Uncovering the significance of expanded CD8+ large granular lymphocytes in inclusion body myositis: Insights into T cell phenotype and functional alterations, and disease severity.

Introduction: Inclusion body myositis (IBM) is a progressive inflammatory myopathy characterised by skeletal muscle infiltration and myofibre invasion by CD8+ T lymphocytes. In some cases, IBM has been reported to be associated with a systemic lymphoproliferative disorder of CD8+ T cells exhibiting a highly differentiated effector phenotype known as T cell Large Granular Lymphocytic Leukemia (T-LGLL). Methods: We investigated the incidence of a CD8+ T-LGL lymphoproliferative disorder in 85 IBM patients and an aged-matched group of 56 Healthy Controls (HC). Further, we analysed the phenotypical characteristics of the expanded T-LGLs and investigated whether their occurrence was associated with any particular HLA alleles or clinical characteristics. Results: Blood cell analysis by flow cytometry revealed expansion of T-LGLs in 34 of the 85 (40%) IBM patients. The T cell immunophenotype of T-LGLHIGH patients was characterised by increased expression of surface molecules including CD57 and KLRG1, and to a lesser extent of CD94 and CD56 predominantly in CD8+ T cells, although we also observed modest changes in CD4+ T cells and γδ T cells. Analysis of Ki67 in CD57+ KLRG1+ T cells revealed that only a small proportion of these cells was proliferating. Comparative analysis of CD8+ and CD4+ T cells isolated from matched blood and muscle samples donated by three patients indicated a consistent pattern of more pronounced alterations in muscles, although not significant due to small sample size. In the T-LGLHIGH patient group, we found increased frequencies of perforin-producing CD8+ and CD4+ T cells that were moderately correlated to combined CD57 and KLRG1 expression. Investigation of the HLA haplotypes of 75 IBM patients identified that carriage of the HLA-C*14:02:01 allele was significantly higher in T-LGLHIGH compared to T-LGLLOW individuals. Expansion of T-LGL was not significantly associated with seropositivity patient status for anti-cytosolic 5'-nucleotidase 1A autoantibodies. Clinically, the age at disease onset and disease duration were similar in the T-LGLHIGH and T-LGLLOW patient groups. However, metadata analysis of functional alterations indicated that patients with expanded T-LGL more frequently relied on mobility aids than T-LGLLOW patients indicating greater disease severity. Conclusion: Altogether, these results suggest that T-LGL expansion occurring in IBM patients is correlated with exacerbated immune dysregulation and increased disease burden.

Mobile phones as fomites for pathogenic microbes: A cross-sectional survey of perceptions and sanitization habits of health care workers in Dubai, United Arab Emirates.

BACKGROUNDS: In 2022, smartphone use continues to expand with the number of smartphone subscriptions surpassing 6 billion and forecasted to grow to 7.5 billion by 2026. The necessity of these 'high touch' devices as essential tools in professional healthcare settings carries great risks of cross-contamination between mobile phones and hands. Current research emphasises mobile phones as fomites enhancing the risk of nosocomial disease dissemination as phone sanitisation is often overlooked. To assess and report via a large-scale E-survey the handling practices and the use of phones by healthcare workers. METHODS: A total of 377 healthcare workers (HCWs) participated in this study to fill in an E-survey online consisting of 14 questions (including categorical, ordinal, and numerical data). Analysis of categorical data used non-parametric techniques such as Pearson's chi-squared test. RESULTS: During an 8-h shift, 92.8% (n/N = 350/377) use their phone at work with 84.6% (n/N = 319/377) considering mobile phones as an essential tool for their job. Almost all HCWs who participated in this survey believe their mobile phones could potentially harbour microorganisms (97.1%; n/N = 366/377). Fifty-seven respondents (15.1%) indicated that they use their phones while wearing gloves and 10.3% (n/N = 39/377) have never cleaned their phones. The majority of respondents (89.3%; n/N = 337/377) agreed that contaminated mobile phones could contribute to dissemination of SARS-CoV-2. CONCLUSION: Mobile phone use is now almost universal and indispensable in healthcare. Medical staff believe mobile phones can act as fomites with a potential risk for dissemination of microbes including SARS-COV-2. There is an urgent call for the incorporation of mobile phone sanitisation in infection prevention protocol. Studies on the use of ultraviolet-C based phone sanitation devices in health care settings are needed.

Use of mixed-type data clustering algorithm for characterizing temporal and spatial distribution of biosecurity border detections of terrestrial non-indigenous species.

Appropriate inspection protocols and mitigation strategies are a critical component of effective biosecurity measures, enabling implementation of sound management decisions. Statistical models to analyze biosecurity surveillance data are integral to this decision-making process. Our research focuses on analyzing border interception biosecurity data collected from a Class A Nature Reserve, Barrow Island, in Western Australia and the associated covariates describing both spatial and temporal interception patterns. A clustering analysis approach was adopted using a generalization of the popular k-means algorithm appropriate for mixed-type data. The analysis approach compared the efficiency of clustering using only the numerical data, then subsequently including covariates to the clustering. Based on numerical data only, three clusters gave an acceptable fit and provided information about the underlying data characteristics. Incorporation of covariates into the model suggested four distinct clusters dominated by physical location and type of detection. Clustering increases interpretability of complex models and is useful in data mining to highlight patterns to describe underlying processes in biosecurity and other research areas. Availability of more relevant data would greatly improve the model. Based on outcomes from our research we recommend broader use of cluster models in biosecurity data, with testing of these models on more datasets to validate the model choice and identify important explanatory variables.