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أدوات الموضوع | طريقة عرض الموضوع |
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#1
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الزنك لنقص الانتباه / فرط النشاط اضطراب: مزدوجة التعمية التجريبية الابتدائية وحده، وجنبا إلى جنب مع المنشطات تسيطر عليها وهمي
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#2
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الزنك وERK الكينازات kinases في الدماغ النامية
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#3
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قاعدة الأدلة لإدارة كبريتات نقص.
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#4
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قاعدة الأدلة لإدارة كبريتات نقص.
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#5
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http://www.biomedcentral.com/1471-2431/11/111/
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#6
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http://www.jmedicalcasereports.com/content/9/1/8
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#7
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http://www.jneuroinflammation.com/content/11/1/4
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#8
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http://www.nature.com/gim/journal/v1...m9201151a.html
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#9
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التوحد هو اضطراب المنشأ العصبي البيولوجي التي تتميز مشاكل في الاتصال والمهارات الاجتماعية والسلوك المتكرر. بعد أكثر من ستة عقود من البحث، ومسببات مرض التوحد ما زال مجهولا، وقد أثبتت المؤشرات الحيوية لا أن يكون سمة من سمات التوحد. وأظهر عدد من الدراسات أن مستويات خلوى في الدم والدماغ، والسائل النخاعي (CSF) من الموضوعات التوحد تختلف من أن الأفراد الأصحاء. على سبيل المثال، سلسلة من الدراسات تشير إلى أن انترلوكين 6 (IL-6)، نخر الورم α factor- (TNF- α)، وγ interferon- (IFN- γ) هي مرتفعة بشكل كبير في الأنسجة المختلفة في المواضيع التوحد. ومع ذلك، والتعبير عن بعض السيتوكينات، مثل IL-1، IL-2، وتحويل β النمو factor- (TGF- β)، وعامل تحفيز مستعمرة الكريات البيضاء بلعم (GM-CSF)، أمر مثير للجدل، والدراسات المختلفة لها العثور على نتائج مختلفة في الأنسجة المختلفة. في هذا الاستعراض، ركزنا على عدة أنواع من proinflammatory والمضادة للالتهابات السيتوكينات التي قد تؤثر على مختلف مسارات إشارة الخلية وتلعب دورا في آلية المرضية في جسم المريض من اضطرابات طيف التوحد.
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#10
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Did you mean: Protocadherin α (PCDHA) as a novel susceptibility gene for autism. J Psychiatry Neurosci. 2012 Oct 2;37(6):120058. doi: 10.1503/jpn.120058. [Epub ahead of print] Anitha A, Thanseem I, Nakamura K, Yamada K, Iwayama Y, Toyota T, Iwata Y, Suzuki K, Sugiyama T, Tsujii M, Yoshikawa T, Mori N. Abstract Background: Synaptic dysfunction has been shown to be involved in the pathogenesis of autism. We hypothesized that the protocadherin α gene cluster (PCDHA), which is involved in synaptic specificity and in serotonergic innervation of the brain, could be a suitable candidate gene for autism. Methods: We examined 14 PCDHA single nucleotide polymorphisms (SNPs) for genetic association with autism in DNA samples of 3211 individuals (841 families, including 574 multiplex families) obtained from the Autism Genetic Resource Exchange. Results: Five SNPs (rs251379, rs1119032, rs17119271, rs155806 and rs17119346) showed significant associations with autism. The strongest association (p < 0.001) was observed for rs1119032 (z score of risk allele G = 3.415) in multiplex families; SNP associations withstand multiple testing correction in multiplex families (p = 0.041). Haplotypes involving rs1119032 showed very strong associations with autism, withstanding multiple testing corrections. In quantitative transmission disequilibrium testing of multiplex fam - ilies, the G allele of rs1119032 showed a significant association (p = 0.033) with scores on the Autism Diagnostic Interview-Revised (ADI-R)_D (early developmental abnormalities). We also found a significant difference in the distribution of ADI-R_A (social interaction) scores between the A/A, A/G and G/G genotypes of rs17119346 (p = 0.002). Limitations: Our results should be replicated in an in - dependent population and/or in samples of different racial backgrounds. Conclusion: Our study provides strong genetic evidence of PCDHA as a potential candidate gene for autism. Predicting the diagnosis of autism spectrum disorder using gene pathway analysis. Mol Psychiatry. 2012 Sep 11. doi: 10.1038/mp.2012.126. [Epub ahead of print] Skafidas E, Testa R, Zantomio D, Chana G, Everall IP, Pantelis C. Abstract Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD.Molecular Psychiatry advance online publication, 11 September 2012; doi:10.1038/mp.2012.126. Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS One. 2012;7(8):e43855. Epub 2012 Aug 27. Wall DP, Dally R, Luyster R, Jung JY, Deluca TF. Abstract The Autism Diagnostic Interview-Revised (ADI-R) is one of the most commonly used instruments for assisting in the behavioral diagnosis of autism. The exam consists of 93 questions that must be answered by a care provider within a focused session that often spans 2.5 hours. We used machine learning techniques to study the complete sets of answers to the ADI-R available at the Autism Genetic Research Exchange (AGRE) for 891 individuals diagnosed with autism and 75 individuals who did not meet the criteria for an autism diagnosis. Our analysis showed that 7 of the 93 items contained in the ADI-R were sufficient to classify autism with 99.9% statistical accuracy. We further tested the accuracy of this 7-question classifier against complete sets of answers from two independent sources, a collection of 1654 individuals with autism from the Simons Foundation and a collection of 322 individuals with autism from the Boston Autism Consortium. In both cases, our classifier performed with nearly 100% statistical accuracy, properly categorizing all but one of the individuals from these two resources who previously had been diagnosed with autism through the standard ADI-R. Our ability to measure specificity was limited by the small numbers of non-spectrum cases in the research data used, however, both real and simulated data demonstrated a range in specificity from 99% to 93.8%. With incidence rates rising, the capacity to diagnose autism quickly and effectively requires careful design of behavioral assessment methods. Ours is an initial attempt to retrospectively analyze large data repositories to derive an accurate, but significantly abbreviated approach that may be used for rapid detection and clinical prioritization of individuals likely to have an autism spectrum disorder. Such a tool could assist in streamlining the clinical diagnostic process overall, leading to faster screening and earlier treatment of individuals with autism. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatry. 2012 Apr 10;2:e100. doi: 10.1038/tp.2012.10. Wall DP, Kosmicki J, Deluca TF, Harstad E, Fusaro VA. Abstract The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization-in particular those focused on assessment of short home videos of children--that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk. Immune function genes CD99L2, JARID2 and TPO show association with autism spectrum disorder. Mol Autism. 2012 Jun 9;3(1):4. [Epub ahead of print] Ramos PS, Sajuthi S, Langefeld CD, Walker SJ. Abstract BACKGROUND: A growing number of clinical and basic research studies have implicated immunological abnormalities as being associated with and potentially responsible for the cognitive and behavioral deficits seen in autism spectrum disorder (ASD) children. Here we test the hypothesis that immune-related gene loci are associated with ASD. FINDINGS: We identified 2,012 genes of known immune-function via Ingenuity Pathway Analysis. Family-based tests of association were computed on the 22,904 single nucleotide polymorphisms (SNPs) from the 2,012 immune-related genes on 1,510 trios available at the Autism Genetic Resource Exchange (AGRE) repository. Several SNPs in immune-related genes remained statistically significantly associated with ASD after adjusting for multiple comparisons. Specifically, we observed significant associations in the CD99 molecule-like 2 region (CD99L2, rs11796490, P = 4.01 x 10-06, OR = 0.68 (0.58-0.80)), in the jumonji AT rich interactive domain 2 (JARID2) gene (rs13193457, P = 2.71 x 10-06, OR = 0.61 (0.49-0.75)), and in the thyroid peroxidase gene (TPO) (rs1514687, P = 5.72 x 10-06, OR = 1.46 (1.24- 1.72)). CONCLUSIONS: This study suggests that despite the lack of a general enrichment of SNPs in immune function genes in ASD children, several novel genes with known immune functions are associated with ASD.
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