Three-Year Mortality of Older Hospitalized Patients with Osteosarcopenia: Data from the OsteoSys Study.

Osteosarcopenia, the concurrent presence of sarcopenia and osteopenia/osteoporosis, poses a significant health risk to older adults, yet its impact on clinical outcomes is not fully understood. The aim of this prospective, longitudinal multicentre study was to examine the impact of osteosarcopenia on 3-year mortality and unplanned hospitalizations among 572 older hospitalized patients (mean age 75.1 ± 10.8 years, 78% female). Sarcopenia and low bone mineral density (BMD) were evaluated using Dual Energy X-ray Absorptiometry and the European Working Group on Sarcopenia in Older People (EWGSOP2) and WHO criteria, respectively. Among participants, 76% had low BMD, 9% were sarcopenic, and 8% had osteosarcopenia. Individuals with osteosarcopenia experienced a significantly higher rate of mortality (46%, p < 001) and unplanned hospitalization (86%, p < 001) compared to those without this condition. Moreover, "healthy" subjects-those without sarcopenia or low BMD-showed markedly lower 3-year mortality (9%, p < 001) and less unplanned hospitalization (53%, p < 001). The presence of osteosarcopenia (p = 0.009) increased the 3-year mortality risk by 30% over sarcopenia alone and by 8% over low BMD alone, underscoring the severe health implications of concurrent muscle and bone deterioration. This study highlights the substantial impact of osteosarcopenia on mortality among older adults, emphasizing the need for targeted diagnostic and therapeutic strategies.

Osteosarcopenia, an Asymmetrical Overlap of Two Connected Syndromes: Data from the OsteoSys Study.

Osteoporosis and sarcopenia are two chronic conditions, which widely affect older people and share common risk factors. We investigated the prevalence of low bone mineral density (BMD) and sarcopenia, including the overlap of both conditions (osteosarcopenia) in 572 older hospitalized patients (mean age 75.1 ± 10.8 years, 78% women) with known or suspected osteoporosis in this prospective observational multicenter study. Sarcopenia was assessed according to the revised definition of the European Working Group on Sarcopenia in Older People (EWGSOP2). Low BMD was defined according to the World Health Organization (WHO) recommendations as a T-score < -1.0. Osteosarcopenia was diagnosed when both low BMD and sarcopenia were present. Low BMD was prevalent in 76% and the prevalence of sarcopenia was 9%, with 90% of the sarcopenic patients showing the overlap of osteosarcopenia (8% of the entire population). Conversely, only few patients with low BMD demonstrated sarcopenia (11%). Osteosarcopenic patients were older and frailer and had lower BMI, fat, and muscle mass, handgrip strength, and T-score compared to nonosteosarcopenic patients. We conclude that osteosarcopenia is extremely common in sarcopenic subjects. Considering the increased risk of falls in patients with sarcopenia, they should always be evaluated for osteoporosis.

Sustainable data analysis with Snakemake.

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

Rapid T-cell receptor interaction grouping with ting.

MOTIVATION: Clustering T-cell receptor repertoire (TCRR) sequences according to antigen specificity is challenging. The previously published tool GLIPH needs several days to weeks for clustering large repertoires, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences. RESULTS: We present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 36 real datasets with up to 62 000 unique CDR3ß sequences using both an implementation of our method called ting, GLIPH and its successor GLIPH2. While GLIPH required multiple weeks, ting only needed about one minute for the same task. GLIPH2 is comparably fast, but uses a different grouping paradigm. In addition, we found that in naïve repertoires, where no or very few antigen-specific CDR3 sequences or clusters should exist, our method indeed selects much fewer motifs and produces smaller clusters. AVAILABILITY AND IMPLEMENTATION: Our method has been implemented in Python as a tool called ting. It is available from GitHub (https://github.com/FelixMoelder/ting) or PyPI under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SLAMF7 and IL-6R define distinct cytotoxic versus helper memory CD8+ T cells.

The prevailing 'division of labor' concept in cellular immunity is that CD8+ T cells primarily utilize cytotoxic functions to kill target cells, while CD4+ T cells exert helper/inducer functions. Multiple subsets of CD4+ memory T cells have been characterized by distinct chemokine receptor expression. Here, we demonstrate that analogous CD8+ memory T-cell subsets exist, characterized by identical chemokine receptor expression signatures and controlled by similar generic programs. Among them, Tc2, Tc17 and Tc22 cells, in contrast to Tc1 and Tc17 + 1 cells, express IL-6R but not SLAMF7, completely lack cytotoxicity and instead display helper functions including CD40L expression. CD8+ helper T cells exhibit a unique TCR repertoire, express genes related to skin resident memory T cells (TRM) and are altered in the inflammatory skin disease psoriasis. Our findings reveal that the conventional view of CD4+ and CD8+ T cell capabilities and functions in human health and disease needs to be revised.

Eleven grand challenges in single-cell data science.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

The Role of Pre-existing Cross-Reactive Central Memory CD4 T-Cells in Vaccination With Previously Unseen Influenza Strains.

Influenza vaccination is a common approach to prevent seasonal and pandemic influenza. Pre-existing antibodies against close viral strains might impair antibody formation against previously unseen strains-a process called original antigenic sin. The role of this pre-existing cellular immunity in this process is, despite some hints from animal models, not clear. Here, we analyzed cellular and humoral immunity in healthy individuals before and after vaccination with seasonal influenza vaccine. Based on influenza-specific hemagglutination inhibiting (HI) titers, vaccinees were grouped into HI-negative and -positive cohorts followed by in-depth cytometric and TCR repertoire analysis. Both serological groups revealed cross-reactive T-cell memory to the vaccine strains at baseline that gave rise to the majority of vaccine-specific T-cells post vaccination. On the contrary, very limited number of vaccine-specific T-cell clones was recruited from the naive pool. Furthermore, baseline quantity of vaccine-specific central memory helper T-cells and clonotype richness of this population directly correlated with the vaccination efficacy. Our findings suggest that the deliberate recruitment of pre-existing cross-reactive cellular memory might help to improve vaccination outcome.