METHODS: A cross-sectional study was conducted at 11 paediatric endocrine units in Malaysia. Blood samples for antithyroglobulin antibodies, antithyroid peroxidase antibodies and thyroid function test were obtained. In patients with pre-existing thyroid disease, information on clinical and biochemical thyroid status was obtained from medical records.
RESULTS: Ninety-seven TS patients with a mean age of 13.4 ± 4.8 years were recruited. Thyroid autoimmunity was found in 43.8% of TS patients. Nineteen per cent of those with thyroid autoimmunity had autoimmune thyroid disease (Hashimoto thyroiditis in 7.3% and hyperthyroidism in 1% of total population). Patients with isochromosome X and patients with 45,X mosaicism or other X chromosomal abnormalities were more prone to have thyroid autoimmunity compared to those with 45,X karyotype (OR 5.09, 95% CI 1.54-16.88, P = 0.008 and OR 3.41, 95% CI 1.32-8.82, P = 0.01 respectively). The prevalence of thyroid autoimmunity increased with age (33.3% for age 0-9.9 years; 46.8% for age 10-19.9 years and 57.1% age for 20-29.9 years) with autoimmune thyroid disease detected in 14.3% during adulthood.
CONCLUSION: Thyroid autoimmunity was significantly associated with the non 45,X karyotype group, particularly isochromosome X. Annual screening of thyroid function should be carried out upon diagnosis of TS until adulthood with more frequent monitoring recommended in the presence of thyroid autoimmunity.
METHODS: Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.
RESULTS: Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).
DISCUSSION: The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.
METHOD: This qualitative cross-sectional study utilised online open-ended, semi-structured focus group interviews to explore the perceptions and experiences of parents of children with Primary Immunodeficiency (PID). Participants were recruited through convenience sampling from the Malaysian Patient Organisation for Primary Immunodeficiencies (MyPOPI), a non-governmental organisation dedicated to providing support and raising awareness about PID. The study spanned from May 2023 to July 2023 and included participants from diverse regions of Malaysia who had undergone different diagnostic journeys in various hospitals.
RESULT: The focus group discussions yielded 11 sub-themes that highlighted the experiences, understanding and challenges of the participants regarding genetic testing based on the semi-structured questions. These sub-themes were then grouped into four main themes that are awareness and understanding of genetic testing, the journey towards diagnosis and treatment, emotional impact and psychological factors, and the importance of medical experts in diagnosing and managing PID, as well as public perception and awareness.
CONCLUSION: In conclusion, this study highlights the diverse knowledge, awareness, and perception surrounding genetic testing for PID. Factors such as access to services, family history, and personal circumstances shape individuals' understanding of genetic testing. The importance of healthcare professionals, along with the need for improved accessibility and targeted communication strategies, is underscored to enhance understanding and reduce stigma surrounding genetic testing for rare diseases like PID.
IMPORTANCE: This study established the largest database of globally circulating HPV6 genomic variants and contributed a total of 130 new, complete HPV6 genome sequences to available sequence repositories. Two HPV6 variant lineages and five sublineages were identified and showed some degree of association with geographical location, anatomical site of infection/disease, and/or gender. We additionally identified several HPV6 lineage- and sublineage-specific SNPs to facilitate the identification of HPV6 variants and determined a representative region within the L2 gene that is suitable for HPV6 whole-genome-based phylogenetic analysis. This study complements and significantly expands the current knowledge of HPV6 genetic diversity and forms a comprehensive basis for future epidemiological, evolutionary, functional, pathogenicity, vaccination, and molecular assay development studies.
METHOD: A literature review was carried out, power and other issues discussed, and planned studies assessed.
RESULTS: Most of the genomic DNA sequence differences between any two people are common (frequency >5%) single nucleotide polymorphisms (SNPs). Because of localized patterns of correlation (linkage disequilibrium), 500,000 to 1,000,000 of these SNPs can test the hypothesis that one or more common variants explain part of the genetic risk for a disease. GWAS technologies can also detect some of the copy number variants (deletions and duplications) in the genome. Systematic study of rare variants will require large-scale resequencing analyses. GWAS methods have detected a remarkable number of robust genetic associations for dozens of common diseases and traits, leading to new pathophysiological hypotheses, although only small proportions of genetic variance have been explained thus far and therapeutic applications will require substantial further effort. Study design issues, power, and limitations are discussed. For psychiatric disorders, there are initial significant findings for common SNPs and for rare copy number variants, and many other studies are in progress.
CONCLUSIONS: GWAS of large samples have detected associations of common SNPs and of rare copy number variants with psychiatric disorders. More findings are likely, since larger GWAS samples detect larger numbers of common susceptibility variants, with smaller effects. The Psychiatric GWAS Consortium is conducting GWAS meta-analyses for schizophrenia, bipolar disorder, major depressive disorder, autism, and attention deficit hyperactivity disorder. Based on results for other diseases, larger samples will be required. The contribution of GWAS will depend on the true genetic architecture of each disorder.
METHODS: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).
RESULTS: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.
CONCLUSIONS: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.