Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Mindfulness-based cognitive therapy for anxiety symptoms in older adults in residential care.

OBJECTIVES: Anxiety in older people is under-diagnosed and poorly treated despite significant impairments that arise from anxiety. Mindfulness-based cognitive therapy (MBCT) has been shown to be a promising treatment for anxiety. The aim of this study was to determine the effect of an MBCT program on anxiety symptoms in older people living in residential care. METHOD: Fifty-two participants (34 females) were randomly allocated into therapy and control groups using a 2 × 3 mixed design. The average age of participants was 83 years. RESULTS: The group effect showed significant improvements on all measures at the end of the seven-week program in the therapy group, while the control group did not show significant changes. CONCLUSION: This study represents one of the first studies of the effectiveness of an MBCT program on anxiety symptoms for older people using a randomized controlled trial. The study has implications for future research that include the effectiveness of MBCT for the treatment of anxiety symptoms in older people, the utility of group therapy programs in residential care and the benefits of using specialized instruments for older populations.

HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach.

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.