VDR, CXCR1, CXCR2, PSCA Polymorphisms and Recurrent Urinary Tract Infections in Women: Genetic Association Study.

INTRODUCTION AND HYPOTHESIS: Urinary tract infection (UTI) is one of the most common human infections. Evidence suggests that there might be a genetic predisposition to UTI. Previous small candidate gene studies have suggested that common variants in genes involved in the immune response to UTI could increase susceptibility to the development of recurrent UTI (rUTI). The objective was to conduct a gene association study to replicate previous gene association studies identifying single nucleotide polymorphisms (SNPs) putatively associated with rUTI in adult women. METHODS: Women with a history of rUTI and healthy controls were recruited (n = 1,008) from gynaecology outpatient clinics. Participants completed a signed consent form and questionnaire for phenotyping. DNA was extracted from blood or saliva samples for each participant. Putative associated SNPs were identified from a comprehensive systematic review of prior gene association studies. Primers for each selected SNP were designed, and genotyping was conducted using a competitive polymerase chain reaction (PCR) method. The Chi-squared test was used to assess the association between each variant and rUTI. Genotyping quality was assessed by checking for deviation from Hardy-Weinberg equilibrium. RESULTS: We found no association between SNPs tested in the VDR (p = 0.16, p = 0.09, p = 0.36), CXCR1 (p = 0.09), CXCR2 (p = 0.39), PSCA (p = 0.74) genes, and rUTI in adult women. CONCLUSIONS: To our knowledge, this is the largest study to date, finding no significant associations. Previously reported positive associations may have been due to type 1 error, or genotyping errors. Future studies should adjust for confounders and employ adequate sample sizes. A greater understanding of the genetic components associated with rUTI may influence future treatment guidelines and screening for susceptible patients.

Genetic polymorphisms associated with urinary tract infection in children and adults: a systematic review and meta-analysis.

INTRODUCTION: The lifetime risk of urinary tract infection is known from first-degree relative studies to be highly heritable. Associations have also been observed across the life course from pediatric urinary tract infection to recurrent urinary tract infection in adulthood, suggesting lifelong susceptibility factors. Candidate gene studies and genome-wide association studies have tested for genetic associations of urinary tract infection; however, no contemporary systematic synthesis of studies is available. OBJECTIVE: We conducted a systematic review to identify all genetic polymorphisms tested for an association with urinary tract infection in children and adults; and to assess their strength, consistency, and risk of bias among reported associations. DATA SOURCES AND STUDY ELIGIBILITY CRITERIA: PubMed, HuGE Navigator and Embase were searched from January 1, 2005 to November 16, 2023, using a combination of genetic and phenotype key words. STUDY APPRAISAL AND SYNTHESIS METHODS: Fixed and random effects meta-analyses were conducted using codominant models of inheritance in metan. The interim Venice criteria were used to assess their credibility of pooled associations. RESULTS: After removing 451 duplicates, 1821 studies reports were screened, with 106 selected for full-text review, 22 were included in the meta-analysis (7 adult studies and 15 pediatric studies). Our meta-analyses demonstrated significant pooled associations for pediatric urinary tract infection with variation in CXCR1, IL8, TGF, TLR4 and VDR; all of which have plausible roles in the pathogenesis of urinary tract infection. Our meta-analyses also demonstrated a significant pooled association for adult urinary tract infection with variation in CXCR1. All significant pooled associations were graded according to their epidemiological credibility, sample sizes, heterogeneity between studies, and risk of bias. CONCLUSION: This systematic review provides a current synthesis of the known genetic architecture of urinary tract infection in childhood and adulthood; and should provide important information for researchers analysing future genetic association studies. Although, overall, the credibility of pooled associations was weak, the consistency of findings for rs2234671 single nucleotide polymorphisms of CXCR1 in both populations suggest a key role in the urinary tract infection pathogenesis.

An integrated cellular and molecular model of gastric neuroendocrine cancer evolution highlights therapeutic targets.

Gastric neuroendocrine carcinomas (G-NEC) are aggressive malignancies with poorly understood biology and a lack of disease models. Here, we use genome sequencing to characterize the genomic landscapes of human G-NEC and its histologic variants. We identify global and subtype-specific alterations and expose hitherto unappreciated gains of MYC family members in a large part of cases. Genetic engineering and lineage tracing in mice delineate a model of G-NEC evolution, which defines MYC as a critical driver and positions the cancer cell of origin to the neuroendocrine compartment. MYC-driven tumors have pronounced metastatic competence and display defined signaling addictions, as revealed by large-scale genetic and pharmacologic screening of cell lines and organoid resources. We create global maps of G-NEC dependencies, highlight critical vulnerabilities, and validate therapeutic targets, including candidates for clinical drug repurposing. Our study gives comprehensive insights into G-NEC biology.

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units.

INTRODUCTION: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. METHOD: Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. RESULTS: Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. CONCLUSIONS: Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review.

BACKGROUND: Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research. METHODS: The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria. RESEARCH: 3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face. CONCLUSION: Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.

Tension band suture technique for the fixation of a displaced posterolateral acromion process fracture with subacromial impingement.

An adult old cyclist presented to our hospital and was referred to the orthopaedic department with a left shoulder posterolateral acromion avulsion fracture and subacromial impingement demonstrated on X-ray and CT. This highly unusual fracture pattern was treated by open reduction and internal fixation with cannulated screws and a tension band suture technique. This fracture went onto successful union with full range of motion and good patient-reported outcome measures by the Oxford Shoulder Score at 3 months. Informed consent was taken for the following case report.