The anaerobic bacterium Propionibacterium acnes is believed to play an important role in the pathophysiology of the common skin disease acne vulgaris. Over the last 10 years our understanding of the taxonomic and intraspecies diversity of this bacterium has increased tremendously, and with it the realisation that particular strains are associated with skin health while others appear related to disease. This extensive review will cover our current knowledge regarding the association of P. acnes phylogroups, clonal complexes and sequence types with acne vulgaris based on multilocus sequence typing of isolates, and direct ribotyping of the P. acnes strain population in skin microbiome samples based on 16S rDNA metagenomic data. We will also consider how multi-omic and biochemical studies have facilitated our understanding of P. acnes pathogenicity and interactions with the host, thus providing insights into why certain lineages appear to have a heightened capacity to contribute to acne vulgaris development, while others are positively associated with skin health. We conclude with a discussion of new therapeutic strategies that are currently under investigation for acne vulgaris, including vaccination, and consider the potential of these treatments to also perturb beneficial lineages of P. acnes on the skin.

Mental health and behavioural problems are common among students commencing university. University life can be stressful and problems often exacerbate during their course of study, while others develop disorders for the first time. The WHO World Mental Health Surveys International College Student Project aims to conduct longitudinal research to examine and monitor student mental health and wellbeing. The Ulster University Student Wellbeing study, which commenced in September 2015 in Northern Ireland (NI), was conducted as part of this initiative (wave 1, n = 739), using the WMH-CIDI to examine psychopathology. Baseline prevalence rates of lifetime and 12-month mental health and substance disorders, ADHD and suicidality were high, with more than half of new undergraduate students reporting any lifetime disorder. Co-morbidity was common with 19.1% of students experiencing three or more disorders. Logistic regression models revealed that females, those over 21, non-heterosexual students, and those from a lower SES background were more likely to have a range of mental health and behavioural problems. Overall, 10% of new entry students received treatment for emotional problems in the previous year. However, 22.3% of students with problems said they would not seek help. The study provides important information for universities, policy makers and practice, on mental health and wellbeing in young people generally but particularly for students commencing university. The findings will assist in the development and implementation of protection and prevention strategies in the university setting and beyond.

Amphibian defensive skin secretions are complex, species-specific cocktails of biologically active molecules, including many uncharacterized peptides. The study of such secretions for novel peptide discovery is time-limited, as amphibians are in rapid global decline. While secretion proteome analysis is non-lethal, transcriptome analysis has until now required killing of specimens prior to skin dissection for cDNA library construction. Here we present the discovery that polyadenylated mRNAs encoding dermal granular gland peptides are present in defensive skin secretions, stabilized by endogenous nucleic acid-binding amphipathic peptides. Thus parallel secretory proteome and transcriptome analyses can be performed without killing the specimen in this model amphibian system—a finding that has important implications in conservation of biodiversity within this threatened vertebrate taxon and whose mechanistics may have broader implications in biomolecular science.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic associated with substantial morbidity and mortality worldwide, with particular risk for severe disease and mortality in the elderly population. SARS-CoV-2 infection is driven by a pathological hyperinflammatory response which results in a dysregulated immune response. Current advancements in aging research indicates that aging pathways have fundamental roles in dictating healthspan in addition to lifespan. Our review discusses the aging immune system and highlights that senescence and aging together, play a central role in COVID-19 pathogenesis. In our review, we primarily focus on the immune system response to SARS-CoV-2 infection, the interconnection between severe COVID-19, immunosenescence, aging, vaccination, and the emerging problem of Long-COVID. We hope to highlight the importance of identifying specific senescent endotypes (or “sendotypes”), which can used as determinants of COVID-19 severity and mortality. Indeed, identified sendotypes could be therapeutically exploited for therapeutic intervention. We highlight that senolytics, which eliminate senescent cells, can target aging-associated pathways and therefore are proving attractive as potential therapeutic options to alleviate symptoms, prevent severe infection, and reduce mortality burden in COVID-19 and thus ultimately enhance healthspan.

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer’s disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.