1. Finegold SM. {{State of the art; microbiology in health and disease intestinal bacterial flora in autism}}. {Anaerobe};2011 (Apr 16)
Autism of the regressive variety is selected as an example of the importance of intestinal bacterial microflora in disease other than classical infection. Our studies have indicated that intestinal bacteria play a role in this disease since it responds to oral vancomycin, a drug that is not absorbed from the GI tract. Pyrosequencing studies document an abnormal gut microflora in regressive autism subjects as compared to controls. Finally, we present preliminary evidence suggesting that Desulfovibrio may play a key role in this disease.
2. Pierce K, Carter C, Weinfeld M, Desmond J, Hazin R, Bjork R, Gallagher N. {{Detecting, Studying, and Treating Autism Early: The One-Year Well-Baby Check-Up Approach}}. {J Pediatr};2011 (Apr 19)
OBJECTIVES: To determine the feasibility of implementing a broadband screen at the 1-year check-up to detect cases of autism spectrum disorders (ASD), language delay (LD), and developmental delay (DD). STUDY DESIGN: The Communication and Symbolic Behavior Scales Developmental Profile Infant-Toddler Checklist was distributed at every 1-year pediatric check-up; 137 pediatricians and 225 infants participated. Screens were scored immediately, and failures referred for further evaluation. RESULTS: Pediatricians screened 10 479 infants at the 1-year check-up; 184 infants who failed the screen were evaluated and tracked. To date, 32 infants received a provisional or final diagnosis of ASD, 56 of LD, nine of DD, and 36 of « other. » Five infants who initially tested positive for ASD no longer met criteria at follow-up. The remainder of the sample was false positive results. Positive predictive value was estimated to be .75. CONCLUSIONS: The 1-Year Well-Baby Check-Up Approach shows promise as a simple mechanism to detect cases of ASD, LD, and DD at 1 year. This procedure offers an alternative to the baby sibling design as a mechanism to study autism prospectively, the results of which will enrich our understanding of autism at an early age.
3. Roesser J. {{Diagnostic Yield of Genetic Testing in Children Diagnosed With Autism Spectrum Disorders at a Regional Referral Center}}. {Clin Pediatr (Phila)};2011 (Apr 27)
The aim was to systematically review genetic testing guidelines in the evaluation of children with autism spectrum disorders (ASDs). The Clinical Report published by the American Academy of Pediatrics (AAP)(1) recommended individualizing the workup, including karyotype and specific DNA testing for fragile X syndrome. A recent publication reported higher rates of abnormalities on CGH microarray (CMA) testing on children with ASD.(2) The medical records of 507 children seen through the Kirch Developmental Services Center were abstracted for genetic testing and factors associated with this testing. Abnormalities were found on karyotype in 2.3% and in DNA for fragile X in 0.04%. The author concludes that the diagnostic yield of the genetic testing was low in this population. Furthermore, their findings support the theory that CMA can be considered as part of the initial genetic screening in children with ASD in most situations. Future studies will need to be done prospectively to evaluate children in a standard fashion.
4. Vatta F, Di Salle F. {{Brain morphometry in autism spectrum disorders: a unified approach for structure-specific statistical analysis of neuroimaging data – biomed 2011}}. {Biomed Sci Instrum};2011;47:135-141.
Autism spectrum disorders (ASD) are a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. It has been assumed in the scientific literature that deviations in regional brain size in clinical samples are directly related to maldevelopment or pathogenesis. The performed clinical studies analyzed specific brain structures that are assumed to be correlated to autistic brain behaviors. Examples of performed analyses, based upon manual or semi-automated segmentation from magnetic resonance imaging (MRI) scans, include volumetric measures of specific brain structures, or small groups of structures, as caudate, corpus callosum, putamen, hippocampus, nucleus accumbens, evaluating differences between groups of subjects with autism and control subjects. Nonetheless, the brain regions analyzed that differ between patients and control subjects have not been always consistent over the performed studies. This inconsistency might be due to the fact that the specific single volume differences that have been reported in the literature for the different brain structures under investigation may, instead, be not independent during pathogenesis. Hence, this issue comes into play in logically framing a comprehensive assessment of putative abnormalities in regional brain volumes. To this aim, a whole brain investigation system for a semi-automated morphometric statistical analysis of brain anatomy is presented in this paper and validated on a selected group of patients diagnosed with ASD that completed a 1.5 T magnetic resonance image (MRI) of the brain. The proposed system, which is mainly built basing upon the FreeSurfer and the 3D Slicer software frameworks for the volumetric analysis of brain imaging data, lies its foundations on the higher statistical power of the region of interest (ROI) approach, but equally aims at a higher exploratory power as it doesnt restrict its focus to a small number of specific regions, thanks to a whole brain unified approach.
