Optimising rigour in focus group analysis : using content/thematic and form/structural approaches to understand British Somali's experiences of policing in London

There is evidence that focus groups are useful to explore issues with socially marginalised groups, notably when participants have shared particular experiences. Focus groups have the methodological potential to highlight group norms and processes, and to illuminate the social and cultural contexts in which individual agency takes place. However, an often cited concern about focus groups is researchers’ inadequate description of the analytical process which then affects the usefulness and credibility of the findings and rigour in analysis. In this article we address this concern and offer an analytical framework which takes account of the content (themes) and form (structure) of focus group data. Framed within an interpretivist paradigm, our analysis is driven by a theoretical interest in how race/ethnicity as social positions shape young British Somali men’s individual and shared experiences of policing in London

The Behaviors of some Counting Functions of ‎g-primes and g-integers as x goes to Infinity‎

 في هذا البحث نركز على تصرفات الدوال الحسابية الموسعة للأعداد الاولية (x) وللأعداد الصحيحة   (x) وكذلك الرابط بينهما عندما x      . هنا دالة ريمان زيتا    (s) ( =  , (s)  > 1 ), تلعب دورا مهما كرابط بين (x) و   (x) . هذا العمل سيتم من خلال سلوك طريقة العالم بلنزاريو ]بلنزاريو،  1998 [   ليست بالتفاصيل والتي عممت من خلال المعموري ] المعموري ، 2013 [ . بالنهاية سوف نرسم مخططا يحدد العلاقة بين  و  ( حيث  و  هما القوى للحدود الخطأ H1(x)  و H2(x) من (x) و   (x) على التوالي . الغرض من هذا البحث هو تحليل تصرفات (x) و   (x) عندما x      . ملاحظة : من المهم والنافع الاشارة بان جهدنا في هذا البحث ليست تغيير بعض قيم الدوال التي استخدمت في طريقة بلنزاريو حيث ان تغيير اي قيمة مهما كانت صغيرة لإحدى دوال طريقة بلنزاريو ربما تقودنا الى خسارة هدف الموضوع بأكمله . ولهذا نبين ايضا قابلية التغيير المسموح بها في قيم بعض الدوال . كذلك سوف نختم البحث بفتح باب لعمل مستقبلي  In this article  we  focus on the behaviors of  the generalised counting  function of primes (x)  and  the counting  function of integers   (x) as well as  the link between them as  x      . Here the Riemann zeta function  (s) ( =  , (s)  > 1 )  play an  important  role  as  a link between   (x)  and  (x)  .  This  work  will  go  through  the  method  ( not  in  details )  adapted  by Balanzario  [Balanzario , 1998]   and  later  generalised  by  AL- Maamori [AL- Maamori , 2013 ] . Finally we shall draw a diagram in order to determine the relation between   and    , (where  and   are the power of the error terms H1(x) , H2(x) of (x) and (x) respectively) . The aim of this work is to analysis  the behaviour of (x)  and   (x) as  x    .   Note that : ʺ  It’s a beneficial to point out that our effort in this paper is not to exchange the values of some functions of  Balanzarioʹs  method . Since , changing any small value of one of the functions of  Balanzarioʹs method may be leads to loss the aim of the work  ʺ  . Therefore , in this article we show  the ability of  changing  the values of  some functions and in which places in the proof we should sort out

Does surgical site infection influence neurological outcome and survival in patients undergoing surgery for metastatic spinal cord compression?

Purpose: Most of the literature on infection after surgery for spinal metastases focuses on incidence and risk factors for surgical site infection (SSI). To the best of our knowledge, there is no report on the influence of infection on neurological outcome and survival in patients undergoing emergent surgery for metastatic spinal cord compression (MSCC).Methods: Our aim was to establish if SSIs adversely affected the neurological outcome and survival in patients with MSCC. We reviewed 318 consecutive patients admitted for surgical intervention for MSCC from October 2005 to October 2012. Morbidity (neurological outcome, length of hospital stay and additional procedures) and survival rates were analysed.Results: During this study period, the incidence of infection was 29/318 (9.1%). The median length of stay in hospital in the infected group was 25 days compared to 13 days in the non-infected group (p = 0.001). Twenty out of the 29 (69%) infected patients underwent an additional procedure (29 procedures in total) compared to 9/289 (3%) non-infected patients (p = 0.001). There was no statistical difference between the two groups with regard to neurological outcome (p = 0.37) but the survival rate was statistically different between the two groups [infected group: median survival 131 days (19–1558) vs. non-infected group: 258 days (5–2696; p = 0.03)].Conclusion: Surgical site infection increased the morbidity with considerably longer hospital stay and requirement for additionalprocedures. Although there was no difference in neurological outcome, the infected group of patients had a significantlyshorter survival

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)–VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis

A dual covariant biomarker approach to Kawasaki disease, implications for coronary pathogenesis using vascular endothelial growth factor A and B gene expression; systematic secondary analysis of clinical datasets

Introduction Kawasaki disease (KD) is the most common vasculitis in young children, with coronary artery lesions (CALs) and coronary aneurysms (CAAs) being responsible for most KD-related deaths. Objective We hypothesized that Vascular Endothelial Growth Factors (VEGFs) are pivotal in KD inflammation and coronary artery lesions. This study assessed VEGF-A and VEGF-B gene expression (GE) as potential biomarkers in KD inflammation. Study design We analyzed NCBI-GEO datasets, categorizing gene expression patterns as inflammatory or non-inflammatory . We focused on TNF-, NFKB1, VEGF-A, and VEGF-B GEs. Datasets were filtered based on differential changes in TNF and NFKB1 levels to isolate those with inflammatory shifts. Results Inflammatory datasets (GSE63881, GSE73464, and GSE68004) displayed elevated TNF, NFKB1, and VEGF-A GE levels during acute KD. VEGF-B GE exhibited a distinctive trend: an initial drop and subsequent rise during recovery, a pattern that was missing in the non-inflammatory group. The treatment response was also studied, with intravenous immunoglobulin (IVIG) responders showing significant downregulation of NFKB1 GE after treatment: GSE16797 [IVIG ± methylprednisolone; p = 8.6443-03], GSE48498 [IVIG; p = 6.618e-02, infliximab; p = 3.240e-03], and GSE18606 [IVIG; p = 3.518e-02]. Considering the similar binding of VEGF-A and VEGF-B to the VEGFR1 receptor, a co-variate and inverse relationship is suggested. Conclusion Temporal VEGF-A, VEGF-B, and GE changes show promise as new post-inflammatory biomarkers for KD. Novelty results with the biomarker approach, with the potential for a dual temporal relationship between VEGF-A and VEGF-A. A comprehensive exploration of VEGF-A and VEGF-B genes and protein analysis in KD is warranted to understand the functional aspects of these changes and how best to utilize the pattern of changes for therapeutic benefit