EXPOSURE: SARS-CoV-2 infection.
MAIN OUTCOMES AND MEASURES: Presence of LC and participant-reported symptoms.
RESULTS: A total of 13 647 participants (11 743 with known SARS-CoV-2 infection and 1904 without known prior SARS-CoV-2 infection; median age, 45 years [IQR, 34-69 years]; and 73% were female) were included. Using the least absolute shrinkage and selection operator analysis regression approach from the 2023 model, symptoms contributing to the updated 2024 index included postexertional malaise, fatigue, brain fog, dizziness, palpitations, change in smell or taste, thirst, chronic cough, chest pain, shortness of breath, and sleep apnea. For the 2024 LC research index, the optimal threshold to identify participants with highly symptomatic LC was a score of 11 or greater. The 2024 index classified 20% of participants with known prior SARS-CoV-2 infection and 4% of those without known prior SARS-CoV-2 infection as having likely LC (vs 21% and 5%, respectively, using the 2023 index) and 39% of participants with known prior SARS-CoV-2 infection as having possible LC, which is a new category for the 2024 model. Cluster analysis identified 5 LC subtypes that tracked quality-of-life measures.
CONCLUSIONS AND RELEVANCE: The 2024 LC research index for adults builds on the 2023 index with additional data and symptoms to help researchers classify symptomatic LC and its symptom subtypes. Continued future refinement of the index will be needed as the understanding of LC evolves
Fokker-Planck diffusion maps of microglial transcriptomes reveal radial differentiation into substates associated with Alzheimer\u27s pathology.
No full textThe identification of microglia subtypes is important for understanding the role of innate immunity in neurodegenerative diseases. Current methods of unsupervised cell type identification assume a small noise-to-signal ratio of transcriptome measurements to produce well-separated cell clusters. However, identification of subtypes can be obscured by gene expression noise, which diminishes the distances in transcriptome space between distinct cell types, blurs boundaries, and reduces reproducibility. Here we use Fokker-Planck (FP) diffusion maps to model cellular differentiation as a stochastic process whereby cells settle into local minima that correspond to cell subtypes, in a potential landscape constructed from transcriptome data using a nearest neighbor graph approach. By applying critical transition fields, we identify individual cells on the verge of transitioning between subtypes, revealing microglial cells in an inactivated, homeostatic state before radially transitioning into various specialized subtypes. Specifically, we show that cells from Alzheimer\u27s disease patients are enriched in a microglia subtype associated to antigen presentation and T-cell recruitment, and are depleted in an anti-inflammatory subtype
The improved prognosis of FLT3-internal tandem duplication but not tyrosine kinase domain mutations in acute myeloid leukemia in the era of targeted therapy: a real-world study using large-scale electronic health record data
No full textNot available
Surgical Management of Thick Primary Cutaneous Melanoma in the US.
No full textBACKGROUND: There remains significant variability in the surgical management of thick melanoma patients with clinically node-negative disease. We evaluated factors influencing overall survival (OS) in these patients, focusing on the surgical management of the primary tumor and nodal basin.
METHODS: Using the National Cancer Database, we identified 7647 patients diagnosed between 2012 and 2017 with thick melanoma (\u3e 4 mm, T4) and clinically node-negative disease. 4332 patients had complete data and met all inclusion criteria. These patients were stratified into three groups based on nodal assessment: sentinel lymph node biopsy (SLNB), elective lymphadenectomy (ELND), or no nodal evaluation (NNE). OS was compared using Kaplan-Meier analyses and multivariable Cox proportional hazard regression.
RESULTS: In the cohort, 2851 (65.8%) had a SLNB, 799 (18.4%) had an ELND, and 682 (15.7%) had NNE. OS significantly decreased for each millimeter of increasing Breslow thickness. Ulceration, lymphovascular invasion, and tumor-positive SLN (+SLN) were associated with worse OS (all p \u3c 0.001). The size of surgical margins was not significantly associated with OS. Five-year OS of patients with SLNB was 67.1% ± 1.2% compared to 57.9% ± 2.3% with ELND and 46.8% ± 2.5% with NNE (p \u3c 0.001). Among +SLN patients, a complete lymph node dissection (CLND) was performed in 400 (62.3%) but was not associated with improved OS (p = 0.67) when compared to the nodal observation group.
CONCLUSION: Our results suggest that increasing Breslow thickness and nodal assessment provide important prognostic information regarding OS for thick melanoma patients, which emphasizes the importance of SLNB for staging and confirm the lack of benefit of CLND after +SLN in thick melanoma. The size of surgical margins did not affect OS
Thinking big and the WE ACT framework for environmentally sustainable critical care nursing.
No full textAre Randomized Trials Better? Comparison of Baseline Covariate Balance of a Propensity Score-Balanced Lumbar Spine IDE Trial and Comparable RCTs.
No full textSTUDY DESIGN: Prospective Observational Propensity Score.
OBJECTIVES: Randomization may lead to bias when the treatment is unblinded and there is a strong patient preference for treatment arms (such as in spinal device trials). This report describes the rationale and methods utilized to develop a propensity score (PS) model for an investigational device exemption (IDE) trial (NCT03115983) to evaluate decompression and stabilization with an investigational dynamic sagittal tether (DST) vs decompression and Transforaminal Lumbar Interbody Fusion (TLIF) for patients with symptomatic grade I lumbar degenerative spondylolisthesis with spinal stenosis.
METHODS: Twenty-five baseline covariates were selected for their expected relationship to patient outcomes or enrollment bias. Subclassification by PS quintiles was used to design a sample of investigational DST patients and TLIF controls with excellent covariate balance in which to estimate causal treatment effects. Additionally, balance in PS covariates was compared to available matching covariates from seven randomized spine IDE trials.
RESULTS: The PS subclassification design resulted in excellent balance across baseline covariates, as evidenced by small standardized mean differences and no significant between group differences after accounting for the PS design (all
CONCLUSION: The PS subclassification design achieved excellent covariate balance between DST investigational and TLIF control participants. This PS designed sample shows covariate balance similar to that observed in published studies in which patients were randomized to investigational or control arms.Clinical trial registered with https://www.clinicaltrials.gov (NCT03115983)
Collaborating across sectors in service of open science, precision oncology, and patients: an overview of the AACR Project GENIE (Genomics Evidence Neoplasia Information Exchange) Biopharma Collaborative (BPC)
No full textThe American Association for Cancer Research (AACR) Project GENIE (Genomics Evidence Neoplasia Information Exchange) Biopharma Collaborative (BPC) is a multi-phase, pre-competitive collaboration between 10 biopharmaceutical companies and select GENIE-participating academic institutions, focused on detailed clinical annotations of a subset of patients within the GENIE Registry. The cohorts focus on 10 solid tumors, and each integrates demographic, diagnosis, genomic, and treatment data with longitudinal, real-world patient outcomes. Data are collected following a structured framework to ensure interoperability and forward compatibility with other data models. Each cohort undergoes a series of rigorous quality control and assurance protocols which ensures consistency, accuracy, and reliability of the data across multiple institutions before public release of the data. Initial analyses of the BPC data have yielded valuable insights, including the validation of treatment-induced resistance mutations and genomic drivers associated with anatomic sites of metastasis. Additionally, the real-world response endpoints compare favorably to published trial results. Central management and a shared knowledgebase help integrate diverse functional teams in the execution of a complex, multi-institutional data collection effort. Future directions aim to automate significant portions of the clinical annotation process to collect clinical data at scale. These efforts will increase the depth and granularity of the BPC data, as well as expand the overall cohort size and range of cancer types represented