Identification of stable endogenous reference genes for real-time PCR in the human fetal gonad using an external standard technique

Peer reviewedPostprin

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology

Autism in the Faroe Islands: diagnostic stability from childhood to early adult life

Childhood autism or autism spectrum disorder (ASD) has been regarded as one of the most stable diagnostic categories applied to young children with psychiatric/developmental disorders. The stability over time of a diagnosis of ASD is theoretically interesting and important for various diagnostic and clinical reasons. We studied the diagnostic stability of ASD from childhood to early adulthood in the Faroe Islands: a total school age population sample (8–17-year-olds) was screened and diagnostically assessed for AD in 2002 and 2009. This paper compares both independent clinical diagnosis and Diagnostic Interview for Social and Communication Disorders (DISCO) algorithm diagnosis at two time points, separated by seven years. The stability of clinical ASD diagnosis was perfect for AD, good for “atypical autism”/PDD-NOS, and less than perfect for Asperger syndrome (AS). Stability of the DISCO algorithm subcategory diagnoses was more variable but still good for AD. Both systems showed excellent stability over the seven-year period for “any ASD” diagnosis, although a number of clear cases had been missed at the original screening in 2002. The findings support the notion that subcategories of ASD should be collapsed into one overarching diagnostic entity with subgrouping achieved on other “non-autism” variables, such as IQ and language levels and overall adaptive functioning

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber crosssectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the “patchy” distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigatorspecific needs and provides novel analytical approaches for quantifying muscle morphology

Vitamin D in the general population of young adults with autism in the Faroe Islands

Vitamin D deficiency has been proposed as a possible risk factor for developing autism spectrum disorder (ASD). 25-Hydroxyvitamin D3 (25(OH)D3) levels were examined in a cross-sectional population-based study in the Faroe Islands. The case group consisting of a total population cohort of 40 individuals with ASD (aged 15–24 years) had significantly lower 25(OH)D3 than their 62 typically-developing siblings and their 77 parents, and also significantly lower than 40 healthy age and gender matched comparisons. There was a trend for males having lower 25(OH)D3 than females. Effects of age, month/season of birth, IQ, various subcategories of ASD and Autism Diagnostic Observation Schedule score were also investigated, however, no association was found. The very low 25(OH)D3 in the ASD group suggests some underlying pathogenic mechanism

Quality of T-cell responses versus reduction in viral load: results from an exploratory phase II clinical study of Vacc-4x, a therapeutic HIV vaccine

Background Immunization with Vacc-4x, a peptide-based therapeutic vaccine for HIV-1, has shown a statistically significant reduction in viral load set point compared to placebo during treatment interruption in an exploratory phase II clinical study enrolling 135 subjects (NCT00659789). This vaccine aims to induce sustained cell-mediated immune responses to conserved domains on HIV p24. Methods After 6 immunizations on ART over 28 weeks, treatment was interrupted for up to 24 weeks (Vacc-4x n=88; placebo n=38). Immunological analyses (ELISPOT, proliferation, intracellular cytokine staining (ICS)) to HIV p24 were carried out at central laboratories. The HLA class I profile (Vacc-4x n=73, placebo n=32) was also determined. Results For subjects that remained off ART until week 52 (Vacc-4x n=56, placebo n=25), there was a log 0.44 reduction in viral load set point between the Vacc-4x and placebo groups (p=0.0397). There was a similar distribution of HLA class I alleles in the two treatment arms, with the exception of the B35 allele (27% of Vacc-4x subjects versus 8% placebo subjects). The viral load of ELISPOT positive Vacc-4x subjects was significantly lower than that of placebo subjects (p=0.023). There was no significant difference in T-cell proliferation responses between Vacc-4x and placebo groups, however, the percentage of subjects showing proliferative CD4 and CD8 T-cell responses to Vacc-4x peptides increased over time only for the Vacc-4x group. ICS analysis showed a predominance of CD8-mediated T-cell responses to p24 that were significantly increased from baseline for the Vacc-4x group (p<0.043) but not for the placebo group(p>0.05). There was also a trend towards higher numbers of polyfunctional T-cells in the Vacc-4x group compared to the placebo group (p=0.188). Conclusion These findings suggest Vacc-4x immunization can influence the quality of immune responses to HIV-1 p24 irrespective of HLA status, and contribute to a reduction in viral load