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Disease activity in rheumatoid arthritis (RA) is influenced by activation of circulating and synovial immune cells. Regulatory T cells (Tregs) and monocytes are key cells that drive inflammation in RA. This study investigated if a relationship exists between disease activity in RA and circulating Treg and monocyte numbers and phenotypes. A potential sialic acid (Sia) mediated link between Tregs and monocytes was also probed in vitro. Peripheral blood mononuclear cells (PBMCs) were isolated from RA patient (n = 62) and healthy control (n = 21) blood using density gradient separation. Flow cytometry was used to count and phenotype Treg and monocyte subsets, and to sort healthy control Tregs for Sia cell culture experiments. The effects of Sia on activated Treg FoxP3 and NFκB expression was assessed by flow cytometry and concentrations of secreted TNFα, IL-10 and IFNγ determined by ELISA. High disease activity RA patients who were unresponsive to disease modifying anti-rheumatic drugs (n = 31), have significantly lower relative numbers (percentages) of CD4+CD25+CD127- Tregs (p < 0.01) and memory CD45RA-FoxP3+ Tregs (p < 0.01), compared to low disease activity responders (n = 24). Relative numbers of non-classical CD169+ monocytes are associated with disease activity in RA (p = 0.012). Sia reduced Treg expression of FoxP3, NFκB and cytokines in vitro. A strong association has been identified between non-classical CD169+ monocytes and post-treatment disease activity in RA. This study also indicates that Sia can reduce Treg activation and cytokine release. We postulate that such a reduction could be mediated by interaction with sialyted proteins captured by CD169+ monocytes.

Objective: To evaluate the prevalence of suicidal ideation (SI), plans and attempts, and non-suicidal self-injury (NSSI) among students with attention deficit hyperactivity disorder (ADHD). Furthermore, we explored the mediating effects of depression, anxiety, alcohol and substance use on the association between ADHD and suicidal behaviors and NSSI. Method: Participants were first-year undergraduate students ( n = 1,829) recruited as part of the World Mental Health International College Student Initiative. Participants completed validated clinical measures online. Results: The prevalence of suicide behaviors and NSSI were significantly higher among students with ADHD than those without. Mediation analyses indicated that ADHD directly and indirectly increased suicidal behaviors and NSSI. While ADHD increased suicidal behaviors and NSSI through depression, ADHD and the co-variates age and gender also had indirect effects on suicidal behaviors via substance use. Conclusions: Specific predictors of risk were identified for students with ADHD which may inform the development of more targeted mental health and suicide prevention strategies across campuses.

Background Many students struggle with psychological problems during their college years. These problems may be even more apparent during the COVID-19 pandemic with the accompanying restrictions and transition to an online learning environment, but few longitudinal studies have been conducted to date. The aim of this study was to compare symptoms of depression, anxiety and suicidality prior to and during the pandemic, and identify stressors. Methods This study was conducted among students attending Ulster University, Northern Ireland (NI) and LYIT, Republic of Ireland (ROI), as part of the World Mental Health International College Student Initiative (WMH-ICS). Data was collected from first year students in September 2019. The completed response rate was 25.22% (NI) and 41.9% (ROI) in relation to the number of first-year students registered. A follow up study was conducted in Autumn 2020, with 884 students fully completing the online survey in both years, equating to just under half of those who completed initially. Results High levels of mental health problems were found in year 1, especially in the ROI. Levels of depression increased significantly in year 2, particularly among students in NI, however, levels of anxiety decreased. No significant variations were found for suicidal behaviour. Several stressors were revealed, including increased social isolation, and worrying about loved ones. Limitations The findings may not be generalised to other student populations. Conclusions This study reveals variation in symptoms of depression and anxiety since the onset of the pandemic. In particular, the large increase in students with depression is of concern.

Background Elevated levels of suicidality, ADHD, mental ill-health and substance disorders are reported among college students globally, yet few receive treatment. Some faculties and courses appear to have more at-risk students than others. The current study aimed to determine if students commencing college in different academic disciplines were at a heightened risk for psychopathology, substance use disorders and suicidal behaviour, and examined variations in help-seeking behaviour. Materials and methods The study utilised data collected from 1,829 first-year undergraduate students as part of the Student Psychological Intervention Trial (SPIT) which commenced in September 2019 across four Ulster University campuses in Northern Ireland and an Institute of Technology, in the North-West of Ireland. The SPIT study is part of the World Mental Health International College Student Initiative (WMH-ICS) which uses the WMH-CIDI to identify 12-month and lifetime disorders. Results Students from Life and Health Sciences reported the lowest rates of a range of psychological problems in the year prior to commencing college, while participants studying Arts and Humanities displayed the highest levels (e.g. depression 20.6%; social anxiety 38.8%). However, within faculty variations were found. For example, psychology students reported high rates, while nursing students reported low rates. Variations in help seeking behaviour were also revealed, with male students less likely to seek help. Conclusions Detecting specific cohorts at risk of psychological disorders and suicidality is challenging. This study revealed that some academic disciplines have more vulnerable students than others, with many reluctant to seek help for their problems. It is important for educators to be aware of such issues and for colleges to provide information and support to students at risk. Tailored interventions and prevention strategies may be beneficial to address the needs of students from different disciplines.

Multidimensional omic datasets often have correlated features leading to the possibility of discovering multiple biological signatures with similar predictive performance for a phenotype. However, their exploration is limited by low sample size and the exponential nature of the combinatorial search leading to high computational cost. To address these issues, we have developed an algorithm muSignAl (multiple signature algorithm) which selects multiple signatures with similar predictive performance while systematically bypassing the requirement of exploring all the combinations of features. We demonstrated the workflow of this algorithm with an example of proteomics dataset. muSignAl is applicable in various bioinformatics-driven explorations, such as understanding the relationship between multiple biological feature sets and phenotypes, and discovery and development of biomarker panels while providing the opportunity of optimising their development cost with the help of equally good multiple signatures. Source code of muSignAl is freely available at https://github.com/ShuklaLab/muSignAl.

A growing body of evidence supports an important role for alterations in the brain-gut-microbiome axis in the aetiology of depression and other psychiatric disorders. The potential role of the oral microbiome in mental health has received little attention, even though it is one of the most diverse microbiomes in the body and oral dysbiosis has been linked to systemic diseases with an underlying inflammatory aetiology. This study examines the structure and composition of the salivary microbiome for the first time in young adults who met the DSM-IV criteria for depression (n = 40) and matched controls (n = 43) using 16S rRNA gene-based next generation sequencing. Subtle but significant differences in alpha and beta diversity of the salivary microbiome were observed, with clear separation of depressed and healthy control cohorts into distinct clusters. A total of 21 bacterial taxa were found to be differentially abundant in the depressed cohort, including increased Neisseria spp. and Prevotella nigrescens, while 19 taxa had a decreased abundance. In this preliminary study we have shown that the composition of the oral microbiome is associated with depression in young adults. Further studies are now warranted, particuarly investigations into whether such shifts play any role in the underling aetiology of depression.

Background: Acute coronary syndrome (ACS) is one of the main causes of death worldwide. The 12-lead electrocardiogram (ECG) is used to help diagnose ACS, along with clinical risk factors (smoking, diabetes mellitus, hypertension, hscTn and positive family history of ACS. These methods however are associated with many limitations resulting in variable sensitivity/specificity. The aim of this study was to use a machine learning approach to develop an optimum panel of blood protein biomarkers capable of independently diagnosing ACS. Methods: A hybrid feature selection and ML prediction algorithms including two classifiers: 1) decision tree (DT) and 2) logistic regression were applied to protein biomarkers (327 proteins) collected from patients with ACS n=91 or non-ACS n=97. Results: Using this approach, 20 proteins out of 327 proteins were able to accurately distinguish between ACS and non-ACS (logistic regression ROC-AUC=0.8 and accuracy=82.5% and DT ROC-A UC=0.6 and accuracy=64.9%. Conclusion: Logistic regression obtained a higher performance compared to DT and showed promising results uncovering a panel of 20 protein biomarkers which included those associated with progressive atherosclerotic plaques, myocardial injury and inflammation. This approach was able to accurately discriminate between patients with ACS and non-ACS.

Predicting response to anti-tumour necrosis factor alpha (anti-TNFα) drugs at baseline remains an elusive goal in rheumatoid arthritis (RA) management. The purpose of this study was to determine if baseline genetic variants of PTPRC, AFF3, myD228, CHUK, MTHFR1, MTHFR2, CD226 and a number of KIR and HLA alleles could predict response to anti-TNF-α in rheumatoid arthritis patients.Peripheral blood samples were collected from 238 RA patients treated with anti-TNFα drugs. Genotyping was performed using biochip array technology by Randox Laboratories Ltd. and sequence specific polymerase chain reaction. Linear regression analysis was performed to investigate the role of these genotypes in predicting response to treatment, as defined by European League Against Rheumatism (EULAR) response classification and absolute change in disease activity score (DAS28).Of 238 RA patients analysed, 50.4% received adalimumab, 29.7% received etanercept, 14.8% received infliximab, 3.4% certoluzimab and 1.7% golimumab. The MTHFR1 variant rs1801133 was significantly associated with the EULAR response, p=0.044. Patients with the HLA-DRB1*0404 allele displayed a significantly larger reduction in DAS28 compared to non-carriers (mean -2.22, -1.67 respectively, p=0.033). CD226 rs763361 was the only SNP variant significantly associated with ΔDAS28 (p=0.029).This study has investigated individual allele associations with reductions in DAS28 across a range of anti-TNFα treatments. A combined predictive model indicates that patients with the HLA-DRB1*0404 allele and without the CD226 rs763361 polymorphism exhibit the largest reduction in DAS28 after anti-TNF-α treatment.

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.

Cellular senescence is a state of growth arrest that occurs after cells encounter various stresses. Senescence contributes to tumour suppression, embryonic development, and wound healing. It impacts on the pathology of various diseases by secreting inflammatory chemokines, immune modulators and other bioactive factors. These secretory biosignatures ultimately cause inflammation, tissue fibrosis, immunosenescence and many ageing-related diseases such as atrial fibrillation (AF). Because the molecular mechanisms underpinning AF development remain unclear, current treatments are suboptimal and have serious side effects. In this review, we summarize recent results describing the role of senescence in AF. We propose that senescence factors induce AF and have a causative role. Hence, targeting senescence and its secretory phenotype may attenuate AF.

This study assessed the metabolic and neuroprotective actions of the sodium glucose cotransporter-2 inhibitor dapagliflozin in combination with the GLP-1 agonist liraglutide in dietary-induced diabetic mice. Mice administered low-dose streptozotocin (STZ) on a high-fat diet received dapagliflozin, liraglutide, dapagliflozin-plus-liraglutide (DAPA-Lira) or vehicle once-daily over 28 days. Energy intake, body weight, glucose and insulin concentrations were measured at regular intervals. Glucose tolerance, insulin sensitivity, hormone and biochemical analysis, dual-energy X-ray absorptiometry densitometry, novel object recognition, islet and brain histology were examined. Once-daily administration of DAPA-Lira resulted in significant decreases in body weight, fat mass, glucose and insulin concentrations, despite no change in energy intake. Similar beneficial metabolic improvements were observed regarding glucose tolerance, insulin sensitivity, HOMA-IR, HOMA-β, HbA1c and triglycerides. Plasma glucagon, GLP-1 and IL-6 levels were increased and corticosterone concentrations decreased. DAPA-Lira treatment decreased alpha cell area and increased insulin content compared to dapagliflozin monotherapy. Recognition memory was significantly improved in all treatment groups. Brain histology demonstrated increased staining for doublecortin (number of immature neurons) in dentate gyrus and synaptophysin (synaptic density) in stratum oriens and stratum pyramidale. These data demonstrate that combination therapy of dapagliflozin and liraglutide exerts beneficial metabolic and neuroprotective effects in diet-induced diabetic mice. Our results highlight important personalised approach in utilising liraglutide in combination with dapagliflozin, instead of either agent alone, for further clinical evaluation in treatment of diabetes and associated neurodegenerative disorders.

Introduction: Management of co- and multi-morbidity continues to represent a major clinical challenge. There remains a lack of understanding of how multiple pathologies or conditions co-evolve or i...

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on the group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer’s disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and k-Nearest Neighbor (kNNreg) for regression and Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (kNNclass) for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to be consistently higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e.,R2= 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Background Acute Coronary Syndrome (ACS) is currently diagnosed using a 12‑lead Electrocardiogram (ECG). Our recent work however has shown that interpretation of the 12‑lead ECG is complex and that clinicians can be sub-optimal in their interpretation. Additionally, ECG does not always identify acute total occlusions in certain patients. Purpose The aim of the present study was to undertake an exploratory analysis to compare protein expression profiles of ACS patients that may in the future augment ECG diagnosis. Methods Patients were recruited consecutively at the cardiac catheterization laboratory at Altnagelvin Hospital over a period of 6 months. A low risk control group was recruited by advertisement. Blood samples were analysed using the multiplex proximity extension assays by OLINK proteomics. Support vector machine (SVM) learning was used as a classifier to distinguish between patient groups on training data. The ST segment elevation level was extracted from each ECG for a subset of patients and combined with the protein markers. Quadratic SVM (QSVM) learning was then used as a classifier to distinguish STEMI from NSTEMI on training and test data. Results Of the 344 participants recruited, 77 were initially diagnosed with NSTEMI, 7 with STEMI, and 81 were low risk controls. The other participants were those diagnosed with angina (stable and unstable) or non-cardiac patients. Of the 368 proteins analysed, 20 proteins together could significantly differentiate between patients with ACS and patients with stable angina (ROC-AUC = 0.96). Six proteins discriminated significantly between the stable angina and the low risk control groups (ROC-AUC = 1.0). Additionally, 16 proteins together perfectly discriminated between the STEMI and NSTEMI patients (ROC-AUC = 1). ECG comparisons with the protein biomarker data for a subset of patients (STEMI n = 6 and NSTEMI n = 6), demonstrated that 21 features (20 proteins + ST elevation) resulted in the highest classification accuracy 91.7% (ROC-AUC = 0.94). The 20 proteins without the ST elevation feature gave an accuracy of 80.6% (ROC-AUC 0.91), while the ST elevation feature without the protein biomarkers resulted in an accuracy of 69.3% (ROC-AUC = 0.81). Conclusions This preliminary study identifies panels of proteins that should be further explored in prospective studies as potential biomarkers that may augment ECG diagnosis and stratification of ACS. This work also highlights the importance for future studies to be designed to allow a comparison of blood biomarkers not only with ECG's but also with cardio angiograms.

Amyotrophic lateral sclerosis (ALS), also known as motor neurone disease, is an incurable neurodegenerative condition, characterized by the loss of upper and lower motor neurons. It affects 1-1.8/100,000 individuals worldwide, and the number of cases is projected to increase as the population ages. Thus, there is an urgent need to identify both therapeutic targets and disease-specific biomarkers - biomarkers that would be useful to diagnose and stratify patients into different sub-groups for therapeutic strategies, as well as biomarkers to follow the efficacy of any treatment tested during clinical trials. There is a lack of knowledge about pathogenesis and many hypotheses. Numerous ‘omics’ studies have been conducted on ALS in the past decade to identify a disease-signature in tissues and circulating biomarkers. The first goal of the present review was to group the molecular pathways that have been implicated in monogenic forms of ALS, to enable the description of patient strata corresponding to each pathway grouping. This strategy allowed us to suggest 14 strata, each potentially targetable by different pharmacological strategies. The second goal of this review was to identify diagnostic/prognostic biomarker candidates consistently observed across the literature. For this purpose, we explore previous biomarker-relevant ‘omics’ studies of ALS and summarize their findings, focusing on potential circulating biomarker candidates. We systematically review 118 papers on biomarkers published during the last decade. Several candidate markers were consistently shared across the results of different studies in either cerebrospinal fluid (CSF) or blood (leukocyte or serum/plasma). Although these candidates still need to be validated in a systematic manner, we suggest the use of combinations of biomarkers that would likely reflect the “health status” of different tissues, including motor neuron health (e.g. pNFH and NF-L, cystatin C, Transthyretin), inflammation status (e.g. MCP-1, miR451), muscle health (miR-338-3p, miR-206) and metabolism (homocysteine, glutamate, cholesterol). In light of these studies and because ALS is increasingly perceived as a multi-system disease, the identification of a panel of biomarkers that accurately reflect features of pathology is a priority, not only for diagnostic purposes but also for prognostic or predictive applications.

Hand osteoarthritis (HOA) includes different subsets; a particular and uncommon form is erosive HOA (EHOA). Interleukin- (IL-) 1βplays a crucial role in the pathogenesis of osteoarthritis (OA); it is synthesized as an inactive precursor which requires the intervention of a cytosolic multiprotein complex, named inflammasome, for its activation. The aim of this study was to investigate the involvement of IL-1βand the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome in patients with EHOA and nonerosive HOA (NEHOA) compared to healthy controls. In particular, we evaluated the gene expression of IL-1βand NLRP3, the serum levels of IL-1β, IL-6, IL-17, and tumor necrosis factor- (TNF-)α, and the protein levels of IL-1βand NLRP3. We also assessed the relationships between IL-1βand NLRP3 and clinical, laboratory, and radiological findings. Fifty-four patients with HOA (25 EHOA and 29 NEHOA) and 20 healthy subjects were included in the study. Peripheral blood mononuclear cell (PBMC) gene and protein expressions of IL-1βand NLRP3 were quantified by quantitative real-time PCR and western blot. IL-1β, IL-6, IL-17, and TNF-αserum levels were determined by ELISA. IL-1βgene expression was significantly reduced (p=0.0208) in EHOA compared to healthy controls. NLRP3 protein levels were significantly increased in the NEHOA group versus the control (p=0.0063) and EHOA groups (p=0.0038). IL-1βserum levels were not significantly different across the groups; IL-6, IL-17, and TNF-αwere not detectable in any sample. IL-1βconcentrations were negatively correlated with the Kellgren-Lawrence score in the whole population (r=−0.446;p=0.0008) and in NEHOA (r=−0.608;p=0.004), while IL-1βgene expression was positively correlated with the number of joint swellings in the EHOA group (r=0.512;p=0.011). Taken together, our results, showing poorly detectable IL-1βconcentrations and minimal inflammasome activity in the PBMCs of HOA patients, suggest a low grade of systemic inflammation in HOA. This evidence does not preclude a possible involvement of these factors at the local level.

Autoimmune diseases (ADs) are currently treated with anti-inflammatory and immunosuppressive drugs, aimed at reducing symptoms of disease in order to improve quality of life for patients. However, for a significant number of patients these therapies are ineffective, leading to an increased risk of irreversible damage and eventual disability in certain cases. Growing evidence has implicated glycosylated proteins and their cognate receptors in modulation of the autoimmune response. This review will summarize these findings with particular focus on sialic acid-binding immunoglobulin-like lectin (Siglec)-1 and Siglec-2 involvement in AD. Fluctuations in these glycosylation-dependent pathways could act as sentinels of disease activity or drug responses. If validated, protein modification and cellular response markers could help clinicians achieve remission earlier.

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.

Inflammatory Bowel Disease (IBD) is an umbrella term for a group of inflammatory diseases of the human gastrointestinal tract, including Crohn’s Disease (CD) and ulcerative colitis (UC). Changes to the intestinal microbiome, the community of micro-organisms that resides in the human gut, have been shown to contribute to the pathogenesis of IBD. IBD diagnosis is often delayed due its non-specific symptoms (e.g. abdominal pain) and an invasive colonoscopy is required for confirmation. Delayed diagnosis is linked to poor growth in children and worse treatment outcomes. Microbial communities are extremely complex and feature selection algorithms are often applied to identify key bacterial groups that drive disease. It has been shown that aggregating Ensemble Feature Selection (EFS) can be used to improve the robustness of feature selection algorithms. The robustness of a feature selector is defined as the variation of feature selector output caused by small changes to the dataset. Typical feature selection algorithms can be used to help build simpler, faster, and easier to understand models - but suffer from poor robustness. Having confidence in the output of a feature selector algorithm is key for enabling knowledge discovery from complex biological datasets. In this work we apply a two-step filter and an EFS process to generate robust feature subsets that can non-invasively predict IBD subtypes from high-resolution microbiome data. The predictive power of the robust feature subsets is the highest reported in literature to date. Furthermore, we identify five biologically plausible bacterial species that have not previously been implicated in IBD aetiology.

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.

Introduction Coronary artery disease (CAD) is the leading cause of death worldwide caused by atherosclerosis, now believed to be a complex inflammatory process. Mounting data indicates that the Nucleotide-binding domain, leucine-rich containing family, pyrin domain-containing-3 (NRLP3) inflammasome is critical in the pathogenesis and/or regulation of a variety of inflammatory diseases including atherosclerosis. NLRP3 is a multiprotein complex responsible for the activation of capase-1 and proteases required for processing and activating IL-1 b and IL-18. Previous evidence suggests that NLRP3 is activated and regulated by the cannabinoid receptor type 1 (CB1 r) and scavenger receptor, cell surface cluster of differentiation 36 (CD36 r) in response to modified lipoproteins through amplification of inflammation or exposure to oxidised low-density lipoproteins (oxLDL). CB1 r and CD36 r are therefore considered risk factors for atherosclerosis, in the promotion of cholesterol accumulation and release of inflammatory mediators. To date, no study has investigated NLRP3, CD36 r and CB1 r collectively as a potential panel of biomarkers to predict major adverse cardiovascular events (MACE). Purpose To measure the expression of the NLRP3 inflammasome and associated receptors (CD36 r and CB1 r) and to determine if levels correlate with cardiovascular risk. Methods Consecutive patients were recruited from the cardiac catheterisation laboratory and outpatient clinics. Group 1 were defined as Very high risk patients (VHR) with a 10-year risk SCORE ≥10% risk of fatal CVD. VHR patients were subdivided in Acute Coronary Syndrome patients (ACS-VHR) and elective percutaneous coronary intervention patients (ELEC-VHR). Group 2 were low risk patients (LR) with a 10-year risk SCORE Results A total of 60 patients were recruited and the cohort consisted of VHR (n=40), which were divided into ACS-VHR (n=20) and ELEC-VHR (n=20), and LR (n=20). NLRP3 gene expression was higher in VHR compared to LR (0.0284 ± 0.0013 vs. 0.0142 ± 0.0019, p Conclusion These results indicate that NLRP3 and CB1 r can potentially differentiate between VHR and LR patients. An ongoing larger cohort study is underway to investigate the utility of NLRP3, CD36 r and CB1 r along with other promising biomarkers to better predict CAD and MACE.

Tumour necrosis factor alpha converting enzyme (TACE/ADAM17) is a member of the A disintegrin and metalloproteinase (ADAM) family of ectodomain shedding proteinases. It regulates many inflammatory processes by cleaving several transmembrane proteins, including tumour necrosis factor alpha (TNFα) and its receptors tumour necrosis factor alpha receptor 1 and tumour necrosis factor alpha receptor 2. There is evidence that TACE is involved in several inflammatory diseases, such as ischaemia, heart failure, arthritis, atherosclerosis, diabetes and cancer as well as neurological and immune diseases. This review summarizes the latest discoveries regarding the mechanism of action and regulation of TACE. It also focuses on the role of TACE in atherosclerosis and coronary artery disease (CAD), highlighting clinical studies that have investigated its expression and protein activity. The multitude of substrates cleaved by TACE make this enzyme an attractive target for therapy and a candidate for biomarker research and development in CAD.

// Qing Wen 1 , Philip D. Dunne 1 , Paul G. O’Reilly 1 , Gerald Li 1 , Anthony J. Bjourson 2 , Darragh G. McArt 1 , Peter W. Hamilton 1, * , Shu-Dong Zhang 1, 2, * 1 Centre for Cancer Research and Cell Biology, Queen’s University Belfast, UK 2 Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, C-TRIC, Londonderry, UK * Joint Senior Author Correspondence to: Qing Wen, email: qwen02@qub.ac.uk Peter W. Hamilton, email: P.Hamilton@qub.ac.uk Keywords: connectivity mapping, differentially expressed genes, colorectal cancer, KRAS mutation, FDA approved drugs Received: May 15, 2016 Accepted: November 30, 2016 Published: December 10, 2016 ABSTRACT Colorectal cancer (CRC) is a life-threatening disease with high prevalence and mortality worldwide. The KRAS oncogene is mutated in approximately 40% of CRCs. While antibody based EGFR inhibitors (cetuximab and panitumumab) represent a major treatment strategy for advanced KRAS wild type (KRAS-WT) CRCs, there still remains no effective therapeutic course for advanced KRAS mutant (KRAS-MT) CRC patients. In this study, we employed a novel and comprehensive approach of gene expression connectivity mapping (GECM) to identify candidate compounds to target KRAS-MT tumors. We first created a combined KRAS-MT gene signature with 248 ranked significant genes using 677 CRC clinical samples. A series of 248 sub-signatures was then created containing an increasing number of the top ranked genes. As an input to GECM analysis, each sub-signature was translated into a statistically significant therapeutic drugs list, which was finally combined to obtain a single list of significant drugs. We identify four antihypertensive angiotensin II receptor blockers (ARBs) within the top 30 significant drugs indicating that these drugs have a mechanism of action that can alter the KRAS-MT CRC oncogenic signaling. A hypergeometric test ( p-value = 6.57 × 10 −6 ) confirmed that ARBs are significantly enriched in our results. These findings support the hypothesis that ARB antihypertensive drugs may directly block KRAS signaling resulting in improvement in patient outcome or, through a reversion to a KRAS wild-type phenotype, improve the response to anti-EGFR treatment. Antihypertensive angiotensin II receptor blockers are therefore worth further investigation as potential therapeutic candidates in this difficult category of advanced colorectal cancers.

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.

Plasma cell myeloma is a clinically heterogeneous malignancy accounting for approximately one to 2% of newly diagnosed cases of cancer worldwide. Treatment options, in addition to long-established cytotoxic drugs, include autologous stem cell transplant, immune modulators, proteasome inhibitors and monoclonal antibodies, plus further targeted therapies currently in clinical trials. Whilst treatment decisions are mostly based on a patient's age, fitness, including the presence of co-morbidities, and tumour burden, significant scope exists for better risk stratification, sub-classification of disease, and predictors of response to specific therapies. Clinical staging, recurring acquired cytogenetic aberrations, and serum biomarkers such as β-2 microglobulin, and free light chains are in widespread use but often fail to predict the disease progression or inform treatment decision making. Recent scientific advances have provided considerable insight into the biology of myeloma. For example, gene expression profiling is already making a contribution to enhanced understanding of the biology of the disease whilst Next Generation Sequencing has revealed great genomic complexity and heterogeneity. Pathways involved in the oncogenesis, proliferation of the tumour and its resistance to apoptosis are being unravelled. Furthermore, knowledge of the tumour cell surface and its interactions with bystander cells and the bone marrow stroma enhance this understanding and provide novel targets for cell and antibody-based therapies. This review will discuss the development in understanding of the biology of the tumour cell and its environment in the bone marrow, the implementation of new therapeutic options contributing to significantly improved outcomes, and the progression towards more personalised medicine in this disorder.

Introduction: Multidrug resistance (MDR) mediated by P-glycoprotein (P-gp) can compromise the successful treatment of many malignancies including plasma cell myeloma (PCM). However, methods do not yet exist that can accurately determine P-gp activity in PCM patient samples. Methods: In this study, we have utilized new advances in flow cytometric methods to determine the activity of P-gp in PCM tumor cells. Furthermore, we have used several PCR-based approaches to perform a pilot study determining the functional impact of ABCB1 SNPs in patients with PCM. Results: No associations were seen between P-gp activity or expression and subgroups of PCM. Similarly, no association was seen between P-gp expression and SNPs within ABCB1 although a nonsignificant reduction in activity was demonstrated for rs1045642 (P = 0.121). Conclusions: We have described a new method for the determination of P-gp and MRP activity suitable for use in clinical studies and have optimized this method to include a gating strategy, allowing routine use on PCM bone marrow aspirate samples. This is the first patient study to consider the full impact of SNPs within ABCB1 all the way from the genome to the proteome in PCM. The methods described here could also be utilized for future studies of “stem cell like” side populations in PCM that are considered to be drug resistant. Furthermore, minor amendments to these methods will facilitate studies of P-gp, MRP, and BCRP activity in other haematological malignancies. © 2012 International Clinical Cytometry Society