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Environment early in life may have a long-lasting impact on mental health through epigenetic mechanisms. We studied the effect of early life adversity (ELA) on high risk subjects for Depression (MDD). 20 unaffected first degree relatives (FHP) and 20 controls (FHN) underwent high resolution MRI. We used CTQ questionnaire to assess ELA. Manual tracing of hippocampal subregions and voxel-based morphometry (VBM) analysis were used. We concluded that FHP individuals had reduced volume of those brain areas of emotional processing, in particular if they had a history of ELA. This suggests that ELA might influence brain structure via epigenetic mechanisms and structural changes may precede MDD.
We determined how the brain-derived neurotrphic factor (BDNF) Val66Met polymorphism and ELA affect volumetric measures of hippocampus. 62 MDD patients and 71 healthy controls underwent high-resolution MRI. We manually teaced hippocampi, assessed childhood adversity with CTQ and genotyped Val66Met BDNF. Met-allele carriers showed significantly smaller hippocampal volumes when they had a history of ELA, both in patients and controls. Our results highlight how relevant stress-gene interactions are for hippocampal volume reductions.
Another 37 patients with MDD and 42 healthy participants underwent Diffussion Tensor Imaging (DTI). Deterministic tractography was applied and Val66Met BDNF polymorphism genotyped. Patients carrying the BDNF met-allele had smaller FA in Uncinate Fasciculus (UF) compared to homozygous for val-allele and controls. The met allele of the BDNF polymorphism seems to render subjects more vulnerable for dysfunctions associated with the UF, a brain region which is very closely related to emotional and cognitive function.
Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.
To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.
Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.
Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.
Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.
Recent infection testing algorithms (RITA) for HIV combine serological assays with epidemiological data to determine likely recent infections, indicators of ongoing transmission. In 2016, we integrated RITA into national HIV surveillance in Ireland to better inform HIV prevention interventions. We determined the avidity index (AI) of new HIV diagnoses and linked the results with data captured in the national infectious disease reporting system. RITA classified a diagnosis as recent based on an AI < 1.5, unless epidemiological criteria (CD4 count <200 cells/mm3; viral load <400 copies/ml; the presence of AIDS-defining illness; prior antiretroviral therapy use) indicated a potential false-recent result. Of 508 diagnoses in 2016, we linked 448 (88.1%) to an avidity test result. RITA classified 12.5% of diagnoses as recent, with the highest proportion (26.3%) amongst people who inject drugs. On multivariable logistic regression recent infection was more likely with a concurrent sexually transmitted infection (aOR 2.59; 95% CI 1.04–6.45). Data were incomplete for at least one RITA criterion in 48% of cases. The study demonstrated the feasibility of integrating RITA into routine surveillance and showed some ongoing HIV transmission. To improve the interpretation of RITA, further efforts are required to improve completeness of the required epidemiological data.