Changes in blood serum parameters in farmed rainbow trout (Oncorhynchus mykiss) during a piscine lactococcosis outbreak

The aquaculture sector plays a vital role in global food security, yet it grapples with significant challenges posed by infectious diseases. Piscine lactococcosis is one of the significant threats in rainbow trout aquaculture due to its potential to cause severe economic losses through mortalities, reduced growth rates, and increased susceptibility to other pathogens. It poses challenges in disease management strategies, impacting the sustainability and profitability of rainbow trout farming. The current study focuses on the variations in serum blood parameters of farmed rainbow trout Oncorhynchus mykiss during a lactococcosis outbreak caused by Lactococcus garvieae. Blood samples were collected for biochemical analysis, fish were examined for parasites and bacteria, and DNA from bacterial colonies was PCR-amplified and sequenced for identification. Overall, 13 biochemical parameters, including proteins, enzymes, lipids, chemicals, and minerals, were measured in serum blood samples from both diseased and healthy fish. The results indicate significant alterations in the levels of these parameters during the outbreak, highlighting the impact of infections on the blood profile of farmed rainbow trout. Urea levels were significantly higher in diseased fish compared to controls, and creatinine, phosphorus, and magnesium also showed similar trends. Alanine aminotransferase and total protein levels were higher in control fish. Chloride levels differed significantly between groups. Iron levels were higher in controls and lower in diseased fish. No significant differences were found in other parameters. This study reveals significant changes in serum blood parameters of rainbow trout during a lactococcosis outbreak caused by L. garvieae. These changes highlight the potential of these parameters as tools for monitoring health status, stress, and aquaculture management. Continuous monitoring can provide valuable insights into disease severity and overall fish health, aiding in the development of improved management practices. The presented data contribute to understanding the pathophysiology of piscine lactococcosis and developing effective mitigation strategies for farmed rainbow trout

Commercial chicken breeds exhibit highly divergent patterns of linkage disequilibrium

The analysis of linkage disequilibrium (LD) underpins the development of effective genotyping technologies, trait mapping and understanding of biological mechanisms such as those driving recombination and the impact of selection. We apply the Malécot-Morton model of LD to create additive LD maps that describe the high-resolution LD landscape of commercial chickens. We investigated LD in chickens (Gallus gallus) at the highest resolution to date for broiler, white egg and brown egg layer commercial lines. There is minimal concordance between breeds of fine-scale LD patterns (correlation coefficient <0.21), and even between discrete broiler lines. Regions of LD breakdown, which may align with recombination hot spots, are enriched near CpG islands and transcription start sites (P<2.2 × 10?16), consistent with recent evidence described in finches, but concordance in hot spot locations between commercial breeds is only marginally greater than random. As in other birds, functional elements in the chicken genome are associated with recombination but, unlike evidence from other bird species, the LD landscape is not stable in the populations studied. The development of optimal genotyping panels for genome-led selection programmes will depend on careful analysis of the LD structure of each line of interest. Further study is required to fully elucidate the mechanisms underlying highly divergent LD patterns found in commercial chickens

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

AbstractAutoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included “machine learning” or “artificial intelligence” and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.</jats:p

Classification of Paediatric Inflammatory Bowel Disease using Machine Learning

AbstractPaediatric inflammatory bowel disease (PIBD), comprising Crohn’s disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified (IBDU) is a complex and multifactorial condition with increasing incidence. An accurate diagnosis of PIBD is necessary for a prompt and effective treatment. This study utilises machine learning (ML) to classify disease using endoscopic and histological data for 287 children diagnosed with PIBD. Data were used to develop, train, test and validate a ML model to classify disease subtype. Unsupervised models revealed overlap of CD/UC with broad clustering but no clear subtype delineation, whereas hierarchical clustering identified four novel subgroups characterised by differing colonic involvement. Three supervised ML models were developed utilising endoscopic data only, histological only and combined endoscopic/histological data yielding classification accuracy of 71.0%, 76.9% and 82.7% respectively. The optimal combined model was tested on a statistically independent cohort of 48 PIBD patients from the same clinic, accurately classifying 83.3% of patients. This study employs mathematical modelling of endoscopic and histological data to aid diagnostic accuracy. While unsupervised modelling categorises patients into four subgroups, supervised approaches confirm the need of both endoscopic and histological evidence for an accurate diagnosis. Overall, this paper provides a blueprint for ML use with clinical data.</jats:p

Expansion of RNA sequence diversity and RNA editing rates throughout human cortical development

ABSTRACTPost-transcriptional modifications by RNA editing are essential for neurodevelopment, yet their developmental and regulatory features remain poorly resolved. We constructed a full temporal view of base-specific RNA editing in the developing human cortex, from early progenitors through fully mature cells found in the adult brain. Developmental regulation of RNA editing is characterized by an increase in editing rates for more than 10,000 selective editing sites, shifting between mid-fetal development and infancy, and a massive expansion of RNA hyper-editing sites that amass in the cortex through postnatal development into advanced age. These sites occur disproportionally in 3’UTRs of essential neurodevelopmental genes. These profiles are preserved in non-human primate and murine models, illustrating evolutionary conserved regulation of RNA editing in mammalian cortical development. RNA editing levels are commonly genetically regulated (editing quantitative trait loci, edQTLs) consistently across development or predominantly during prenatal or postnatal periods. Both consistent and temporal-predominant edQTLs co-localize with risk loci associated with neurological traits and disorders, including attention deficit hyperactivity disorder, schizophrenia, and sleep disorders. These findings expand the repertoire of highly regulated RNA editing sites in the brain and provide insights of how epitranscriptional sequence diversity by RNA editing contributes to neurodevelopment.</jats:p

Spatiotemporal and genetic regulation of A-to-I editing throughout human brain development

Posttranscriptional RNA modifications by adenosine-to-inosine (A-to-I) editing are abundant in the brain, yet elucidating functional sites remains challenging. To bridge this gap, we investigate spatiotemporal and genetically regulated A-to-I editing sites across prenatal and postnatal stages of human brain development. More than 10,000 spatiotemporally regulated A-to-I sites were identified that occur predominately in 3' UTRs and introns, as well as 37 sites that recode amino acids in protein coding regions with precise changes in editing levels across development. Hyper-edited transcripts are also enriched in the aging brain and stabilize RNA secondary structures. These features are conserved in murine and non-human primate models of neurodevelopment. Finally, thousands of cis-editing quantitative trait loci (edQTLs) were identified with unique regulatory effects during prenatal and postnatal development. Collectively, this work offers a resolved atlas linking spatiotemporal variation in editing levels to genetic regulatory effects throughout distinct stages of brain maturation

Trends of determinants of hormone therapy use in Italian women attending menopause clinics, 1997-2003

OBJECTIVE: Analysis of patterns of hormone therapy (HT) use among postmenopausal Italian women, before and after publication of results from the Heart and Estrogen/progestin Replacement Study and the Women's Health Initiative. DESIGN: This was a cross-sectional study conducted between 1997 and 2003 on the characteristics of women around the age of menopause. The study population consisted of 106,784 women (mean age 53 y) attending menopause clinics in Italy. Postmenopausal women were defined as women with surgical menopause (ie, bilateral oophorectomy with or without hysterectomy), women older than 55 years who underwent hysterectomy without bilateral oophorectomy, and women whose menstrual cycles had stopped more than 1 year before their interview. RESULTS: A total of 15,657 women (14.7%) reported ever using HT. The prevalence of HT prescription was 17.6% among women observed in 1997-1998, 14.9% in 1999, 12.2% in 2000, 12.1% in 2001, and 11.4% in 2002-2003. HT use was related to age at menopause and level of education in all the periods considered and was more frequent in women reporting surgical menopause. The odds ratio of HT prescription tended to decrease in women with surgical menopause, with slight fluctuations in the intermediate years. Ever users of oral contraceptives and nulliparae were more frequently HT users. CONCLUSIONS: In our population the percentage of current HT users dropped from 17.6% in 1997-1998 to 11.4% in 2002-2003. However, the determinants of use were largely unchanged during the study period: women with higher education, nulliparae, and smokers reported more frequent HT use. ©2008The North American Menopause Society